This resource offers a comprehensive analysis of vulnerability factors, including the impacts of climate change, natural disasters, and socio-economic challenges. This map reveals how risks are distributed across the state, equipping stakeholders with the knowledge needed for informed decision-making and effective resilience planning.

The West Africa Coastal Vulnerability Mapping: Social Vulnerability Indices data set includes three indices: Social Vulnerability, Population Exposure, and Poverty and Adaptive Capacity. The Social Vulnerability Index (SVI) was developed using six indicators: population density (2010), population growth (2000-2010), subnational poverty and extreme poverty (2005), maternal education levels circa 2008, market accessibility (travel time to markets) circa 2000, and conflict data for political violence (1997-2013). Because areas of high population density and growth (high vulnerability) are generally associated with urban areas that have lower levels of poverty and higher degrees of adaptive capacity (low vulnerability), to some degree, the population factors cancel out the poverty and adaptive capacity indicators. To account for this, the data set includes two sub-indices, a Population Exposure Index (PEI), which only includes population density and population growth; and a Poverty and Adaptive Capacity Index (PACI), composed of subnational poverty, maternal education levels, market accessibility, and conflict. These sub-indices are able to isolate the population indicators from the poverty and conflict metrics. The indices represent Social Vulnerability in the West Africa region within 200 kilometers of the coast.

The West Africa Coastal Vulnerability Mapping: Social Vulnerability Indices data set includes three indices: Social Vulnerability, Population Exposure, and Poverty and Adaptive Capacity. The Social Vulnerability Index (SVI) was developed using six indicators: population density (2010), population growth (2000-2010), subnational poverty and extreme poverty (2005), maternal education levels circa 2008, market accessibility (travel time to markets) circa 2000, and conflict data for political violence (1997-2013). Because areas of high population density and growth (high vulnerability) are generally associated with urban areas that have lower levels of poverty and higher degrees of adaptive capacity (low vulnerability), to some degree, the population factors cancel out the poverty and adaptive capacity indicators. To account for this, the data set includes two sub-indices, a Population Exposure Index (PEI), which only includes population density and population growth; and a Poverty and Adaptive Capacity Index (PACI), composed of subnational poverty, maternal education levels, market accessibility, and conflict. These sub-indices are able to isolate the population indicators from the poverty and conflict metrics. The indices represent Social Vulnerability in the West Africa region within 200 kilometers of the coast.

Certain populations are particularly vulnerable to changing climate effects. The term "vulnerable populations" refers to people or groups that may be more susceptible to the health effects of climate change. Vulnerability to climate change varies across time and location, across communities, and among individuals within communities. People and communities differ in their exposures, their inherent sensitivity, and their capacity to respond to, adapt to and cope with climate change related health impacts.

The West Africa Coastal Vulnerability Mapping: Population Projections, 2030 and 2050 data set is based on an unreleased working version of the Gridded Population of the World (GPW), Version 4, year 2010 population count raster but at a coarser 5 arc-minute resolution. Bryan Jones of Baruch College produced country-level projections based on the Shared Socioeconomic Pathway 4 (SSP4). SSP4 reflects a divided world where cities that have relatively high standards of living, are attractive to internal and international migrants. In low income countries, rapidly growing rural populations live on shrinking areas of arable land due to both high population pressure and expansion of large-scale mechanized farming by international agricultural firms. This pressure induces large migration flow to the cities, contributing to fast urbanization, although urban areas do not provide many opportUnities for the poor and there is a massive expansion of slums and squatter settlements. This scenario may not be the most likely for the West Africa region, but it has internal coherence and is at least plausible.

This dataset is available for use for non-commercial purposes only on request as AfA248 dataset Groundwater Vulnerability Maps (2017). For commercial use please contact the British Geological Survey.

The Groundwater Vulnerability Maps show the vulnerability of groundwater to a pollutant discharged at ground level based on the hydrological, geological, hydrogeological and soil properties within a single square kilometre. The 2017 publication has updated the groundwater vulnerability maps to reflect improvements in data mapping, modelling capability and understanding of the factors affecting vulnerability Two map products are available: • The combined groundwater vulnerability map. This product is designed for technical specialists due to the complex nature of the legend which displays groundwater vulnerability (High, Medium, Low), the type of aquifer (bedrock and/or superficial) and aquifer designation status (Principal, Secondary, Unproductive). These maps require that the user is able to understand the vulnerability assessment and interpret the individual components of the legend.

• The simplified groundwater vulnerability map. This was developed for non-specialists who need to know the overall risk to groundwater but do not have extensive hydrogeological knowledge or the time to interpret the underlying data. The map has five risk categories (High, Medium-High, Medium, Medium-Low and Low) based on the likelihood of a pollutant reaching the groundwater (i.e. the vulnerability), the types of aquifer present and the potential impact (i.e. the aquifer designation status). The two maps also identify areas where solution features that enable rapid movement of a pollutant may be present (identified as stippled areas) and areas where additional local information affecting vulnerability is held by the Environment Agency (identified as dashed areas).

The West Africa Coastal Vulnerability Mapping: Subset of JRC Map of Accessibility data set is a 30 arc-second raster of travel time to major cities in West Africa within 200 kilometers of the coast. Extensive literature shows that road networks and market accessibility play an important role in development and access to health care and other social services. Greater spatial isolation is assumed to produce higher vulnerability to climate stressors. Market accessibility is defined as the travel time to a location of interest using land (road/off road) or water (navigable river, lake, and ocean) based travel. A team at the Joint Research Centre (JRC) in Ispra, Italy, created a global raster of accessibility using a cost-distance algorithm which computes the "cost" (in Units of time) of traveling between two locations on a regular raster grid. The raster grid cells contain values which represent the cost required to travel across them, hence this raster grid is often termed a friction-surface. The friction-surface contains information on the transport network, and environmental and political factors that affect travel times between locations. Transport networks can include road and rail networks, navigable rivers, and shipping lanes. The locations of interest are termed targets, and in the case of this data set, the targets are cities with a population of 50,000 or greater in the year 2000.

Various biophysical and socio-economic impacts may be associated with unconventional oil and gas (UOG) extraction. A vulnerability map may assist governments during environmental assessments, spatial planning and the regulation of UOG extraction, as well as decision-making around UOG extraction in fragile areas. A regional interactive vulnerability map was developed for UOG extraction in South Africa. This map covers groundwater, surface water, vegetation, socio-economics and seismicity as mapping themes, based on impacts that may emanate from UOG extraction. The mapping themes were developed using a normative approach, where expert input during the identification and classification of vulnerability indicators may increase the acceptability of the resultant map. This article describes the development of the interactive vulnerability map for South Africa, where UOG extraction is not yet allowed and where regulations are still being developed to manage this activity. The importance and po...

In order to better understand the combined impacts of climate change and spatially identify where changes are anticipated to be most extreme, we developed a climate change vulnerability map for the Midwest Region. The vulnerability map is watershed-based (Hydrologic Unit Code-8) and combines fifteen climate change indicators evenly divided into three categories: temperature, precipitation, and hydrology that were selected by resource managers working in Region 3 of the United States Fish and Wildlife Service. The projected change in each of these indicators from the baseline period (1986-2005) to the future period (2040-2059) was aggregated into a composite score for each watershed. Landscape-scale metrics reflective of a watershed’s adaptive capacity were combined with the climate change impact indicators to produce a vulnerability score. We found sub-regional variation in vulnerability to climate change with the greatest vulnerability in Iowa, central Illinois, and northwest Ohio. Greater potential impact was seen in the higher emissions scenario resource concentration pathway (RCP) 8.5 compared to the lower emissions scenario RCP 4.5, but similar spatial vulnerabilities were identified between the two emissions scenarios. By quantifying and mapping hotspots of climate change vulnerability, resource managers can use climate impact information to adjust adaptation strategies and identify areas of prioritization accordingly.

This Layer contains the Vertical Vulnerability Map of the transboundary aquifer located within the Skadar/Shkoder - Buna/Bojana area shared by the two countries elaborated in the context of the Pilot Project "Design and testing of a multipurpose transboundary groundwater monitoring network in the Extended Drin River Basin" (DRIN) 2017 – 2020.Vertical vulnerability refers to the vulnerability of contamination threats from land-based pollution. For each polygon of the map, the numeric value of the Vertical Vulnerability class is in the field VV.

The West Africa Coastal Vulnerability Mapping: Point and Gridded Locations of Fatalities, 2008-2013 data set consists of two layers: points representing the location of conflict events with fatalities within 200 kilometers from the coast during the time period from 2008 to 2013, and a raster layer created from the points using a kernel density interpolation of the number of fatalities. These layers were created from the Armed Conflict Location and Event Dataset (ACLED), which codes the dates and locations of all reported political violence events in over 50 developing countries. Political violence includes events that occur within civil wars and periods of instability. Armed conflict reduces human security and increases the sensitivity of populations to climate stressors.

The West Africa Coastal Vulnerability Mapping: Point and Gridded Locations of Fatalities, 2008-2013 data set consists of two layers: points representing the location of conflict events with fatalities within 200 kilometers from the coast during the time period from 2008 to 2013, and a raster layer created from the points using a kernel density interpolation of the number of fatalities. These layers were created from the Armed Conflict Location and Event Dataset (ACLED), which codes the dates and locations of all reported political violence events in over 50 developing countries. Political violence includes events that occur within civil wars and periods of instability. Armed conflict reduces human security and increases the sensitivity of populations to climate stressors.

This dataset tracks the updates made on the dataset "CDC Social Vulnerability Index (SVI) Mapping Dashboard" as a repository for previous versions of the data and metadata.

The Groundwater Vulnerability map shows land areas across Ireland where groundwater can be easily polluted. It also shows areas where it is well protected by the subsoil layers. The vulnerability category given to a site or an area is based on how easy it is for water which may contain pollutants to reach the groundwater. Geologists map and record information on the subsoils above the bedrock. They find out how deep the subsoil is and how permeable it is (how easy water can pass through it). They use information from quarries, deep pits and from boreholes (a deep narrow round hole drilled in the ground). Subsoil depth, type and permeability maps are combined to work out the groundwater vulnerability at that location. Landforms found in the Irish landscape like sinkholes and sinking streams (‘karst’ landforms) are categorised as extremely vulnerable as water can pass straight through. Where the water table is close to the surface in sand and gravel aquifers, groundwater vulnerability is also extremely vulnerable. This Groundwater Vulnerability map is to the scale 1:40,000. This means it should be viewed at that scale. When printed at that scale 1cm on the map relates to a distance of 400m. It is a vector dataset. Vector data portray the world using points, lines, and polygons (areas). The groundwater vulnerability data are shown as polygons. Each polygon holds information on the vulnerability category (X, E, H, M or L), a description explaining this (‘Extreme – rock at or near surface/karst’, ‘Extreme’, ‘High’, ‘Moderate’ or ‘Low’) and a unique id. .hidden { display: none }

The Gulf Coast Vulnerability Assessment (GCVA) used an expert opinion approach to qualitatively assess the vulnerability of four ecosystems: mangrove, oyster reef, tidal emergent marsh, and barrier islands, and a suite of wildlife species that depend on them. More than 50 individuals participated in the completion of the GCVA, facilitated via Ecosystem and Species Expert Teams. The GCVA made use of the Standardized Index of Vulnerability and Value Assessment (SIVVA) (Reece and Noss 2014) to provide an objective framework for evaluating vulnerability by guiding assessors through a series of questions related to the changes an ecosystem or species might experience due to climate change and other threats. Assessors used their best professional judgment, available empirical data, and numerical model outputs to complete the assessments for certain species and ecosystems. The SIVVA tool enabled the Assessment Team to then assess both the relative vulnerability of those ecosystems and sp ...

A map used in the Hazard Risk Assessment Maps app and the Hazard Explorer app to visualize social vulnerability.

The West Africa Coastal Vulnerability Mapping: Demographic and Health Survey Data Sets present grids of maternal education levels and household wealth based on Demographic and Health Survey (DHS) cluster level data for ten West African countries. While the maternal education levels are comparable across countries, owing to different underlying indicators, the household wealth index is not. Education can directly influence risk perception, skills and knowledge and indirectly reduce poverty, improve health, and promote access to information and resources. When facing natural hazards or climate risks, educated individuals, households, and societies are assumed to be more empowered and more adaptive in their response to, preparation for, and recovery from disasters. Education is a key background indicator that helps contextualize a country's health and development situation. The household wealth index is a composite measure of a household's cumulative living standard. The wealth index is calculated using easy-to-collect data on a household's ownership of selected assets, such as televisions and bicycles, materials used for housing construction, and types of water access and sanitation facilities. Bayesian spatial interpolation methods were employed to create country level grids based on DHS cluster point data for each country. Data are from the following dates by country: Benin (2006), Cameroon (2011), Cote d'Ivoire (2012), Ghana (2008), Guinea (2012), Liberia (2011), Nigeria (2010), Sierra Leone (2008), and Togo (1998).

The West Africa Coastal Vulnerability Mapping: Subset of OpenStreetMap (OSM) Roads data set includes roads within 200 kilometers of the coast and was extracted from the full OSM data set in March 2014. OSM is a global crowdsourced road and street map and is continually being updated. To provide roads linking rural production to urban markets in the coastal zone of West Africa that represent an important exposed infrastructure asset.

The Localized Flood Map for Climate Vulnerability Screening layer shows potential surface flooding locations in the landscape for the Twin Cities Metro area. These locations, called bluespots, are areas that may be subject to flood during short-term, extreme rain events. The Council's local flood screening tool uses information about the topography of the earth contained in the State of Minnesota's 3-meter digital elevation model (DEM) built from the state's LiDAR effort. Localized flooding locations are determined solely based on depressions in the DEM; no data of existing stormwater infrastructure is considered because this information does not currently exist at a regional scale. This layer should only be used as a screening tool. A low spot shown as a bluespot on this map does not indicate that the area will definitively flood; instead, the area has the potential to flood if a rain event is intense enough and stormwater infrastructure not sufficient.

For more information, visit the Council's Climate Vulnerability Assessment website at: www.metrocouncil.org/cva.

Wisconsin DNR Web map displaying the CDC Social Vulnerability Index 2018 at the census tract Level, centered on Wisconsin. The 2018 Social Vulnerability Index (SVI) layer was created by the Centers for Disease Control and Prevention (CDC) / Agency for Toxic Substances and Disease Registry (ATSDR) / Geospatial Research, Analysis, and Services Program (GRASP). Visit https://www.atsdr.cdc.gov/placeandhealth/svi/index.html for more information.