\n Create Account\n
\n {this.props.errors.length > 0 ? (\n\n \n
\n Already have an Account ? Login\n \n \n \n );\n }\n}\n\nconst mapStateToProps = (state) => {\n let errors = [];\n if (state.auth.errors) {\n errors = Object.keys(state.auth.errors).map((field) => {\n return { field, message: state.auth.errors[field] };\n });\n }\n return {\n errors,\n isAuthenticated: state.auth.isAuthenticated,\n };\n};\n\nconst mapDispatchToProps = (dispatch) => {\n return {\n register: (\n first_name,\n last_name,\n username,\n email,\n password,\n confirm_password,\n company_name\n ) =>\n dispatch(\n auth.register(\n first_name,\n last_name,\n username,\n email,\n password,\n confirm_password,\n company_name\n )\n ),\n };\n};\n\nexport default connect(mapStateToProps, mapDispatchToProps)(Register);\n","import React, { Component } from \"react\";\nimport { ReCaptcha } from \"react-recaptcha-google\";\nimport {\n Form,\n Button,\n Input,\n TextArea,\n Grid,\n Container,\n Message,\n Image,\n Header,\n Icon,\n Dropdown,\n Checkbox,\n} from \"semantic-ui-react\";\nimport { Redirect } from \"react-router-dom\";\nimport login from \"../assets/login.png\";\nimport logo from \"../assets/logo1.png\";\nimport { connect } from \"react-redux\";\nimport { auth } from \"../actions\";\n\nimport Spinner from \"./spinner\";\n\nimport styles from \"./login.css\";\n\nconst AnalysisOptions = [\n { key: \"HongKong\", value: \"local\", text: \"HongKong\" },\n { key: \"Global\", value: \"global\", text: \"Global\" },\n];\n\nclass Login extends Component {\n state = {\n username: \"\",\n password: \"\",\n company_code: \"\",\n loading: false,\n remember: true,\n analysis: \"\",\n };\n\n componentDidMount() {\n if (this.captchaDemo) {\n this.captchaDemo.reset();\n }\n }\n onLoadRecaptcha() {\n if (this.captchaDemo) {\n this.captchaDemo.reset();\n }\n }\n\n onSubmit = (e) => {\n e.preventDefault();\n let formdata = new FormData();\n formdata.append(\"username\", this.state.username);\n formdata.append(\"password\", this.state.password);\n formdata.append(\"company_code\", this.state.company_code);\n\n this.setState({ loading: true }, () => {\n this.props.login(formdata);\n });\n };\n handleAnalysis = (e, value) => {\n this.setState({ analysis: value.value }, () =>\n console.log(\"this.state.ana\", this.state.analysis)\n );\n };\n\n render() {\n if (auth.isUserAuthenticated()) {\n return
Sign In
\n {this.props.errors.length > 0 ? (\n\n \n
\n {/*Don't have an Account ? Create an Account*/}\n \n Forgot password ?\n \n
Creating Portfolio
',\n\t\t'Mapping Dashboard
',\n\t\t'Calculating Risk
',\n\t\t'Analysing Asset
',\n\t\t'Preparing Dashboard
',\n\t\t'Loading....
'\n\t]\n\t$(\".loader p\").html(\"\"+text[counter]+\"\");\n}\n\nloading=()=>{\n\tlet num=0;\n\t for( let i=0; i<=100; i++) {\n setTimeout(function() { \n $('.loader span').html(num+'%');\n\n if(num == 100) {\n \n }\n num++;\n },i*3600);\n };\n}\n\ncomponentDidMount(){\n\tthis.interval = setInterval(this.setInterval,3000)\n\tthis.loading();\n}\n\n\n\n\nrender(){\n\n\treturn(\nMark locations to analyze
\n \n \n\n
\n\n
\n\n {/*
Mark locations to analyse
*/}\n\n {/*\n {this.props.location.state ? (\n
\n
\n */}\n
\n
\n \n Upload CSV
\n\n \n Note(Click on Header to Expand)\n
\n- \n
- \n For submitting multiple assets on the\n BlueSwan platform to conduct the physical\n climate risk analysis, the user will need to\n download the sample CSV file to input data\n following the format as prescribed in the\n CSV file and upload the completed CSV file.\n Essential input data to conduct the analysis\n includes asset name and location\n information, while optional input data would\n be helpful to increase the accuracy of the\n amount for Climate Value at Risk (financial\n loss in dollar value).\n \n
- \n For location information, consider the\n granularity of analysis provided by the\n BlueSwan platform, it is important to input\n an accurate location of the asset in the CSV\n file. If the user refers to Google Maps for\n mapping the asset location, please refer to\n the satellite image of the map. Except for\n asset location, most input data can be\n updated later under the “Asset Table”\n Section of the Analysis result Dashboard.\n \n
- \n For inputting asset location information, it\n is observed that there is a shift in\n location on Google Maps’ satellite imagery\n for an asset located in mainland China.\n Special attention has to be paid to\n cross-check the location found on Google\n Maps with the user’s internal asset location\n information or other online map resources\n (such as Baidu map, Gaode map, or\n open-street map).\n \n
\n This function is to account for the flood risk\n reduction capacity of three flood mitigation\n measures that are near the asset and connect\n to the main flood control / stormwater\n management system of the area: a) manhole, b)\n catchpit, and c) underground flood tunnel.\n These mitigation measures are effective to\n reduce rainfall and river flooding, and they\n include any existing or to-be-built measures.\n It does not apply to storm surge flooding, as\n when there is a rise in sea level on the\n coastal side, the floodwater is very likely to\n block most nearby outlets of the stormwater\n management system. The level of flood risk\n reduction depends on below two key factors and\n assumption:\n
\n 1. Distance between any mitigation measure and\n the asset. To have a risk reduction impact,\n the closer the mitigation measure to the\n asset, the greater level of risk reduction.\n Any mitigation measure that is more than 20\n meters far from the asset is assumed to have\n no risk reduction effect.\n
\n 2. The number of available mitigation\n measures. The more the number of mitigation\n measures, the greater level of risk reduction.\n It can be in any combination of item a, b, and\n c as listed above, e.g., asset with two nearby\n manholes plus one nearby underground flood\n tunnel is counted as a total of 3 measures for\n that asset.\n
\n
\n \n {\" \"}\n A. High level of flood risk reduction –\n Either:\n \n
\n i. Having at least three measures in place\n near the asset. Two measures both are at least\n within a distance of ≤ 10 meters, plus the\n remaining measure is at least within 15-20\n meters; or\n
\n ii. Having at least three measures in place\n near the asset. Two measures both are at least\n within a distance of 10-15 meters, plus the\n remaining measure is at least within ≤ 10\n meters.\n
\n
\n \n B. Median level of flood risk reduction –\n Either:\n \n
\n i. Having at least three measures in place.\n Two measure both are at least within 15-20\n meters, plus the remaining measure is at least\n within 10-15 meters; or\n
\n ii. Having at least two measures in place near\n the asset. One measure is at least within ≤ 10\n meters, plus another one is at least within\n 15-20 meters; or\n
\n iii. Having at least two measures in place\n near the asset, and two measures both are at\n least within 10-15 meters.\n
\n
\n \n {\" \"}\n C. Low level of flood risk reduction –\n Either:\n \n
\n i. Having at least two measures in place, and\n these two measures both are at least within\n 15-20 meters; or\n
\n ii. Having at least one measure in place, and\n this measure is at least within a distance of\n ≤ 10 meters.\n
\n
\n Example: An asset has nearby two flood\n mitigation measures (no matter what\n combination of a) manhole, b) catchpit or c)\n underground flood tunnel). One measure (e.g.,\n manhole) is within 10-15 meters and another\n measure (e.g., catchpit or another manhole) is\n at 15-20 meters far from the asset. Both\n measures are connected to the city-level\n stormwater management system. In this example,\n as it can’t fulfill the requirement of the\n median level of flood risk reduction of points\n B) ii and B) iii above, it falls into the\n category of low level of flood risk reduction.\n
\n
\n\n
\n
\n
All Assets
\nSelected Assets
\n\n \n Upload CSV
\n \n
\n\n
\n
\n
All Assets
\nSelected Assets
\n\n
\n \n Search Company
\n\n \n {this.props.name.error ? (\n
No Data Found
\n\n
\n {companyLocationCards && companyLocationCards.length > 0 ? (\n <>\n
\n \n Upload CSV (Address)
\n\n Upload CSV
\n \n \n\n \n
\n \n \n
\n
All Assets
\nSelected Assets
\nSelected Index:
\n{this.state.index}
\n\n Number of Countries:\n {this.props.indices.number_of_countries}\n
\n\n Number of Companies:\n {this.props.indices.number_of_companies}\n
\nIndices:
\n \n\n Do you want to upload CSV with these{\" \"}\n ?\n
\n\n\n
\n
\n
\n
\n
\n
Do you want to upload CSV with these ?
\n \n
\n
\n
\n \n
\n
\n
\n You are only allowed to add{\" \"}\n {this.props.permissions.assets_allowed} assets for Analysis\n
\n\n {errorMessage}\n
\n ))}\n\n
\n
\n\n {/* Unit Dropdown \n \n
\n\n
\n
*/}\n
\n
\n
\n Who we{\" \"}\n \n serve?\n \n
\n - \n
- \n
CORPORATIONS\n
\n - \n
FINANCIAL\n INSTITUTIONS\n
\n - \n
REAL ESTATE\n INVESTORS\n
\n - \n
COMMUNTIES\n
\n
\n
\n
\n
\n Why Intensel is{\" \"}\n \n better product?\n \n
\n \n Scientifically Rigorous\n
\n\n In-house scientific simulations with peer-reviewed\n dynamical and data-driven models.\n
\nExpert Validation
\n\n Validation & verification integrated throughout the\n modeling chain.\n
\nData Transparency
\n\n Transparency in all modeling components, from metrics\n to scoring to loss. No black boxes\n
\nForward-Looking
\n\n Latest predictive climate models; not a\n backward-looking catastrophic risk model approach\n
\n\n Quantified Uncertainty\n
\n\n Measure and provide known sources of modeling\n uncertainty.\n
\n\n Scenario-based Ability\n
\n\n Ability to traverse a range of emissions and response\n scenarios.\n
\nData Access
\n\n Flexible data access via API and SaaS applications—\n all delivered at enterprise-grade\n
\nDownscaled
\n\n Downscaling methods optimized for portfolio & facility\n analyses.\n
\nExpanding Catalog
\n\n We are rapidly expanding climate metrics tuned to\n customer needs.\n
\nData Sources
\n- \n
- \n \n {this.dataSourceDialogueBox(\"Global Climate Models\")}\n\n
- \n
- Sea - Level Rise \n
- Higher Temperatures \n
- Storm Intensification \n
- Altered Rain Patterns \n
\n - \n {this.dataSourceDialogueBox(\"Historical data\")}\n
- \n
- Reanalysis Data \n
- Satellite Observations \n
- In-Situ Observations \n
\n - \n {this.dataSourceDialogueBox(\"Additional Models\")}\n
- \n
- Severe Weather \n
- Hyrdrology \n
- Wildlife \n
- Evapotranspiration \n
- Global EQ Hazard \n
\n - \n {this.dataSourceDialogueBox(\"Local Conditions\")}\n
- \n
- Elevation \n
- Land Use \n
- Vegatation \n
\n
Simulation Of Future Perils
\n- \n
- \n Every 20 Years\n
\n 2020-2100\n \n - \n Any Location\n
\n Worldwide\n \n - \n Multiple Carbon\n
\n Scenarios\n \n
\n INTENSEL{\" \"}\n \n Climate Solutions\n \n
\n \n As businesses, investors and governments explore the\n transition to a net-zero world,\n
\n\n Intensel is committed to creating climate solutions to\n support real time, accurate decision making\n
\n\n
- \n
- Portfolio Physical Climate Risk \n
- Key Risk Identifications \n
- Asset Level Exposure To Climate \n
- Supply Chain Risk Exposure \n
\n
- \n
- \n Climate-Related Potential Losses For The Portfolio\n \n
- \n Climate-Related Potential Losses On Company And\n Asset Level\n \n
- \n Climate Credit Spread Estimation On Portfolio And\n Company Level\n \n
\n
- \n
- Forward-Time Analysis \n
- RCP Related climate Scenarios \n
- Return Period Risk And Dollar loss Exposure \n
\n
- \n
- Emission Estimate Using Satellite Data \n
- Footprint Portfolio Level Estimation \n
- \n Relative Or Attribution Analysis Versus Benchmarks\n \n
- Price Risk Assessment \n
- Emission Gap Assessment \n
- Contribution By Sector And Asset/Company \n
\n
- \n
- State of the art model \n
- Broad coverage across equities, fixed income and real estate \n
\n \n Intensel\n {\" \"}\n SaaS advantage\n
\n \n We provide real-time to calculate portfolio and asset\n financial value-at-risk(VAR) accurate climate analytics\n
\n\n Enables reporting and disclosure consistent with the\n TCFD framework.\n
\n\n Reports transition, physical risks & opportunities in\n the financial terms to empower decision - making.\n
\n\n Powered by transparent methodology and rigorous science.\n
\n\n Provides global coverage with property/asset to\n portfolio-level analysis, multiple climate scenarios\n covering present day to the year 2100.\n
\nClimate risk
\n\n Extreme heat is a period of high heat and humidity with\n temperatures above 90 degrees for at least two to three\n days. In extreme heat your body works extra hard to\n maintain a normal temperature, which can lead to death.\n
\n \n Learn more\n \n
EXTREME HEAT
\n\n A flood is an overflow of water on normally dry ground.\n This is most commonly due to an overflowing river, a dam\n break, SnowFall, or heavy rainfall. Less commonly\n happening are tsunamis, storm surge.\n
\n \n Learn more\n \n
FLOOD
\n\n A typhoon is a mature tropical cyclone that develops\n between 180° and 100°E in the Northern Hemisphere. It is\n the most active tropical cyclone basin on Earth,\n accounting for almost one-third of the world's annual\n tropical cyclones.\n
\n \n Learn more\n \n
TYPHOONS
\n\n
\n \n \n );\n }\n}\nexport default Home;\n","import {\n\n\tMENU_SET_CLASSNAMES,\n\tMENU_CONTAINER_ADD_CLASSNAME,\n\tMENU_CLICK_MOBILE_MENU,\n\tMENU_CHANGE_DEFAULT_CLASSES,\n\tMENU_CHANGE_HAS_SUB_ITEM_STATUS,\n\tMENU_CHANGE_TITLE\n} from '../actions';\n\nimport {defaultMenuType, subHiddenBreakpoint, menuHiddenBreakpoint } from '../../constants/defaultValues'\n\n\nconst INIT_STATE = {\n\tcontainerClassnames: defaultMenuType,\n\tsubHiddenBreakpoint,\n\tmenuHiddenBreakpoint,\n\tmenuClickCount: 0,\n\tselectedMenuHasSubItems: defaultMenuType===\"menu-default\", //if you use menu-sub-hidden as default menu type, set value of this variable to false\n\ttitle: \"\",\n};\n\nexport default (state = INIT_STATE, action) => {\n\tswitch (action.type) {\n\n\t\tcase MENU_CHANGE_HAS_SUB_ITEM_STATUS:\n\t\treturn Object.assign({}, state, {\n\t\t\tselectedMenuHasSubItems: action.payload\n\t\t})\n\n\t\tcase MENU_SET_CLASSNAMES:\n\t\treturn Object.assign({}, state, {\n\t\t\tcontainerClassnames: action.payload.containerClassnames,\n\t\t\tmenuClickCount:action.payload.menuClickCount\n\t\t})\n\n\t\tcase MENU_CLICK_MOBILE_MENU:\n\t\treturn Object.assign({}, state, {\n\t\t\tcontainerClassnames: action.payload.containerClassnames,\n\t\t\tmenuClickCount:action.payload.menuClickCount\n\t\t})\n\n\t\tcase MENU_CONTAINER_ADD_CLASSNAME:\n\t\treturn Object.assign({}, state, {\n\t\t\tcontainerClassnames: action.payload\n\t\t})\n\n\t\tcase MENU_CHANGE_DEFAULT_CLASSES:\n\t\t\treturn Object.assign({}, state, {\n\t\t\t\tcontainerClassnames: action.payload\n\t\t\t})\n\n\t\tcase MENU_CHANGE_TITLE:\n\t\t\treturn Object.assign({}, state, {\n\t\t\t\ttitle: action.payload\n\t\t\t})\n\t\t\t\n\t\tdefault: return { ...state };\n\t}\n}\n","/* \nMenu Types:\n\"menu-default\", \"menu-sub-hidden\", \"menu-hidden\"\n*/\nexport const defaultMenuType = \"menu-default\";\n\nexport const subHiddenBreakpoint = 1440;\nexport const menuHiddenBreakpoint = 450;\nexport const defaultLocale = \"en\";\nexport const localeOptions = [\n { id: \"en\", name: \"English - LTR\", direction: \"ltr\" },\n { id: \"es\", name: \"Español\", direction: \"ltr\" },\n { id: \"enrtl\", name: \"English - RTL\", direction: \"rtl\" }\n];\n\nexport const firebaseConfig = {\n apiKey: \"AIzaSyBBksq-Asxq2M4Ot-75X19IyrEYJqNBPcg\",\n authDomain: \"gogo-react-login.firebaseapp.com\",\n databaseURL: \"https://gogo-react-login.firebaseio.com\",\n projectId: \"gogo-react-login\",\n storageBucket: \"gogo-react-login.appspot.com\",\n messagingSenderId: \"216495999563\"\n};\n\nexport const searchPath = \"/app/pages/search\";\nexport const servicePath = \"https://api.coloredstrategies.com\";\n\n/* \nColor Options:\n\"light.purple\", \"light.blue\", \"light.green\", \"light.orange\", \"light.red\", \"dark.purple\", \"dark.blue\", \"dark.green\", \"dark.orange\", \"dark.red\"\n*/\nexport const themeColorStorageKey=\"__theme_color\"\nexport const isMultiColorActive = true;\nexport const defaultColor = \"light.purple\";\nexport const isDarkSwitchActive = true;\nexport const defaultDirection = \"ltr\";\nexport const themeRadiusStorageKey = \"__theme_radius\";\nexport const isDemo = true;","const initialState = {\n token: localStorage.getItem(\"token\"),\n isAuthenticated: null,\n isLoading: true,\n user: null,\n errors: {},\n log_data: {},\n};\n\nexport default function auth(state = initialState, action) {\n switch (action.type) {\n case \"USER_LOADING\":\n return { ...state, isLoading: true };\n\n case \"USER_LOADED\":\n case \"USER_UPDATED\":\n return {\n ...state,\n isAuthenticated: true,\n isLoading: false,\n user: action.user,\n };\n\n case \"LOGIN_SUCCESSFUL\":\n localStorage.setItem(\"token\", action.data.token);\n return {\n ...state,\n log_data: action.data,\n isAuthenticated: true,\n isLoading: false,\n errors: null,\n };\n case \"REGISTRATION_SUCCESSFUL\":\n return {\n ...state,\n ...action.data,\n isAuthenticated: true,\n isLoading: false,\n errors: null,\n };\n\n case \"AUTHENTICATION_ERROR\":\n case \"LOGIN_FAILED\":\n case \"REGISTRATION_FAILED\":\n case \"LOGOUT_SUCCESSFUL\":\n localStorage.removeItem(\"token\");\n return {\n ...state,\n errors: action.data,\n token: null,\n user: null,\n isAuthenticated: false,\n isLoading: false,\n };\n\n default:\n return state;\n }\n}\n","const initialState = {\n locus: [],\n errors: {},\n isLoading: true,\n assets: [],\n permissions: [],\n name: [],\n company_name: [],\n company_location: [],\n point: [],\n indices: [],\n index: [],\n transition: [],\n sbti: [],\n modify_transition: [],\n notification: [],\n property: [],\n keyword: [],\n faqs: [],\n map: [],\n map_response: [],\n download_map: [],\n download_map_value: [],\n dataset: [],\n};\n\nexport default function locus(state = initialState, action) {\n switch (action.type) {\n case \"LOCATION_ADDING\":\n return { ...state, isLoading: true };\n case \"CREATE_MAP\":\n return { ...state, isLoading: true };\n case \"ADD_LOCATION\":\n localStorage.setItem(\"variables\", JSON.stringify(action.locus.variables));\n localStorage.setItem(\n \"property_types\",\n JSON.stringify(action.locus.property_types)\n );\n\n return { ...state, locus: action.locus, isLoading: false };\n case \"MAP_ADD\":\n return { ...state, map: action.map, isLoading: false };\n case \"ADD_INDEX\":\n return { ...state, index: action.index, isLoading: false };\n case \"GET_TRANSITION\":\n return { ...state, transition: action.transition, isLoading: false };\n case \"MODIFY_TRANSITION\":\n return { ...state, modify_transition: action.modify_transition };\n case \"GET_SBTI\":\n return { ...state, sbti: action.sbti, isLoading: false };\n case \"NOTIFICATION_SUCCESS\":\n return { ...state, notification: action.notification, isLoading: false };\n case \"GENERATE_SUCCESS\":\n return { ...state, map_response: action.map_response, isLoading: false };\n case \"DOWNLOAD_MAP_SUCCESS\":\n return { ...state, download_map: action.download_map, isLoading: false };\n case \"DOWNLOAD_MAP_VALUE_SUCCESS\":\n return {\n ...state,\n download_map_value: action.download_map_value,\n isLoading: false,\n };\n case \"DATASET_SUCCESS\":\n return {\n ...state,\n dataset: action.dataset,\n isLoading: false,\n };\n case \"ADD_NONBUILDING\":\n return { ...state, locus: action.locus, isLoading: false };\n case \"POST_INDICES\":\n return { ...state, indices: action.indices };\n case \"FETCH_LOCATION\":\n return { ...state, locus: action.locus, isLoading: true };\n case \"FETCH_COORDINATES\":\n return { ...state, point: action.point };\n case \"FETCH_SEARCH\":\n return { ...state, name: action.name };\n case \"FETCH_COMPANY_SEARCH\":\n return { ...state, company_name: action.company_name };\n case \"FETCH_COMPANY_LOCATION\":\n return { ...state, company_location: action.company_location };\n case \"PROPERTY_RISK\":\n return { ...state, property: action.property, isLoading: false };\n case \"GET_PROPERTY_RISK\":\n return { ...state, property: action.property, isLoading: false };\n case \"KEYWORD\":\n return { ...state, keyword: action.keyword, isLoading: false };\n case \"FAQS\":\n return { ...state, faqs: action.faqs, isLoading: false };\n case \"FETCH_PERMISSION\":\n return { ...state, permissions: action.permissions };\n case \"FETCH_ASSET\":\n return { ...state, assets: action.assets };\n case \"AUTHENTICATION_ERROR\":\n case \"LOCATION_FAIL\":\n return { ...state, errors: action.data, locus: null, isLoading: false };\n case \"MAP_FAIL\":\n return { ...state, errors: action.data, map: null, isLoading: false };\n case \"PERMISSION_FAIL\":\n return { ...state, errors: action.data, permissions: null };\n case \"SEARCH_FAIL\":\n return { ...state, errors: action.data, name: null };\n case \"COMPANY_SEARCH_FAIL\":\n return { ...state, errors: action.data, company_name: null };\n case \"COMPANY_LOCATION_FAIL\":\n return { ...state, errors: action.data, company_location: null };\n case \"NONBUILDING_FAIL\":\n return { ...state, errors: action.data, locus: null, isLoading: false };\n case \"COORDINATES_FAIL\":\n return { ...state, errors: action.data, point: null };\n case \"INDICES_FAIL\":\n return { ...state, errors: action.data, indices: null };\n case \"NOTIFICATION_FAIL\":\n return { ...state, errors: action.data, notification: null };\n case \"GENERATE_FAIL\":\n return { ...state, errors: action.data, map_response: null };\n case \"DOWNLOAD_MAP_FAIL\":\n return { ...state, errors: action.data, download_map: null };\n case \"DOWNLOAD_MAP_VALUE_FAIL\":\n return { ...state, errors: action.data, download_map_value: null };\n case \"DATASET_FAIL\":\n return { ...state, errors: action.data, dataset: null };\n case \"INDEX_FAIL\":\n return { ...state, errors: action.data, index: null };\n case \"SBTI_FAIL\":\n return { ...state, errors: action.data, index: null };\n case \"FAQS_FAIL\":\n return { ...state, errors: action.data, faqs: null };\n case \"PROPERTY_RISK_FAIL\":\n return { ...state, errors: action.data, property: null };\n case \"GET_PROPERTY_RISK_FAIL\":\n return { ...state, errors: action.data, property: null };\n case \"KEYWORD_FAIL\":\n return { ...state, errors: action.data, keyword: null };\n case \"TRANSITION_FAIL\":\n return { ...state, errors: action.data, transition: null };\n case \"MODIFY_TRANSITION_FAIL\":\n return { ...state, errors: action.data, modify_transition: null };\n default:\n return state;\n }\n}\n","const initialState ={\n\tcompany:[],\n\terrors:{}\n};\n\nexport default function company(state=initialState,action){\n\tswitch(action.type){\n\t\n\t\tcase 'FETCH_COMPANY':\n\t\t\treturn {...state,company:action.company};\n\t\tcase 'AUTHENTICATION_ERROR':\n\t\tcase 'COMPANY_FAIL':\n\t\t\treturn {...state,errors:action.data,company:null};\n\t\tdefault:\n\t\t\treturn state;\n\t}\n}","const initialState = {\n project: [],\n errors: {},\n csv: [],\n invoice: [],\n report: [],\n detailreport: [],\n windreport: [],\n lossreport: [],\n countrywiselossreport: [],\n overallmapimage: [],\n assetmapimage: [],\n tcfd: [],\n ppt: [],\n map: [],\n overall_map: [],\n link: [],\n list: [],\n transiton_project: [],\n carbon_project: [],\n carbon_project_data: [],\n delete_carbon_project_data: [],\n download_carbon_project_data: [],\n filter: [],\n};\n\nexport default function project(state = initialState, action) {\n switch (action.type) {\n case \"ADD_PROJECT\":\n return { ...state, project: action.project };\n case \"ADD_REPORT\":\n return { ...state, report: action.report };\n case \"DELETE_PROJECT\":\n return { ...state, project: action.project };\n case \"FETCH_PROJECT\":\n return { ...state, project: action.project };\n case \"CARBON_PROJECT\":\n return { ...state, carbon_project: action.carbon_project };\n case \"GET_CARBON_PROJECT\":\n return { ...state, carbon_project_data: action.carbon_project_data };\n case \"DELETE_CARBON_PROJECT\":\n return {\n ...state,\n delete_carbon_project_data: action.delete_carbon_project_data,\n };\n case \"DOWNLOAD_CARBON_PROJECT\":\n return {\n ...state,\n download_carbon_project_data: action.download_carbon_project_data,\n };\n case \"UPDATE_FILTER\":\n return {\n ...state,\n filter: action.filter,\n };\n case \"FETCH_PROJECT_LIST\":\n return { ...state, list: action.list };\n case \"FETCH_INVOICE\":\n return { ...state, invoice: action.invoice };\n case \"FETCH_REPORT\":\n return { ...state, report: action.report };\n case \"TRANSITION_PROJECT\":\n return { ...state, transition_project: action.transition_project };\n case \"FETCH_DETAILREPORT\":\n return { ...state, detailreport: action.detailreport };\n case \"FETCH_LOSSREPORT\":\n return { ...state, lossreport: action.lossreport };\n case \"FETCH_WINDREPORT\":\n return { ...state, windreport: action.windreport };\n case \"FETCH_COUNTRYWISELOSSREPORT\":\n return { ...state, countrywiselossreport: action.countrywiselossreport };\n case \"FETCH_OVERALLMAPIMAGE\":\n return { ...state, overallmapimage: action.overallmapimage };\n case \"FETCH_ASSETMAPIMAGE\":\n return { ...state, assetmapimage: action.assetmapimage };\n case \"FETCH_TCFDREPORT\":\n return { ...state, tcfd: action.tcfd };\n case \"FETCH_PPTREPORT\":\n return { ...state, ppt: action.ppt };\n case \"FETCH_ASSETMAP\":\n return { ...state, map: action.map };\n case \"FETCH_MAP\":\n return { ...state, overall_map: action.overall_map };\n case \"FETCH_LINK\":\n return { ...state, link: action.link };\n case \"FETCH_INSURANCE\":\n return { ...state, insurance: action.insurance };\n case \"AUTHENTICATION_ERROR\":\n case \"PROJECT_FAIL\":\n return { ...state, errors: action.data, project: null };\n case \"CARBON_PROJECT_FAIL\":\n return { ...state, errors: action.data, carbon_project: null };\n case \"DELETE_CARBON_PROJECT_FAIL\":\n return { ...state, errors: action.data, delete_carbon_project: null };\n case \"DOWNLOAD_CARBON_PROJECT_FAIL\":\n return {\n ...state,\n errors: action.data,\n download_carbon_project_data: null,\n };\n case \"UPDATE_FILTER_FAIL\":\n return {\n ...state,\n errors: action.data,\n filter: null,\n };\n case \"PROJECT_LIST_FAIL\":\n return { ...state, errors: action.data, list: null };\n case \"TRANSITION_PROJECT_FAIL\":\n return { ...state, errors: action.data, transition_project: null };\n case \"GET_CARBON_PROJECT_FAIL\":\n return { ...state, errors: action.data, carbon_project_data: null };\n case \"GET_CSV\":\n return { ...state, csv: action.csv };\n case \"CSV_FAIL\":\n return { ...state, errors: action.data, csv: null };\n case \"CSV_ERROR\":\n return { ...state, csv: action.csv, errors: null };\n case \"INVOICE_FAIL\":\n return { ...state, errors: action.data, invoice: null };\n case \"REPORT_FAIL\":\n return { ...state, errors: action.data, report: null };\n case \"DETAILREPORT_FAIL\":\n return { ...state, errors: action.data, detailreport: null };\n case \"WINDREPORT_FAIL\":\n return { ...state, errors: action.data, windreport: null };\n case \"LOSSREPORT_FAIL\":\n return { ...state, errors: action.data, lossreport: null };\n case \"COUNTRYWISELOSSREPORT_FAIL\":\n return { ...state, errors: action.data, countrywiselossreport: null };\n case \"OVERALLMAPIMAGE_FAIL\":\n return { ...state, errors: action.data, overallmapimage: null };\n case \"ASSETMAPIMAGE_FAIL\":\n return { ...state, errors: action.data, assetmapimage: null };\n case \"TCFDREPORT_FAIL\":\n return { ...state, errors: action.data, tcfd: null };\n case \"PPTREPORT_FAIL\":\n return { ...state, errors: action.data, ppt: null };\n case \"ASSETMAP_FAIL\":\n return { ...state, errors: action.data, map: null };\n case \"MAP_FAIL\":\n return { ...state, errors: action.data, overall_map: null };\n case \"LINK_FAIL\":\n return { ...state, errors: action.data, link: null };\n case \"INSURANCE_FAIL\":\n return { ...state, errors: action.data, insurance: null };\n default:\n return state;\n }\n}\n","const initialState = {\n feedback: [],\n feedback_ssp: [],\n benchmark: [],\n errors: {},\n building: [],\n detail: [],\n detailyear: [],\n isLoading: true,\n summary: [],\n};\n\nexport default function feedback(state = initialState, action) {\n switch (action.type) {\n case \"DASHBOARD_LOADING\":\n return { ...state, isLoading: true };\n case \"ADD_DASHBOARD\":\n return { ...state, feedback: action.feedback, isLoading: false };\n case \"ADD_BENCHMARK\":\n return { ...state, benchmark: action.benchmark, isLoading: false };\n /*case \"ADD_SSP_DASHBOARD\":\n return { ...state, feedback: action.feedback, isLoading: false };*/\n case \"GET_BUILDING\":\n return { ...state, building: action.building, isLoading: false };\n case \"GET_SUMMARY\":\n return { ...state, summary: action.summary, isLoading: false };\n case \"GET_DETAIL\":\n return { ...state, detail: action.detail };\n case \"GET_DETAIL_YEAR\":\n return { ...state, detailyear: action.detailyear };\n case \"AUTHENTICATION_ERROR\":\n case \"DASHBOARD_FAIL\":\n return {\n ...state,\n errors: action.data,\n feedback: null,\n isLoading: false,\n };\n case \"BENCHMARK_FAIL\":\n return {\n ...state,\n errors: action.data,\n benchmark: null,\n isLoading: false,\n };\n /*case \"SSP_DASHBOARD_FAIL\":\n return {\n ...state,\n errors: action.data,\n feedback: null,\n isLoading: false,\n };*/\n case \"BUILDING_FAIL\":\n return { ...state, errors: action.data, building: null };\n case \"DETAIL_FAIL\":\n return { ...state, errors: action.data, detail: null };\n case \"SUMMARY_FAIL\":\n return { ...state, errors: action.data, summary: null };\n case \"DETAIL_YEAR_FAIL\":\n return { ...state, errors: action.data, detailyear: null };\n default:\n return state;\n }\n}\n","const initialState = {\n asset: [],\n typhoon: [],\n errors: {},\n isLoading: true,\n};\n\nexport default function asset(state = initialState, action) {\n switch (action.type) {\n case \"ASSET_LOADING\":\n return { ...state, isLoading: true };\n case \"FETCH_ASSET\":\n return { ...state, asset: action.asset };\n case \"TYPHOON_TRACK\":\n return { ...state, typhoon: action.typhoon };\n case \"DELETE_ASSET\":\n return { ...state, asset: action.asset };\n case \"UPDATE_ASSET\":\n return { ...state, asset: action.asset, isLoading: false };\n case \"AUTHENTICATION_ERROR\":\n case \"ASSET_FAIL\":\n case \"TYPHOON_FAIL\":\n return { ...state, errors: action.data, asset: null, isLoading: false };\n default:\n return state;\n }\n}\n","import { combineReducers } from 'redux';\nimport menu from './menu/reducer';\nimport auth from '../reducers/auth';\nimport locus from '../reducers/location';\nimport company from '../reducers/company';\nimport project from '../reducers/project';\nimport feedback from '../reducers/dashboard';\nimport asset from '../reducers/asset';\n\nconst reducers = combineReducers({\n menu,\n auth,locus,company,project,feedback,asset\n});\n\nexport default reducers;\n","import React, { Component } from \"react\";\nimport {\n Form,\n Button,\n Input,\n TextArea,\n Grid,\n Container,\n Message,\n Image,\n Header,\n Menu,\n Select,\n Dropdown,\n Icon,\n Table,\n Modal,\n Popup,\n StepTitle,\n Progress,\n} from \"semantic-ui-react\";\nimport logo from \"../assets/logo.png\";\nimport \"./project.css\";\nimport { connect } from \"react-redux\";\nimport { locus, auth, project } from \"../actions\";\nimport { Row, Col, Card, Tabs, Tab } from \"react-bootstrap\";\nimport Spinner from \"./loader\";\nimport CsvDownloadButton from \"react-json-to-csv\";\nimport dropbox from \"../assets/dropbox.png\";\nimport team1 from \"../assets/team_1.png\";\nimport team2 from \"../assets/team_2.png\";\n\nimport var_eg from \"../assets/var_eg.jpg\";\n//import Report from \"./report\";\nimport { Popconfirm } from \"antd\";\nimport \"antd/dist/antd.css\";\nimport pptxgen from \"pptxgenjs\";\nimport { runppt } from \"./ppt\";\nimport { changeTitle } from \"../redux/actions\";\nimport { Redirect, withRouter } from \"react-router-dom\";\nimport HorizontalTimeline from \"react-horizontal-timeline\";\n\nlet options = [];\n\nconst VALUES = [\n \"2021-1-02\",\n \"2021-1-01\",\n \"2021-2-01\",\n \"2021-3-01\",\n \"2021-4-01\",\n \"2021-5-01\",\n \"2021-6-01\",\n \"2021-7-01\",\n \"2021-8-01\",\n \"2021-9-01\",\n \"2021-10-01\",\n \"2021-11-01\",\n \"2021-12-01\",\n];\n\nconst StatusOptions = [\n { key: \"active\", value: \"Active\", text: \"Active\" },\n { key: \"finished\", value: \"Finished\", text: \"Finished\" },\n];\nconst csv = [];\nconst othersOptions = [\n { key: \"valuation\", value: \"valuation\", text: \"valuation\" },\n { key: \"type\", value: \"type\", text: \"type\" },\n { key: \"Area\", value: \"Area\", text: \"Area\" },\n { key: \"Ownership\", value: \"ownership\", text: \"Ownership\" },\n { key: \"floors\", value: \"floors\", text: \"floors\" },\n {\n key: \"Construction Cost\",\n value: \"construction_cost\",\n text: \"Construction Cost\",\n },\n { key: \"With Content\", value: \"With Content\", text: \"With Content\" },\n];\nlet variableOptions = [];\nconst rcpOptions = [\n { key: \"0.0\", value: \"0.0\", text: \"0.0\" },\n { key: \"2.6\", value: \"2.6\", text: \"2.6\" },\n { key: \"4.5\", value: \"4.5\", text: \"4.5\" },\n { key: \"8.5\", value: \"8.5\", text: \"8.5\" },\n];\nconst yearOptions = [\n { key: \"2020\", value: \"2020\", text: \"2020\" },\n { key: \"2030\", value: \"2030\", text: \"2030\" },\n { key: \"2050\", value: \"2050\", text: \"2050\" },\n];\nconst PortfolioLossOptions = [\n {\n key: \"Rainfall Flood Loss\",\n value: \"Rainfall Flood Loss\",\n text: \"Rainfall Flood Loss\",\n },\n {\n key: \"River Flood Loss\",\n value: \"River Flood Loss\",\n text: \"River Flood Loss\",\n },\n {\n key: \"Storm Surge Loss\",\n value: \"Storm Surge Loss\",\n text: \"Storm Surge Loss\",\n },\n { key: \"Typhoon Loss\", value: \"Typhoon Loss\", text: \"Typhoon Loss\" },\n {\n key: \"Temprature/heat Loss\",\n value: \"Temprature/heat Loss\",\n text: \"Temprature/heat Loss\",\n },\n {\n key: \"Solar Production Loss\",\n value: \"Solar Production Loss\",\n text: \"Solar Production Loss\",\n },\n {\n key: \"Wind Production Loss\",\n value: \"Wind Production Loss\",\n text: \"Wind Production Loss\",\n },\n {\n key: \"Hydro Production Loss\",\n value: \"Hydro Production Loss\",\n text: \"Hydro Production Loss\",\n },\n {\n key: \"Wheat Production Loss\",\n value: \"Wheat Production Loss\",\n text: \"Wheat Production Loss\",\n },\n {\n key: \"Rice Production Loss\",\n value: \"Rice Production Loss\",\n text: \"Rice Production Loss\",\n },\n {\n key: \"Maize Production Loss\",\n value: \"Maize Production Loss\",\n text: \"Maize Production Loss\",\n },\n {\n key: \"Fruit Production Loss\",\n value: \"Fruit Production Loss\",\n text: \"Fruit Production Loss\",\n },\n {\n key: \"Vegetable Production Loss\",\n value: \"Vegetable Production Loss\",\n text: \"Vegetable Production Loss\",\n },\n];\n\nclass TransitionProject extends Component {\n state = {\n name: \"\",\n description: \"\",\n status: \"\",\n assets: [],\n loading: false,\n activeItemName: \"\",\n modalOpen: false,\n variables: [],\n others: \"\",\n year: \"\",\n rcp: \"\",\n modalloading: false,\n lossmodalOpen: false,\n reportmodal: false,\n portfolios_transition: \"\",\n downloadmodalOpen: false,\n new_project: \"\",\n portfolio_loss: \"\",\n modalEdit: false,\n loading: false,\n dropbox: true,\n value: 0,\n previous: 0,\n share: false,\n };\n\n componentDidMount() {\n this.props.getProjectList(\"transition_risk\");\n }\n\n onSubmit = (e) => {\n e.preventDefault();\n let formdata = new FormData();\n formdata.append(\"oldname\", this.state.activeItemName);\n formdata.append(\"status\", this.state.status);\n formdata.append(\"description\", this.state.description);\n formdata.append(\"name\", this.state.new_project);\n this.setState({ loading: true }, () => {\n this.props.addProject(formdata);\n });\n };\n handleChange = (value, key) => {\n this.setState({ [key]: value }, () =>\n console.log(\"othres\", this.state.others)\n );\n };\n\n handlePptReport(portfolio) {\n let formdata = new FormData();\n formdata.append(\"project_name\", portfolio);\n\n this.setState({ loading: true }, () => {\n this.props.getPptReport(formdata);\n });\n }\n handleShare = () => {\n this.setState({ share: true });\n };\n\n handleSubmit = (e) => {\n e.preventDefault();\n let formdata = new FormData();\n formdata.append(\"portfolio\", this.state.activeItemName.slice(10));\n formdata.append(\"variables\", JSON.stringify(this.state.variables));\n formdata.append(\"others\", JSON.stringify(this.state.others));\n formdata.append(\"year\", JSON.stringify(this.state.year));\n formdata.append(\"rcp\", JSON.stringify(this.state.rcp));\n formdata.append(\n \"portfolio_loss\",\n JSON.stringify(this.state.portfolio_loss)\n );\n\n this.setState({ modalloading: true }, () => {\n this.props.getCSV(formdata);\n });\n };\n handleView = () => {\n console.log(\"view \", this.props.csv.success);\n this.props.history.push({\n pathname: \"/pivottable\",\n state: { data: this.props.csv.success },\n });\n };\n handleLossSubmit = (e) => {\n e.preventDefault();\n let formdata = new FormData();\n formdata.append(\"portfolio\", this.state.activeItemName.slice(10));\n formdata.append(\"year\", this.state.year);\n this.setState({ modalloading: true }, () => {\n this.props.getLoss(formdata);\n });\n };\n\n handleSummarySubmit = (e) => {\n e.preventDefault();\n let formdata = new FormData();\n formdata.append(\"portfolio\", this.state.activeItemName.slice(10));\n this.setState({ modalloading: true }, () => {\n this.props.getSummary(formdata);\n });\n };\n\n handleLogout = () => {\n this.props.logout();\n };\n handleOpen = (portfolio, variable) =>\n this.setState(\n { modalOpen: true, activeItemName: portfolio, activeVariable: variable },\n () => this.handleVariable()\n );\n\n handleVariable() {\n console.log(\"Climate Variable\", this.state.activeVariable);\n variableOptions = [];\n for (let i = 0; i < this.state.activeVariable.length; i++) {\n variableOptions.push({\n key: this.state.activeVariable[i],\n value: this.state.activeVariable[i],\n text: this.state.activeVariable[i],\n });\n }\n }\n handleEdit = (portfolio) => {\n this.setState({ modalEdit: true, activeItemName: portfolio });\n };\n\n handleLossModalOpen = (portfolio) =>\n this.setState({ lossmodalOpen: true, activeItemName: portfolio }, () =>\n console.log(this.state.activeItemName)\n );\n\n handleReport = (portfolio) =>\n this.setState({ reportmodal: true, activeItemName: portfolio }, () =>\n console.log(this.state.activeItemName)\n );\n\n handleLossModalClose = () => {\n this.setState({ lossmodalOpen: false }, () =>\n window.location.reload(false)\n );\n };\n handleSummaryModalOpen = (portfolio) =>\n this.setState({ summarymodalOpen: true, activeItemName: portfolio });\n\n handleSummaryModalClose = () => {\n this.setState({ summarymodalOpen: false }, () =>\n window.location.reload(false)\n );\n };\n\n handleDropbox = (base, map) => {\n if (this.props.link.url == undefined) {\n console.log(\"function called\", map);\n let formdata = new FormData();\n formdata.append(\"ppt\", base);\n formdata.append(\"project_name\", map);\n this.props.Dropbox(formdata);\n }\n };\n\n handleClose = () =>\n this.setState(\n {\n modalOpen: false,\n lossmodalOpen: false,\n reportmodal: false,\n modalEdit: false,\n dropbox: false,\n },\n () => window.location.reload(false)\n );\n\n handleModify = (portfolio) => {\n this.props.history.push({\n pathname: \"/edit-transition\",\n state: { carbon_project_name: portfolio.project_name },\n });\n };\n\n handleDelete = (portfolio) => {\n let formdata = new FormData();\n formdata.append(\"portfolio_name\", portfolio.project_name);\n this.props.deleteCarbonProject(formdata);\n };\n handleDownload = (portfolio) => {\n let formdata = new FormData();\n formdata.append(\"portfolio_name\", portfolio);\n this.props.downloadCarbonProject(formdata);\n };\n handleDownloadModalOpen = (portfolio) =>\n this.setState({\n downloadmodalOpen: true,\n activeItemName: portfolio.project_name,\n });\n\n handleDownloadModalClose = () => {\n this.setState({ downloadmodalOpen: false }, () =>\n window.location.reload(false)\n );\n };\n\n render() {\n const {\n value,\n others,\n variables,\n rcp,\n year,\n status,\n portfolio_loss,\n } = this.state;\n let user_id = localStorage.getItem(\"user_id\");\n\n if (this.state.loading) {\n return ;\n }\n if (this.props.list.data && this.props.list.data.length > 0) {\n let assets = this.props.list.data;\n this.state.portfolios_transition = assets;\n }\n console.log(\"portfolio transition\", this.state.portfolios_transition);\n\n return (\n
\n \n All Projects For {\" \"}\n \n\n {this.props.list.username}\n
\n \n\n \n Total Number of Projects \n \n\n {this.props.list.data.length}\n
\n \n\n
\n\n {this.state.modalloading &&\n !this.props.download_carbon_project_data.length > 0 ? (\n
\n \n All Projects For {\" \"}\n \n\n {this.props.list.username}\n
\n \n\n \n Total Number of Projects \n \n\n {this.props.list.data.length}\n
\n \n\n
\n All Projects For {\" \"}\n {this.props.list_physical.username}\n
\n \n\n Total Project {\" \"}\n {this.props.list_physical.data.length}\n
\n \n\n
\n\n
\n\n
\n +{portfolio.shared_with.length}\n {\"\"} more\n
\n \n\n
delete{\" \"}\n {portfolio.name}\n \n }\n onConfirm={() =>\n this.delete(portfolio.name)\n }\n okText=\"Yes\"\n cancelText=\"No\"\n >\n
{\" \"}\n delete {portfolio.name}\n \n }\n onConfirm={() =>\n this.delete(portfolio.name)\n }\n okText=\"Yes\"\n cancelText=\"No\"\n >\n
\n Assets\n
\n {portfolio.assets}\n
\n
\n CVaR\n
$ {portfolio.loss.toFixed(2)} million\n
\n
\n
\n
\n
\n Max Exposure
$ {portfolio.value}{\" \"}\n million\n
\n
\n \n\n
\n {/*
\n \n Climate Score\n \n \n {portfolio.overall}%\n \n
\n\n\n {/*
\n +{portfolio.shared_with.length}\n {\"\"} more\n
\n \n\n
{\" \"}\n delete {portfolio.name}\n \n }\n onConfirm={() =>\n this.delete(portfolio.name)\n }\n okText=\"Yes\"\n cancelText=\"No\"\n >\n
\n {portfolio.date ? (\n
\n \n Climate Score\n \n
\n\n\n\n
\n\n
\n +{portfolio.shared_with.length}\n {\"\"} more\n
\n \n\n
delete{\" \"}\n {portfolio.name}\n \n }\n onConfirm={() =>\n this.delete(portfolio.name)\n }\n okText=\"Yes\"\n cancelText=\"No\"\n >\n
{this.props.errors.Error}
\n ) : null}\n\n {/*
\n
\n\n {this.state.loading && this.props.errors.Error == undefined ? (\n
User Default Options
\nChange User Default Options for more insights
\nAnalysis Type
\nRCP
\nSSP
\nYear
\n {this.state.scenario == \"rcp\" ? (\n\n\n
\n\n
\n
Project Name
\n\n {/*
\n
Select Users to Share Project with
\n\n
Permissions
\n\n
Analysis Type
\n\n\n {/*
\n {\" \"}\n
Currency
\n\n
\n
\n
\n
\n
\n\n {!this.state.loading ? (\n
\n
\n
{this.props.csv.error}
\nSelect Scenario
\n\nSelect Year
\n\n {this.state.csv_scenario == \"rcp\" ? (\nSelect RCP
\n\nSelect SSP
\n\nSelect RCP
\n\nSelect SSP
\n\nSelect Climate Variable
\n\nSelect Portfolio Losses
\n\nMitigation Measures
\n\nSelect Content
\nSelect Currency
\n\n Account for Long-term Impact of Climate Change on Economic\n Growth\n
\nTechnical Insurance Premium in %
\nAgriculture Variables
\nEnergy Variables
\n\n
{this.props.csv.error}
\nSelect Scenario
\nSelect Year
\n\n {this.state.csv_scenario === \"rcp\"\n ? \"Select RCP\"\n : \"Select SSP\"}\n
\nClimate Variable
\nPortfolio Losses
\nContent
\nSelect Currency
\n\n Account for Long-term Impact of Climate Change on Economic\n Growth\n
\n\n
Select Year
\n\n\n {this.state.modalloading &&\n !this.props.csv.length === undefined ? (\n
Select Scenario
\n\n
Mitigation Measures
\n\n\n\n {this.state.modalloading &&\n !this.props.csv.length === undefined ? (\n
\n Organization Name: \n
\n {this.props.list.organization}\n
\n User Name: \n
\n {this.props.list.username}\n
\n Number of Project: {\" \"}\n
\n {this.props.list.data.length}\n
\n Active Project: {\" \"}\n
\n {this.props.list.data.length}\n
\n
\n \n All Projects For {\" \"}\n \n\n {this.props.list.username}\n
\n \n\n \n Total Number of Projects \n \n\n {this.props.list.length}\n
\n \n\n\n {this.state.modalloading &&\n !this.props.download_carbon_project_data.length > 0 ? (\n
\n
\n
\n
Please upgrade tier to see content
\n \nTotal Loss: $ ${string.total_loss.toFixed(\n 2\n )} Mil
Total Loss(in %): ${string.percentage.toFixed(\n 2\n )}
Top Risk: ${\n string.top_hazard_string\n }
${hazard} Risk: ${string.hazard_loss[hazard].toFixed(2)}`\n )\n .addTo(map);\n });\n el.addEventListener(\"mouseleave\", (e) => {\n popup.remove();\n });\n });\n }\n map.on(\"load\", () => {\n map.addSource(\"places\", {\n type: \"geojson\",\n data: stores,\n });\n\n map.addLayer({\n id: \"places\",\n type: \"geojson\",\n source: \"places\",\n layout: {\n // Make the layer visible by default.\n visibility: \"visible\",\n },\n });\n\n map.addSource(\"raster-tiles\", {\n type: \"raster\",\n tiles: [\n \"https://intensel-tiles.s3.amazonaws.com/hazard/extreme_heat/rcp/85_2050/plasma/{z}/{x}/{y}.png\",\n ],\n scheme: \"tms\",\n tileSize: 256,\n });\n map.addLayer({\n id: \"Extreme Heat\",\n type: \"raster\",\n source: \"raster-tiles\",\n layout: {\n // Make the layer visible by default.\n visibility: \"visible\",\n },\n minzoom: 0,\n maxzoom: 22,\n });\n\n /* map.addSource(\"radar\", {\n type: \"image\",\n url: \"https://intensel-tiles.s3.amazonaws.com/hazard/extreme_heat/rcp/85_2050/plasma/{z}/{x}/{y}.png\",\n coordinates: [\n [-80.425, 46.437],\n [-71.516, 46.437],\n [-71.516, 37.936],\n [-80.425, 37.936],\n ],\n });\n map.addLayer({\n id: \"radar-layer\",\n type: \"raster\",\n source: \"radar\",\n paint: {\n \"raster-fade-duration\": 0,\n },\n });*/\n\n addMarkers();\n });\n map.on(\"idle\", () => {\n /* let mark = document.getElementsByClassName(\"worldmarker\");\n \n\n if (map.getZoom() <=4) {\n for (let i = 0; i < mark.length; i++) {\n mark[i].style.backgroundColor = \"#00008b\";\n }\n }*/\n // If these two layers were not added to the map, abort\n if (!map.getLayer(\"Extreme Heat\")) {\n return;\n }\n\n // Enumerate ids of the layers.\n const toggleableLayerIds = [\"Extreme Heat\"];\n\n // Set up the corresponding toggle button for each layer.\n for (const id of toggleableLayerIds) {\n // Skip layers that already have a button set up.\n if (document.getElementById(id)) {\n continue;\n }\n\n // Create a link.\n const link = document.createElement(\"a\");\n link.id = id;\n link.href = \"#\";\n link.textContent = id;\n link.className = \"active\";\n\n // Show or hide layer when the toggle is clicked.\n link.onclick = function(e) {\n const clickedLayer = this.textContent;\n e.preventDefault();\n e.stopPropagation();\n\n const visibility = map.getLayoutProperty(\n clickedLayer,\n \"visibility\"\n );\n\n // Toggle layer visibility by changing the layout object's visibility property.\n if (visibility === \"visible\") {\n map.setLayoutProperty(clickedLayer, \"visibility\", \"none\");\n this.className = \"\";\n } else {\n this.className = \"active\";\n map.setLayoutProperty(clickedLayer, \"visibility\", \"visible\");\n }\n };\n\n const layers = document.getElementById(\"mapmenu\");\n layers.appendChild(link);\n }\n });\n }\n }\n\n render() {\n return (\n
Total Loss: $ ${string.total_loss.toFixed(\n 2\n )} Mil
Total Loss(in %): ${string.percentage.toFixed(\n 2\n )}
Top Risk: ${\n string.top_hazard_string\n }
${hazard} Risk: ${string.hazard_loss[hazard].toFixed(2)}`\n )\n .addTo(map);\n });\n el.addEventListener(\"mouseleave\", (e) => {\n popup.remove();\n });\n });\n }\n map.on(\"load\", () => {\n map.addSource(\"places\", {\n type: \"geojson\",\n data: stores,\n });\n\n map.addLayer({\n id: \"places\",\n type: \"geojson\",\n source: \"places\",\n layout: {\n // Make the layer visible by default.\n visibility: \"visible\",\n },\n });\n\n map.addSource(\"raster-tiles\", {\n type: \"raster\",\n tiles: [\n \"https://intensel-tiles.s3.amazonaws.com/hazard/river_flood/rcp/85_2050/blues/{z}/{x}/{y}.png\",\n ],\n scheme: \"tms\",\n tileSize: 256,\n });\n map.addLayer({\n id: \"River Flood\",\n type: \"raster\",\n source: \"raster-tiles\",\n layout: {\n // Make the layer visible by default.\n visibility: \"visible\",\n },\n minzoom: 0,\n maxzoom: 22,\n });\n\n /* map.addSource(\"radar\", {\n type: \"image\",\n url: \"https://intensel-tiles.s3.amazonaws.com/hazard/extreme_heat/rcp/85_2050/plasma/{z}/{x}/{y}.png\",\n coordinates: [\n [-80.425, 46.437],\n [-71.516, 46.437],\n [-71.516, 37.936],\n [-80.425, 37.936],\n ],\n });\n map.addLayer({\n id: \"radar-layer\",\n type: \"raster\",\n source: \"radar\",\n paint: {\n \"raster-fade-duration\": 0,\n },\n });*/\n\n addMarkers();\n });\n map.on(\"idle\", () => {\n /* let mark = document.getElementsByClassName(\"worldmarker\");\n \n\n if (map.getZoom() <=4) {\n for (let i = 0; i < mark.length; i++) {\n mark[i].style.backgroundColor = \"#00008b\";\n }\n }*/\n // If these two layers were not added to the map, abort\n if (!map.getLayer(\"River Flood\")) {\n return;\n }\n\n // Enumerate ids of the layers.\n const toggleableLayerIds = [\"River Flood\"];\n\n // Set up the corresponding toggle button for each layer.\n for (const id of toggleableLayerIds) {\n // Skip layers that already have a button set up.\n if (document.getElementById(id)) {\n continue;\n }\n\n // Create a link.\n const link = document.createElement(\"a\");\n link.id = id;\n link.href = \"#\";\n link.textContent = id;\n link.className = \"active\";\n\n // Show or hide layer when the toggle is clicked.\n link.onclick = function(e) {\n const clickedLayer = this.textContent;\n e.preventDefault();\n e.stopPropagation();\n\n const visibility = map.getLayoutProperty(\n clickedLayer,\n \"visibility\"\n );\n\n // Toggle layer visibility by changing the layout object's visibility property.\n if (visibility === \"visible\") {\n map.setLayoutProperty(clickedLayer, \"visibility\", \"none\");\n this.className = \"\";\n } else {\n this.className = \"active\";\n map.setLayoutProperty(clickedLayer, \"visibility\", \"visible\");\n }\n };\n\n const layers = document.getElementById(\"mapmenu\");\n layers.appendChild(link);\n }\n });\n }\n }\n\n render() {\n return (\n
Total Loss: $ ${string.total_loss.toFixed(\n 2\n )} Mil
Total Loss(in %): ${string.percentage.toFixed(\n 2\n )}
Top Risk: ${\n string.top_hazard_string\n }
${hazard} Risk: ${string.hazard_loss[hazard].toFixed(2)}`\n )\n .addTo(map);\n });\n el.addEventListener(\"mouseleave\", (e) => {\n popup.remove();\n });\n });\n }\n map.on(\"load\", () => {\n map.addSource(\"places\", {\n type: \"geojson\",\n data: stores,\n });\n\n map.addLayer({\n id: \"places\",\n type: \"geojson\",\n source: \"places\",\n layout: {\n // Make the layer visible by default.\n visibility: \"visible\",\n },\n });\n\n map.addSource(\"raster-tiles\", {\n type: \"raster\",\n tiles: [\n \"https://intensel-tiles.s3.amazonaws.com/hazard/typhoon/rcp/85_2050/viridis/{z}/{x}/{y}.png\",\n ],\n scheme: \"tms\",\n tileSize: 256,\n });\n map.addLayer({\n id: \"Typhoon\",\n type: \"raster\",\n source: \"raster-tiles\",\n layout: {\n // Make the layer visible by default.\n visibility: \"visible\",\n },\n minzoom: 0,\n maxzoom: 22,\n });\n\n /* map.addSource(\"radar\", {\n type: \"image\",\n url: \"https://intensel-tiles.s3.amazonaws.com/hazard/extreme_heat/rcp/85_2050/plasma/{z}/{x}/{y}.png\",\n coordinates: [\n [-80.425, 46.437],\n [-71.516, 46.437],\n [-71.516, 37.936],\n [-80.425, 37.936],\n ],\n });\n map.addLayer({\n id: \"radar-layer\",\n type: \"raster\",\n source: \"radar\",\n paint: {\n \"raster-fade-duration\": 0,\n },\n });*/\n\n addMarkers();\n });\n map.on(\"idle\", () => {\n /* let mark = document.getElementsByClassName(\"worldmarker\");\n \n\n if (map.getZoom() <=4) {\n for (let i = 0; i < mark.length; i++) {\n mark[i].style.backgroundColor = \"#00008b\";\n }\n }*/\n // If these two layers were not added to the map, abort\n if (!map.getLayer(\"Typhoon\")) {\n return;\n }\n\n // Enumerate ids of the layers.\n const toggleableLayerIds = [\"Typhoon\"];\n\n // Set up the corresponding toggle button for each layer.\n for (const id of toggleableLayerIds) {\n // Skip layers that already have a button set up.\n if (document.getElementById(id)) {\n continue;\n }\n\n // Create a link.\n const link = document.createElement(\"a\");\n link.id = id;\n link.href = \"#\";\n link.textContent = id;\n link.className = \"active\";\n\n // Show or hide layer when the toggle is clicked.\n link.onclick = function(e) {\n const clickedLayer = this.textContent;\n e.preventDefault();\n e.stopPropagation();\n\n const visibility = map.getLayoutProperty(\n clickedLayer,\n \"visibility\"\n );\n\n // Toggle layer visibility by changing the layout object's visibility property.\n if (visibility === \"visible\") {\n map.setLayoutProperty(clickedLayer, \"visibility\", \"none\");\n this.className = \"\";\n } else {\n this.className = \"active\";\n map.setLayoutProperty(clickedLayer, \"visibility\", \"visible\");\n }\n };\n\n const layers = document.getElementById(\"mapmenu\");\n layers.appendChild(link);\n }\n });\n }\n }\n\n render() {\n return (\n
Total Loss: $ ${string.total_loss.toFixed(\n 2\n )} Mil
Total Loss(in %): ${string.percentage.toFixed(\n 2\n )}
Top Risk: ${\n string.top_hazard_string\n }
${hazard} Risk: ${string.hazard_loss[hazard].toFixed(2)}`\n )\n .addTo(map);\n });\n el.addEventListener(\"mouseleave\", (e) => {\n popup.remove();\n });\n });\n }\n map.on(\"load\", () => {\n map.addSource(\"places\", {\n type: \"geojson\",\n data: stores,\n });\n\n console.log(\"add Marker\", map.getZoom());\n\n /* map.addSource(\"radar\", {\n type: \"image\",\n url: \"https://intensel-tiles.s3.amazonaws.com/hazard/extreme_heat/rcp/85_2050/plasma/{z}/{x}/{y}.png\",\n coordinates: [\n [-80.425, 46.437],\n [-71.516, 46.437],\n [-71.516, 37.936],\n [-80.425, 37.936],\n ],\n });\n map.addLayer({\n id: \"radar-layer\",\n type: \"raster\",\n source: \"radar\",\n paint: {\n \"raster-fade-duration\": 0,\n },\n });*/\n\n addMarkers();\n });\n }\n }\n\n render() {\n return (\n
\n \n Analysis of River Flood Damage\n \n
\n\n\n \n Analysis of Storm Damage\n \n
\n {/*\n \n Analysis of Rainfall Flood Damage\n \n
\n {data2.length > 0 ? (\n\n For the given year {this.props.year}, the projected loss depends\n on the carbon concentration, captured by certain{\" \"}\n {this.props.analysis.toUpperCase()}s. The difference between{\" \"}\n {this.props.analysis.toUpperCase()} 8.5 AND{\" \"}\n {this.props.analysis.toUpperCase()} 2.6 reflects the Value of\n Transition Policies which is US${\"\"}\n \n {\"\"}\n {this.props.key_find.value_of_transition_policies}\n \n Million by {this.props.year}. This is calculated by\n difference between the {this.props.analysis.toUpperCase()} 8.5\n CVaR of US$\n \n {this.props.key_find.cvar_85.toFixed(2)} Million\n {\" \"}\n and the {this.props.analysis.toUpperCase()} 2.6 CVaR of US$\n \n {this.props.key_find.cvar_26.toFixed(2)} Million.\n {\" \"}\n
\n ) : null}\n\n {this.state.analysis == \"rcp\" ? (\n \n This indicates the Loss projection for 2030 and 2050\n relative to the historical baseline of 2020 under the\n Business-as-Usual case. For{\" \"}\n {this.state.analysis.toUpperCase()}{\" \"}\n {this.props.rcp} , Climate\n Value at Risk (CVaR) increases{\" \"}\n \n {\" \"}\n {this.props.key_find.increased_by_2030}{\" \"}\n {\" \"}\n times by 2030 and{\" \"}\n \n {this.props.key_find.increased_by_2050}{\" \"}\n {\" \"}\n times by 2050 compared to current losses (2020).\n \n ) : (\n \n This indicates the Loss projection for 2030 , 2050 and 2100\n relative to the historical baseline of 2020 under the\n Business-as-Usual case. For{\" \"}\n {this.state.analysis.toUpperCase()}{\" \"}\n {this.props.ssp} , Climate\n Value at Risk (CVaR) increases{\" \"}\n \n {\" \"}\n {this.props.key_find.increased_by_2030}{\" \"}\n {\" \"}\n times by 2030 and{\" \"}\n \n {this.props.key_find.increased_by_2050}{\" \"}\n {\" \"}\n times by 2050 and{\" \"}\n \n {this.props.key_find.increased_by_2100} times by 2100\n \n compared to current losses (2020).\n \n )}\n
\n ) : null}\n\n {props.variable[i]}\n {props.hazard.acute ? (\n \n {props.hazard.acute[props.variable[i]][0]}\n {props.hazard.acute[props.variable[i]][1]}\n \n ) : null}\n
\n {props.newdata.acute[props.variable[i]] == 0 ? (\n \n ) : (\n\n {props.variable[i]}\n \n {props.hazard.chronic[props.variable[i]][0]}{\" \"}\n {props.hazard.chronic[props.variable[i]][1]}\n \n
\n {props.newdata.chronic[props.variable[i]] == 0 ? (\n \n ) : (\n\n Rainfall Flood\n \n {props.hazard.both[\"Rainfall Flood\"][0]}{\" \"}\n {props.hazard.both[\"Rainfall Flood\"][1]} \n {props.hazard.both[\"Rainfall Flood\"][2] > 0 == 0 ? (\n \n [No Risk]\n \n ) : props.hazard.both[\"Rainfall Flood\"][2] > 0 &&\n props.hazard.both[\"Rainfall Flood\"][2] < 25 ? (\n \n [Low]\n \n ) : props.hazard.both[\"Rainfall Flood\"][2] >= 25 &&\n props.hazard.both[\"Rainfall Flood\"][2] < 50 ? (\n \n [Medium]\n \n ) : props.hazard.both[\"Rainfall Flood\"][2] >= 50 &&\n props.hazard.both[\"Rainfall Flood\"][2] < 75 ? (\n \n [Medium-High]\n \n ) : props.hazard.both[\"Rainfall Flood\"][2] >= 75 &&\n props.hazard.both[\"Rainfall Flood\"][2] < 100 ? (\n \n [High]\n \n ) : props.hazard.both[\"Rainfall Flood\"][2] >= 100 ? (\n \n [Very High]\n \n ) : null}\n \n
\n {props.newdata.both[\"Rainfall Flood\"] == 0 ? (\n\n River Flood\n \n {props.hazard.both[\"River Flood\"][0]}{\" \"}\n {props.hazard.both[\"River Flood\"][1]} \n {props.hazard.both[\"River Flood\"][2] > 0 == 0 ? (\n \n [No Risk]\n \n ) : props.hazard.both[\"River Flood\"][2] > 0 &&\n props.hazard.both[\"River Flood\"][2] < 25 ? (\n \n [Low]\n \n ) : props.hazard.both[\"River Flood\"][2] >= 25 &&\n props.hazard.both[\"River Flood\"][2] < 50 ? (\n \n [Medium]\n \n ) : props.hazard.both[\"River Flood\"][2] >= 50 &&\n props.hazard.both[\"River Flood\"][2] < 75 ? (\n \n [Medium-High]\n \n ) : props.hazard.both[\"River Flood\"][2] >= 75 &&\n props.hazard.both[\"River Flood\"][2] < 100 ? (\n \n [High]\n \n ) : props.hazard.both[\"River Flood\"][2] >= 100 ? (\n \n [Very High]\n \n ) : null}\n \n
\n {props.newdata.both[\"River Flood\"] == 0 ? (\n\n Storm Surge\n \n {props.hazard.both[\"Storm Surge\"][0]}{\" \"}\n {props.hazard.both[\"Storm Surge\"][1]} \n {props.hazard.both[\"Storm Surge\"][2] > 0 == 0 ? (\n \n [No Risk]\n \n ) : props.hazard.both[\"Storm Surge\"][2] > 0 &&\n props.hazard.both[\"Storm Surge\"][2] < 25 ? (\n \n [Low]\n \n ) : props.hazard.both[\"Storm Surge\"][2] >= 25 &&\n props.hazard.both[\"Storm Surge\"][2] < 50 ? (\n \n [Medium]\n \n ) : props.hazard.both[\"Storm Surge\"][2] >= 50 &&\n props.hazard.both[\"Storm Surge\"][2] < 75 ? (\n \n [Medium-High]\n \n ) : props.hazard.both[\"Storm Surge\"][2] >= 75 &&\n props.hazard.both[\"Storm Surge\"][2] < 100 ? (\n \n [High]\n \n ) : props.hazard.both[\"Storm Surge\"][2] >= 100 ? (\n \n [Very High]\n \n ) : null}\n \n
\n {props.newdata.both[\"Storm Surge\"] == 0 ? (\n\n Typhoon\n \n {props.hazard.both[\"Typhoon\"][0]}{\" \"}\n {props.hazard.both[\"Typhoon\"][1]} \n {props.hazard.both[\"Typhoon\"][2] > 0 == 0 ? (\n \n [No Risk]\n \n ) : props.hazard.both[\"Typhoon\"][2] > 0 &&\n props.hazard.both[\"Typhoon\"][2] < 25 ? (\n \n [Low]\n \n ) : props.hazard.both[\"Typhoon\"][2] >= 25 &&\n props.hazard.both[\"Typhoon\"][2] < 50 ? (\n \n [Medium]\n \n ) : props.hazard.both[\"Typhoon\"][2] >= 50 &&\n props.hazard.both[\"Typhoon\"][2] < 75 ? (\n \n [Medium-High]\n \n ) : props.hazard.both[\"Typhoon\"][2] >= 75 &&\n props.hazard.both[\"Typhoon\"][2] < 100 ? (\n \n [High]\n \n ) : props.hazard.both[\"Typhoon\"][2] >= 100 ? (\n \n [Very High]\n \n ) : null}\n \n
\n {props.newdata.both[\"Typhoon\"] == 0 ? (\n \n ) : (\n\n Landslide\n \n {props.hazard.both[\"Landslide\"][0]} \n {props.hazard.both[\"Landslide\"][1]}\n {props.hazard.both[\"Landslide\"][2] > 0 == 0 ? (\n \n [No Risk]\n \n ) : props.hazard.both[\"Landslide\"][2] > 0 &&\n props.hazard.both[\"Landslide\"][2] < 25 ? (\n \n [Low]\n \n ) : props.hazard.both[\"Landslide\"][2] >= 25 &&\n props.hazard.both[\"Landslide\"][2] < 50 ? (\n \n [Medium]\n \n ) : props.hazard.both[\"Landslide\"][2] >= 50 &&\n props.hazard.both[\"Landslide\"][2] < 75 ? (\n \n [Medium High]\n \n ) : props.hazard.both[\"Landslide\"][2] >= 75 &&\n props.hazard.both[\"Landslide\"][2] < 100 ? (\n \n [High]\n \n ) : props.hazard.both[\"Landslide\"][2] >= 100 ? (\n \n Very [High]\n \n ) : null}\n \n
\n {props.newdata.both[\"Landslide\"] == 0 ? (\n\n Extreme heat\n \n {props.hazard.both[\"Extreme heat\"][0]}{\" \"}\n {props.hazard.both[\"Extreme heat\"][1]} \n {props.hazard.both[\"Extreme heat\"][2] > 0 == 0 ? (\n \n [No Risk]\n \n ) : props.hazard.both[\"Extreme heat\"][2] > 0 &&\n props.hazard.both[\"Extreme heat\"][2] < 25 ? (\n \n [Low]\n \n ) : props.hazard.both[\"Extreme heat\"][2] >= 25 &&\n props.hazard.both[\"Extreme heat\"][2] < 50 ? (\n \n [Medium]\n \n ) : props.hazard.both[\"Extreme heat\"][2] >= 50 &&\n props.hazard.both[\"Extreme heat\"][2] < 75 ? (\n \n [Medium-High]\n \n ) : props.hazard.both[\"Extreme heat\"][2] >= 75 &&\n props.hazard.both[\"Extreme heat\"][2] < 100 ? (\n \n [High]\n \n ) : props.hazard.both[\"Extreme heat\"][2] >= 100 ? (\n \n [Very High]\n \n ) : null}\n \n
\n {props.newdata.both[\"Extreme heat\"] == 0 ? (\n\n Extreme Cold\n \n {props.hazard.both[\"Extreme Cold\"][0]}{\" \"}\n {props.hazard.both[\"Extreme Cold\"][1]} \n {props.hazard.both[\"Extreme Cold\"][2] > 0 == 0 ? (\n \n [No Risk]\n \n ) : props.hazard.both[\"Extreme Cold\"][2] > 0 &&\n props.hazard.both[\"Extreme Cold\"][2] < 25 ? (\n \n [Low]\n \n ) : props.hazard.both[\"Extreme Cold\"][2] >= 25 &&\n props.hazard.both[\"Extreme Cold\"][2] < 50 ? (\n \n [Medium]\n \n ) : props.hazard.both[\"Extreme Cold\"][2] >= 50 &&\n props.hazard.both[\"Extreme Cold\"][2] < 75 ? (\n \n [Medium-High]\n \n ) : props.hazard.both[\"Extreme Cold\"][2] >= 75 &&\n props.hazard.both[\"Extreme Cold\"][2] < 100 ? (\n \n [High]\n \n ) : props.hazard.both[\"Extreme Cold\"][2] >= 100 ? (\n \n [Very High]\n \n ) : null}\n \n
\n {props.newdata.both[\"Extreme Cold\"] == 0 ? (\n\n Snowfall\n \n {props.hazard.both[\"Snow Fall\"][0]}{\" \"}\n {props.hazard.both[\"Snow Fall\"][1]} \n {props.hazard.both[\"Snow Fall\"][2] > 0 == 0 ? (\n \n [No Risk]\n \n ) : props.hazard.both[\"Snow Fall\"][2] > 0 &&\n props.hazard.both[\"Snow Fall\"][2] < 25 ? (\n \n [Low]\n \n ) : props.hazard.both[\"Snow Fall\"][2] >= 25 &&\n props.hazard.both[\"Snow Fall\"][2] < 50 ? (\n \n [Medium]\n \n ) : props.hazard.both[\"Snow Fall\"][2] >= 50 &&\n props.hazard.both[\"Snow Fall\"][2] < 75 ? (\n \n [Medium-High]\n \n ) : props.hazard.both[\"Snow Fall\"][2] >= 75 &&\n props.hazard.both[\"Snow Fall\"][2] < 100 ? (\n \n [High]\n \n ) : props.hazard.both[\"Snow Fall\"][2] >= 100 ? (\n \n [Very High]\n \n ) : null}\n \n
\n {props.newdata.both[\"Snow Fall\"] == 0 ? (\n\n Sea Level Rise\n \n {props.hazard.both[\"Sea Level Rise\"][0]}{\" \"}\n {props.hazard.both[\"Sea Level Rise\"][1]} \n {props.hazard.both[\"Sea Level Rise\"][2] > 0 == 0 ? (\n \n [No Risk]\n \n ) : props.hazard.both[\"Sea Level Rise\"][2] > 0 &&\n props.hazard.both[\"Sea Level Rise\"][2] < 100 ? (\n \n [Very High]\n \n ) : null}\n \n
\n {props.newdata.both[\"Sea Level Rise\"] == 0 ? (\n\n Drought\n \n {props.hazard.both[\"Drought\"][0]}{\" \"}\n {props.hazard.both[\"Drought\"][1]} \n {props.hazard.both[\"Drought\"][2] <= 0 ? (\n \n [No Risk]\n \n ) : props.hazard.both[\"Drought\"][2] > 0 &&\n props.hazard.both[\"Drought\"][2] < 25 ? (\n \n [Low]\n \n ) : props.hazard.both[\"Drought\"][2] >= 25 &&\n props.hazard.both[\"Drought\"][2] < 50 ? (\n \n [Medium]\n \n ) : props.hazard.both[\"Drought\"][2] >= 50 &&\n props.hazard.both[\"Drought\"][2] < 75 ? (\n \n [Medium-High]\n \n ) : props.hazard.both[\"Drought\"][2] >= 75 &&\n props.hazard.both[\"Drought\"][2] < 100 ? (\n \n [High]\n \n ) : props.hazard.both[\"Drought\"][2] >= 100 ? (\n \n [Very High]\n \n ) : null}\n \n
\n {props.newdata.both[\"Drought\"] == 0 ? (\n\n Wildfire\n \n {props.hazard.both[\"Wild Fire\"][0]}{\" \"}\n {props.hazard.both[\"Wild Fire\"][1]}\n {props.hazard.both[\"Wild Fire\"][2] <= 0 ? (\n \n [No Risk]\n \n ) : props.hazard.both[\"Wild Fire\"][2] > 0 &&\n props.hazard.both[\"Wild Fire\"][2] < 25 ? (\n \n [Low]\n \n ) : props.hazard.both[\"Wild Fire\"][2] >= 25 &&\n props.hazard.both[\"Wild Fire\"][2] < 50 ? (\n \n [Medium]\n \n ) : props.hazard.both[\"Wild Fire\"][2] >= 50 &&\n props.hazard.both[\"Wild Fire\"][2] < 75 ? (\n \n [[Medium]-[High]]\n \n ) : props.hazard.both[\"Wild Fire\"][2] >= 75 &&\n props.hazard.both[\"Wild Fire\"][2] < 100 ? (\n \n [High]\n \n ) : props.hazard.both[\"Wild Fire\"][2] >= 100 ? (\n \n Very [High]\n \n ) : null}\n \n
\n {props.newdata.both[\"Wild Fire\"] == 0 ? (\n\n Extreme heat\n \n {props.hazard.both[\"Extreme heat\"][0]}{\" \"}\n {props.hazard.both[\"Extreme heat\"][1]} \n {props.hazard.both[\"Extreme heat\"][2] > 0 == 0 ? (\n \n [No Risk]\n \n ) : props.hazard.both[\"Extreme heat\"][2] > 0 &&\n props.hazard.both[\"Extreme heat\"][2] < 25 ? (\n \n [Low]\n \n ) : props.hazard.both[\"Extreme heat\"][2] >= 25 &&\n props.hazard.both[\"Extreme heat\"][2] < 50 ? (\n \n [Medium]\n \n ) : props.hazard.both[\"Extreme heat\"][2] >= 50 &&\n props.hazard.both[\"Extreme heat\"][2] < 75 ? (\n \n [Medium-High]\n \n ) : props.hazard.both[\"Extreme heat\"][2] >= 75 &&\n props.hazard.both[\"Extreme heat\"][2] < 100 ? (\n \n [High]\n \n ) : props.hazard.both[\"Extreme heat\"][2] >= 100 ? (\n \n [Very High]\n \n ) : null}\n \n
\n {props.newdata.both[\"Extreme heat\"] == 0 ? (\n\n Snowfall\n \n {props.hazard.both[\"Snow Fall\"][0]}{\" \"}\n {props.hazard.both[\"Snow Fall\"][1]} \n {props.hazard.both[\"Snow Fall\"][2] > 0 == 0 ? (\n \n [No Risk]\n \n ) : props.hazard.both[\"Snow Fall\"][2] > 0 &&\n props.hazard.both[\"Snow Fall\"][2] < 25 ? (\n \n [Low]\n \n ) : props.hazard.both[\"Snow Fall\"][2] >= 25 &&\n props.hazard.both[\"Snow Fall\"][2] < 50 ? (\n \n [Medium]\n \n ) : props.hazard.both[\"Snow Fall\"][2] >= 50 &&\n props.hazard.both[\"Snow Fall\"][2] < 75 ? (\n \n [Medium-High]\n \n ) : props.hazard.both[\"Snow Fall\"][2] >= 75 &&\n props.hazard.both[\"Snow Fall\"][2] < 100 ? (\n \n [High]\n \n ) : props.hazard.both[\"Snow Fall\"][2] >= 100 ? (\n \n [Very High]\n \n ) : null}\n \n
\n {props.newdata.both[\"Snow Fall\"] == 0 ? (\n\n Sea Level Rise\n \n {props.hazard.both[\"Sea Level Rise\"][0]}{\" \"}\n {props.hazard.both[\"Sea Level Rise\"][1]} \n {props.hazard.both[\"Sea Level Rise\"][2] > 0 == 0 ? (\n \n [No Risk]\n \n ) : props.hazard.both[\"Sea Level Rise\"][2] > 0 &&\n props.hazard.both[\"Sea Level Rise\"][2] < 100 ? (\n \n [Very High]\n \n ) : null}\n \n
\n {props.newdata.both[\"Sea Level Rise\"] == 0 ? (\n\n Drought\n \n {props.hazard.both[\"Drought\"][0]}{\" \"}\n {props.hazard.both[\"Drought\"][1]} \n {props.hazard.both[\"Drought\"][2] <= 0 ? (\n \n [No Risk]\n \n ) : props.hazard.both[\"Drought\"][2] > 0 &&\n props.hazard.both[\"Drought\"][2] < 25 ? (\n \n [Low]\n \n ) : props.hazard.both[\"Drought\"][2] >= 25 &&\n props.hazard.both[\"Drought\"][2] < 50 ? (\n \n [Medium]\n \n ) : props.hazard.both[\"Drought\"][2] >= 50 &&\n props.hazard.both[\"Drought\"][2] < 75 ? (\n \n [Medium-High]\n \n ) : props.hazard.both[\"Drought\"][2] >= 75 &&\n props.hazard.both[\"Drought\"][2] < 100 ? (\n \n [High]\n \n ) : props.hazard.both[\"Drought\"][2] >= 100 ? (\n \n [Very High]\n \n ) : null}\n \n
\n {props.newdata.both[\"Drought\"] == 0 ? (\n\n Landslide\n \n {props.hazard.both[\"Landslide\"][0]} \n {props.hazard.both[\"Landslide\"][1]}\n {props.hazard.both[\"Landslide\"][2] > 0 == 0 ? (\n \n [No Risk]\n \n ) : props.hazard.both[\"Landslide\"][2] > 0 &&\n props.hazard.both[\"Landslide\"][2] < 25 ? (\n \n [Low]\n \n ) : props.hazard.both[\"Landslide\"][2] >= 25 &&\n props.hazard.both[\"Landslide\"][2] < 50 ? (\n \n [Medium]\n \n ) : props.hazard.both[\"Landslide\"][2] >= 50 &&\n props.hazard.both[\"Landslide\"][2] < 75 ? (\n \n [Medium High]\n \n ) : props.hazard.both[\"Landslide\"][2] >= 75 &&\n props.hazard.both[\"Landslide\"][2] < 100 ? (\n \n [High]\n \n ) : props.hazard.both[\"Landslide\"][2] >= 100 ? (\n \n Very [High]\n \n ) : null}\n \n
\n {props.newdata.both[\"Landslide\"] == 0 ? (\n\n Rainfall Flood\n \n {props.hazard.both[\"Rainfall Flood\"][0]}{\" \"}\n {props.hazard.both[\"Rainfall Flood\"][1]} \n {props.hazard.both[\"Rainfall Flood\"][2] > 0 == 0 ? (\n \n [No Risk]\n \n ) : props.hazard.both[\"Rainfall Flood\"][2] > 0 &&\n props.hazard.both[\"Rainfall Flood\"][2] < 25 ? (\n \n [Low]\n \n ) : props.hazard.both[\"Rainfall Flood\"][2] >= 25 &&\n props.hazard.both[\"Rainfall Flood\"][2] < 50 ? (\n \n [Medium]\n \n ) : props.hazard.both[\"Rainfall Flood\"][2] >= 50 &&\n props.hazard.both[\"Rainfall Flood\"][2] < 75 ? (\n \n [Medium-High]\n \n ) : props.hazard.both[\"Rainfall Flood\"][2] >= 75 &&\n props.hazard.both[\"Rainfall Flood\"][2] < 100 ? (\n \n [High]\n \n ) : props.hazard.both[\"Rainfall Flood\"][2] >= 100 ? (\n \n [Very High]\n \n ) : null}\n \n
\n {props.newdata.both[\"Rainfall Flood\"] == 0 ? (\n\n River Flood\n \n {props.hazard.both[\"River Flood\"][0]}{\" \"}\n {props.hazard.both[\"River Flood\"][1]} \n {props.hazard.both[\"River Flood\"][2] > 0 == 0 ? (\n \n [No Risk]\n \n ) : props.hazard.both[\"River Flood\"][2] > 0 &&\n props.hazard.both[\"River Flood\"][2] < 25 ? (\n \n [Low]\n \n ) : props.hazard.both[\"River Flood\"][2] >= 25 &&\n props.hazard.both[\"River Flood\"][2] < 50 ? (\n \n [Medium]\n \n ) : props.hazard.both[\"River Flood\"][2] >= 50 &&\n props.hazard.both[\"River Flood\"][2] < 75 ? (\n \n [Medium-High]\n \n ) : props.hazard.both[\"River Flood\"][2] >= 75 &&\n props.hazard.both[\"River Flood\"][2] < 100 ? (\n \n [High]\n \n ) : props.hazard.both[\"River Flood\"][2] >= 100 ? (\n \n [Very High]\n \n ) : null}\n \n
\n {props.newdata.both[\"River Flood\"] == 0 ? (\n\n Storm Surge\n \n {props.hazard.both[\"Storm Surge\"][0]}{\" \"}\n {props.hazard.both[\"Storm Surge\"][1]} \n {props.hazard.both[\"Storm Surge\"][2] > 0 == 0 ? (\n \n [No Risk]\n \n ) : props.hazard.both[\"Storm Surge\"][2] > 0 &&\n props.hazard.both[\"Storm Surge\"][2] < 25 ? (\n \n [Low]\n \n ) : props.hazard.both[\"Storm Surge\"][2] >= 25 &&\n props.hazard.both[\"Storm Surge\"][2] < 50 ? (\n \n [Medium]\n \n ) : props.hazard.both[\"Storm Surge\"][2] >= 50 &&\n props.hazard.both[\"Storm Surge\"][2] < 75 ? (\n \n [Medium-High]\n \n ) : props.hazard.both[\"Storm Surge\"][2] >= 75 &&\n props.hazard.both[\"Storm Surge\"][2] < 100 ? (\n \n [High]\n \n ) : props.hazard.both[\"Storm Surge\"][2] >= 100 ? (\n \n [Very High]\n \n ) : null}\n \n
\n {props.newdata.both[\"Storm Surge\"] == 0 ? (\n\n Typhoon\n \n {props.hazard.both[\"Typhoon\"][0]}{\" \"}\n {props.hazard.both[\"Typhoon\"][1]} \n {props.hazard.both[\"Typhoon\"][2] > 0 == 0 ? (\n \n [No Risk]\n \n ) : props.hazard.both[\"Typhoon\"][2] > 0 &&\n props.hazard.both[\"Typhoon\"][2] < 25 ? (\n \n [Low]\n \n ) : props.hazard.both[\"Typhoon\"][2] >= 25 &&\n props.hazard.both[\"Typhoon\"][2] < 50 ? (\n \n [Medium]\n \n ) : props.hazard.both[\"Typhoon\"][2] >= 50 &&\n props.hazard.both[\"Typhoon\"][2] < 75 ? (\n \n [Medium-High]\n \n ) : props.hazard.both[\"Typhoon\"][2] >= 75 &&\n props.hazard.both[\"Typhoon\"][2] < 100 ? (\n \n [High]\n \n ) : props.hazard.both[\"Typhoon\"][2] >= 100 ? (\n \n [Very High]\n \n ) : null}\n \n
\n {props.newdata.both[\"Typhoon\"] == 0 ? (\n \n ) : (\n\n Wildfire\n \n {props.hazard.both[\"Wild Fire\"][0]}{\" \"}\n {props.hazard.both[\"Wild Fire\"][1]}\n {props.hazard.both[\"Wild Fire\"][2] <= 0 ? (\n \n [No Risk]\n \n ) : props.hazard.both[\"Wild Fire\"][2] > 0 &&\n props.hazard.both[\"Wild Fire\"][2] < 25 ? (\n \n [Low]\n \n ) : props.hazard.both[\"Wild Fire\"][2] >= 25 &&\n props.hazard.both[\"Wild Fire\"][2] < 50 ? (\n \n [Medium]\n \n ) : props.hazard.both[\"Wild Fire\"][2] >= 50 &&\n props.hazard.both[\"Wild Fire\"][2] < 75 ? (\n \n [[Medium]-[High]]\n \n ) : props.hazard.both[\"Wild Fire\"][2] >= 75 &&\n props.hazard.both[\"Wild Fire\"][2] < 100 ? (\n \n [High]\n \n ) : props.hazard.both[\"Wild Fire\"][2] >= 100 ? (\n \n Very [High]\n \n ) : null}\n \n
\n {props.newdata.both[\"Wild Fire\"] == 0 ? (\n\n Extreme Cold\n \n {props.hazard.both[\"Extreme Cold\"][0]}{\" \"}\n {props.hazard.both[\"Extreme Cold\"][1]} \n {props.hazard.both[\"Extreme Cold\"][2] > 0 == 0 ? (\n \n [No Risk]\n \n ) : props.hazard.both[\"Extreme Cold\"][2] > 0 &&\n props.hazard.both[\"Extreme Cold\"][2] < 25 ? (\n \n [Low]\n \n ) : props.hazard.both[\"Extreme Cold\"][2] >= 25 &&\n props.hazard.both[\"Extreme Cold\"][2] < 50 ? (\n \n [Medium]\n \n ) : props.hazard.both[\"Extreme Cold\"][2] >= 50 &&\n props.hazard.both[\"Extreme Cold\"][2] < 75 ? (\n \n [Medium-High]\n \n ) : props.hazard.both[\"Extreme Cold\"][2] >= 75 &&\n props.hazard.both[\"Extreme Cold\"][2] < 100 ? (\n \n [High]\n \n ) : props.hazard.both[\"Extreme Cold\"][2] >= 100 ? (\n \n [Very High]\n \n ) : null}\n \n
\n {props.newdata.both[\"Extreme Cold\"] == 0 ? (\n\n River Flood\n \n {props.hazard.mitigation[\"River Flood\"][0]}{\" \"}\n {props.hazard.mitigation[\"River Flood\"][1]} \n {props.hazard.mitigation[\"River Flood\"][2] > 0 == 0 ? (\n \n [No Risk]\n \n ) : props.hazard.mitigation[\"River Flood\"][2] > 0 &&\n props.hazard.mitigation[\"River Flood\"][2] < 25 ? (\n \n [Low]\n \n ) : props.hazard.mitigation[\"River Flood\"][2] >= 25 &&\n props.hazard.mitigation[\"River Flood\"][2] < 50 ? (\n \n [Medium]\n \n ) : props.hazard.mitigation[\"River Flood\"][2] >= 50 &&\n props.hazard.mitigation[\"River Flood\"][2] < 75 ? (\n \n [Medium-High]\n \n ) : props.hazard.mitigation[\"River Flood\"][2] >= 75 &&\n props.hazard.mitigation[\"River Flood\"][2] < 100 ? (\n \n [High]\n \n ) : props.hazard.mitigation[\"River Flood\"][2] >= 100 ? (\n \n [Very High]\n \n ) : null}\n \n
\n {props.hazard.both[\"River Flood\"] == 0 ? (\n\n River Flood\n \n {props.hazard.mitigation[\"Rainfall Flood\"][0]}{\" \"}\n {props.hazard.mitigation[\"Rainfall Flood\"][1]} \n {props.hazard.mitigation[\"Rainfall Flood\"][2] > 0 == 0 ? (\n \n [No Risk]\n \n ) : props.hazard.mitigation[\"Rainfall Flood\"][2] > 0 &&\n props.hazard.mitigation[\"Rainfall Flood\"][2] < 25 ? (\n \n [Low]\n \n ) : props.hazard.mitigation[\"Rainfall Flood\"][2] >= 25 &&\n props.hazard.mitigation[\"Rainfall Flood\"][2] < 50 ? (\n \n [Medium]\n \n ) : props.hazard.mitigation[\"Rainfall Flood\"][2] >= 50 &&\n props.hazard.mitigation[\"Rainfall Flood\"][2] < 75 ? (\n \n [Medium-High]\n \n ) : props.hazard.mitigation[\"Rainfall Flood\"][2] >= 75 &&\n props.hazard.mitigation[\"Rainfall Flood\"][2] < 100 ? (\n \n [High]\n \n ) : props.hazard.mitigation[\"Rainfall Flood\"][2] >= 100 ? (\n \n [Very High]\n \n ) : null}\n \n
\n {props.hazard.both[\"Rainfall Flood\"] == 0 ? (\n\n Solar Production\n {props.data.energy[\"solar\"]}%\n
\n {props.newdata.energy[\"solar\"] == 0 ? (\n \n ) : (\n\n Wind Production\n {props.data.energy[\"wind\"]}%\n
\n {props.newdata.energy[\"wind\"] == 0 ? (\n \n ) : (\n\n Hydro Production\n {props.data.energy[\"hydro\"]}%\n
\n {props.newdata.energy[\"hydro\"] == 0 ? (\n \n ) : (\n\n Wheat Production\n \n {props.data.agriculture[\"wheat\"]}%\n \n
\n {props.newdata.agriculture[\"wheat\"] == 0 ? (\n \n ) : (\n\n Rice Production\n \n {props.data.agriculture[\"rice\"]}%\n \n
\n {props.newdata.agriculture[\"rice\"] == 0 ? (\n \n ) : (\n\n Maize Production\n \n {props.data.agriculture[\"maize\"]}%\n \n
\n {props.newdata.agriculture[\"maize\"] == 0 ? (\n \n ) : (\n\n Fruit Production\n \n {props.data.agriculture[\"fruit\"]}%\n \n
\n {props.newdata.agriculture[\"fruit\"] == 0 ? (\n \n ) : (\n\n Vegetable Production\n \n {props.data.agriculture[\"vegetable\"]}%\n \n
\n {props.newdata.agriculture[\"vegetable\"] == 0 ? (\n \n ) : (\n\n {props.data[\"general\"][\"Rainfall Flood Loss\"] ? (\n
\n \n Rainfall Flood Loss{\" \"}\n \n \n {props.data[\"general\"][\"Rainfall Flood Loss\"][0] < 1000 ? (\n \n $\n {props.data[\"general\"][\"Rainfall Flood Loss\"][0].toFixed(2)}{\" \"}\n Million\n \n ) : (\n \n $\n {(\n props.data[\"general\"][\"Rainfall Flood Loss\"][0] / 1000\n ).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n River Flood Loss \n \n {props.data[\"general\"][\"River Flood Loss\"][0] < 1000 ? (\n \n ${props.data[\"general\"][\"River Flood Loss\"][0].toFixed(2)}{\" \"}\n Million\n \n ) : (\n \n $\n {(\n props.data[\"general\"][\"River Flood Loss\"][0] / 1000\n ).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n Storm Surge Loss \n \n {props.data[\"general\"][\"Storm Surge Loss\"][0] < 1000 ? (\n \n ${props.data[\"general\"][\"Storm Surge Loss\"][0].toFixed(2)}{\" \"}\n Million\n \n ) : (\n \n $\n {(\n props.data[\"general\"][\"Storm Surge Loss\"][0] / 1000\n ).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n Typhoon Loss \n \n {props.data[\"general\"][\"Typhoon Loss\"][0] < 1000 ? (\n \n ${props.data[\"general\"][\"Typhoon Loss\"][0].toFixed(2)} Million\n \n ) : (\n \n $\n {(props.data[\"general\"][\"Typhoon Loss\"][0] / 1000).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n \n Temperature/heat Loss{\" \"}\n \n \n {props.data[\"general\"][\"Temprature/heat loss\"][0] < 1000 ? (\n \n $\n {props.data[\"general\"][\"Temprature/heat loss\"][0].toFixed(\n 2\n )}{\" \"}\n Million\n \n ) : (\n \n $\n {(\n props.data[\"general\"][\"Temprature/heat loss\"][0] / 1000\n ).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n
\n Total Loss \n \n {props.data[\"total\"][\"Total Loss\"][0] < 1000 ? (\n \n ${props.data[\"total\"][\"Total Loss\"][0].toFixed(2)} Million\n \n ) : (\n \n ${(props.data[\"total\"][\"Total Loss\"][0] / 1000).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n \n Asset Rainfall Flood Damage{\" \"}\n \n \n {props.data[\"detailed\"][\"Asset Rainfall Flood Damage\"][0] <\n 1000 ? (\n \n $\n {props.data[\"detailed\"][\n \"Asset Rainfall Flood Damage\"\n ][0].toFixed(2)}{\" \"}\n Million\n \n ) : (\n \n $\n {(\n props.data[\"detailed\"][\"Asset Rainfall Flood Damage\"][0] /\n 1000\n ).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n ) : null}\n {props.newdata[\"detailed\"][\"Asset Rainfall Flood Damage\"] ? (\n\n \n Asset River Flood Damage{\" \"}\n \n \n {props.data[\"detailed\"][\"Asset River Flood Damage\"][0] < 1000 ? (\n \n $\n {props.data[\"detailed\"][\n \"Asset River Flood Damage\"\n ][0].toFixed(2)}{\" \"}\n Million\n \n ) : (\n \n $\n {(\n props.data[\"detailed\"][\"Asset River Flood Damage\"][0] / 1000\n ).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n \n Asset Storm Surge Damage{\" \"}\n \n \n {props.data[\"detailed\"][\"Asset Storm Surge Damage\"][0] < 1000 ? (\n \n $\n {props.data[\"detailed\"][\n \"Asset Storm Surge Damage\"\n ][0].toFixed(2)}{\" \"}\n Million\n \n ) : (\n \n $\n {(\n props.data[\"detailed\"][\"Asset Storm Surge Damage\"][0] / 1000\n ).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n \n Asset Typhoon Damage{\" \"}\n \n \n {props.data[\"detailed\"][\"Asset Typhoon Damage\"][0] < 1000 ? (\n \n $\n {props.data[\"detailed\"][\"Asset Typhoon Damage\"][0].toFixed(2)}{\" \"}\n Million\n \n ) : (\n \n $\n {(\n props.data[\"detailed\"][\"Asset Typhoon Damage\"][0] / 1000\n ).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n \n Operational Rainfall Flood Loss{\" \"}\n \n \n {props.data[\"detailed\"][\"Operational Rainfall Flood Loss\"][0] <\n 1000 ? (\n \n $\n {props.data[\"detailed\"][\n \"Operational Rainfall Flood Loss\"\n ][0].toFixed(3)}{\" \"}\n Million\n \n ) : (\n \n $\n {(\n props.data[\"detailed\"][\n \"Operational Rainfall Flood Loss\"\n ][0] / 1000\n ).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n ) : null}\n {props.newdata[\"detailed\"][\"Operational Rainfall Flood Loss\"] ? (\n\n \n Operational River Flood Loss{\" \"}\n \n \n {props.data[\"detailed\"][\"Operational River Flood Loss\"][0] <\n 1000 ? (\n \n $\n {props.data[\"detailed\"][\n \"Operational River Flood Loss\"\n ][0].toFixed(2)}{\" \"}\n Million\n \n ) : (\n \n $\n {(\n props.data[\"detailed\"][\"Operational River Flood Loss\"][0] /\n 1000\n ).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n \n Operational Storm Surge Loss{\" \"}\n \n \n {props.data[\"detailed\"][\"Operational Storm Surge Loss\"][0] <\n 1000 ? (\n \n $\n {props.data[\"detailed\"][\n \"Operational Storm Surge Loss\"\n ][0].toFixed(2)}{\" \"}\n Million\n \n ) : (\n \n $\n {(\n props.data[\"detailed\"][\"Operational Storm Surge Loss\"][0] /\n 1000\n ).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n \n Operational Typhoon Loss{\" \"}\n \n \n {props.data[\"detailed\"][\"Operational Typhoon Loss\"][0] < 1000 ? (\n \n $\n {props.data[\"detailed\"][\n \"Operational Typhoon Loss\"\n ][0].toFixed(2)}{\" \"}\n Million\n \n ) : (\n \n $\n {(\n props.data[\"detailed\"][\"Operational Typhoon Loss\"][0] / 1000\n ).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n \n Temperature/Heat Loss\n \n \n {props.data[\"detailed\"][\"Temprature/heat loss\"][0] < 1000 ? (\n \n $\n {props.data[\"detailed\"][\"Temprature/heat loss\"][0].toFixed(\n 2\n )}{\" \"}\n Million\n \n ) : (\n \n $\n {(\n props.data[\"detailed\"][\"Temprature/heat loss\"][0] / 1000\n ).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n ) : null}\n {props.newdata[\"detailed\"][\"Temprature/heat loss\"] ? (\n\n\n
\n Total Energy Loss\n \n {props.data['detailed'].energy[\"total_loss\"][0] < 1000 ? (\n \n ${props.data['detailed'].energy[\"total_loss\"][0].toFixed(2)} Million\n \n ) : (\n \n ${(props.data['detailed'].energy[\"total_loss\"][0] / 1000).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n \n Solar Production Loss\n \n \n {props.data['detailed'].energy[\"solar_loss\"][0] < 1000 ? (\n \n ${props.data['detailed'].energy[\"solar_loss\"][0].toFixed(2)} Million\n \n ) : (\n \n ${(props.data['detailed'].energy[\"solar_loss\"][0] / 1000).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n \n Wind Production Loss\n \n \n {props.data['detailed'].energy[\"wind_loss\"][0] < 1000 ? (\n \n ${props.data['detailed'].energy[\"wind_loss\"][0].toFixed(2)} Million\n \n ) : (\n \n ${(props.data['detailed'].energy[\"wind_loss\"][0] / 1000).toFixed(2)} Billion\n \n )}\n \n
\n\n \n Hydro Production Loss\n \n \n {props.data['detailed'].energy[\"hydro_loss\"][0] < 1000 ? (\n \n ${props.data['detailed'].energy[\"hydro_loss\"][0].toFixed(2)} Million\n \n ) : (\n \n ${(props.data['detailed'].energy[\"hydro_loss\"][0] / 1000).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n
\n \n Total Agriculture Loss\n \n \n {props.data['detailed'].agriculture[\"total_loss\"][0] < 1000 ? (\n \n ${props.data['detailed'].agriculture[\"total_loss\"][0].toFixed(2)} Million\n \n ) : (\n \n ${(props.data['detailed'].agriculture[\"total_loss\"][0] / 1000).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n \n Wheat Production Loss\n \n \n {props.data['detailed'].agriculture[\"wheat\"][0] < 1000 ? (\n \n ${props.data['detailed'].agriculture[\"wheat\"][0].toFixed(2)} Million\n \n ) : (\n \n ${(props.data['detailed'].agriculture[\"wheat\"][0] / 1000).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n \n Rice Production Loss\n \n \n {props.data['detailed'].agriculture[\"rice\"][0] < 1000 ? (\n \n ${props.data['detailed'].agriculture[\"rice\"][0].toFixed(2)} Million\n \n ) : (\n \n ${(props.data['detailed'].agriculture[\"rice\"][0] / 1000).toFixed(2)} Billion\n \n )}\n \n
\n\n \n Maize Production Loss\n \n \n {props.data['detailed'].agriculture[\"maize\"][0] < 1000 ? (\n \n ${props.data['detailed'].agriculture[\"maize\"][0].toFixed(2)} Million\n \n ) : (\n \n ${(props.data['detailed'].agriculture[\"maize\"][0] / 1000).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n \n Fruit Production Loss\n \n \n {props.data['detailed'].agriculture[\"fruit\"][0] < 1000 ? (\n \n ${props.data['detailed'].agriculture[\"fruit\"][0].toFixed(2)} Million\n \n ) : (\n \n ${(props.data['detailed'].agriculture[\"fruit\"][0] / 1000).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n \n Vegetable Production Loss\n \n \n {props.data['detailed'].agriculture[\"vegetable\"][0] < 1000 ? (\n \n ${props.data['detailed'].agriculture[\"vegetable\"][0].toFixed(2)} Million\n \n ) : (\n \n ${(props.data['detailed'].agriculture[\"vegetable\"][0] / 1000).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n Total Loss\n \n {props.data[\"total\"][\"Total Loss\"][0] < 1000 ? (\n \n ${props.data[\"total\"][\"Total Loss\"][0].toFixed(2)} Million\n \n ) : (\n \n ${(props.data[\"total\"][\"Total Loss\"][0] / 1000).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n Total Damage\n \n {props.data[\"total\"][\"Total Damage\"][0] < 1000 ? (\n \n ${props.data[\"total\"][\"Total Damage\"][0].toFixed(2)} Million\n \n ) : (\n \n ${(props.data[\"total\"][\"Total Damage\"][0] / 1000).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n Total Operational\n \n {props.data[\"total\"][\"Total Operational\"][0] < 1000 ? (\n \n ${props.data[\"total\"][\"Total Operational\"][0].toFixed(2)}{\" \"}\n Million\n \n ) : (\n \n $\n {(props.data[\"total\"][\"Total Operational\"][0] / 1000).toFixed(\n 2\n )}{\" \"}\n Billion\n \n )}\n \n
\n\n \n River Flood {props.data[\"mitigation\"][\"severity\"]} Loss\n \n \n {props.data[\"mitigation\"][`River Flood ${severity} Loss`][0] <\n 1000 ? (\n \n $\n {props.data[\"mitigation\"][\n `River Flood ${severity} Loss`\n ][0].toFixed(2)}{\" \"}\n Million\n \n ) : (\n \n $\n {(\n props.data[\"mitigation\"][\n `River Flood ${severity} Loss`\n ][0] / 1000\n ).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n \n Rainfall Flood {severity} Loss\n \n \n {props.data[\"mitigation\"][\n `Rainfall Flood ${severity} Loss`\n ][0] < 1000 ? (\n \n $\n {props.data[\"mitigation\"][\n `Rainfall Flood ${severity} Loss`\n ][0].toFixed(2)}{\" \"}\n Million\n \n ) : (\n \n $\n {(\n props.data[\"mitigation\"][\n `Rainfall Flood ${severity} Loss`\n ][0] / 1000\n ).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n| Asset Name | \nLoss | \n%Loss | \n
| {location.name.slice(0, 20)} | \n{location.loss.toFixed(2)} | \n{location.percentage.toFixed(2)}% | \n
Countries
\n {listings}\nClimate spread (% loss adjusted by 1:100 return period)
RCP 2.6\n RCP 4.5\n \n \n RCP 8.5\n \n
\n {this.state.analysis == \"rcp\" ? (\n \n This indicates the Loss projection for 2030 and 2050\n relative to the historical baseline of 2020 under the\n Business-as-Usual case. For{\" \"}\n {this.state.analysis.toUpperCase()}{\" \"}\n {this.props.rcp} , Climate\n Value at Risk (CVaR) increases{\" \"}\n \n {\" \"}\n {this.props.key_find.increased_by_2030}{\" \"}\n {\" \"}\n times by 2030 and{\" \"}\n \n {this.props.key_find.increased_by_2050}{\" \"}\n {\" \"}\n times by 2050 compared to current losses (2020).\n \n ) : (\n \n This indicates the Loss projection for 2030 , 2050 and 2100\n relative to the historical baseline of 2020 under the\n Business-as-Usual case. For{\" \"}\n {this.state.analysis.toUpperCase()}{\" \"}\n {this.props.ssp} , Climate\n Value at Risk (CVaR) increases{\" \"}\n \n {\" \"}\n {this.props.key_find.increased_by_2030}{\" \"}\n {\" \"}\n times by 2030 and{\" \"}\n \n {this.props.key_find.increased_by_2050}{\" \"}\n {\" \"}\n times by 2050 and{\" \"}\n \n {this.props.key_find.increased_by_2100} times by 2100\n \n compared to current losses (2020).\n \n )}\n
\n ) : null}\n\n For the given year {this.props.year}, the projected loss depends\n on the carbon concentration, captured by certain{\" \"}\n {this.props.analysis.toUpperCase()}s. The difference between{\" \"}\n {this.props.analysis.toUpperCase()} 8.5 AND{\" \"}\n {this.props.analysis.toUpperCase()} 2.6 reflects the Value of\n Transition Policies which is US${\"\"}\n \n {\"\"}\n {this.props.key_find.value_of_transition_policies}\n \n Million by {this.props.year}. This is calculated by\n difference between the {this.props.analysis.toUpperCase()} 8.5\n CVaR of US$\n \n {this.props.key_find.cvar_85.toFixed(2)} Million\n {\" \"}\n and the {this.props.analysis.toUpperCase()} 2.6 CVaR of US$\n \n {this.props.key_find.cvar_26.toFixed(2)} Million.\n {\" \"}\n
\n ) : null}\nTotal Loss: $ ${string.total_loss.toFixed(\n 2\n )} Mil
Total Loss(in %): ${string.percentage.toFixed(\n 2\n )}
Top Risk: ${\n string.top_hazard_string\n }
${hazard} Risk: ${string.hazard_loss[hazard].toFixed(2)}`\n )\n .addTo(map);\n });\n el.addEventListener(\"mouseleave\", (e) => {\n popup.remove();\n });\n });\n }\n map.on(\"load\", () => {\n map.addSource(\"places\", {\n type: \"geojson\",\n data: stores,\n });\n\n map.addLayer({\n id: \"places\",\n type: \"geojson\",\n source: \"places\",\n layout: {\n // Make the layer visible by default.\n visibility: \"visible\",\n },\n });\n\n map.addSource(\"raster-tiles\", {\n type: \"raster\",\n tiles: [\n \"https://intensel-tiles.s3.amazonaws.com/hazard/landslide/rcp/85_2050/viridis/{z}/{x}/{y}.png\",\n ],\n scheme: \"tms\",\n tileSize: 256,\n });\n map.addLayer({\n id: \"Landslide\",\n type: \"raster\",\n source: \"raster-tiles\",\n layout: {\n // Make the layer visible by default.\n visibility: \"visible\",\n },\n minzoom: 0,\n maxzoom: 22,\n });\n\n /* map.addSource(\"radar\", {\n type: \"image\",\n url: \"https://intensel-tiles.s3.amazonaws.com/hazard/extreme_heat/rcp/85_2050/plasma/{z}/{x}/{y}.png\",\n coordinates: [\n [-80.425, 46.437],\n [-71.516, 46.437],\n [-71.516, 37.936],\n [-80.425, 37.936],\n ],\n });\n map.addLayer({\n id: \"radar-layer\",\n type: \"raster\",\n source: \"radar\",\n paint: {\n \"raster-fade-duration\": 0,\n },\n });*/\n\n addMarkers();\n });\n map.on(\"idle\", () => {\n /* let mark = document.getElementsByClassName(\"worldmarker\");\n \n\n if (map.getZoom() <=4) {\n for (let i = 0; i < mark.length; i++) {\n mark[i].style.backgroundColor = \"#00008b\";\n }\n }*/\n // If these two layers were not added to the map, abort\n if (!map.getLayer(\"Landslide\")) {\n return;\n }\n\n // Enumerate ids of the layers.\n const toggleableLayerIds = [\"Landslide\"];\n\n // Set up the corresponding toggle button for each layer.\n for (const id of toggleableLayerIds) {\n // Skip layers that already have a button set up.\n if (document.getElementById(id)) {\n continue;\n }\n\n // Create a link.\n const link = document.createElement(\"a\");\n link.id = id;\n link.href = \"#\";\n link.textContent = id;\n link.className = \"active\";\n\n // Show or hide layer when the toggle is clicked.\n link.onclick = function(e) {\n const clickedLayer = this.textContent;\n e.preventDefault();\n e.stopPropagation();\n\n const visibility = map.getLayoutProperty(\n clickedLayer,\n \"visibility\"\n );\n\n // Toggle layer visibility by changing the layout object's visibility property.\n if (visibility === \"visible\") {\n map.setLayoutProperty(clickedLayer, \"visibility\", \"none\");\n this.className = \"\";\n } else {\n this.className = \"active\";\n map.setLayoutProperty(clickedLayer, \"visibility\", \"visible\");\n }\n };\n\n const layers = document.getElementById(\"mapmenu\");\n layers.appendChild(link);\n }\n });\n }\n }\n\n render() {\n return (\n
Asset & Loss
\n| Asset Name | \nLoss | \n%Loss | \n
| {location.name.slice(0, 20)} | \n{location.loss.toFixed(2)} | \n{location.percentage.toFixed(2)}% | \n
Countries
\n {listings}\n\n\n {props.data[\"general\"][\"Rainfall Flood Loss\"] ? (\n
\n \n Rainfall Flood Loss{\" \"}\n \n \n {props.data[\"general\"][\"Rainfall Flood Loss\"][2] ? (\n \n {props.data[\"general\"][\"Rainfall Flood Loss\"][2].toFixed(2)}{\" \"}\n %\n \n ) : null}\n \n
\n\n River Flood Loss \n \n {props.data[\"general\"][\"River Flood Loss\"][2] ? (\n \n {props.data[\"general\"][\"River Flood Loss\"][2].toFixed(2)} %\n \n ) : null}\n \n
\n\n Storm Surge Loss \n \n {props.data[\"general\"][\"Storm Surge Loss\"][2] ? (\n \n {props.data[\"general\"][\"Storm Surge Loss\"][2].toFixed(2)} %\n \n ) : null}\n \n
\n\n Typhoon Loss \n \n {props.data[\"general\"][\"Typhoon Loss\"][2] ? (\n \n {props.data[\"general\"][\"Typhoon Loss\"][2].toFixed(2)} %\n \n ) : null}\n \n
\n\n \n Temperature/Heat Loss{\" \"}\n \n \n {props.data[\"general\"][\"Temprature/heat loss\"][2] ? (\n \n {props.data[\"general\"][\"Temprature/heat loss\"][2].toFixed(\n 2\n )}{\" \"}\n %\n \n ) : null}\n \n
\n\n
\n Total Loss \n \n {props.data[\"total\"][\"Total Loss\"][2] ? (\n \n {props.data[\"total\"][\"Total Loss\"][2].toFixed(2)} %\n \n ) : null}\n \n
\n\n \n Asset Rainfall Flood Damage{\" \"}\n \n \n {props.data[\"detailed\"][\"Asset Rainfall Flood Damage\"][2] ? (\n \n {props.data[\"detailed\"][\n \"Asset Rainfall Flood Damage\"\n ][2].toFixed(2)}\n %\n \n ) : null}\n \n
\n ) : null}\n {props.newdata[\"detailed\"][\"Asset Rainfall Flood Damage\"] ? (\n\n \n Asset River Flood Damage{\" \"}\n \n \n {props.data[\"detailed\"][\"Asset River Flood Damage\"][2] ? (\n \n {props.data[\"detailed\"][\n \"Asset River Flood Damage\"\n ][2].toFixed(2)}\n %\n \n ) : null}\n \n
\n\n \n Asset Storm Surge Damage{\" \"}\n \n \n {props.data[\"detailed\"][\"Asset Storm Surge Damage\"][2] ? (\n \n {props.data[\"detailed\"][\n \"Asset Storm Surge Damage\"\n ][2].toFixed(2)}\n %\n \n ) : null}\n \n
\n\n \n Asset Typhoon Damage{\" \"}\n \n \n {props.data[\"detailed\"][\"Asset Typhoon Damage\"][2] ? (\n \n {props.data[\"detailed\"][\"Asset Typhoon Damage\"][2].toFixed(2)}{\" \"}\n %\n \n ) : null}\n \n
\n\n \n Operational Rainfall Flood Loss{\" \"}\n \n \n {props.data[\"detailed\"][\n \"Operational Rainfall Flood Loss\"\n ][2] ? (\n \n {props.data[\"detailed\"][\n \"Operational Rainfall Flood Loss\"\n ][2].toFixed(2)}\n %\n \n ) : null}\n \n
\n ) : null}\n {props.newdata[\"detailed\"][\"Operational Rainfall Flood Loss\"] ? (\n\n \n Operational River Flood Loss{\" \"}\n \n \n {props.data[\"detailed\"][\"Operational River Flood Loss\"][2] ? (\n \n {props.data[\"detailed\"][\n \"Operational River Flood Loss\"\n ][2].toFixed(2)}{\" \"}\n %\n \n ) : null}\n \n
\n\n \n Operational Storm Surge Loss{\" \"}\n \n \n {props.data[\"detailed\"][\"Operational Storm Surge Loss\"][2] ? (\n \n {props.data[\"detailed\"][\n \"Operational Storm Surge Loss\"\n ][2].toFixed(2)}{\" \"}\n %\n \n ) : null}\n \n
\n\n \n Operational Typhoon Loss{\" \"}\n \n \n {props.data[\"detailed\"][\"Operational Typhoon Loss\"][2] ? (\n \n {props.data[\"detailed\"][\n \"Operational Typhoon Loss\"\n ][2].toFixed(2)}{\" \"}\n %\n \n ) : null}\n \n
\n\n \n Temperature/Heat Loss\n \n \n {props.data[\"detailed\"][\"Temprature/heat loss\"][2] ? (\n \n {props.data[\"detailed\"][\"Temprature/heat loss\"][2].toFixed(\n 2\n )}{\" \"}\n %\n \n ) : null}\n \n
\n ) : null}\n {props.newdata[\"detailed\"][\"Temprature/heat loss\"] ? (\n\n\n
\n Total Energy Loss\n \n {props.data.energy[\"total_loss\"][0] < 1000 ? (\n \n ${props.data.energy[\"total_loss\"][0].toFixed(2)} Million\n \n ) : (\n \n ${(props.data.energy[\"total_loss\"][0] / 1000).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n \n Solar Production Loss\n \n \n {props.data.energy[\"solar_loss\"][0] < 1000 ? (\n \n ${props.data.energy[\"solar_loss\"][0].toFixed(2)} Million\n \n ) : (\n \n ${(props.data.energy[\"solar_loss\"][0] / 1000).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n \n Wind Production Loss\n \n \n {props.data.energy[\"wind_loss\"][0] < 1000 ? (\n \n ${props.data.energy[\"wind_loss\"][0].toFixed(2)} Million\n \n ) : (\n \n ${(props.data.energy[\"wind_loss\"][0] / 1000).toFixed(2)} Billion\n \n )}\n \n
\n\n \n Hydro Production Loss\n \n \n {props.data.energy[\"hydro_loss\"][0] < 1000 ? (\n \n ${props.data.energy[\"hydro_loss\"][0].toFixed(2)} Million\n \n ) : (\n \n ${(props.data.energy[\"hydro_loss\"][0] / 1000).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n
\n \n Total Agriculture Loss\n \n \n {props.data.agriculture[\"total_loss\"][0] < 1000 ? (\n \n ${props.data.agriculture[\"total_loss\"][0].toFixed(2)} Million\n \n ) : (\n \n ${(props.data.agriculture[\"total_loss\"][0] / 1000).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n \n Wheat Production Loss\n \n \n {props.data.agriculture[\"wheat\"][0] < 1000 ? (\n \n ${props.data.agriculture[\"wheat\"][0].toFixed(2)} Million\n \n ) : (\n \n ${(props.data.agriculture[\"wheat\"][0] / 1000).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n \n Rice Production Loss\n \n \n {props.data.agriculture[\"rice\"][0] < 1000 ? (\n \n ${props.data.agriculture[\"rice\"][0].toFixed(2)} Million\n \n ) : (\n \n ${(props.data.agriculture[\"rice\"][0] / 1000).toFixed(2)} Billion\n \n )}\n \n
\n\n \n Maize Production Loss\n \n \n {props.data.agriculture[\"maize\"][0] < 1000 ? (\n \n ${props.data.agriculture[\"maize\"][0].toFixed(2)} Million\n \n ) : (\n \n ${(props.data.agriculture[\"maize\"][0] / 1000).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n \n Fruit Production Loss\n \n \n {props.data.agriculture[\"fruit\"][0] < 1000 ? (\n \n ${props.data.agriculture[\"fruit\"][0].toFixed(2)} Million\n \n ) : (\n \n ${(props.data.agriculture[\"fruit\"][0] / 1000).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n \n Vegetable Production Loss\n \n \n {props.data.agriculture[\"vegetable\"][0] < 1000 ? (\n \n ${props.data.agriculture[\"vegetable\"][0].toFixed(2)} Million\n \n ) : (\n \n ${(props.data.agriculture[\"vegetable\"][0] / 1000).toFixed(2)}{\" \"}\n Billion\n \n )}\n \n
\n\n Total Loss\n \n {props.data[\"total\"][\"Total Loss\"][2] ? (\n \n {props.data[\"total\"][\"Total Loss\"][2].toFixed(2)} %\n \n ) : null}\n \n
\n\n Total Damage\n \n {props.data[\"total\"][\"Total Damage\"][2] ? (\n \n {props.data[\"total\"][\"Total Damage\"][2].toFixed(2)}%\n \n ) : null}\n \n
\n\n Total Operational\n \n {props.data[\"total\"][\"Total Operational\"][2] ? (\n \n {props.data[\"total\"][\"Total Operational\"][2].toFixed(2)} %\n \n ) : null}\n \n
\n\n \n River Flood {severity} Loss\n \n \n {props.data[\"mitigation\"][`River Flood ${severity} Loss`][2] ? (\n \n {props.data[\"mitigation\"][\n `River Flood ${severity} Loss`\n ][2].toFixed(2)}{\" \"}\n %\n \n ) : null}\n \n
\n\n \n Rainfall Flood {severity} Loss\n \n \n {props.data[\"mitigation\"][\n `Rainfall Flood ${severity} Loss`\n ][2] ? (\n \n {props.data[\"mitigation\"][\n `Rainfall Flood ${severity} Loss`\n ][2].toFixed(2)}{\" \"}\n %\n \n ) : null}\n \n
\nTotal Loss: $ ${string.total_loss.toFixed(\n 2\n )} Mil
Total Loss(in %): ${string.percentage.toFixed(\n 2\n )}
Top Risk: ${\n string.top_hazard_string\n }
${hazard} Score: ${string.hazard_loss[hazard].toFixed(2)}`\n )\n .addTo(map);\n });\n\n el.addEventListener(\"mouseleave\", () => {\n popup.remove();\n });\n });\n }\n\n map.on(\"load\", () => {\n map.addSource(\"places\", {\n type: \"geojson\",\n data: stores,\n });\n\n map.addLayer({\n id: \"places\",\n type: \"geojson\",\n source: \"places\",\n layout: {\n visibility: \"visible\",\n },\n });\n\n addMarkers();\n });\n }\n }, [data, hazard]);\n\n return (\n
\n
\n
\n
\n
\n {\" \"}\n
\n\n {this.state.subscription.overall_dashboard &&\n this.state.subscription.overall_dashboard.overview_card ? (\n
\n
\n
\n
\n\n
\n
\n {this.props.feedback.created_at ? (\n
\n
\n\n
\n
\n
\n
\n
\n
\n {this.state.overall[\"Max_exposure\"] < 1000 ? (\n
\n $\n {(\n this.state.overall[\"Max_exposure\"] / 1000\n ).toFixed(2)}{\" \"}\n Billion\n
\n )}{\" \"}\n \n {this.state.losses[\"detailed\"] &&\n this.state.exposure_variable != \"Total\" &&\n this.state.exposure_variable != \"Extreme heat\" ? (\n\n {this.state.overall[\n `${this.state.exposure_variable}_exposure`\n ] < 1000 ? (\n
\n {\" \"}\n ${\" \"}\n {this.state.overall[\n `${this.state.exposure_variable}_exposure`\n ].toFixed(2)}\n Million{\" \"}\n
\n ) : (\n\n ${\" \"}\n {(\n this.state.overall[\n `${this.state.exposure_variable}_exposure`\n ] / 1000\n ).toFixed(2)}\n Billion{\" \"}\n
\n )}{\" \"}\n \n\n\n {\" \"}\n ${\" \"}\n {(\n parseFloat(\n this.state.losses[\"detailed\"][\n `Asset ${this.state.exposure_variable} Damage`\n ]\n ) +\n parseFloat(\n this.state.losses[\"detailed\"][\n `Operational ${this.state.exposure_variable} Loss`\n ]\n )\n ).toFixed(2)}{\" \"}\n Million\n
\n ) : (\n\n ${\" \"}\n {(\n (parseFloat(\n this.state.losses[\"detailed\"][\n `Asset ${this.state.exposure_variable} Damage`\n ]\n ) +\n parseFloat(\n this.state.losses[\"detailed\"][\n `Operational ${this.state.exposure_variable} Loss`\n ]\n )) /\n 1000\n ).toFixed(2)}{\" \"}\n Billion\n
\n )}\n \n \n\n {this.state.overall[\n `${this.state.exposure_variable}_exposure`\n ] < 1000 ? (\n
\n ${\" \"}\n {this.state.overall[\n `${this.state.exposure_variable}_exposure`\n ].toFixed(2)}{\" \"}\n Million\n
\n ) : (\n\n ${\" \"}\n {(\n this.state.overall[\n `${this.state.exposure_variable}_exposure`\n ] / 1000\n ).toFixed(2)}{\" \"}\n Billion\n
\n )}\n \n\n\n {\" \"}\n ${\" \"}\n {parseFloat(\n this.state.losses[\"detailed\"][\n `Temprature/heat loss`\n ]\n )}{\" \"}\n Million\n
\n ) : (\n\n ${\" \"}\n {parseFloat(\n this.state.losses[\"detailed\"][\n `Temprature/heat loss`\n ]\n )}{\" \"}\n Billion\n
\n )}\n \n \n\n
\n
\n
\n {/*
\n Technical Insurance Premium in % at \n {this.state.overall.tip_in_perc}{\" \"}\n
\n ) : null}\n \n
\n {\" \"}\n \n {\" \"}\n Year Loss for {this.state.analysis.toUpperCase()}{\" \"}\n {this.state.analysis == \"rcp\" ? (\n {this.state.rcp}\n ) : (\n {this.state.ssp}\n )}{\" \"}\n \n
{\" \"}\n\n {\" \"}\n \n {\" \"}\n {this.state.analysis.toUpperCase()} Loss for Year{\" \"}\n {this.state.year}{\" \"}\n \n
{\" \"}\n\n \n Loss Contribution Analysis\n \n
\n
\n
\n Projected Impact\n \n
\n\n
\n \n Asset Level Risk Map\n \n
\n \n\n\n
\n\n {this.state.combined_data &&\n this.state.hazard_map == \"Rainfall Flood\" ? (\n
\n \n Climate Variable Intensity\n \n
\n {this.state.risk_2020 && this.state.hazard ? (\n
\n Projected Impact\n \n
Climate Risk Index
\n\n {this.state.subscription.overall_dashboard &&\n this.state.subscription.overall_dashboard.climate_heatmap ? (\n
\n \n Climate Risk Heat Map{\" \"}\n \n
\n\n
\n
\n
\n\n
\n {this.state.subscription.overall_dashboard &&\n this.state.subscription.overall_dashboard.asset_table ? (\n
\n \n Asset Table{\" \"}\n
{\" \"}\n {this.state.table_condition && (\n
{\" \"}\n {this.state.table_condition && (\n
\n {this.state.table_condition && (\n
\n \n Flood Mitigation Measures{\" \"}\n \n
\n\n {\" \"}\n Extreme Wind Speeds
Maximum wind\n speed at multiple return periods
\n
\n Fraction of building, contents and inventory\n damaged by extreme wind events
\n
\n Annual average loss to building, contents and\n inventory\n
\n Number of days of business interruption\n {\" \"}\n {\" \"}\n \n
\n {\" \"}\n \n {\" \"}\n \n Extreme Rainfall
\n
\n Maximum daily precipitation across multiple return\n periods\n {\" \"}\n {\" \"}\n \n
\n {\" \"}\n \n {\" \"}\n \n Wildfire Risk
\n
\n The annual number of wildfires, which ignite and\n grow to detectable size,
\n that are expected in a 1 square km grid cell\n {\" \"}\n {\" \"}\n \n
\n {\" \"}\n \n {\" \"}\n \n Hail And Thunderstorm Probability
\n
\n Number of days per year where large hail (>2 in\n / 5 cm in diameter) is possible\n
\n
\n Number of days per year where environmental\n conditions are conducive to severe thunderstorm\n formation\n {\" \"}\n {\" \"}\n \n
\n {\" \"}\n \n {\" \"}\n \n Drought Frequency
\n
\n Months per year where the rolling 3-month or\n 6-month average\n
\n
\n Standardized Precipitation Evapotranspiration\n Index (SPEI) is below -2\n {\" \"}\n {\" \"}\n \n
\n {\" \"}\n \n {\" \"}\n \n Flood Depth of Water
\n
\n Flood depth of water across multiple return\n periods\n
\n
\n Percentage of the central square flooded across\n multiple return periods\n
\n
\n Fraction of building, contents and inventory\n damaged by flood events\n
\n {\" \"}\n Extreme Heat
\n
\n Days per year exceeding 35°C, 38°C and the\n historical 99th percentile\n
\n
\n Heating and cooling degree days\n
\n
\n Days exceeding high (32°C) and dangerously high\n (35°C) Wet-Bulb Globe Temperature\n {\" \"}\n {\" \"}\n \n
\n
\n
\n {this.state.analysis == \"rcp\" ? (\n
\n
\n {this.state.single_asset_overall[0][\n \"Max_exposure\"\n ] < 1000 ? (\n
\n $\n {(\n this.state.single_asset_overall[0][\n \"Max_exposure\"\n ] / 1000\n ).toFixed(2)}{\" \"}\n Billion\n
\n )}{\" \"}\n \n {this.state.single_asset_loss[\n \"Asset River Flood Damage\"\n ] &&\n this.state.exposure_variable != \"Total\" &&\n this.state.exposure_variable !=\n \"Extreme heat\" ? (\n\n {this.state.single_asset_overall[0][\n `${this.state.exposure_variable}_exposure`\n ] < 1000 ? (\n
\n {\" \"}\n ${\" \"}\n {this.state.single_asset_overall[0][\n `${this.state.exposure_variable}_exposure`\n ].toFixed(2)}\n Million{\" \"}\n
\n ) : (\n\n ${\" \"}\n {(\n this.state\n .single_asset_overall[0][\n `${this.state.exposure_variable}_exposure`\n ] / 1000\n ).toFixed(2)}\n Billion{\" \"}\n
\n )}{\" \"}\n \n\n\n {\" \"}\n ${\" \"}\n {(\n parseFloat(\n this.state.single_asset_loss[\n `Asset ${this.state.exposure_variable} Damage`\n ]\n ) +\n parseFloat(\n this.state.single_asset_loss[\n `Operational ${this.state.exposure_variable} Loss`\n ]\n )\n ).toFixed(2)}{\" \"}\n Million\n
\n ) : (\n\n ${\" \"}\n {(\n (parseFloat(\n this.state.single_asset_loss[\n `Asset ${this.state.exposure_variable} Damage`\n ]\n ) +\n parseFloat(\n this.state\n .single_asset_loss[\n `Operational ${this.state.exposure_variable} Loss`\n ]\n )) /\n 1000\n ).toFixed(2)}{\" \"}\n Billion\n
\n )}\n \n \n\n {this.state.single_asset_overall[0][\n `${this.state.exposure_variable}_exposure`\n ] < 1000 ? (\n
\n ${\" \"}\n {this.state.single_asset_overall[0][\n `${this.state.exposure_variable}_exposure`\n ].toFixed(2)}{\" \"}\n Million\n
\n ) : (\n\n ${\" \"}\n {(\n this.state\n .single_asset_overall[0][\n `${this.state.exposure_variable}_exposure`\n ] / 1000\n ).toFixed(2)}{\" \"}\n Billion\n
\n )}\n \n\n\n {\" \"}\n ${\" \"}\n {parseFloat(\n this.state.single_asset_loss[\n `Temprature/heat loss`\n ]\n )}{\" \"}\n Million\n
\n ) : (\n\n ${\" \"}\n {parseFloat(\n this.state.single_asset_loss[\n `Temprature/heat loss`\n ]\n )}{\" \"}\n Billion\n
\n )}\n \n \n\n\n
\n
\n {/*
\n {\" \"}\n \n {\" \"}\n Year Loss for{\" \"}\n {this.state.analysis.toUpperCase()}{\" \"}\n {this.state.analysis == \"rcp\" ? (\n {this.state.rcp}\n ) : (\n {this.state.ssp}\n )}{\" \"}\n \n
\n\n {\" \"}\n \n {\" \"}\n {this.state.analysis.toUpperCase()} Loss for\n Year {this.state.year}{\" \"}\n \n
\n\n \n Climate Score Table\n \n
\n\n \n Asset Map Location\n \n
\n\n {/*
\n
\n Projected Impact\n \n
\n
\n
\n
\n Projected Impact\n \n
\n
\n \n {this.state.analysis.toUpperCase()} 2.6 vs{\" \"}\n {this.state.analysis.toUpperCase()} 4.5 vs{\" \"}\n {this.state.analysis.toUpperCase()} 8.5 for Year\n 2050\n \n
\n\n \n Year 2020 vs Year 2030 vs Year 2050 for{\" \"}\n {this.state.analysis.toUpperCase()}{\" \"}\n {this.state.rcp}\n \n
\n ) : (\n\n \n Year 2020 vs Year 2030 vs Year 2050 vs Year\n 2100 for {this.state.analysis.toUpperCase()}{\" \"}\n {this.state.ssp}\n \n
\n )}\n\n
\n \n Please select appropriate flood risk reduction level from High\n to Low on the pull-down manual by referring to the below\n guidance.{\" \"}\n \n
\n This function is to account for the flood risk reduction\n capacity of three flood mitigation measures that are near the\n asset and connect to the main flood control / stormwater\n management system of the area: a) manhole, b) catchpit, and c)\n underground flood tunnel. These mitigation measures are\n effective to reduce rainfall and river flooding, and they\n include any existing or to-be-built measures. It does not apply\n to storm surge flooding, as when there is a rise in sea level on\n the coastal side, the floodwater is very likely to block most\n nearby outlets of the stormwater management system.\n
\n
The level of flood risk reduction depends on below two\n key factors and assumption:
\n 1. Distance between any mitigation measure and the asset. To\n have a risk reduction impact, the closer the mitigation measure\n to the asset, the greater level of risk reduction. Any\n mitigation measure that is more than 20 meters far from the\n asset is assumed to have no risk reduction effect.\n
2. The number of available mitigation measures. The more\n the number of mitigation measures, the greater level of risk\n reduction. It can be in any combination of item a, b, and c as\n listed above, e.g., asset with two nearby manholes plus one\n nearby underground flood tunnel is counted as a total of 3\n measures for that asset.\n
\n A. High level of flood risk reduction – Either:\n
i. Having at least three measures in place near\n the asset. Two measures both are at least within a distance of ≤\n 10 meters, plus the remaining measure is at least within 15-20\n meters; or
\n ii. Having at least three measures in place near the\n asset. Two measures both are at least within a distance of 10-15\n meters, plus the remaining measure is at least within ≤ 10\n meters.\n
\n B. Median level of flood risk reduction – Either:\n
i. Having at least three measures in place. Two\n measure both are at least within 15-20 meters, plus the\n remaining measure is at least within 10-15 meters; or\n
ii. Having at least two measures in place near the\n asset. One measure is at least within ≤ 10 meters, plus another\n one is at least within 15-20 meters; or\n
iii. Having at least two measures in place near\n the asset, and two measures both are at least within 10-15\n meters.\n
\n
\n C. Low level of flood risk reduction – Either:
\n i. Having at least two measures in place, and these two\n measures both are at least within 15-20 meters; or
\n ii. Having at least one measure in place, and this\n measure is at least within a distance of ≤ 10 meters.\n
\n
Example: An asset has nearby two flood mitigation\n measures (no matter what combination of a) manhole, b) catchpit\n or c) underground flood tunnel). One measure (e.g., manhole) is\n within 10-15 meters and another measure (e.g., catchpit or\n another manhole) is at 15-20 meters far from the asset. Both\n measures are connected to the city-level stormwater management\n system. In this example, as it can’t fulfill the requirement of\n the median level of flood risk reduction of points B) ii and B)\n iii above, it falls into the category of low level of flood risk\n reduction.\n
\n Climate value at risk (CVaR) is a measure for estimating the\n maximum amount of loss over a one year period due to co-occurring\n extreme events. CVaR can be thought of as a potential loss due to\n the exposure of an asset, displayed as a $ value or a percentage\n (%).\n
\n\n Make use of the “Advance Filter” to view detailed analysis results\n of the same project under different variables (e.g., scenario and\n year), toggle to view the Climate Value at Risk in $ loss or as a\n percentage (%).\n
\n Simply select the desired variable/criteria, and press “Apply\n Filter”.\n
\n \n Climate Variable Intensity\n \n
\n\n\n {this.state.hazard && this.state.risk_2020 ? (\n
\n \n Portfolio Losses\n \n
\n\n \n Climate Risk Heat Map (0 to 100){\" \"}\n \n
\nAdvanced Filter Options
\nFiltering your data for more insight
\nProjects
\nAnalysis Type
\nRCP
\nSSP
\nCount
\nScenario Type
\n\nYear
\n {this.state.analysis == \"rcp\" ? (\nContent Type
\n\n Account for Long-term Impact of Climate Change on Economic\n Growth\n
\nMitigation
\nSelect Currency
\nCVaR in Percentage
\n\n Note for *: For the outputs under “Operational Loss Adjusted\n with Long-term Climate Change Impact”, in addition to the\n operational loss due to acute hazards, it includes loss\n associated with the impact from long-term climate change\n (chronic hazards related e.g., raising temperatures on labour\n productivity, long-term sea level rise causing erosion on\n coastline, etc).\n
\n\n\n
\n {/*
Property Type
\n\n
\n Maximum exposure refers to the sum of accumulated asset values\n (physical exposure) of all assets plus the accumulated annual\n operational revenue (operational exposure) of all assets that are\n exposed to climate hazards. Any asset that is not exposed to any\n physical damage, its asset value and operational revenue would not\n be included under max exposure.\n
\n\n
\n
- \n
- \n Adaptation Toolkits: Provide a guidance for overall\n understanding of key concept in relation to adaptation and\n resilience action, adaptation planning process, as well as\n categories and example in general.\n \n
\n reflects the cost-saving benefits from the reduced physical\n climate risk that will occur if appropriate steps are taken now to\n implement climate action and policies toward a net-zero emissions\n target. It is the difference in the portfolio Climate\n Value-at-Risk between that of a high emissions scenario (RCP\n 8.5/SSP 5-8.5) and the net-zero emissions scenario (RCP 2.6/SSP\n 1-2.6) for a specified time horizon.\n
\n\n reflects the maximum amount of loss expected to be incurred over a\n one-year period (for all climate hazards that affect the asset).\n
\n\n reflects the equally weighted average of climate hazard severity\n across all hazards and all assets analyzed within a portfolio.\n
\n\n For {this.state.analysis.toUpperCase()}{\" \"}\n {this.state.analysis == \"rcp\" ? (\n {this.state.rcp}\n ) : (\n {this.state.ssp}\n )}\n , Climate Value at Risk of{\" \"}\n {this.state.key_finding_data.cvar_85_perc.toFixed(2)}% means\n that by {this.state.year} in any given one year{\" \"}\n {this.state.year == \"2100\" ? (\n \n ({this.state.year - 19}-{this.state.year})\n \n ) : (\n \n ({this.state.year - 9}-{this.state.year + 10})\n \n )}\n , we expect there to be a 1% chance that{\" \"}\n {this.state.key_finding_data.cvar_85_perc.toFixed(2)}% of the\n US${this.state.overall[\"Max_exposure\"].toFixed(2)} Million\n results in a maximum loss of US${\"\"}\n {this.state.losses[\"total\"][\"Total Loss\"][0].toFixed(2)}{\" \"}\n Million.\n
\n ) : null}\n- \n
- \n Total CVaR in % = Total CVaR / Portfolio Exposure (i.e. Total\n asset value + total annual revenue income of ALL assets in the\n portfolio disregard if any asset is not exposed to any hazard\n damage.)\n \n
- \n Damage CVaR in % = Total Physical damage loss / total asset\n value for ALL assets in the portfolio\n \n
- \n Operational CVaR in % = Total operational loss / total annual\n revenue income for ALL assets in the portfolio\n \n
\n Technical Insurance Premium (TIP) is the annual expected cost of\n damage assuming all hazards are insured, which is equal to the\n physical loss of the asset, using the replacement cost as the\n basis. TIP in % is the TIP as a percentage of the replacement cost\n of the asset.\n
\n\n For the assets analyzed, the portfolio is exposed to chronic &\n acute climate events to the extent identified below. The red bar\n above each hazard indicates the severity of that hazard for the\n year 2020 (historical), and the blue bar indicates the severity\n of that hazard by the year under consideration (e.g., by the\n year{\" \"}\n {this.state.analysis == \"rcp\" ? (\n 2030 / 2050\n ) : (\n 2030/2050/2100\n )}\n ). Users can compare the length of the red bar with the blue bar\n to get a sense of how the risk severity for each hazard changes\n across years.\n
\n
\n {this.state.hazard.both[\"Typhoon\"] ? (\n
\n \n Typhoon : {this.state.hazard.both[\"Typhoon\"][0]}\n {this.state.hazard.both[\"Typhoon\"][1]}\n {\" \"}\n Typhoons bring intense wind gusts and rainfall that\n may result in physical damage to assets, power outages and\n business interruption. Typhoon risk models estimate the\n spatial extent and wind speed intensity of a typhoon from\n historic tropical cyclone track, temperature and wind speed\n data.\n
\n ) : null}\n\n {this.state.hazard.both[\"Rainfall Flood\"] ? (\n
\n \n Rainfall Flood :{\" \"}\n {this.state.hazard.both[\"Rainfall Flood\"][0]}\n {this.state.hazard.both[\"Rainfall Flood\"][1]}\n {\" \"}\n Rainfall flood refers to inundation resulting from\n rainfall, elevation and climate change. Flood hazard\n severity is derived from flood models, river bathymetry,\n maximum rainfall in a 24-hour period, and extreme rainfall\n events.Intense precipitation may bring rainfall floods or\n storm surges that result in physical damages to assets or\n temporary facility closures.\n
\n ) : null}\n\n {this.state.hazard.both[\"River Flood\"] ? (\n
\n \n River Flood : {this.state.hazard.both[\"River Flood\"][0]}\n {this.state.hazard.both[\"River Flood\"][1]}{\" \"}\n {\" \"}\n River flood refers to inundation as a result of\n overflows from a river channel. This may result in physical\n damage to assets and business interruption. Flood hazard\n information is obtained from in-house hydrology and\n hydraulic models. \n
\n ) : null}\n\n {this.state.hazard.both[\"Extreme heat\"] ? (\n
\n {\" \"}\n \n Extreme Heat : {this.state.hazard.both[\"Extreme heat\"][0]}\n {this.state.hazard.both[\"Extreme heat\"][1]}\n {\" \"}\n Extreme Heat is experienced when humidity and\n temperature are high for prolonged period. Extreme heat\n values reflected are maximum values derived from the impacts\n of climate change on temperature and relative humidity.\n Prolonged extreme heat conditions may lead to undesirable\n effects on health and well-being, productivity losses and\n increased energy consumption.\n
\n ) : null}\n\n {this.state.hazard.both[\"Sea Level Rise\"] ? (\n
\n {\" \"}\n \n Sea Level Rise :{\" \"}\n {this.state.hazard.both[\"Sea Level Rise\"][0]}\n {this.state.hazard.both[\"Sea Level Rise\"][1]}{\" \"}\n {\" \"}\n Sea Level Rise refers to inundation estimated from\n shoreline and elevation models. It considers mean sea level\n projections, the affected catchment area near shorelines,\n elevation models and forecasting analysis based on the\n representative scenarios from Global Circulation\n Models. Sea level rise as a chronic hazard may result in\n inundation of areas or road networks surrounding an asset\n over a long time horizon.\n
\n ) : null}\n\n {this.state.hazard.both[\"Storm Surge\"] ? (\n
\n {\" \"}\n \n Storm Surge : {this.state.hazard.both[\"Storm Surge\"][0]}\n {this.state.hazard.both[\"Storm Surge\"][1]}\n {\" \"}\n Storm surge refers to the extreme rise in sea level\n due to typhoons, typhoon tracks, elevations, and climate\n change. This may result in physical damage to assets and\n business interruption. Storm surge hazard inundation depths\n are derived from hydrodynamic models. \n
\n ) : null}\n\n {this.state.hazard.both[\"Snow Fall\"] ? (\n
\n {\" \"}\n \n Snowfall : {this.state.hazard.both[\"Snow Fall\"][0]}\n {this.state.hazard.both[\"Snow Fall\"][1]}{\" \"}\n {\" \"}\n Precipitation in the form of snow it is derived from\n CMIP6 GCM models. it is expressed in units of kg m⁻² s⁻¹,\n which is equivalent to mm/s (assuming a water density of\n 1000 kg/m³). To analyze snowfall over time, daily snowfall\n data is aggregated to calculate the annual cumulative\n snowfall, represented in mm/yr. The data is further\n processed to compute aggregated means for specific CMIP6\n timelines, such as 2030, 2050, and 2100. This dataset is\n particularly valuable for studying hydrological impacts of\n climate change and assessing avalanche risks in mountainous\n regions with extreme snowfall\n
\n ) : null}\n\n {this.state.hazard.both[\"Landslide\"] ? (\n
\n {\" \"}\n \n Landslide : {this.state.hazard.both[\"Landslide\"][0]}\n {this.state.hazard.both[\"Landslide\"][1]}{\" \"}\n {\" \"}\n A landslide is a movement of a mass of rock, debris,\n or earth down a slope. The hazard is calculated as a\n frequency or annual probability of landslide. \n
\n ) : null}\n\n {this.state.hazard.both[\"Drought\"] ? (\n
\n {\" \"}\n \n Drought : {this.state.hazard.both[\"Drought\"][0]}\n {this.state.hazard.both[\"Drought\"][1]}\n {\" \"}\n
\n Drought is a prolonged absence or marked deficiency of\n precipitation or dry weather which results in water shortage\n or a hydrological imbalance. Drought scores are derived from\n the Standard Precipitation Index.\n
A negative number indicates a drought, meaning it is\n drier. A positive number indicates wetter conditions.\n
\n
\n Note :: Above hazard values are averages across all assets\n within the portfolio\n
\n \n ) : null}\n\n Users can compare the length of the red bar with the blue bar to\n visualize changes in the Climate Value-at-Risk (financial loss in\n dollar or % value) across dimensions.\n
\n\n Users can use the “Edit” function for each asset under the\n “Action” column to update different variables, including asset\n name, property type, area (single floor construction area in\n square meter), floors (total no. of floors), value (asset\n valuation per square meter), etc. So as long as the user is within\n its subscription period, the analysis results can be updated with\n more accurate internal information when this information is\n available later. However, the location information (latitude and\n longitude) can not be updated once the project is created. Simply\n amend the content of the variables and click “Save”, the analysis\n results will update accordingly.\n
\n\n\t\t\t
{portfolio.name}\n
\n \n \nSelect Data You want to Download
\n\t\tSelect Climate Variable
\n\t\tSelect RCP
\n\t\tSelect Year
\n\t\t\n\n\t\t{(this.state.modalloading && (!this.props.csv.length===undefined))?:\n\t\t\t\t}\n\t\t\n\t\t{(this.props.csv.length===undefined)?
Select Year
\n\t\t\n\n\t\t{(this.state.modalloading && (!this.props.csv.length===undefined))?:\n\t\t\t\t}\n\t\t\n\t\t{(this.props.csv.length===undefined)?
\n\n\t\t{(this.state.modalloading && (!this.props.csv.length===undefined))?:\n\t\t\t\t}\n\t\t\n\t\t{(this.props.csv.length===undefined)?
\n
Summary
\n\n\n Last edited: Nov 24 2021\n
\n\n\n The BlueSwan Interface provides first-hand access to\n projections of physical climate risks at the asset level. It\n shows maps and graphs illustrating the projected changes in\n climate conditions, resulting in impacts and damages on\n selected sectors for several global warming levels, also how\n they will play out over time according to various\n policy-relevant emission scenarios by IPCC. All projections\n are calculated assuming that socio-economic conditions\n (population, land-use, management practices, etc.) will\n remain constant as of 2020, therefore BlueSwan isolates the\n sole effect of climate change on the indicators it provides\n information on.{\" \"}\n
\n\n\n
- \n
- \n Projections of climate impacts at the asset level on\n yearly scales:\n
- \n
- \n Including uncertainty ranges encompassing both the\n global climate sensitivity to emissions and the\n response of local impacts to global warming\n \n
- \n Aggregation at the continental, national and\n subnational levels using weighted averages by either\n area, GDP, or population\n \n
\n - \n Time evolution of future impacts for policy-relevant\n scenarios from Representative Concentration Pathways\n (RCP)\n \n
- \n Hazard and Country loss maps for different warming\n levels containing information on the robustness of the\n projections, based on the agreement between the various\n climate and impact models used to derive them.\n \n
- \n Climate and climate impact indicators covering several\n biophysical sectors and economic damages from selected\n extreme events\n \n
- \n The possibility to download all displayed data, as well\n as the data underlying them\n \n
- \n The possibility to download a comprehensive ppt - report\n covering all the assets, corresponding losses, graphs\n with explanations\n \n
\n The Intensel Blueswan SaaS provides a comprehensive,\n globally consistent dataset of physical risk projections for\n different climate scenarios. These datasets are supplemented\n by regional data to asset level granularity and financial\n modeling to translate hazards into dollar Loss. However,\n these losses and climate hazards are scenarios and\n projection-based thus could not be compared with exact\n events. The datasets from the platform can be used for\n indicative risk and thus should be used in supplementing\n national or regional risk assessments from governments or\n consultants.\n
\n\n
\n
\n
\n
\n
\n
\n
\n
\n
\n 1. The Climate Value at Risk represents the Potential\n YEARLY losses of the portfolio or asset in 2030-2050 or\n 2050-2070
\n
\n 2. The projected losses are the average of the maximum\n extreme events under radiative forcing corresponding to\n RCP
\n 8.5, that is business as usual with no transition policies\n in place.\n
\n
\n 3. The projected losses are based on the damages to the\n buildings, that is the estimated value of the property\n using
\n indicated area and the price per sqm. They can be\n readjusted using either the construction value or the\n replacement value.
\n These data are internal data specific to the company and\n assets under consideration.\n
\n
\n 4. It is the sum of physical damage + operation disruption\n due to extreme events.\n
\n
\n 5. The projected/potential loss is different for Expected\n loss. The Expected loss is therefore the probability\n adjusted.
\n Projected losses for the given carbon concentration\n scenario (assume an RCPs 8.5 has a probability of 10%) and\n the probability\n
\n that the extreme event is happening (ie 1:100 year return\n period=1%). For example, the Expected loss of a\n USD100million is
\n = USD100million *10%*1%=USD100,000\n
\n
\n 6. The % losses are independent of the value of the\n property. It is determined from the severity of climate\n risk,
\n geographical location, and the building characteristics\n such as building material, height, terrain elevation, and\n surrounding
\n infrastructure.
\n
\n 7. No time value is taking into consideration as the\n discount factors is a subjective and company-specific\n number.\n
\n
\n 8. When projecting losses related to climate risk, no\n asset-specific resilience measure is taken into\n consideration.\n
\n Nonetheless, we do use the best available satellite data\n to check the surrounding infrastructure, ie roads, and\n other
\n water catchments to the degree that this information is\n publicly available.\n
\n
\n 9. No insurance coverage is taken into account. That means\n that part of the historical losses is covered from\n insurance,\n
\n hence company reported losses are only a fraction of\n realized losses.\n
\n
\n 10. Both the area and the price of the properties are kept\n constant during the analysis, as the only variable here is\n the
\n climate change-related risk. Should one integrate the\n impact of climate on GDP, valuation, and productivity,\n
\n the losses will most likely be much higher than the ones\n projected in this analysis.\n
\n
\n
\n
\n
\n
\n
\n Electricity Loss CalculationPredicted_Consumption =\n Model(t,h,a)\n
\n\n Here:
\n
- \n
- Model : Machine Learning Model. \n
- t : Temperature in Degree Celsius \n
- h : Humidity \n
- a : Area of Building \n
\n so we have used effective_electricity_cost(kwh) for\n that building.\n \n\n
\n Effective Electricy_Cost(kwh) =\n ec-min(ec*k_factor,ec*area/100000)\n
\n\n {\" \"}\n Here:
\n
- \n
- ec : Electricity Cost (kwh) \n
\n Predicted_Consumption = Model(t,a,h)
{\" \"}\n Predicted_Total_Cost =\n (Predicted_Cosumption)*Effective_Electricity_Cost(kwh)\n
\n Note: Effective Electricity Cost can be used for all\n areas because for small areas the
modification\n is negligible.\n
\n Validation Metrics\n
\n\n\n
\n
\n
\n
\n
\n
\n Intensel's Flood Modelling{\" \"}\n
\nAchieving Higher Accuracy by
\n\n
- \n
- \n {\" \"}\n DEM Preprocessing (Fill sink and\n Reconditioning)\n \n
- \n {\" \"}\n Using high resolution Rainfall data and\n considering
\n Land use Land cover parameter for assigning{\" \"}\n
\n roughness coefficient which controls flow\n velocity.\n \n - \n {\" \"}\n Using Full momentum equation in HECRAS model &{\" \"}\n
\n Flo 2D model as all parameter like Gravity,\n Pressure,
\n Friction, Corollis effect, Acceleration and\n Turbulent
\n eddy viscosity is taken into consideration.\n \n - \n {\" \"}\n Simulated results are validated using\n satellite images
\n of Synthetic Aperture Radar (SAR) and uses our{\" \"}\n
\n developed tool for estimating performance of\n flood
\n simulation models based on the counts of\n pixels
\n which are correctly predicted by the model,\n under
\n predicted by the model and over predicted by\n the model.\n \n
\n
\n
\n
\n
\n Validation Statistics\n
\n\n With Respect to:-
\n FEMACorrectly Predicted cells (A)- 277065O
\n verpredicted cells – (B) - 178042
\n Under predicted cells – (C) - 84995
\n % increased in the flood area – 35%
\n Measures of Fit
\n F1 = A /(A+B+C)
\n F1 = 0.51
\n Note – F1 ranges from 0 to 1
\n The value =1 is 100% accuracy\n
\n
\n
\n
\n
\n Flood Validation with FEMA (Depth)\n
\n\n
\n
\n
\n Vietnam Flood Modelling Validation\n
\n\n
\n
\n
\n Vietnam Flood Modelling Validation\n
\n\n
\n
\n
\n
\n
\n Storm Surge Simulation\n
\n\n The surge experienced for historic storms is\n simulated using GeoClaw.
\n
\n It estimates surge using Riemann solvers are\n used to solve 2d depth-averaged
\n shallow water equations for flow over varying\n topography.
\n
\n It takes storm properties from IBTrACS record.{\" \"}\n
\n
\n The surge experienced is estimated from the\n solution files generated.
\n
\n Bathymetric data at 500m resolution is used{\" \"}\n
\n for the simulation.\n
\n Sampling Surge Values\n
\n\n The maximum surge experienced is sampled near{\" \"}\n
\n the coast as a 0 representative value.
\n
\n Surge values were estimated at grid size of 1km\n x 1km.
\n
\n The simulation values were validated with\n recorded
\n maximum surge for the storm Manghut.\n
| Station | \nObserved | \nSimulated | \nError | \n
|---|---|---|---|
| Quarry Bay | \n3.88 | \n3.10 | \n19.8% | \n
| Shek Pik | \n3.89 | \n3.28 | \n15.5% | \n
| Tai Po Kau | \n4.71 | \n2.95 | \n37.2% | \n
| Tsim Bei Tsu | \n4.18 | \n3.40 | \n18.5% | \n
\n Estimating Inundation\n
\n\n The inundation of surge into the land is\n simulated
\n using a high resolution DEM.
\n
\n A connected component based segmentation is used\n to estimate
\n the submerged regions.
\n
\n The domain is divided into numerous subdomains\n for easy processing.
\n
\n The largest continuous zero value patch in DEM\n is considered as ocean
\n and is used as seed region.
\n
\n All submerged regions connected to the ocean\n segment is evaluated
\n and added to the result.
\n
\n
\n {\" \"}\n In one space dimension, the clawpack routine can\n be used
\n to solve a system of equations of the form κ(x)q\n t + f (q) x = ψ(q, x, t),
\n where q = q(x, t) lR m .
\n The standard case of a homogeneous conservation\n law has ∈κ ≡ 1 and ψ ≡ 0,
\n q t + f (q) x = 0.
\n The flux function f (q) can also depend\n explicitly on x and t as well as on q.
\n Hyperbolic systems that are not in conservation\n form ,
\n q t + A(x, t)q x = 0, can also be solved.
{\" \"}\n
\n
\n For simulating the surge due to a typhoon, the\n conservation laws are solved for
\n a given bathymetry and wind forcing over the\n surface.
\n
\n The temporal simulation gives the depth averaged\n volume and velocity components
\n of the disturbance. This is used to estimate the\n surge due to disturbance.
\n
\n
\n
\n
\n
\n
\n {\" \"}\n \n {\" \"}\n Objective{\" \"}\n \n : Forecast the entire track of the cyclone based on\n the single origin point and the time of forming.{\" \"}\n
\n
\n \n Input Features\n \n :
\n 1)Latitude and Longitude of Origin.\n
\n 2)Date and Time of forming the Origin.\n
\n
\n
\n \n Additional Input Features\n \n : (these will be searched on the fly by our model\n itself)
\n
\n 1)Temperatures (1 millibar to 1000 millibar)\n
\n 2)UWind & VWind\n
\n 3)SLI (Surface Lifted Index)\n
\n 4)SLP (Sea level pressure)\n
\n 5)Sea Temperatures\n
\n 6)Omega\n
\n 7)Geopotential Height\n
\n
\n \n Output Features\n \n : The output will be a csv file containing\n forecasted time, latitude
\n and longitude of the eye of the cyclone at that\n time.\n
\n
\n
\n\n
\n We have selected GRU+Attention Model for this\n task, it is basically a deep neural network.{\" \"}\n
\n
\n \n GRU Model\n \n :
\n GRU model basically has a temporal hidden memory\n and it also has some selective gates
\n for what to store and what not to store. This\n model stores useful information of previous
\n points of the same track in hidden memory much\n like the human brain.
\n \n
\n \n Attention Mechanism\n \n :
\n Attention is basically used to improve the\n performance of GRU models because GRU
\n vanishes some information if there is a very long\n sequence or very long typhoon track,
\n so attention can focus on any previous point.{\" \"}\n
\n \n
\n
\n
\n
\n
\n This is the Basic GRU Model Working with our\n dataset\n \n
\n
\n
\n
\n Error Metric for Average Distance Error
\n
\n
\n
\n
\n \n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n {\" \"}\n \n {\" \"}\n Objective{\" \"}\n \n : Predict the damage based on any Cyclone, suppose\n a cyclone brings x amount
\n of rain with y wind speed and we have GDP Per\n Capital of Landfall location then based on
\n these we can Predict how much damage due to this\n cyclone.
\n
\n \n Input Features\n \n :
\n 1)Rainfall
\n 2)Wind Speed (max)
\n 3)Population (200km)
\n 4)Total GDP (200km)
\n 5)GDP per capita
\n Note : Rainfall Data can be collected using a\n separate model, same with wind speed data can{\" \"}\n
\n be collected from a different deep neural network\n model.
\n
\n
\n \n Output Features\n \n :
1)Total damage due to cyclone
\n
\n \n Model Description\n \n :
We used random forest model totrain on\n the historical dataset,
\n inputs are the wind speed rainfall etc then the\n model will predict total damage in US $ 2015,{\" \"}\n
\n due to those extreme conditions.\n
\n
\n
\n \n
\n
\n
\n
\n
\n
\n 1)R2 Accuracy :\n
\n\n 2)R2 Confidence intervals :\n
\n3)RMSE Error :
\n\n 4)Feature Importance :\n
\n\n 5)Scatter Plot real vs Predicted :\n
\n\n
\n
\n
\n
\n
\n
\n
\n
- \n
- \n The drought methodology is based and derived from\n RCM projections of
“rainfall in the driest\n quarter”\n \n - \n The formula used to calculate the drought index\n which ranges between
0 to 100 is following\n \n
\n
\n
\n
\n
\n
\n Notes:\n
\n- \n
- \n If drought risk is 100% that means the rainfall in\n future projection would
reduce equivalent\n to “rainfall in driest quarter historical”\n \n - \n If drought is 0% means that the condition in the\n driest quarter is either the same
or\n receiving more rainfall than “rainfall in driest\n quarter historical”\n \n
\n
Extreme Heat
\n| Temperature(Celsius) | \nRisk Severity | \nRisk Score | \nEvery Degree Rise | \n
|---|---|---|---|
| 0-27 | \nNo Risk | \n0 | \n0 | \n
| 27-32 | \nLow | \n25 | \n0 + (5) | \n
| 32-41 | \nMedium | \n50 | \n25 + (2.77) | \n
| 41-54 | \nMedium High | \n75 | \n50 + (1.92) | \n
| 54-65 | \nHigh | \n100 | \n75 + (2.27) | \n
| >65 | \n\n Very High\n | \n100 | \n100 | \n
Rainfall Flood
\n| Depth (m) | \nRisk Severity | \nRisk Score | \nIncrease in 0.1m Rise | \n
|---|---|---|---|
| 0 | \nNo Risk | \n0 | \n0 | \n
| Less than 0.5 | \nLow | \n25 | \n0 + (0.5) | \n
| 0.5 - 1 | \nMedium | \n50 | \n25 + (0.5) | \n
| 1 - 2 | \nMedium High | \n75 | \n50 + (0.25) | \n
| 2 - 5 | \nHigh | \n100 | \n75 + (0.083) | \n
| >5 | \n\n Very High\n | \n100 | \n100 | \n
River Flood
\n| Depth (m) | \nRisk Severity | \nRisk Score | \nIncrease in 0.1m Rise | \n
|---|---|---|---|
| 0 | \nNo Risk | \n0 | \n0 | \n
| Less than 0.5 | \nLow | \n25 | \n0 + (0.5) | \n
| 0.5 - 1 | \nMedium | \n50 | \n25 + (0.5) | \n
| 1 - 2 | \nMedium High | \n75 | \n50 + (0.25) | \n
| 2 - 5 | \nHigh | \n100 | \n75 + (0.083) | \n
| >5 | \n\n Very High\n | \n100 | \n100 | \n
Typhoon
\n| Wind Speed (Km/h) | \nRisk Severity | \nRisk Score | \nIncrease in Wind Speed | \n
|---|---|---|---|
| Less Than 119 | \nNo Risk | \n0 | \n0 | \n
| 119 - 153 | \nLow | \n25 | \n0 + (0.42) | \n
| 178 - 208 | \nMedium | \n50 | \n25 + (0.83) | \n
| 209 - 251 | \nMedium High | \n75 | \n50 + (0.58) | \n
| 251 - 350 | \nHigh | \n100 | \n75 + (0.25) | \n
| >350 | \nVery High | \n100 | \n100 | \n
Storm Surge
\n| Depth (m) | \nRisk Severity | \nRisk Score | \nIncrease in 0.1m Rise | \n
|---|---|---|---|
| 0 | \nNo Risk | \n0 | \n0 | \n
| Less Than 0.5 | \nLow | \n25 | \n0 + 0.5 | \n
| 0.5 - 1 | \nMedium | \n50 | \n25 + (0.5) | \n
| 1 - 2 | \nMedium High | \n75 | \n50 + (0.25) | \n
| 2 - 5 | \nHigh | \n100 | \n75 + (0.083) | \n
| >5 | \n\n Very High\n | \n100 | \n100 | \n
Sea Level Rise
\n| Level Rise(m) | \nRisk Severity | \nRisk Score | \nIncrease in m(s) | \n
|---|---|---|---|
| 0 | \nNo Risk | \n0 | \n0 | \n
| >0 | \nVery High | \n100 | \n100 | \n
Drought
\n| % Decrease in Driest Quarter | \nRisk Severity | \nRisk Score | \n% Decrese in Rainfall | \n
|---|---|---|---|
| 0 | \nNo Risk | \n0 | \n0 | \n
| 0 - 25 | \nLow | \n25 | \n0 + (1) | \n
| 25 - 50 | \nMedium | \n50 | \n25 + (1) | \n
| 50 - 75 | \nMedium High | \n75 | \n50 + (1) | \n
| 75 - 100 | \nHigh | \n100 | \n75 + (1) | \n
| More then 100 | \nVery High | \n100 | \n100 | \n
Snow Fall
\n| \n Snow Fall Quantity 10-6 (Kg-m)\n | \nRisk Severity | \nRisk Score | \nIncrease in Snow-Melt | \n
|---|---|---|---|
| 0 | \nNo Risk | \n0 | \n0 | \n
| 6 - 7 | \nLow | \n25 | \n0 + (2.5) | \n
| 7 - 8 | \nMedium | \n50 | \n25 + (2.5) | \n
| 8 - 8.5 | \nMedium High | \n75 | \n50 + (5) | \n
| 8.5 - 9 | \nHigh | \n100 | \n75 + (5) | \n
| >9 | \nVery High | \n100 | \n100 | \n
Landslide
\n| Score out of 100 | \nRisk Severity | \nRisk Score | \nIncrease in Score | \n
|---|---|---|---|
| 0 | \nNo Risk | \n0 | \n0 | \n
| 0 - 25 | \nLow | \n25 | \n0 + (1) | \n
| 25 - 50 | \nMedium | \n50 | \n25 + (1) | \n
| 50- 75 | \nMedium High | \n75 | \n50 + (1) | \n
| 75 - 100 | \nHigh | \n100 | \n75 + (1) | \n
| 100 | \nVery High | \n100 | \n100 | \n
\n RCPs usually refer to the portion of the concentration\n pathway extending up to 2100, for which Integrated\n Assessment Models produced corresponding emission\n scenarios:
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n The 100-yr flood peak applies for flood plain\n development, medium-sized flood protection works, and\n regional urban drainage facilities.\n \n
\n
\n
\n Figure: Design of Levee based on 250 year return\n period of river at Tecate Creek, Baja California,\n Mexico\n
\n
\n
\n\n
\n
| No. | \nType of project or feature | \nReturn Period(yr) | \n
|---|---|---|
| 1 | \nUrban drainage [low risk] (up to 100 ha) | \n5 to 10 | \n
| 2 | \n\n Urban drainage [medium risk] (more than 100 ha)\n | \n25 to 250 | \n
| 3 | \nRoad drainage | \n25 to 100 | \n
| 4 | \nPrincipal spillways (dams) | \n25 to 100 | \n
| 5 | \nHighway drainage | \n50 to 250 | \n
| 6 | \nLevees [medium risk] | \n50 to 250 | \n
| 7 | \n\n Urban drainage [high risk] (more than 1,000 ha)\n | \n50 to 250 | \n
| 8 | \nFlood plain development | \n100 | \n
| >9 | \nInsurance premium calculations | \n250 | \n
| 10 | \nBridge design (piers) | \n100 to 500 | \n
| 11 | \nLevees [high risk] | \n200 to 1000 | \n
\n Fig. 1 {\" \"}\n Figure shows change in Dollar losses and with respect to\n years and Representative concentration pathways\n
\n\n Here climate value at risk is the expected maximum\n losses from climate hazard per year while maximum\n exposure is the sum of highest loss the assets in the\n portfolio from a particular climate hazard.\n \n
\n Fig. 2 {\" \"}\n Figure shows severity of climate hazard along with max\n probable yearly loss and max expected exposure from any\n hazard posing highest loss on the portfolio\n
\n \n\n\n Fig. 3 {\" \"}\n Figure shows the breakdown of climate hazard severity\n and a loss breakdown corresponding to different\n variables using progress charts\n
{\" \"}\n \n\n\n Fig. 4 {\" \"}\n Figure shows heat scores of all the selected climate\n variables. The minimum score is 0 (no risk) and max\n score is 10 (highest risk). The size of the bubbles\n represents intensity of hazard\n
\n \n \n\n Individual Climate Value at Risk % is calculated\n by Taking the Individual Asset Loss and Dividing\n this number by the asset valuation. Total\n Climate value at risk is calculated by taking\n the Total combined losses and dividing by the\n total Maximum Exposure.\n
\n\n Intensel uses HK Observatory district data set\n with granularity at 30 meters. We use SRTM\n (Shuttle Radar Topography Mission) as DEM\n (Digital elevation Model) data as these are more\n accurate than Coastal Dem (used by Climate\n Central) data especially for small islands such\n as HK Island.\n
\n Intensel's results do not reflect the access to\n the assets due to the inundation of road network\n and surrounding area.\n
\n The focus should be on the asset with higher\n potential loss and higher exposure to climate\n hazards (exposure=probability 1% *severity of\n climate). The probability/likelihood is the 1%\n across all hazards, but the severity of climate\n hazards is not the same on various assets.\n Nonetheless, to be able to use the dollar loss\n Intensel would recommend doing so only when each\n asset has All the asset relevant data, ie\n revenues generated in that location, area, cost,\n valuation, ownership etc. At least need to be\n more accurate on how much of that loss you own\n (ownership). If you do not have that\n information, the financial dollar loss number is\n potentially overstated.\n
\n\n Maximum Exposure is calculated as the valuation\n of all assets that receive climate risk loss,\n those that do not receive damage will not be\n included in the Maximum exposure.\n
\n\n There are over nine climate risk variables on\n the Intensel blue-swan platform, estimations of\n most variables vary from one to another. We try\n to model these variables according to the best\n standards and peer-reviewed models. Further, as\n the platform provides global analysis, we’re\n developing a uniformed modelling approach\n globally. However, several inputs are\n region-specific, for example, DEMs, bathymetry,\n footprints, historical data; thus, model output\n is both scalable and includes region specific\n information, Pluvial/fluvial Flood Modelling:\n In-house models using HEC (Hydrologic\n Engineering Centre) and Flow-2d. Storm Surge:\n HEC and Geo hydrodynamic models.’ Extreme heat:\n outputs from integrated GCM-RCM models. These\n are in-house models with validation available\n upon request.\n
\n\n The outputs are based on recommendations by\n TCFD, where the recommendations are to provide\n physical climate risk for two time periods,\n 2030-2050 and 2050 -2070 only. Though we can\n generate these events yearly, we neither\n recommend nor prefer this for two reasons: 1. As\n we provide asset-level granularity, the size of\n the datasets increases exponentially and can go\n beyond 100 terabytes as 9 variables, global\n yearly for 70+ years. This much data is\n challenging to handle as cloud infrastructure\n handling becomes arduous. 2. Climate data does\n not make sense yearly, unlike the weather.\n Climate data’s mean over the time variables\n makes more sense as the uncertainty is reduced\n when averaged over the time period. It gives a\n much better estimation of extremes and trends.\n
\n\n The losses are based on the insurance approach\n wherein Mean Damage Ratio (MDR) is modelled for\n different levels of floods and surges for types,\n floors, basements of assets. The MDR approach is\n documented in various journals. Temperature loss\n models are trained on historical losses because\n of the increase/decrease in energy consumption\n and change in temperature and humidity; both are\n modelled from outputs of integrated GCM-RCMs.\n (Ref: 10.1007/s11069-013-1017-z\n https://doi.org/10.1007/s11069-013-1017-z ,\n http://hdl.handle.net/2117/22402 etc.) The\n typhoon loss is based on calculations of\n coefficients for factors that lead to typhoon\n losses and trained with historical data. This\n was developed at Intensel with our PhD who also\n published a paper on this loss calculation\n approach (Ref:\n https://doi.org/10.3389/fbuil.2020.575534)\n
\n\n We provide the ability to adjust property\n valuation according to the client both using\n APIs and UI (User Interface). A client who has\n already calculated an increase/decrease in\n valuation can provide these inputs on their end.\n This is done as an increase and decrease in\n property valuation depends on several variables\n and varies from region to region; modelling this\n is not our expertise. The operations loss\n considers the increase/decrease in economic\n activity in a year; however, this is done with\n the assumption that increase/decrease in all the\n regions is directly proportional to the\n increase/decrease in GDP of a country in the\n coming 60-year period.\n
\n\n While aggregating the losses, we made sure that\n these individual variable-losses were mutually\n exhaustive and did not overlap. As all the\n outputs are probable max loss, what we give\n clients is a total and break down individual\n losses to better understand the risk. Though the\n losses are mutually exhaustive, two events might\n occur together, like extreme temperature and\n storm surge. The co-occurrence is very difficult\n to model accurately. Hence the client can use\n the individual losses and can create his own set\n of co-occurring events.\n
\n\n Intensel uses multiple in-house models for\n typhoon risk (mainly rain and wind from\n typhoons). These models take into consideration\n some of the essential factors for modelling\n typhoon risk, such as changes in wind intensity\n over time, change in 5 days cumulative rainfall\n from the typhoon, deviation in typhoon tracks\n and landfalls (this has a significant impact on\n exposure) and lastly the prediction of\n month/year and location of origin of typhoons.\n
\n\n Summary SaaS model worksheet for BD Team (Updated on 1 JUN\n 2022)\n
\n| \n Hazard\n | \n\n SAAS\n | \n\n Deep Analysis\n | \n\n Risk Index\n | \n\n Notes\n | \n|||||
|---|---|---|---|---|---|---|---|---|---|
| \n | Modelled | \nNot Modelled | \nModelled | \nModelled | \n\n | ||||
| \n | Name (source) | \nValidation/Accuracy | \nModel not available | \nData source | \nName (source) | \nValidation/Accuracy | \nName if YES | \nValidation/Accuracy | \n\n |
| Rainfall Flood | \n\n [Widely Adapted Model] HEC-RAS (Rain on grid),\n Flo-2d (urban)\n | \n80% | \n\n | \n | HEC-RAS (result with Time Step) | \n80% | \nAnalytical Hierarchy Process | \nNA (As Judgemental) | \n\n |
| River flood | \nN/A | \nN/A | \n\n | GLOBAL RASTER DATASET | \nHECRAS (Discharge input to river cells) | \n80% | \nAnalytical Hierarchy Process | \nNA (As Judgemental) | \n\n |
| Snow Fall | \n\n No Model/Downscaled for Improve the resolution\n using (SGAN Model)\n | \n\n | \n | Chelsa/IPCC-CMIP5-GCM's | \n\n | \n | \n | \n | \n |
| Temperature | \n\n No Model/Downscaled for Improve the resolution\n using (SGAN Model)\n | \n\n | \n | Chelsa/IPCC-CMIP5-GCM's | \n\n | \n | \n | \n | \n |
| Sea Level Rise | \n\n [Intensel Model] Global Coastal areas Inundation\n based on Mean sea level rise projections using\n Region growing Model\n | \n\n | \n | \n | \n | \n | \n | \n | \n |
| Storm Surge | \n\n [Intensel Model] Based on peer reviwed article and\n inhouse developed technique for shore connected\n region segmentation{\" \"}\n | \n\n | \n | \n GESLA‑2: Global extreme sea level analysis version\n 2 (tide gauge data).\n | \n\n | \n | \n Normalised maximum surge depth over 9x9 nearest\n neighbour\n | \n\n | \n {\" \"}\n Refe article: Kirezci, E., Young, I.R.,\n Ranasinghe, R. et al. Projections of global-scale\n extreme sea levels and resulting episodic coastal\n flooding over the 21st Century. Sci Rep 10, 11629\n (2020). https://doi.org/10.1038/s41598-020-67736-6\n | \n
| Extreme Heat | \n\n [Widely Adapted Model] Window based max over\n 5years from GCM predictions\n | \n\n | \n | IPCC-CMIP5 GCMs | \n\n | \n | \n | \n | \n |
| Landslide | \n\n No Model/Downscaled for Improve the resolution\n using (SGAN Model)\n | \n\n | \n | NASA | \n\n | \n | \n | \n | Historical land silde data | \n
| Typhoon | \n\n [Intensel RCP Model but Lost the methodology]. We\n are trying to build a model.{\" \"}\n | \n\n | \n | NASA | \n\n | \n | Analytical Hierarchy Process | \nNA (As Judgemental) | \n|
| Drought | \n\n No Model/Downscaled for Improve the resolution\n using (SGAN Model){\" \"}\n | \n\n | \n | SPI - IPCC -CMIP6 - GCM | \n\n | \n | \n | \n |
\n
\n
\n {/*
Summary SaaS model worksheet for BD Team (Updated on 1 JUN 2022)
\n\n| Hazard | \nSAAS | \nDeep Analysis | \nRisk Index | \nNotes | \n|||||
|---|---|---|---|---|---|---|---|---|---|
| \n | Modelled | \nNot Modelled | \nModelled | \nModelled | \n\n | ||||
| \n | Name (source) | \nValidation/Accuracy | \nModel not available | \nData source | \nName (source) | \nValidation/Accuracy | \nName if YES | \nValidation/Accuracy | \n\n |
| Rainfall Flood | \n[Widely Adapted Model] HEC-RAS (Rain on grid), Flo-2d (urban) | \n80% | \n\n | \n | HEC-RAS (result with Time Step) | \n80% | \nAnalytical Hierarchy Process | \nNA (As Judgemental) | \n\n |
| River flood | \nN/A | \nN/A | \n\n | GLOBAL RASTER DATASET | \nHECRAS (Discharge input to river cells) | \n80% | \nAnalytical Hierarchy Process | \nNA (As Judgemental) | \n\n |
| Snow Fall | \nNo Model | \n\n | \n | IPCC-CMIP5-GCM's | \n\n | \n | \n | \n | \n |
| Temperature | \nNo Model/Downscaled for Improve the resolution using (SGAN Model) | \n\n | \n | IPCC-CMIP5-GCM's | \n\n | \n | \n | \n | \n |
| Sea Level Rise | \n[Intensel Model] Global Coastal areas Inundation based on Mean sea level rise projections using Region growing Model | \n\n | \n | \n | \n | \n | \n | \n | \n |
| Storm Surge | \n[Intensel Model] Based on peer reviwed article and inhouse developed technique for shore connected region segmentation | \n\n | \n | GESLA‑2: Global extreme sea level analysis version 2 (tide gauge data). | \n\n | \n | Normalised maximum surge depth over 9x9 nearest neighbour | \n\n | Refe article: Kirezci, E., Young, I.R., Ranasinghe, R. et al. Projections of global-scale extreme sea levels and resulting episodic coastal flooding over the 21st Century. Sci Rep 10, 11629 (2020). https://doi.org/10.1038/s41598-020-67736-6 | \n
| Extreme Heat | \n[Widely Adapted Model] Window based max over 5years from GCM predictions | \n\n | \n | IPCC-CMIP5 GCMs | \n\n | \n | \n | \n | \n |
| Landslide | \nNo Model | \n\n | \n | NASA | \n\n | \n | \n | \n | Historical land silde data | \n
| Typhoon | \n[Intensel RCP Model but Lost the methodology]. We are trying to build a model. | \n\n | \n | NASA | \n\n | \n | Analytical Hierarchy Process | \nNA (As Judgemental) | \n|
| Drought | \nNo Model/Downscaled for Improve the resolution using (SGAN Model) | \n\n | \n | SPI - IPCC -CMIP6 - GCM | \n\n | \n | \n | \n |
\n
Input Data
\nRequired Field
\n ) : null}\nRequired Field
\n ) : null}\n\n {this.props.transition.error}\n
\n ) : null}\n\n
\n
\n
\n \n Upload CSV \n \n \n Download Sample CSV\n \n \n \n \n
\n\n {this.props.transition.error ? (\n \n {this.props.transition.error}\n
\n ) : null}\n\n\n
\n
\n
\n
\n\n
\n
\n
All Assets
\nSelected Assets
\n\n
\n
\n\n
\n
\n {\" \"}\n \n {\" \"}\n Year Loss for{\" \"}\n {this.state.analysis.toUpperCase()}{\" \"}\n {this.state.analysis == \"rcp\" ? (\n {this.state.rcp}\n ) : (\n {this.state.ssp}\n )}{\" \"}\n \n
{\" \"}\n\n {\" \"}\n \n {\" \"}\n Year Loss for{\" \"}\n {this.state.analysis.toUpperCase()}{\" \"}\n {this.state.analysis == \"rcp\" ? (\n {this.state.rcp}\n ) : (\n {this.state.ssp}\n )}{\" \"}\n \n
{\" \"}\n\n {\" \"}\n \n {\" \"}\n {this.state.analysis.toUpperCase()} Loss for\n Year {this.state.year}{\" \"}\n \n
{\" \"}\n\n {\" \"}\n \n {\" \"}\n {this.state.analysis.toUpperCase()} Loss for\n Year {this.state.year}{\" \"}\n \n
{\" \"}\n\n \n Climate Risk Hazard{\" \"}\n \n
\n\n Company\n \n
\n
\n\n
\n \n Climate Risk Heat Map{\" \"}\n \n
\n\n\n
\n \n Global Score/CVaR Maps\n \n
\n\n
\n \n Asset Table{\" \"}\n \n
\n\n
{\" \"}\n {this.state.table_condition && (\n
{\" \"}\n {this.state.table_condition && (\n
\n {this.state.table_condition && (\n
\n {\" \"}\n Extreme Wind Speeds
Maximum wind\n speed at multiple return periods
\n
\n Fraction of building, contents and inventory\n damaged by extreme wind events
\n
\n Annual average loss to building, contents and\n inventory\n
\n Number of days of business interruption\n {\" \"}\n {\" \"}\n \n
\n {\" \"}\n \n {\" \"}\n \n Extreme Rainfall
\n
\n Maximum daily precipitation across multiple return\n periods\n {\" \"}\n {\" \"}\n \n
\n {\" \"}\n \n {\" \"}\n \n Wildfire Risk
\n
\n The annual number of wildfires, which ignite and\n grow to detectable size,
\n that are expected in a 1 square km grid cell\n {\" \"}\n {\" \"}\n \n
\n {\" \"}\n \n {\" \"}\n \n Hail And Thunderstorm Probability
\n
\n Number of days per year where large hail (>2 in\n / 5 cm in diameter) is possible\n
\n
\n Number of days per year where environmental\n conditions are conducive to severe thunderstorm\n formation\n {\" \"}\n {\" \"}\n \n
\n {\" \"}\n \n {\" \"}\n \n Drought Frequency
\n
\n Months per year where the rolling 3-month or\n 6-month average\n
\n
\n Standardized Precipitation Evapotranspiration\n Index (SPEI) is below -2\n {\" \"}\n {\" \"}\n \n
\n {\" \"}\n \n {\" \"}\n \n Flood Depth of Water
\n
\n Flood depth of water across multiple return\n periods\n
\n
\n Percentage of the central square flooded across\n multiple return periods\n
\n
\n Fraction of building, contents and inventory\n damaged by flood events\n
\n {\" \"}\n Extreme Heat
\n
\n Days per year exceeding 35°C, 38°C and the\n historical 99th percentile\n
\n
\n Heating and cooling degree days\n
\n
\n Days exceeding high (32°C) and dangerously high\n (35°C) Wet-Bulb Globe Temperature\n {\" \"}\n {\" \"}\n \n
Advanced Filter Options
\nFiltering your data for more insight
\nProjects
\nAnalysis Type
\nRCP
\nSSP
\nCount
\nScenario Type
\n\nYear
\n {this.state.analysis == \"rcp\" ? (\nIndex Name
\n\n
\n
\n
\n
\n
\n {/*
Additional Factor
\n\n
\n {/*
Property Type
\n\n
FAQs
\n\n \n\n There are over nine climate risk variables on the intensel blue-swan platform, and the estimation of most variables varies from one another. \n We try to model these variables according to the best standards and peer-reviewed models. Further, as the platform provides global analysis, \n we have a uniform modelling approach globally. However, several inputs are region-specific, for example, DEMs, bathymetry, footprints, historical data; thus, \n model output is both scalable and is also has regional information,\n Pluvial/fluvial Flood Modelling: In-house models using HEC and Flow-2d. \n Storm Surge: HEC and Geo hydrodynamic models.’\n Extreme heat: outputs from integrated GCM-RCM models \n These are in-house run models with validation ppt available on request.\n
\n\n The outputs are based on recommendations of TCFD, where the recommendations are to provide physical climate risk for two time periods, 2030-2050 and 2050 -2070 only. Though we can generate these events yearly, we neither recommend nor prefer it because for two reasons:\n\n 1. As we provide asset-level granularity, the size of the datasets increases exponentially and can go beyond 100 terabytes as 9 variables, global yearly for 70+ years. This much data is challenging to handle also cloud infrastructure handling becomes arduous.\n 2. Climate data does not make sense yearly, unlike the weather. Climate data mean over the time variables makes more sense as the uncertainty is reduced when averaged over the time period. It gives a much better estimation of extremes and trends.\n
\n\n The losses are based on the insurance approach wherein Mean Damage Ratio (MDR) is modelled for different levels of floods and surges for types, floors, basements of assets. The MDR approach is documented in various journals. \nTemperature loss models are trained on historical losses because of the increase/decrease in energy consumption and change in temperature and humidity; both are modelled from outputs of integrated GCM-RCMs.\n(Ref: 10.1007/s11069-013-1017-z https://doi.org/10.1007/s11069-013-1017-z , http://hdl.handle.net/2117/22402 etc)\nThe typhoon loss is based on calculations of coefficients for factors that lead to typhoon losses and training it for historical data. This was developed at intensel with our PhD who also published a paper on this loss calculation approach\n (Ref: https://doi.org/10.3389/fbuil.2020.575534).\n
\n\n LThere is facilitation to adjust property valuation according to the clients boh using APIs and UI. A client who has already calculated an increase/decrement in valuation can provide those inputs from his end. This is done as an increase and decrease in property valuation depends on several variables and varies from region to region; modelling this is not our expertise.\n\n
\n\n \nThe operations loss considers the increase/decrease in economic activity with year; however, this is done by the assumption that increase/decrease in all the regions is directly proportional to the increase/decrease in GDP of a country in the coming 60 years.\n
\n\n While aggregating the losses, we made sure that these individual variable-losses were mutually exhaustive and did not overlap. As all the outputs are probable max loss, what we give clients is total and also break down individual losses to better understand the risk. \n
\n\n Though the losses are mutually exhaustive, two events might occur together, like extreme temperature and storm surge. The co-occurrence is very difficult to model for anyone. Hence the client can use the individual losses and can create his own set of co-occurring events,\n
\n\n Intensel uses multiple models in-house to model typhoon risk (mainly rain and wind from typhoons). These models take into consideration some of the essential factors for modelling typhoon risk, such as changes in wind intensity over time, change in 5 days cumulative rainfall from the typhoon, deviation in typhoon tracks and landfalls (this has a significant impact on exposure) and lastly the prediction of month/year and location of origin of typhoons. \n
\n\r\n Getting Started\r\n
\r\n\r\nStep 0: Base Endpoint for API calls
\r\n\r\n {\" \"}\r\n >{\" \"}\r\n \r\n https://api.intensel.net/apiv1/\r\n \r\n
\r\n\r\nStep 1: Prepare API key
\r\n\r\n You have to pass api key in headers for authorization\r\n
\r\n\r\n Authorization: Api-Key {\"{\" \"}\r\n
Step 2: Make Request
\r\n\r\n import requests
\r\n URL = \"https://api.intensel.net/apiv1/create/\"
\r\n headers ={'{\"Authorization\":\"Api-Key API_KEY\"}'}\r\n
\r\n payload ={'{\"project_name\": \"My Awesome Project\"}'}\r\n
\r\n response = requests.post(URL, json=payload, headers=headers){\" \"}\r\n
\r\n data = response.json()\r\n
\r\n Make Sure to include API_KEY in headers for authentication\r\n
\r\n\r\nStep 3: Done
\r\n\r\n Congrates you have make your first query.\r\n
\r\n \r\n\r\n Create Project\r\n
\r\n\r\nCreate A Project
\r\n\r\n Project Contains All Your Assets And It Allows To Analyze All Of\r\n Your Submitted Locations At Once
\r\n Endpoint:\r\n
\r\n {\" \"}\r\n >{\" \"}\r\n \r\n https://api.intensel.net/apiv1/create/\r\n \r\n
\r\n\r\n| Parameters | \r\nValue | \r\n
|---|---|
| project_name | \r\nName Of the Project | \r\n
Sample Request
\r\n\r\n URL: https://api.intensel.net/apiv1/create/
\r\n
\r\n data:{\" \"}\r\n {\r\n \r\n {' \"project_name\": \"My Awesome Project\", '} \r\n }\r\n
\r\n
\r\n \"variables\":[\"Rainfall Flood\", \"Extreme Heat\"],
\r\n
}{\" \"}\r\n
\r\n Variable Name Are Climate Variables Which You Wish To Analyze\r\n
\r\n\r\nClimate Variables
\r\n| Variables* | \r\n
|---|
\r\n * Marked Are Required And Cant Be Skipped\r\n
\r\n\r\nCode Example
\r\n\r\n import requests URL = \"https://api.intensel.net/apiv1/create/\"{\" \"}\r\n
\r\n
\r\n \r\n {' headers = {\"Authorization\":\"Api-Key API_KEY\"} '} {\" \"}\r\n
\r\n
\r\n {' payload = {\"project_name\": \"My Awesome Project\"} '}\r\n
\r\n
\r\n response = requests.post(URL, json=payload, headers=headers){\" \"}\r\n
\r\n
\r\n data = response.json()
\r\n
\r\n
Expected Response
\r\n\r\n status: HTTP 201\r\n {' {\"success\":\"Project Created Successfully\"} '}\r\n
\r\n Add Single Asset\r\n
\r\nAdd Single Asset To Project
\r\n\r\n Using This API You Can Add Single Location To Get Analyzed And\r\n Added To Your Project Endpoint:
>\r\n \r\n https://api.intensel.net/apiv1/add/\r\n \r\n
Api Parameters
\r\n| Parameters | \r\nValue | \r\n
|---|---|
| project_name | \r\nName of the Project | \r\n
| asset_data | \r\nValid Json Data Containing Information About Location | \r\n
Sample Project
\r\n\r\n \r\n URL: https://api.intensel.net/apiv1/add/\r\n \r\n
\r\n data: {\"{\"}\r\n
\r\n \"project_name\": \"My Awesome Project\",
\r\n \"asset_data\" : {\"{\"}\r\n
\r\n {\" \"}\"name\": \"Hong Kong Park\",
\r\n {\" \"}\"latitude\": 22.424645494250665,
\r\n {\" \"}\"longitude\" : 114.2135547607693,
\r\n {\" \"}\"area\": 2509,
\r\n {\" \"}\"valuation\": 2117.25,
\r\n {\" \"}\"stories\": 3,\r\n
\r\n {\"}\"}\r\n
\r\n {\"}\"}\r\n
asset_data paramters
\r\n| Parameters | \r\nValue | \r\n
|---|---|
| name (optional) | \r\n\r\n Name Of The location (If Not Provided Will Get identified By\r\n Us){\" \"}\r\n | \r\n
| latitude * | \r\nLatitude Of Location | \r\n
| Longitude * | \r\nLongitude Of Location | \r\n
| area (optional) | \r\n\r\n Area of location in sqm (If Not Provided Will Get identified\r\n By Us){\" \"}\r\n | \r\n
| Valuation (Optional) | \r\n\r\n Valuation Of Location in USD/sqm (If Not Provided Will Get\r\n identified By Us){\" \"}\r\n | \r\n
| Stories (Optional) | \r\n\r\n Number Of Floors/Stories Of The Location (If Not Provided\r\n Will Get identified By Us){\" \"}\r\n | \r\n
Code Example
\r\n\r\n \r\n import requests\r\n \r\n
\r\n URL = \"https://api.intensel.net/apiv1/add/\"\r\n
\r\n headers = {\"{\"}\"Authorization\":\"Api-Key API_KEY\"{\"}\"}\r\n payload = {\"{\"}
\r\n \"project_name\": \"My Awesome Project\",
\r\n \"asset_data\" : {\"{\"}\r\n \"name\": \"Hong Kong Park\",
\r\n \"latitude\": 22.424645494250665,
\r\n \"longitude\" : 114.2135547607693,
\r\n \"area\": 2509,
\r\n \"valuation\": 2117.25,
\r\n \"stories\": 3,
\r\n {\"}\"}
\r\n {\"}\"}
\r\n response = requests.post(URL, json=payload, headers=headers)\r\n
\r\n data = response.json()\r\n
Expected Response
\r\n\r\n status: HTTP 201\r\n {\"{\"}\"success\":\"Asset Added To Project My Awesome Project With\r\n Name Hong Kong Park\"{\"}\"}\r\n \r\n \r\n \r\n \r\n Add Bulk Assets\r\n
\r\nAdd Multiple Asset To Project
\r\n\r\n Using This API You Can Add Multiple Location To Get Analyzed And\r\n Added To Your Project In One Single API Call\r\n
Endpoint:
>\r\n \r\n https://api.intensel.net/apiv1/add/\r\n \r\n
Api Parameters
\r\n| Parameters | \r\nValue | \r\n
|---|---|
| project_name | \r\nName of the Project | \r\n
| asset_data | \r\n\r\n Valid Json Data Containing Information About Multiple\r\n Locations\r\n | \r\n
Sample Project
\r\n\r\n \r\n URL: https://api.intensel.net/apiv1/add/\r\n \r\n
\r\n data: {\"{\"}\r\n
\r\n \"project_name\": \"My Awesome Project\",
\r\n \"asset_data\" : [{\"{\"}\r\n
\r\n {\" \"}\"name\": \"Hong Kong Park\",
\r\n {\" \"}\"latitude\": 22.424645494250665,
\r\n {\" \"}\"longitude\" : 114.2135547607693,
\r\n {\" \"}\"area\": 2509,
\r\n {\" \"}\"valuation\": 2117.25,
\r\n {\" \"}\"stories\": 3,\r\n
\r\n {\"}\"},
\r\n {\"{\"}\r\n
\r\n {\" \"}\"name\": \"Canal Rd West\",
\r\n {\" \"}\"latitude\": 22.27806830351389,
\r\n {\" \"}\"longitude\" : 114.18119155173112,
\r\n {\" \"}\"area\": 1810,
\r\n {\" \"}\"valuation\": 2117.25,
\r\n {\" \"}\"stories\": 3,\r\n
\r\n {\"}\"}]\r\n
\r\n {\"}\"}\r\n
asset_data paramters
\r\n\r\n asset_data is Same As Single API\r\n
\r\n| Parameters | \r\nValue | \r\n
|---|---|
| name (optional) | \r\n\r\n Name Of The location (If Not Provided Will Get identified By\r\n Us){\" \"}\r\n | \r\n
| latitude * | \r\nLatitude Of Location | \r\n
| Longitude * | \r\nLongitude Of Location | \r\n
| area (optional) | \r\n\r\n Area of location in sqm (If Not Provided Will Get identified\r\n By Us){\" \"}\r\n | \r\n
| Valuation (Optional) | \r\n\r\n Valuation Of Location in USD/sqm (If Not Provided Will Get\r\n identified By Us){\" \"}\r\n | \r\n
| Stories (Optional) | \r\n\r\n Number Of Floors/Stories Of The Location (If Not Provided\r\n Will Get identified By Us){\" \"}\r\n | \r\n
Code Example
\r\n\r\n \r\n import requests\r\n \r\n
\r\n URL = \"https://api.intensel.net/apiv1/add/\"\r\n
\r\n headers = {\"{\"}\"Authorization\":\"Api-Key API_KEY\"{\"}\"}\r\n payload = {\"{\"}
\r\n \"project_name\": \"My Awesome Project\",
\r\n \"asset_data\" : \"asset_data\" : [{\"{\"}\r\n
\r\n {\" \"}\"name\": \"Hong Kong Park\",
\r\n {\" \"}\"latitude\": 22.424645494250665,
\r\n {\" \"}\"longitude\" : 114.2135547607693,
\r\n {\" \"}\"area\": 2509,
\r\n {\" \"}\"valuation\": 2117.25,
\r\n {\" \"}\"stories\": 3,\r\n
\r\n {\"}\"},
\r\n {\"{\"}\r\n
\r\n {\" \"}\"name\": \"Canal Rd West\",
\r\n {\" \"}\"latitude\": 22.27806830351389,
\r\n {\" \"}\"longitude\" : 114.18119155173112,
\r\n {\" \"}\"area\": 1810,
\r\n {\" \"}\"valuation\": 2117.25,
\r\n {\" \"}\"stories\": 3,\r\n
\r\n {\"}\"}]
\r\n {\"}\"}
\r\n response = requests.post(URL, json=payload, headers=headers)\r\n
\r\n data = response.json()\r\n
Expected Response
\r\n\r\n status: HTTP 201\r\n {\"{\"}\"success\":\"2 assets added to project My Awesome Project\"\r\n {\"}\"}\r\n \r\n \r\n \r\n Add Multiple Asset To Project\r\n
\r\nAdd Single Asset To Project
\r\n\r\n Using This API You Can Analyze complete Project
\r\n Endpoint:
>\r\n \r\n https://api.intensel.net/apiv1/getanalysis/\r\n \r\n
Api Parameters
\r\n| Parameters | \r\nValue | \r\n
|---|---|
| project_name | \r\nName of the Project | \r\n
Sample Request
\r\n\r\n {\" \"}\r\n \r\n URL: https://api.intensel.net/apiv1/getanalysis/\r\n \r\n
\r\n data: {\"{\"}\r\n
\r\n \"project_name\": \"My Awesome Project\",
\r\n {\"}\"}\r\n
Code Example
\r\n\r\n \r\n import requests\r\n \r\n
\r\n URL = \"https://api.intensel.net/apiv1/getanalysis/\"\r\n
\r\n headers = {\"{\"}\"Authorization\":\"Api-Key API_KEY\"{\"}\"}\r\n payload = {\"{\"}
\r\n \"project_name\": \"My Awesome Project\",
\r\n {\"}\"}
\r\n response = requests.post(URL, json=payload, headers=headers)\r\n
\r\n data = response.json()\r\n
Expected Response
\r\n\r\n \r\n status: HTTP 201\r\n {\"{\"}\"RCP_{\"{\"}rcp{\"}\"}-YEAR_{\"{\"}year{\"}\"}\":{\"{\"}data{\"}\"}\r\n {\"}\"}\r\n \r\n
\r\n * Returned Data Will Be Risk Score Out Of 100
\r\n You Will Get Data For Following RCPS And Years
\r\n RCP: 26, 45, 85
\r\n Year: 2030, 2050\r\n
Output Data
\n\n Carbon Intensity:{this.props.carbon_project.carbon_intensity}\n
\n\n
\n Carbon FootPrints:{this.props.carbon_project.carbon_footprint}\n
\n\n
\n Total Carbon Emission:\n {this.props.carbon_project.total_carbon_emission_portfolio}\n
\n\n
\n
Input Data
\nOutput Data
\nPortfolio Total
\n \n\n {\n this.props.carbon_project\n .projected_scope_1_and_2_portfolio\n }\n
\n \n\n {\n this.props.carbon_project\n .weighted_average_carbon_intensity_portfolio\n }\n
\n \n\n {\n this.props.carbon_project\n .total_carbon_emission_portfolio\n }\n
\n \n{this.props.carbon_project.carbon_footprint}
\n \n{this.props.carbon_project.carbon_intensity}
\n \n\n
\n
\n
\n {this.props.location.state ? (\n
\n
\n \n \n \n
\n \n Upload CSV
\n\n \n
\n\n
\n
\n
All Assets
\nSelected Assets
\nUpload CSV
\n \n \n\n \n
\n \n \n
\n
All Assets
\nSelected Assets
\n\n Do you want to upload CSV with these{\" \"}\n ?\n
\n\n\n
\n
\n
\n
\n
\n
Do you want to upload CSV with these ?
\n \n
\n
\n
\n \n
\n
\n
\n You are only allowed to add{\" \"}\n {this.props.permissions.assets_allowed} assets for Analysis\n
\nAsset & Risk Scores
\n| Asset Name | \nFlood Scores | \nTyphoon Scores | \n
| {popup.properties.address} | \n{popup.properties.flood_score} | \n{popup.properties.typhoon_score} | \n
\n
Property Risk Analysis
\n\n
\n Number of Assets
\n {this.props.property.length} \n
\n Mean Flood Score
\n {flood_score_average.toFixed(2)} \n
\n Mean Typhoon Score
\n {typhoon_score_average.toFixed(2)}\n
\n
\n Mean Combined Risk Score\n
\n {((flood_score_average + typhoon_score_average) / 2).toFixed(\n 2\n )}\n
\n
\n \n Property Risk Heat Map (0 to 100)\n \n
\n\n \n Property risk Map\n \n
\n\n \n Flood Risk Score\n \n
\n\n
\n \n Typhoon Risk Score\n \n
\n\n
\n
Know More about
\n\nCVaR
\n\n Conditional Value at Risk (CVaR) ,also known as the\n expected shortfall ,is a risk assessment measure that\n quantifies the amount of trail risk an investment\n
\n \nCVaR
\n\n Conditional Value at Risk (CVaR) ,also known as the\n expected shortfall ,is a risk assessment measure that\n quantifies the amount of trail risk an investment\n
\n \nCVaR
\n\n Conditional Value at Risk (CVaR) ,also known as the\n expected shortfall ,is a risk assessment measure that\n quantifies the amount of trail risk an investment\n
\n \nCVaR
\n\n Conditional Value at Risk (CVaR) ,also known as the\n expected shortfall ,is a risk assessment measure that\n quantifies the amount of trail risk an investment\n
\n \nCVaR
\n\n Conditional Value at Risk (CVaR) ,also known as the\n expected shortfall ,is a risk assessment measure that\n quantifies the amount of trail risk an investment\n
\n \nCVaR
\n\n Conditional Value at Risk (CVaR) ,also known as the\n expected shortfall ,is a risk assessment measure that\n quantifies the amount of trail risk an investment\n
\n \n\n
Physical Risk Maps
\nClimate Variable
\nScenario
\nYear
\n\n
News
\n\n Climate Tech company, Intensel, to join Taronga Ventures’ ESG\n Impact innovation program in 2023\n
\n
\n Intensel: Providing Cutting-Edge Climate Risk Intelligence\n with Machine Learning\n
\n
\n Intensel Selected as Top Ten Finalist for Accenture’s 2023\n FinTech Innovation Lab APAC Program\n
\n
Events
\nLorem Ipsum
\n\n Lorem Ipsum is simply dummy text of the printing and\n typesetting industry. Lorem Ipsum has been the industry's\n standard dummy text ever since the 1500s, when an unknown\n printer took a galley of type and scrambled it to make a\n type specimen book\n
\nLorem Ipsum
\n\n Lorem Ipsum is simply dummy text of the printing and\n typesetting industry. Lorem Ipsum has been the industry's\n standard dummy text ever since the 1500s, when an unknown\n printer took a galley of type and scrambled it to make a\n type specimen book\n
\nLorem Ipsum
\n\n Lorem Ipsum is simply dummy text of the printing and\n typesetting industry. Lorem Ipsum has been the industry's\n standard dummy text ever since the 1500s, when an unknown\n printer took a galley of type and scrambled it to make a\n type specimen book\n
\nLorem Ipsum
\n\n Lorem Ipsum is simply dummy text of the printing and\n typesetting industry. Lorem Ipsum has been the industry's\n standard dummy text ever since the 1500s, when an unknown\n printer took a galley of type and scrambled it to make a\n type specimen book\n
\nLorem Ipsum
\n\n Lorem Ipsum is simply dummy text of the printing and\n typesetting industry. Lorem Ipsum has been the industry's\n standard dummy text ever since the 1500s, when an unknown\n printer took a galley of type and scrambled it to make a\n type specimen book\n
\nLorem Ipsum
\n\n Lorem Ipsum is simply dummy text of the printing and\n typesetting industry. Lorem Ipsum has been the industry's\n standard dummy text ever since the 1500s, when an unknown\n printer took a galley of type and scrambled it to make a\n type specimen book\n
\nLorem Ipsum
\n\n Lorem Ipsum is simply dummy text of the printing and\n typesetting industry. Lorem Ipsum has been the industry's\n standard dummy text ever since the 1500s, when an unknown\n printer took a galley of type and scrambled it to make a\n type specimen book\n
\nLorem Ipsum
\n\n Lorem Ipsum is simply dummy text of the printing and\n typesetting industry. Lorem Ipsum has been the industry's\n standard dummy text ever since the 1500s, when an unknown\n printer took a galley of type and scrambled it to make a\n type specimen book\n
\nLorem Ipsum
\n\n Lorem Ipsum is simply dummy text of the printing and\n typesetting industry. Lorem Ipsum has been the industry's\n standard dummy text ever since the 1500s, when an unknown\n printer took a galley of type and scrambled it to make a\n type specimen book\n
\n\n
\n
\n
\n
\n
\n
Change log
\n\n\n Version 4.5 - Feature Update\n
\n\nAdded Features
\n- \n
- New Variables: Extreme Cold and Snowfall \n
- Bounding Box-Based Location Selection in Maps \n
- Plinth Height Inputs and Calculations \n\n
- \n Gross Area, Total Valuation, and Total Content Value in Upload CSV\n \n
Improvements
\n- \n
- \n New Wildfire: Enhanced resolution to 30m with additional input\n variables\n \n
- Improved granularity for Rainfall Flood \n
- Malaysia, Singapore, Mongolia, Turkey - 30m \n
- Eastern Coast of Australia – 5m \n
- London, UK – 2m \n
- \n New Global Storm Surge model with Delta-DEM (100-year Return\n period)\n \n
- \n Extreme Cold: Predicted minimum temperature from CMIP6 datasets\n \n
- Snowfall: Annual snowfall in mm/year \n
- Extreme Heat: Updated to 1km resolution \n
- \n
\n Version 4.0 - Feature Update\n
\n\n {/* Added Features Section */}\nAdded Features
\n- \n
- Map Module (Version 1.0) \n
- Global coverage \n
- Create maps from 1km to 50 km \n
- Rainfall data and Storm surge data are available \n
- PDF reports (Version 1.0) \n
- Generate report automatically \n
- \n
- \n
Improvements
\n- \n
- Rainfall Flood \n
- Houston - 1m \n
- New York - 1m \n
- Los Angeles - 1m \n
- Storm Surge Delta DEM \n
- 30m Globally \n
- SSP 5-year interval on dashboard \n
- Input CSV \n
- \n Total inputs (gross area, total valuation, total content value)\n in input CSV\n \n
- \n
- \n
- \n
\n Version 3.0 - Feature Update\n
\n {/* Added Features Section */}\nAdded Features
\n- \n
- Updated user interface \n
- Building level flood adaptation (Mitigation) \n
- Allows three (3) different levels of flood mitigation \n
- Integrated into both Hazard and Financial analysis \n
- Applicable for Rainfall and River Flood \n
- \n Building footprint database generated covering 1.3 billion\n buildings globally integrating Microsoft, Google, and HK footprint\n data\n \n
- \n New damage functions specific to asset types (infrastructure) and\n regions\n \n
- Ability to add basement as an asset-level variable \n
- Ability to share projects within an organization \n
Improvements
\n- \n
- \n Enhancements to calculation of business interruption days for\n Floods and Typhoons\n \n
Hazard Resolution
\n- \n
- Storm Surge \n
- 30m Globally (FABDEM) \n
- Rainfall Flood \n
- Malaysia, Singapore, Mongolia, Turkey - 30m \n
- Eastern Coast of Australia – 5m \n
- London, UK – 2m \n
- \n
- \n
\n Version 4.0 - Feature Update\n
\n\n {/* Added Features Section */}\nAdded Features
\n- \n
- Map Module (Version 1.0) \n
- Global coverage \n
- Create maps from 1km to 50 km \n
- Rainfall data and Storm surge data are available \n
- PDF reports (Version 1.0) \n
- Generate report automatically \n
- \n
- \n
Improvements
\n- \n
- Rainfall Flood \n
- Houston - 1m \n
- New York - 1m \n
- Los Angeles - 1m \n
- Storm Surge Delta DEM \n
- 30m Globally \n
- SSP 5-year interval on dashboard \n
- Input CSV \n
- \n Total inputs (gross area, total valuation, total content value)\n in input CSV\n \n
- \n
- \n
- \n
- {linkMarkup}
\n
{question}
\n\n \n
\n {image.length > 0 &&\n image.map((img, index) => (\n
\n
\n
\n {/* Sidebar */}\n
Flood Depth at Location ${value.toFixed(2)}
`)\n .addTo(this.map);\n })\n .catch((error) => {\n console.error(\"Error fetching WMS GetFeatureInfo data:\", error);\n });\n };\n\n removePopup = () => {\n this.popup.remove();\n };\n\n handleAnalyse = (record) => {\n this.setState({ selectedCity: record, modalVisible: true });\n };\n\n closeModal = () => {\n this.setState({ modalVisible: false });\n if (this.map) {\n this.map.remove();\n this.map = null;\n }\n };\n\n handleMapStyleChange = (checked) => {\n const newStyle = checked\n ? \"mapbox://styles/mapbox/streets-v11\"\n : \"mapbox://styles/mapbox/satellite-v9\";\n this.setState({ mapStyle: newStyle });\n };\n\n handlePeriodChange = (checked) => {\n const returnPeriod = checked ? \"100\" : \"250\";\n this.setState({ return_period: returnPeriod });\n };\n\n handleScenarioChange = (checkedValues) => {\n this.setState({ selectedScenarios: checkedValues });\n };\n\n handleYearChange = (e) => {\n this.setState({ year: e.target.value });\n };\n\n handleRCPChange = (e) => {\n this.setState({ scenario: e.target.value });\n };\n\n openFilterModal = () => {\n this.setState({ filterModalVisible: true });\n };\n\n closeFilterModal = () => {\n this.setState({ filterModalVisible: false });\n };\n\n applyFilter = () => {\n const updatedDataSource = this.updateDataSource(\n this.state.dataSource,\n this.props.map_response,\n this.state.scenario,\n this.state.year\n );\n this.setState(\n {\n dataSource: updatedDataSource,\n filterModalVisible: false,\n },\n this.updateRasterLayer\n );\n };\n\n handleTableVisibility = (checked) => {\n this.setState({ showTable: checked });\n };\n\n render() {\n console.log(\"mapbound\", this.state.mapBounds);\n const columns = [\n {\n title: \"Name\",\n dataIndex: \"name\",\n key: \"name\",\n },\n {\n title: \"Country Code\",\n dataIndex: \"country_code\",\n key: \"country_code\",\n },\n {\n title: \"Rainfall Flood Avg (in m)\",\n dataIndex: \"rainfall_flood_avg\",\n key: \"rainfall_flood_avg\",\n },\n {\n title: \"Rainfall Flood Max (in m)\",\n dataIndex: \"rainfall_flood_max\",\n key: \"rainfall_flood_max\",\n },\n {\n title: \"Rainfall Flood Min(in m)\",\n dataIndex: \"rainfall_flood_min\",\n key: \"rainfall_flood_min\",\n },\n {\n title: \"GDP(USD/capita)\",\n dataIndex: \"gdp\",\n key: \"gdp\",\n },\n {\n title: \"Population Density(per sqkm)\",\n dataIndex: \"populationDensity\",\n key: \"populationDensity\",\n render: (text) => {text} people/sq km,\n },\n ];\n\n return (\n\n
\n {this.state.showTable && (\n
\n
Year Options
\nScenario Options
\n\n
Return Period
\n\n Mark locations to analyze\n
\n\n
\n
\n
\n Do you want to proceed further? The map will be charged\n separately.\n
\n\n
\n You are only allowed to add{\" \"}\n {this.props.permissions.assets_allowed} assets for Analysis\n
\n\" +\n \"Type: \" +\n (properties.type || \"N/A\") +\n \"
\" +\n \"Average Generation: \" +\n (properties.capacity || \"N/A\") +\n \" MW\";\n\n while (Math.abs(e.lngLat.lng - coordinates[0]) > 180) {\n coordinates[0] += e.lngLat.lng > coordinates[0] ? 360 : -360;\n }\n\n new mapboxgl.Popup()\n .setLngLat(coordinates)\n .setHTML(description)\n .addTo(this.map);\n });\n\n this.map.on(\"mouseenter\", \"power-plants-layer\", () => {\n this.map.getCanvas().style.cursor = \"pointer\";\n });\n\n this.map.on(\"mouseleave\", \"power-plants-layer\", () => {\n this.map.getCanvas().style.cursor = \"\";\n });\n\n const centerMarkerElement = document.createElement(\"div\");\n centerMarkerElement.className = \"center-marker\";\n centerMarkerElement.style.backgroundImage =\n \"url('../assets/red_marker.png')\";\n\n centerMarkerElement.style.width = \"40px\";\n centerMarkerElement.style.height = \"40px\";\n centerMarkerElement.style.backgroundSize = \"contain\";\n centerMarkerElement.style.zIndex = \"9999\"; // Highest z-index\n centerMarkerElement.style.position = \"absolute\";\n\n // Add the DOM marker\n new mapboxgl.Marker({ element: centerMarkerElement })\n .setLngLat([this.state.longitude, this.state.latitude]) // Set to map center\n .addTo(this.map);*/\n });\n };\n\n initializeMapFeatures = () => {\n console.log(\"Initializing map features...\");\n\n // Add layers and controls\n this.addRasterLayer();\n console.log(\"Raster layer added\");\n\n this.addLegendControl();\n console.log(\"Legend control added\");\n\n // Set map as loaded and then set initial scale\n this.setState({ isMapLoaded: true }, () => {\n console.log(\"Map loaded, setting initial scale...\");\n // Add a small delay to ensure all layers are fully rendered\n setTimeout(() => {\n this.setInitialScale();\n console.log(\"Initial scale set\");\n }, 100);\n });\n };\n\n setInitialScale = () => {\n if (!this.map || !this.state.distance) {\n console.warn(\"Map or distance not available\");\n return;\n }\n\n // Target distance in meters\n const targetDistance = this.state.distance * 1000;\n const center = this.map.getCenter();\n\n // Binary search for the correct zoom level\n let minZoom = 0;\n let maxZoom = 22;\n let currentZoom = 12;\n\n const getMapDistance = (zoom) => {\n this.map.setZoom(zoom);\n const bounds = this.map.getBounds();\n const ne = bounds.getNorthEast();\n const sw = bounds.getSouthWest();\n return this.getDistance(ne.lng, ne.lat, sw.lng, sw.lat);\n };\n\n // Perform binary search for up to 6 iterations\n for (let i = 0; i < 6; i++) {\n const currentDistance = getMapDistance(currentZoom);\n console.log(\n `Iteration ${i}: zoom ${currentZoom}, distance ${currentDistance /\n 1000}km (target: ${targetDistance / 1000}km)`\n );\n\n // If we're within 10% of target distance, we're done\n if (Math.abs(currentDistance - targetDistance) / targetDistance < 0.1) {\n break;\n }\n\n if (currentDistance > targetDistance) {\n minZoom = currentZoom;\n currentZoom = (currentZoom + maxZoom) / 2;\n } else {\n maxZoom = currentZoom;\n currentZoom = (currentZoom + minZoom) / 2;\n }\n }\n\n // Set final zoom and update scale\n this.map.setZoom(currentZoom);\n this.updateScale();\n\n console.log(\n `Final zoom set to ${currentZoom} for target distance ${this.state.distance}km`\n );\n };\n\n add3DBuildingsLayer = () => {\n const layers = this.map.getStyle().layers;\n const labelLayerId = layers.find(\n (layer) => layer.type === \"symbol\" && layer.layout[\"text-field\"]\n ).id;\n\n if (this.map.getLayer(\"add-3d-buildings\")) {\n return; // If the layer already exists, do nothing\n }\n\n this.map.addLayer(\n {\n id: \"add-3d-buildings\",\n source: \"composite\",\n \"source-layer\": \"building\",\n filter: [\"==\", \"extrude\", \"true\"],\n type: \"fill-extrusion\",\n minZoom: 16,\n paint: {\n \"fill-extrusion-color\": \"#ffffff\",\n \"fill-extrusion-height\": [\n \"interpolate\",\n [\"linear\"],\n [\"zoom\"],\n 15,\n 0,\n 15.05,\n [\"get\", \"height\"],\n ],\n \"fill-extrusion-base\": [\n \"interpolate\",\n [\"linear\"],\n [\"zoom\"],\n 15,\n 0,\n 15.05,\n [\"get\", \"min_height\"],\n ],\n \"fill-extrusion-opacity\": [\n \"interpolate\",\n [\"linear\"],\n [\"zoom\"],\n 15,\n 0, // Opacity is zero below zoom level 15\n 15.05,\n 0.6,\n ],\n },\n },\n labelLayerId\n );\n };\n add2DBuildingsLayer = () => {\n const layers = this.map.getStyle().layers;\n const labelLayerId = layers.find(\n (layer) => layer.type === \"symbol\" && layer.layout[\"text-field\"]\n ).id;\n\n if (this.map.getLayer(\"building-2d\")) {\n return; // If the layer already exists, do nothing\n }\n\n this.map.addLayer(\n {\n id: \"building-2d\",\n source: \"composite\",\n \"source-layer\": \"building\",\n type: \"fill\",\n minzoom: 15, // Ensure visibility at all zoom levels\n\n paint: {\n \"fill-color\": \"#d6d6d6\",\n \"fill-opacity\": [\n \"interpolate\",\n [\"linear\"],\n [\"zoom\"],\n 10,\n 0.2, // At zoom level 14, fill-opacity is 0.6\n 15,\n 0.6, // At higher zoom levels, opacity increases slightly for clarity\n ],\n },\n },\n labelLayerId\n );\n };\n remove2DBuildingsLayer = () => {\n if (this.map.getLayer(\"building-2d\")) {\n this.map.removeLayer(\"building-2d\");\n this.map.removeSource(\"composite\");\n }\n };\n\n remove3DBuildingsLayer = () => {\n if (this.map.getLayer(\"add-3d-buildings\")) {\n this.map.removeLayer(\"add-3d-buildings\");\n this.map.removeSource(\"composite\");\n }\n };\n\n handle3DViewChange = (checked) => {\n this.setState({ is3DViewEnabled: checked }, () => {\n if (this.state.is3DViewEnabled) {\n this.add3DBuildingsLayer();\n this.remove2DBuildingsLayer();\n } else {\n this.remove3DBuildingsLayer();\n this.add2DBuildingsLayer();\n }\n });\n };\n\n addLegendControl = () => {\n console.log(\"legend\");\n const legend = new LegendControl();\n this.map.addControl(legend, \"bottom-right\");\n };\n handleMapHover = (e) => {\n const longitude = e.lngLat.lng;\n const latitude = e.lngLat.lat;\n const country_code = this.state.dataSource[0]?.country_code || \"\";\n const { map_response } = this.props;\n\n console.log(\"Map hover coordinates:\", { longitude, latitude });\n\n let formdata = new FormData();\n formdata.append(\"latitude\", latitude);\n formdata.append(\"longitude\", longitude);\n formdata.append(\"country_code\", country_code);\n formdata.append(\"duration_data\", this.state.duration_data);\n formdata.append(\"variable\", this.state.variable);\n if (map_response.subregion != null) {\n formdata.append(\"subregion\", map_response.subregion);\n }\n\n this.props.MapValue(formdata);\n\n this.setState({ longitude, latitude }, () => {\n console.log(\"State updated with new coordinates:\", {\n longitude,\n latitude,\n });\n // this.showPopup(longitude, latitude);\n });\n };\n\n // Separate function to show the popup\n showPopup = (lng, lat) => {\n const {\n mapValueResponse: { hand, elevation, no_of_days, slope, flood, value },\n variable,\n } = this.state;\n\n const hazard = variable === \"rainfall_flood\" ? \"Flood\" : \"Storm Surge\";\n\n // Remove existing popup if present\n if (this.popup) {\n this.popup.remove();\n }\n\n // Validate necessary data\n if (!this.map || hand == null || elevation == null) {\n console.error(\"Popup data is missing or map is not initialized\");\n return;\n }\n\n // Update state with coordinates\n this.setState({\n latitude: lat,\n longitude: lng,\n geolocationText: `Geolocation Popup`,\n });\n\n // Construct popup content\n const popupContent = `\n ${hazard} (in m): ${value || \"0\"}
\n Elevation (in m): ${elevation || \"0\"}
\n HAND (in m): ${hand || \"0\"} \n ℹ️
\n Number of Days: ${no_of_days || \"0\"}
\n Slope: ${slope || \"0\"} %\n `;\n\n // Create and add the popup to the map\n this.popup = new mapboxgl.Popup({\n closeOnClick: false,\n className: \"custom-popup\",\n })\n .setLngLat([lng, lat])\n .setHTML(popupContent)\n .addTo(this.map);\n\n // Add event listener for the info icon\n\n // Wait for popup content to render\n setTimeout(() => {\n const handInfo = document.getElementById(\"hand-info\");\n if (handInfo) {\n handInfo.addEventListener(\"click\", () => {\n console.log(\"HAND info clicked\");\n this.showHandDescriptionPopup(lng, lat);\n });\n handInfo.addEventListener(\"keypress\", (e) => {\n if (e.key === \"Enter\" || e.key === \" \") {\n this.showHandDescriptionPopup(lng, lat);\n }\n });\n } else {\n console.warn(\"HAND info icon not found!\");\n }\n }, 300);\n\n // Reset to center coordinates on popup close\n this.popup.on(\"close\", () => {\n const center = this.map.getCenter();\n this.setState({\n latitude: center.lat,\n longitude: center.lng,\n geolocationText: `Geolocation Center`,\n });\n });\n };\n\n showHandDescriptionPopup = (lng, lat) => {\n // Remove any existing HAND info popup\n if (this.handPopup) {\n this.handPopup.remove();\n }\n\n // Create and display a new popup with HAND info\n this.handPopup = new mapboxgl.Popup({\n closeOnClick: true,\n })\n .setLngLat([lng, lat])\n .setHTML(\n `\n
\n
\n {this.state.showTable && (\n
\n
\n Storm Surge {this.state.scenario / 10} Year {this.state.year}{\" \"}\n {this.state.distance} km\n
\n ) : (\n\n Rainfall Flood 1:{this.state.return_period} Return Period{\" \"}\n {this.state.distance} km\n
\n )}\n \n\n {this.state.geolocationText}: {this.state.latitude.toFixed(5)}\n , \n {this.state.longitude.toFixed(5)}\n
\n ) : null}\n \n \n\n\n
404
\nPage Not Found
\n\n The link you have clicked is broken or the page may have been\n removed or renamed.\n
\n \n Go Back\n \n