Climate Exposure-Fragility [Bivariate Assessment] (USAID, 2018)
Data and Resources
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Additional Info
| Field | Value |
|---|---|
| Source | https://app.mapx.org/static.html?views=MX-CEY5L-ZB219-4GL64&zoomToViews=true#JAAc6 |
| Author | UNEP/GRID-Geneva |
| Maintainer | UNEP/GRID-Geneva |
| Last Updated | December 7, 2022, 08:11 (UTC) |
| Created | December 7, 2022, 08:11 (UTC) |
| GUID | MX-CEY5L-ZB219-4GL64 |
| Issued | 2019-11-29 12:56:14 |
| Language | EN |
| Modified | 2021-11-26 20:05:13 |
| Publisher email | info@mapx.org |
| Publisher name | UNEP/GRID-Geneva |
| Theme | Web Map |
| data_type | geospatial |
| keywords_m49 | WLD |
| projects_description | NEAT+ Global |
| projects_id | MX-WJO-FOV-NNB-1BN-SZN |
| projects_title | NEAT+ Global |
| range_end_at_year | 2021 |
| range_start_at_year | 2018 |
| source_abstract | The first mechanism of compound risk assessment—a bivariate map—integrates the fragility and climate data before they are mapped to show the intersection of fragility and climate risks at the first administrative division level for all countries for which data are available. This approach allows comparison of countries across all fragility categories on a single map. However, it loses some granularity in the subnational climate exposure data, which must be aggregated to the first administrative division to be cross-tabulated with the fragility data. The bivariate map uses the total climate exposure data and total fragility data. It is created using the first administrative division polygon files from the Global Administrative Areas database version 2.8. In this map, each country’s national-level fragility category is applied to all administrative divisions in the country. The bivariate map thus retains the five fragility categories seen in the total fragility measure, classifying areas as having low, some, moderate, high, or highest fragility. The climate exposure category is based on a k-median clustering algorithm. The median climate exposure score for each administrative division is determined with spatial analysis in ArcGIS. These median scores are then clustered into five categories of low to high exposure using the k-median algorithm contained in Stata.15. The cross-tabulation of the five fragility categories and the five climate exposure categories are represented on the final bivariate map. The total exposure to climate retain the five exposure categories of low to high exposure (1 to 5) based on the k-median clustering algorithm. The median climate exposure score for each administrative division is determined with spatial analysis in ArcGIS. These median scores are then clustered into five categories of low to high exposure using the k-median algorithm contained in Stata. The climate exposure portion of this study aims to identify places most likely vulnerable to a combination of climate hazards. Geographic location makes some countries more susceptible to climate hazards. Within countries, some areas, such as the coasts, have more exposure to certain kinds of climate hazards. This study assesses climate hazard exposure using historical data on the frequency and magnitude of six hazards, including cyclones, flood events, wildfire events, rainfall anomalies, and chronic aridity. The sixth hazard is a measure of low-elevation coastal zones, which may be susceptible to storm surges and future sea-level rise. The goal is to identify places that in the recent historical record have faced high exposure to climaterelated hazards. This seeks to get a snapshot of places of chronic concern over a long enough time period to say that these are places that have been historically affected by climate-related hazards. The indicators included here use the most recent data available. Some of these indicators, like cyclones, have longerterm data available, while others like floods have data for fewer years. In all cases, this project uses the most recent and broadest set of years for which global data are available to get the best snapshot of climate exposure in the recent past, as close as possible to the present. The total fragility data retain the five fragility categories seen in the total fragility measure, classifying areas as having low, some, moderate, high, or highest fragility. To help achieve this study’s goal of identifying the intersection of fragility and climate risks globally, this study develops a new fragility measure. Developing a new measure specifically for this purpose provides a comprehensive fragility measure while avoiding use of existing fragility measures that include environmental indicators and thus should not be overlaid on climate hazards. This study uses open-source data to create a measure of state fragility that is similar in composition and outcome to USAID’s internal methods and framework for analyzing fragility.11 Like USAID’s internal measure, the new measure assesses fragility in state effectiveness and legitimacy in four key spheres: political, security, economic, and social. This is based on an understanding of fragility as being rooted in poor state capacity and poor state-society relationships, both of which can contribute to instability. Poor state capacity and state-society relationships can lead to and perpetuate other forms of overt instability, including conflict or an inability to address and mitigate stresses such as a changing environment, difficult global financial situations, or conflict in neighboring states. This document [https://pdf.usaid.gov/pdf_docs/PA00TBFH.pdf] provide a detailed approach on the generation of this bivariate map and its attributes. |
| source_title | Climate Exposure-Fragility [Bivariate Assessment] (USAID, 2018) |
| spatial | WLD |