The Centers for Disease Control Social Vulnerability Index shows which communities are especially at risk during public health emergencies because of factors like socioeconomic status, household composition, racial composition of neighborhoods, or housing type and transportation. The CDC SVI uses 15 U.S. census variables to identify communities that may need support before, during, or after disasters. Learn more here. The condition is the overall ranking of four social theme rankings where lower values indicate high vulnerability and high values indicate low vulnerability. Quintiles for this condition were determined for all the Census tracts in King County. Quintile 1 is the most vulnerable residents, Quintile 5 is the least vulnerable residents. Data is released every 2 years following the American Community Survey release in December of the year following the Survey. The most recent data for 2018 was downloaded from the ATSDR website.

The Social Vulnerability Index (SoVI®) 2006-10 measures the social vulnerability of U.S. Census tracts to environmental hazards. The index is a comparative metric that facilitates the examination of the differences in social vulnerability among counties. SoVI® is a valuable tool for policy makers and practitioners. It graphically illustrates the geographic variation in social vulnerability. It shows where there is uneven capacity for preparedness and response and where resources might be used most effectively to reduce the pre-existing vulnerability. SoVI® also is useful as an indicator in determining the differential recovery from disasters.

The index synthesizes 30 socioeconomic variables, which the research literature suggests contribute to reduction in a community’s ability to prepare for, respond to, and recover from hazards. SoVI® data sources include primarily those from the United States Census Bureau.

The data are compiled and processed by the Hazards and Vulnerability Research Institute at the University of South Carolina with funding via the NOAA Office for Coastal Management. The data are standardized and placed into a principal components analysis to reduce the initial set of variables into a smaller set of statistically optimized components. Adjustments are made to the components’ cardinality (positive (+) or negative (-)) to insure that positive component loadings are associated with increased vulnerability, and negative component loadings are associated with decreased vulnerability. Once the cardinalities of the components are determined, the components are added together to determine the numerical social vulnerability score for each census tract.

SoVI® 2006-10 marks a change in the formulation of the SoVI® metric from earlier versions. New directions in the theory and practice of vulnerability science emphasize the constraints of family structure, language barriers, vehicle availability, medical disabilities, and healthcare access in the preparation for and response to disasters, thus necessitating the inclusion of such factors in SoVI®. Extensive testing of earlier conceptualizations of SoVI®, in addition to the introduction of the U.S. Census Bureau’s five-year American Community Survey (ACS) estimates, warrants changes to the SoVI® recipe, resulting in a more robust metric. These changes, pioneered with the ACS-based SoVI® 2005-09 carry over to SoVI® 2006-10, which combines the best data available from both the 2010 U.S. Decennial Census and five-year estimates from the 2006-2010 ACS at the census tract level.

These data are available for download from: http://www.coast.noaa.gov/digitalcoast/data/sovi

"ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) created Centers for Disease Control and Prevention Social Vulnerability Index (CDC SVI or simply SVI, hereafter) to help public health officials and emergency response planners identify and map the communities that will most likely need support before, during, and after a hazardous event.

SVI indicates the relative vulnerability of every U.S. Census tract. Census tracts are subdivisions of counties for which the Census collects statistical data. SVI ranks the tracts on 15 social factors, including unemployment, minority status, and disability, and further groups them into four related themes. Thus, each tract receives a ranking for each Census variable and for each of the four themes, as well as an overall ranking."

For more see https://www.atsdr.cdc.gov/place-health/php/svi/svi-data-documentation-download.html

This dataset is the 1km*1km grid version of SVI nationwide. It is based on inputs at the census-tract level for each of the CDC's 15 social vulnerability variables and aligns with SEDAC's Gridded Population of the World dataset. Article: https://www.earthdata.nasa.gov/learn/articles/sedac-social-vulnerability-dataset;At a Glance: https://www.atsdr.cdc.gov/placeandhealth/svi/at-a-glance_svi.html;Data Download: https://sedac.ciesin.columbia.edu/data/set/usgrid-us-social-vulnerability-index/data-download;Interactive Map: https://dataviz.ei.columbia.edu/social-vulnerability-index-map/;

The CDC/ATDSR developed a national Social Vulnerability Index (SVI) to bring together many different factors at once and estimate places in greatest need during an emergency. This was done with a national-level analysis and does not account for the impact of Arizona-specific conditions on a community’s vulnerability such as extreme heat. The Arizona Social Vulnerability Index (AZSVI) incorporates an additional theme (Arizona Theme 5) into the index using factors determined by the Arizona Health Improvement Plan (AzHIP) Data Advisory Committee.The AZSVI presents factors that Arizona communities face as they pursue health, community strength, and data to inform action. The AZSVI provides the Arizona public health workforce, health care providers, policy makers and public a tool to assess the factors impacting Arizona communities, with the aim of addressing disparities and fostering equity. The AZSVI is a product of the Arizona Health Improvement Plan (AzHIP) Data Advisory Committee, created in partnership with Arizona State University, Arizona Department of Health Services GIS, and the ADHS Office of Health Equity. Funding for this project was provided through the Centers for Disease Control and Prevention (CDC) Health Disparities Grant OT21 2103. Data may be downloaded in full or in part, by adding a filter before selecting your download file type. To view information about field definitions, data sources, and analysis methods for the Arizona Theme 5, download this data documentation:Technical Data DocumentationTechnical Data DictionaryTo view data documentation for the first four themes, which come directly from the CDC/ATSDR SVI, please visit their website and select "CDC/ATSDR SVI Documentation 2020".

This dataset represents a water shortage social vulnerability analysis performed by DWR using Census 2021 block groups as the unit of analysis. This feature class includes water shortage social vulnerability indicators and scores from an analysis done by CA Department of Water Resources, joined to the 2021 Census Block Groups. Most of the indicators were pulled from the ACS (American Communities Survey). These indicators were joined to the block groups to represent a spatial analysis of the social vulnerability of communities to water shortages. The goal of this data is to provide a spatial representation of social and economic factors that can affect water shortage vulnerability in the state of California. Model indicators included in the attribute table are percent of the population 65 and older, percent of households with no vehicles, percent of population 25 and older without a high school diploma. Please note that all of these model indicators are estimated values pulled from the ACS (American Communities Survey). All model indicators are added together using sum-rank methods outlined in the Drought and Water Shortage Risk Scoring: California's Domestic Wells and State Smalls Systems document and CDC standards for sum-rank methods. This data is for the 2024 analysis using 2017-2021 ACS estimates and 2021 Census block groups.

This is the Social Vulnerability Index (SVI) developed by the U.S. Centers for Disease Control (CDC) [1]. This is often used by the emergency response community to anticipate areas where social support systems are weaker, and residents may be more likely to need help. A map viewer for the national database can be found here [2]. Documentation is available here [3] which is also included for download below.

Subsets of the national coverage for the Hurricane Harvey and Hurricane Irma hydrologic study areas can be downloaded below.

[1] SVI web site [http://svi.cdc.gov] [2] CDC’s Social Vulnerability Index (SVI) – 2014 overall SVI, census tract level (web feature layer) [http://cuahsi.maps.arcgis.com/home/item.html?id=f951e0df78604cf0ab1fda61a575be6b] [3] SVI Documentation [https://svi.cdc.gov/Documents/Data/2014_SVI_Data/SVI2014Documentation.pdf] [4] ArcGIS Online feature service (CONUS) [https://services3.arcgis.com/ZvidGQkLaDJxRSJ2/arcgis/rest/services/Overall_2014_Tracts/FeatureServer]

The 2004 Environmental Vulnerability Index (EVI) portion of the Compendium of Environmental Sustainability Indicator Collections contains 111 variables for 235 countries and territories. This index is designed to be used with economic and social vulnerability indices to provide insights into the processes that can negatively influence the sustainable development of countries. It was developed through consultation and collaborations with countries, institutions and experts across the globe by the South Pacific Applied Geoscience Commission (SOPAC), the United Nations Environment Programme (UNEP) and their partners. The data are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).

The USACE South Atlantic Coastal Study’s CDC Social Vulnerability Index leverages the CDC’s published Social Vulnerability Index (SVI) across the SACS study area. The SACS study area includes tidally influenced coastal areas to the inland extent of NOAA’s Category 5 Maximum of Maximum storm surge hazard layer, from North Carolina to Mississippi, including Puerto Rico and the US Virgin Islands.The methodology and original SVI was published in 2011 and was most recently updated in 2014. The Social Vulnerability Index (SVI) indicates the relative vulnerability of every U.S. Census tract. The SVI ranks the tracts on 15 social factors, including unemployment, minority status, and disability, and further groups them into four related themes. Thus each tract receives a ranking for each Census variable and for each of the four themes, as well as an overall ranking.This Tier 1 dataset is available for download here:Tier 1 Risk Assessment DownloadThe SVI contains the following criteria: 1. Socioeconomic Status (ST)Below PovertyUnemployedIncomeNo High School Diploma2. Household Composition and Disability (HCD)Aged 65 or OlderAged 17 or YoungerCivilian with a DisabilitySingle-Parent Households3. Minority Status and Language (MSL)MinoritySpeaks English “less than well”4. Housing and Transportation (HT)Multi-Unit StructuresMobile HomesCrowdingNo VehicleGroup QuartersThe SACS CDC Social Vulnerability Index was developed by ranking all census tracts within the study area on the RPL theme value of the CDC’s SVI dataset, and then normalizing this percentile index ranking on a value of 0 to 1. The resulting dataset was then converted to a grid using this normalized value. For more information regarding the CDC’s methodology, please reference the following:https://svi.cdc.gov/https://svi.cdc.gov/Documents/Data/A%20Social%20Vulnerability%20Index%20for%20Disaster%20Management.pdfhttps://svi.cdc.gov/Documents/Data/2016_SVI_Data/SVI2016Documentation.pdf

This page is meant to act as an archival repository for previous Water Shortage Vulnerability analyses, which includes the Water Shortage Vulnerability Sections, Water Shortage Vulnerability for Small Water Systems, and Social Vulnerability Index by Block Groups. All data are presented in their original format. Please read the documentation found at https://water.ca.gov/Programs/Water-Use-And-Efficiency/SB-552/SB-552-Tool for more informatoin.

Per California Water Code Section 10609.80 (a), DWR has released an update to the indicators analyzed for the rural communities water shortage vulnerability analysis and a new interactive tool to explore the data. This page remains to archive the original dataset, but for more current information, please see the following pages: - https://water.ca.gov/Programs/Water-Use-And-Efficiency/SB-552/SB-552-Tool - https://data.cnra.ca.gov/dataset/water-shortage-vulnerability-technical-methods - https://data.cnra.ca.gov/dataset/i07-water-shortage-vulnerability-sections - https://data.cnra.ca.gov/dataset/i07-water-shortage-social-vulnerability-blockgroup This dataset is made publicly available pursuant to California Water Code Section 10609.42 which directs the California Department of Water Resources to identify small water suppliers and rural communities that may be at risk of drought and water shortage vulnerability and propose to the Governor and Legislature recommendations and information in support of improving the drought preparedness of small water suppliers and rural communities. As of March 2021, two datasets are offered here for download. The background information, results synthesis, methods and all reports submitted to the legislature are available here: https://water.ca.gov/Programs/Water-Use-And-Efficiency/2018-Water-Conservation-Legislation/County-Drought-Planning Two online interactive dashboards are available here to explore the datasets and findings. https://dwr.maps.arcgis.com/apps/MapSeries/index.html?appid=3353b370f7844f468ca16b8316fa3c7b The following datasets are offered here for download and for those who want to explore the data in tabular format. (1) Small Water Suppliers: In total, 2,419 small water suppliers were examined for their relative risk of drought and water shortage. Of these, 2,244 are community water systems. The remaining 175 systems analyzed are small non-community non-transient water systems that serve schools for which there is available spatial information. This dataset contains the final risk score and individual risk factors for each supplier examined. Spatial boundaries of water suppliers' service areas were used to calculate the extent and severity of each suppliers' exposure to projected climate changes (temperature, wildfire, and sea level rise) and to current environmental conditions and events. The boundaries used to represent service areas are available for download from the California Drinking Water System Area Boundaries, located on the California State Geoportal, which is available online for download at https://gispublic.waterboards.ca.gov/portal/home/item.html?id=fbba842bf134497c9d611ad506ec48cc (2) Rural Communities: In total 4,987 communities, represented by US Census Block Groups, were analyzed for their relative risk of drought and water shortage. Communities with a record of one or more domestic well installed within the past 50 years are included in the analysis. Each community examined received a numeric risk score, which is derived from a set of indicators developed from a stakeholder process. Indicators used to estimate risk represented three key components: (1) the exposure of suppliers and communities to hazardous conditions and events, (2) the physical and social vulnerability of communities to the exposure, and (3) recent history of shortage and drought impacts. The unit of analysis for the rural communities, also referred to as "self-supplied communities" is U.S. Census Block Groups (ACS 2012-2016 Tiger Shapefile). The Census Block Groups do not necessarily represent socially-defined communities, but they do cover areas where population resides. Using this spatial unit for this analysis allows us to access demographic information that is otherwise not available in small geographic units.

This layer contains Census Tracts that have been designated as Qualified Opportunity Zones and contains additional data determined by the EPA to be of interest to users who are seeking revitalization-oriented information about these tracts. Based on nominations of eligible census tracts by the Chief Executive Officers of each State, Treasury has completed its designation of Qualified Opportunity Zones. Each State nominated the maximum number of eligible tracts, per statute, and these designations are final. The statute and legislative history of the Opportunity Zone designations, under IRC § 1400Z, do not contemplate an opportunity for additional or revised designations after the maximum number of zones allowable have been designated in a State or Territory. The data in this layer was updated in January 2021. For more information on Opportunity Zones, please visit: https://www.cdfifund.gov/Pages/Opportunity-Zones.aspx

EPA has added these indicators to the QOZ tracts list:

1. Count of Superfund facilities from EPA National Priorities List (NPL). Count was generated by performing spatial join of Tract boundaries to NPL points—yielding per tract counts. Spatial Extent: all US states and territories. Source: https://www.epa.gov/superfund/superfund-data-and-reports

2. Count of Brownfields properties from EPA Assessment, Cleanup and Redevelopment Exchange System (ACRES). Count was generated by performing spatial join of Tract boundaries to ACRES points--yielding per tract counts. Spatial Extent: all US states and territories. Source: https://edap-oei-data-commons.s3.amazonaws.com/EF/GIS/EF_ACRES.csv

3. Technical Assistance Communities from EPA Office of Community Revitalization (OCR). 13 layers were merged into one; count was generated by performing spatial join of Tract boundaries to combined point layer—yielding per tract counts. Please note that technical assistance communities are often serving areas larger than a single Census tract. Please contact OCR with questions. Spatial Extent: all US states and territories. Source: https://epa.maps.arcgis.com/home/item.html?id=b8795575db194340a4ad1c251e4d6ca1

4. Lead Paint Index from Environmental Justice Screening and Mapping Tool (EJSCREEN). Block group-level values were population weighted and summed to produce a tract-level estimate. The “raw” values were converted to tract-level percentiles. Spatial Extent: all US states and Puerto Rico. Source: https://gaftp.epa.gov/EJSCREEN/2019/

5. Air Toxics Respiratory Index from EJSCREEN. Block group-level values were population weighted and summed to produce a tract-level estimate. The “raw” values were converted to tract-level percentiles. Spatial Extent: all US states and Puerto Rico. Source: https://gaftp.epa.gov/EJSCREEN/2019/

6. Demographic Index Indicator from EJSCREEN. Block group-level values were population weighted and summed to produce a tract-level estimate. The “raw” values were converted to tract-level percentiles. Spatial Extent: all US states and Puerto Rico. Source: https://gaftp.epa.gov/EJSCREEN/2019/

7. Estimated Floodplain Indicator from EPA EnviroAtlas. Floodplain raster was converted to polygon feature class; Y/N indicator was generated by performing a spatial join of Tract boundaries to the Floodplain polygons. Spatial Extent: Continental US. Source: https://gaftp.epa.gov/epadatacommons/ORD/EnviroAtlas/Estimated_floodplain_CONUS.zip

8. National Walkability Index from EPA Smart Location Tools. The National Walkability Index is a nationwide geographic data resource that ranks block groups according to their relative walkability. Tract values assigned by averaging values from block group-level table. Spatial Extent: all US states and territories. Source: EPA Office of Policy—2020 NWI update

9. Impaired Waters Indicator from EPA Office of Water (OW). Y/N indicator was generated by performing spatial joins of Tract boundaries to 3 separate impaired waters layers (point, line and polygon). Y was assigned for all intersected geographies. Extent: all US states and Puerto Rico. Source: https://watersgeo.epa.gov/GEOSPATIALDOWNLOADS/rad_303d_20150501_fgdb.zip

10. Tribal Areas Indicator from EPA. Y/N indicator was generated by performing spatial joins of Tract boundaries to 4 separate Tribal areas layers (Alaska Native Villages, Alaska Allotments, Alaska Reservations, Lower 48 Tribes). Y as assigned for all intersected geographies. Spatial Extent: Alaska and Continental US. Source: https://edg.epa.gov/data/PUBLIC/OEI/OIAA/TRIBES/EPAtribes.zip

11. Count of Resource Conservation and Recovery Act (RCRA) Corrective Action facilities. Count was generated by performing spatial join of Tract boundaries to Corrective Action points—yielding per tract counts. Spatial Extent: all US states and territories. Source: https://www.epa.gov/cleanups/cimc-web-map-service-and-more

12. Count of Toxics Release Inventory facilities from EPA. Count was generated by performing spatial join of Tract boundaries to TRI points—yielding per tract counts. Spatial Extent: all US states and territories. Source: https://edap-oei-data-commons.s3.amazonaws.com/EF/GIS/EF_TRI.csv

13. Social Vulnerability Index (SVI) Housing/Transportation Index from CDC, published in 2018. The Housing/Transportation Index includes ACS 2014-2018 data on crowding in housing and no access to vehicle, among others. County values assigned to tracts by joining Tracts to county-level table. For detailed documentation: https://svi.cdc.gov/Documents/Data/2018_SVI_Data/SVI2018Documentation.pdfSpatial Extent: all US states. Source: https://epa.maps.arcgis.com/home/item.html?id=cbd68d9887574a10bc89ea4efe2b8087

14. Low Access to Food Store Indicator from USDA Food Access Atlas. Y/N indicator was generated by performing a table join of Tracts to the Food Access table records meeting the test criteria. Spatial Extent: all US states. Source: https://www.ers.usda.gov/data-products/food-access-research-atlas/download-the-data/

15. Overall Social Vulnerability Index (SVI) from CDC. Values (RPL_THEMES) assigned by joining the Tract boundaries to source Tract-level table. Spatial Extent: All US states. Source: https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html

16. Rural Communities Indicator from USDA Economic Research Service (ERS). Source tract-level table was flagged as rural where RUCA Codes in 4-10 or 2 and 3 where area >= 400 sq. miles and pop density

Dates of Images:Pre-Event: April 2024Post-Event: 5/18/2024, 5/19/2024, 5/20/2024Date of Next Image:UnknownSummary:The Black Marble High-Definition (BMHD) images were created by the NASA Black Marble Science team, with directed funding the NASA-Google Partnership program. The images map the impact of extreme heat in Houston, Texas on electric grids. The baseline image is from April 2024, a cloud-free, moon-free composite, and the “after" image is from May 18, 2024 - May 20, 2024. There is a layer to display where clouds are present in the "after" images. This comparison between the images is meant as a visual assessment of outage impacts from the extreme heat to aid various partners who are working to deliver emergency aids to local communities. Power outage maps like these help disaster response efforts in the short-term as well as long-term monitoring during the crucial stages of disaster recovery.From the BMHD data, zonal statistics were collated with FEMA's National Risk Index (NRI) Social Vulnerability score to identify areas where vulnerable populations were affected by power outages. Higher (red) census tract areas indicate a higher amount of power lost relative to the second date listed. For example, a red polygon in the Baseline (April) - May 18th layer indicates that specific area lost disproportionately more power and is in an area with high social vulnerability risk when comparing the normal power available (baseline) to when the disaster occurred.Suggested Use:NOTE: Black Marble HD images are downscaled from NASA’s Black Marble nighttime lights product (VNP46), and as such are a “modelled” or “best guess” estimate of how lights are distributed at a 30m resolution. These images should be used for visualization purposes, not for quantitative analysis.The image is in a yellow-red color scale. Red indicates more severe impacts. Grey polygons are acquired from cloud cover and represent areas where no data was available on a given day.Satellite/Sensor:The primary data source, NASA’s Black Marble nighttime lights product suite (VNP46), utilized to generate this product is derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP) along with high resolution base layers - Landsat derived normalized index products (NDVI and NDWI) and OpenStreetMap (OSM) derived road layerResolution:Scaled resolution of 30 metersCredits:NASA Black Marble Science teamFEMA National Risk Index (NRI) TeamPlease cite the following two references when using this data:Román MO, Stokes EC, Shrestha R, Wang Z, Schultz L, Carlo EA, Sun Q, Bell J, Molthan A, Kalb V, Ji C. Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PloS one. 2019 Jun 28;14(6):e0218883.Román MO, Wang Z, Sun Q, Kalb V, Miller SD, Molthan A, Schultz L, Bell J, Stokes EC, Pandey B, Seto KC. NASA's Black Marble nighttime lights product suite. Remote Sensing of Environment. 2018 Jun 1;210:113-43.Point of Contact:Ranjay ShresthaNASA Goddard Space Flight CenterE-mail: ranjay.m.shrestha@nasa.govAdditional Links:NASA’s Black Marble Product SuiteRomán, M.O. et al. (2019) Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PLoS One, 14 (6).Román, M.O. et al. (2018) NASA’s Black Marble nighttime lights product suite. Remote Sensing of Environment. 210, 113–143.FEMA's National Risk Index Map: https://hazards.fema.gov/nri/data-resources#shpDownloadEsri REST Endpoint:See URL section on right side of page.WMS Endpoint:https://maps.disasters.nasa.gov/ags03/services/texas_extremeheat_202405/Relative_Power_Loss_Web_Map/MapServer/WMSServer?request=GetCapabilities&service=WMSData Download:https://maps.disasters.nasa.gov/download/gis_products/event_specific/2024/texas_extremeheat_202405/blackmarble_hd/

The DRIVES Project collects and processes low frequency and high frequency naturalistic driving data in order to study their association with cognitive decline in older drivers. Both sets of data are obtained daily from an off-the-shelf telematics datalogger that is installed our participants' vehicles. The low frequency data is collected at 1 Hz in 30 second intervals, whereas the high frequency data is collected at 24 Hz in one second intervals. The low frequency data is collected in the form of four CSV files: 1) A breadcrumbs file that contains the periodic driving data, 2) An activity file that provides detailed trip information, 3) An events file that provides detailed information on all adverse events 4) A a summary file that aggregates all daily trips carried out by each vehicle a day. The high frequency data is collected in the form of JSON files; each JSON file contains data for a single trip taken by a single vehicle on a given day. Each JSON is processed into four data tables: 1) A trip_info table that provides the periodic driving data 2) An activity table that details all adverse events that occurred during the trip (i.e. speeding, hard braking, idling etc.) 3) A braking table that details all hard braking events that occurred during the trip, and 4) A idling table that details each time the vehicle was idle during a trip.In addition to naturalistic driving data, the DRIVES Project collects clinical and neuropsychological data from our enrolled participants. Our participants undergo a variety of neuropsychological evaluations from which the DRIVES Project derives this data from (see attached data descriptor for more details). The DRIVES Project also collects data related to social determinants of health (SDoH). In particular, the DRIVES Project uses our participants' primary home addresses to obtain their Area of Deprivation Index (ADI) and Social Vulnerability Index (SVI) rankings. These rankings are provided by the Center of Health Disparities Research at the University of Wisconsin, Madison and the Center for Disease Control’s Agency for Toxic Substances and Disease Registry.The DRIVES Project uses two Python scripts to process the raw data files for the LFD and HFD. The scripts remove data and transforms the raw data files as needed to create the processed tables. In this repository, we provide a short demo of how our scripts processes our raw data in preparation for subsequent analysis or data storage. The demo code provides a walkthrough on how our scripts process 4 LFD CSV files that the DRIVES Project collected on March 31st, 2023 and a single HFD trip JSON that the project collected on March 31st, 2023. In the raw_data folder, we have provided four 'Spring2023' CSV files that contain the combined daily files that we download for the breadcrumbs, activity, events, and summary LFD data from March 1st, 2023 to May 31st, 2023. We've also provided three tarballs (.tar.gz files) that contain all of the HFD trip JSONs that we downloaded during the same time period; each tarball corresponds to the HFD trip JSONs we downloaded in a month (i.e. March, April, May). We've included these comprehensive files in case users would like to experiment with our scripts on more data.See attached metadata file for an explanation on the features for each table.

DefinitionThis indicator identifies the vulnerability of communities to impacts of natural disasters, pollution, climate change, and other health burdens within the Midwest Landscape. It prioritizes areas based on climate, environmental, and socioeconomic burdens. Pixels can take the following values:1 – Most vulnerable census tracts, not a natural asset2 – Most vulnerable census tracts, natural assetsSelectionThis indicator was chosen as a targetable, important feature of the MLI goals that will be used to track conditions over time and prioritize areas for conservation. Indicators were defined through elicitation and prioritization exercises with federal and state participants. Criteria for the indicators includes 1) actionable, 2) measurable, 3) relevant to multiple groups across the region, and/or 4) representative of other social and/or environmental values.Input Data & Mapping StepsThis indicator originates from the CDC Social Vulnerability Index and the Climate and Economic Justice Screening tool. To create this layer, MLI partners, members, and staff completed the following mapping steps: projected all input data to NAD83 (2011) UTM Zone 15N, classified the Social Vulnerability tracts as above or below-average vulnerability, mosaicked with the Climate and Economic Justice Screening tool disadvantaged tracts, and emphasized natural assets within the most vulnerable tracts for a raster with the following values: 1 – Most vulnerable census tracts, not a natural asset, 2 – Most vulnerable census tracts, natural assets. Finally, we removed highly altered areas using our Highly Altered Areas Mask. For full mapping details, please refer to the Midwest Conservation Blueprint 2024 Development Process. For a complete download of all Blueprint input and output data, visit the Midwest Conservation Blueprint 2024 Data Download.

The Canadian Index of Multiple Deprivation (CIMD) is an area-based index which used 2016 Census of Population microdata to measure four key dimensions of deprivation at the dissemination area (DA)-level: residential instability, economic dependency, situational vulnerability and ethno-cultural composition. Using factor analysis, DA-level factor scores were calculated for each dimension. Within a dimension, ordered scores were assigned a quintile value, 1 through 5, where 1 represents the least deprived and 5 represents the most deprived. The CIMD allows for an understanding of inequalities in various measures of health and social well-being. While it is a geographically-based index of deprivation and marginalization, it can also be used as a proxy for an individual. The CIMD has the potential to be widely used by researchers on a variety of topics related to socio-economic research. Other uses for the index may include: policy planning and evaluation, or resource allocation.

Abstract

The Integrated Household Survey is one of the primary instruments implemented by the Government of Malawi through the National Statistical Office (NSO) roughly every 3-5 years to monitor and evaluate the changing conditions of Malawian households. The IHS data have, among other insights, provided benchmark poverty and vulnerability indicators to foster evidence-based policy formulation and monitor the progress of meeting the Millennium Development Goals (MDGs), the goals listed as part of the Malawi Growth and Development Strategy (MGDS) and now the Sustainable Development Goals (SDGs).

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Consumption expenditure commodities/items
• Communities
• Agricultural household/ Holder/ Crop
• Market

Universe

Members of the following households are not eligible for inclusion in the survey: • All people who live outside the selected EAs, whether in urban or rural areas. • All residents of dwellings other than private dwellings, such as prisons, hospitals and army barracks. • Members of the Malawian armed forces who reside within a military base. (If such individuals reside in private dwellings off the base, however, they should be included among the households eligible for random selection for the survey.) • Non-Malawian diplomats, diplomatic staff, and members of their households. (However, note that non-Malawian residents who are not diplomats or diplomatic staff and are resident in private dwellings are eligible for inclusion in the survey. The survey is not restricted to Malawian citizens alone.) • Non-Malawian tourists and others on vacation in Malawi.

Kind of data

Sample survey data [ssd]

Sampling procedure

The IHS5 sampling frame is based on the listing information and cartography from the 2018 Malawi Population and Housing Census (PHC); includes the three major regions of Malawi, namely North, Center and South; and is stratified into rural and urban strata. The urban strata include the four major urban areas: Lilongwe City, Blantyre City, Mzuzu City, and the Municipality of Zomba. All other areas are considered as rural areas, and each of the 27 districts were considered as a separate sub-stratum as part of the main rural stratum. The sampling frame further excludes the population living in institutions, such as hospitals, prisons and military barracks. Hence, the IHS5 strata are composed of 32 districts in Malawi.

A stratified two-stage sample design was used for the IHS5.

Note: Detailed sample design information is presented in the "Fifth Integrated Household Survey 2019-2020, Basic Information Document" document.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

HOUSEHOLD QUESTIONNAIRE The Household Questionnaire is a multi-topic survey instrument and is near-identical to the content and organization of the IHS3 and IHS4 questionnaires. It encompasses economic activities, demographics, welfare and other sectoral information of households. It covers a wide range of topics, dealing with the dynamics of poverty (consumption, cash and non-cash income, savings, assets, food security, health and education, vulnerability and social protection). Although the IHS5 household questionnaire covers a wide variety of topics in detail it intentionally excludes in-depth information on topics covered in other surveys that are part of the NSO’s statistical plan (such as maternal and child health issues covered at length in the Malawi Demographic and Health Survey).

AGRICULTURE QUESTIONNAIRE All IHS5 households that are identified as being involved in agricultural or livestock activities were administered the agriculture questionnaire, which is primarily modelled after the IHS3 counterpart. The modules are expanding on the agricultural content of the IHS4, IHS3, IHS2, AISS, and other regional agricultural surveys, while remaining consistent with the NACAL topical coverage and methodology. The development of the agriculture questionnaire was done with input from the aforementioned stakeholders who provided input on the household questionnaire as well as outside researchers involved in research and policy discussions pertaining to the Malawian agriculture. The agriculture questionnaire allows, among other things, for extensive agricultural productivity analysis through the diligent estimation of land areas, both owned and cultivated, labor and non-labor input use and expenditures, and production figures for main crops, and livestock. Although one of the major foci of the agriculture data collection effort was to produce smallholder production estimates for major crops, it is also possible to disaggregate the data by gender and main geographical regions. The IHS5 cross-sectional households supply information on the last completed rainy season (2017/2018 or 2018/2019) and the last completed dry season (2018 or 2019) depending on the timing of their interview.

FISHERIES QUESTIONNAIRE The design of the IHS5 fishery questionnaire is identical to the questionnaire designed for IHS3. The IHS3 fisheries questionnaire was informed by the design and piloting of a fishery questionnaire by the World Fish Center (WFC), which was supported by the LSMS-ISA project for the purpose of assembling a fishery questionnaire that could be integrated into multi-topic household-surveys. The WFC piloted the draft instrument in November 2009 in the Lower Shire region, and the NSO team considered the revised draft in designing the IHS5 fishery questionnaire.

COMMUNITY QUESTIONNAIRE The content of the IHS5 Community Questionnaire follows the content of the IHS3 & IHS4 Community Questionnaires. A “community” is defined as the village or urban location surrounding the enumeration area selected for inclusion in the sample and which most residents recognize as being their community. The IHS5 community questionnaire was administered to each community associated with the cross-sectional EAs interviewed. Identical to the IHS3 and IHS4 approach, to a group of several knowledgeable residents such as the village headman, the headmaster of the local school, the agricultural field assistant, religious leaders, local merchants, health workers and long-term knowledgeable residents. The instrument gathers information on a range of community characteristics, including religious and ethnic background, physical infrastructure, access to public services, economic activities, communal resource management, organization and governance, investment projects, and local retail price information for essential goods and services.

MARKET QUESTIONNAIRE The Market Survey consisted of one questionnaire which is composed of four modules. Module A: Market Identification, Module B: Seasonal Main Crops, Module C: Permanents Crops, and Module D: Food Consumption.

Cleaning operations

DATA ENTRY PLATFORM To ensure data quality and timely availability of data, the IHS5 was implemented using the World Bank’s Survey Solutions CAPI software. To carry out IHS5, 1 laptop computer and a wireless internet router were assigned to each team supervisor, and each enumerator had an 8–inch GPS-enabled Lenovo tablet computer. The use of Survey Solutions allowed for the real-time availability of data as the completed data was completed, approved by the Supervisor and synced to the Headquarters server as frequently as possible. While administering the first module of the questionnaire the enumerator(s) also used their tablets to record the GPS coordinates of the dwelling units. In Survey Solutions, Headquarters can then see the location of the dwellings plotted on a map of Malawi to better enable supervision from afar – checking both the number of interviews performed and the fact that the sample households lie within EA boundaries. Geo-referenced household locations from that tablet complemented the GPS measurements taken by the Garmin eTrex 30 handheld devices and these were linked with publically available geospatial databases to enable the inclusion of a number of geospatial variables - extensive measures of distance (i.e. distance to the nearest market), climatology, soil and terrain, and other environmental factors - in the analysis.

The range and consistency checks built into the application was informed by the LSMS-ISA experience in previous IHS waves. Prior programming of the data entry application allowed for a wide variety of range and consistency checks to be conducted and reported and potential issues investigated and corrected before closing the assigned enumeration area. Headquarters (NSO management) assigned work to supervisors based on their regions of coverage. Supervisors then made assignments to the enumerators linked to their Supervisor account. The work assignments and syncing of completed interviews took place through a Wi-Fi connection to the IHS5 server. Because the data was available in real time it was monitored closely throughout the entire data collection period and upon receipt of the data at headquarters, data was exported to STATA for other consistency checks, data cleaning, and analysis.

DATA MANAGEMENT The IHS5 Survey Solutions CAPI based data entry application was designed to stream-line the data collection process from the field. IHS5 Interviews were collected in “sample” mode (assignments generated from headquarters) as opposed to “census” mode (new interviews created by interviewers from a template) for the NSO to have more control over the sample.

The range and consistency checks built into the application was informed by the LSMS-ISA experience in previous IHS waves. Prior programming of the data

Reason for Selection Protected natural areas help foster a conservation ethic by providing opportunities for people to connect with nature, and also support ecosystem services like offsetting heat island effects (Greene and Millward 2017, Simpson 1998), water filtration, stormwater retention, and more (Hoover and Hopton 2019). In addition, parks, greenspace, and greenways can help improve physical and psychological health in communities (Gies 2006). However, parks are not equitably distributed within easy walking distance for everyone. It also complements the urban park size indicator by capturing the value of potential new parks. Input Data

  The Trust for Public Land (TPL) ParkServe database, accessed 8-8-2021: Park priority areas (ParkServe_ParkPriorityAreas_08062021) 
    From the TPL ParkServe documentation:

The ParkServe database maintains an inventory of parks for every urban area in the U.S., including Puerto Rico. This includes all incorporated and Census-designated places that lie within any of the country’s 3,000+ census-designated urban areas. All populated areas in a city that fall outside of a 10-minute walk service area are assigned a level of park priority, based on a comprehensive index of six equally weighted demographic and environmental metrics:Population densityDensity of low-income households – which are defined as households with income less than 75 percent of the urban area median household incomeDensity of people of colorCommunity health – a combined index based on the rate of poor mental health and low physical activity from the 2020 CDC PLACES census tract datasetUrban heat islands – surface temperature at least 1.25o greater than city mean surface temperature from The Trust for Public Land, based on Landsat 8 satellite imageryPollution burden - Air toxics respiratory hazard index from 2020 EPA EJScreen The 10-minute walkFor each park, we create a 10-minute walkable service area using a nationwide walkable road network dataset provided by Esri. The analysis identifies physical barriers such as highways, train tracks, and rivers without bridges and chooses routes without barriers.

  CDC Social Vulnerability Index 2018: RPL_Themes 

Social vulnerability refers to the capacity for a person or group to “anticipate, cope with, resist and recover from the impact” of a natural or anthropogenic disaster such as extreme weather events, oil spills, earthquakes, and fires. Socially vulnerable populations are more likely to be disproportionately affected by emergencies (Wolkin et al. 2018).

In this indicator, we use the “RPL_THEMES” attribute from the Social Vulnerability Index, described here. “The Geospatial Research, Analysis, and Services Program (GRASP) at Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry developed the Social Vulnerability Index (SVI). The SVI is a dataset intended to help state, local, and tribal disaster management officials identify where the most socially vulnerable populations occur (Agency for Toxic Substances and Disease Registry [ATSDR] 2018)” (Flanagan et al. 2018).

“The SVI database is regularly updated and includes 15 census variables (ATSDR 2018). Each census variable was ranked from highest to lowest vulnerability across all census tracts in the nation with a nonzero population. A percentile rank was calculated for each census tract for each variable. The variables were then grouped among four themes.... A tract-level percentile rank was also calculated for each of the four themes. Finally, an overall percentile rank for each tract as the sum of all variable rankings was calculated. This process of percentile ranking was then repeated for the individual states” (Flanagan et al. 2018).

Base Blueprint 2022 extent
Southeast Blueprint 2023 extent

Mapping Steps

Convert the ParkServe park priority areas layer to a raster using the ParkRank field. Note: The ParkRank scores are calculated using metrics classified relative to each city. Each city contains park rank values that range from 1-3. For the purposes of this indicator, we chose to target potential park areas to improve equity. Because the ParkRank scores are relative for each city, a high score in one city is not necessarily comparable to a high score from another city. In an effort to try to bring more equity into this indicator, we also use the CDC Social Vulnerability Index to narrow down the results.
Reclassify the ParkServe raster to make NoData values 0. 
Convert the SVI layer from vector to raster based on the “RPL_Themes” field. 
To limit the ParkRank layer to areas with high SVI scores, first identify census tracts with an “RPL_Themes” field value >0.65. Make a new raster that assigns a value of 1 to census tracts that score >0.65, and a value of 0 to everything else. Take the resulting raster times the ParkRank layer.
Reclassify this raster into the 4 classes seen in the final indicator below.
Clip to the spatial extent of Base Blueprint 2022.
As a final step, clip to the spatial extent of Southeast Blueprint 2023. 

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator values Indicator values are assigned as follows: 3 = Very high priority for a new park that would create nearby equitable access

2 = High priority for a new park that would create nearby equitable access1 = Moderate priority for a new park that would create nearby equitable access 0 = Not identified as a priority for a new park that would create nearby equitable access (within urban areas) Known Issues

This indicator could overestimate park need in areas where existing parks are missing from the ParkServe database. TPL regularly updates ParkServe to incorporate the best available park data. If you notice missing parks or errors in the park boundaries or attributes, you can submit corrections through the ParkReviewer tool or by contacting TPL staff.
Within a given area of high park need, the number of people served by the creation of a new park depends on its size and how centrally located it is. This indicator does not account for this variability. Similarly, while creating a new park just outside an area of high park need would create access for some people on the edge, the indicator does not capture the benefits of new parks immediately adjacent to high-need areas. For a more granular analysis of new park benefits, ParkServe’s ParkEvaluator tool allows you to draw a new park, view its resulting 10-minute walk service area, and calculate who would benefit.
Beyond considering distance to a park and whether it is open to the public, this indicator does not account for other factors that might limit park access, such as park amenities or public safety. The TPL analysis excludes private or exclusive parks that restrict access to only certain individuals (e.g., parks in gated communities, fee-based sites). The TPL data includes a wide variety of parks, trails, and open space as long as there is no barrier to entry for any portion of the population.
The indicator does not incorporate inequities in access to larger versus smaller parks. In predicting where new parks would benefit nearby people who currently lack access, this indicator treats all existing parks equally.
This indicator identifies areas where parks are needed, but does not consider whether a site is available to become a park. We included areas of low intensity development in order to capture vacant lots, which can serve as new park opportunities. However, as a result, this indicator also captures some areas that are already used for another purpose (e.g., houses, cemeteries, and businesses) and are unlikely to become parks. In future updates, we would like to use spatial data depicting vacant lots to identify more feasible park opportunities.
This indicator underestimates places in rural areas where many people within a socially vulnerable census tract would benefit from a new park. ParkServe covers incorporated and Census-designated places within census-designated urban areas, which leaves out many rural areas. We acknowledge that there are still highly socially vulnerable communities in rural areas that would benefit from the development of new parks. However, based on the source data, we were not able to capture those places in this version of the indicator. 

Other Things to Keep in MindThe zero values in this indicator contain three distinct types of areas that we were unable to distinguish between in the legend: 1) Areas that are not in a community analyzed by ParkServe (ParkServe covers incorporated and Census-designated places within census-designated urban areas); 2) Areas in a community analyzed by ParkServe that were not identified as a priority; 3) Areas that ParkServe identifies as a priority but do not meet the SVI threshold used to represent areas in most need of improved equitable access.This indicator only includes park priority areas that fall within the 65th percentile or above from the Social Vulnerability Index. We did not perform outreach to community leaders or community-led organizations for feedback on this threshold. This indicator is intended to generally help identify potential parks that can increase equitable access but should not be solely used to inform the creation of new parks. As the social equity component relies on information summarized by census tract, it should only be used in conjunction with local knowledge and in discussion with local communities (NRPA 2021, Manuel-Navarete et al. 2004). Disclaimer: Comparing with Older Indicator Versions There are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov). Literature Cited Centers for

The HVA tool evaluates coastal hazard vulnerability from four components: 1.) Storm surge, 2.) Shoreline change rate (erosion or accretion), 3.) Flooding, and 4.) Social/economic vulnerability (SoVI®). The final products are vulnerability indices on a scale of 1 to 5, with 1 being the least risk, and 5 being the most risk. Storm surge data is based on the SLOSH (Sea, Lake and Overland Surges from Hurricanes) model, which was developed by the NOAA’s National Weather Service. SLOSH produces MEOWs (Maximum Envelope of Water) and MOMs (Maximum of MEOWs). The MEOW is a composite of many hypothetic model runs, with identical hurricane category, speed, and direction, but different landfalls. The maximum value for each grid is plotted. The MOMs (Maximum of MEOWs) is a composite of MEOWs for each category hurricane, plotting the maximum surge for each category. The MOM (AGL) data was used for the Charleston Harbor basin. SLOSH data can be obtained here after getting a password: https://slosh.nws.noaa.gov/sdp/download.php Go here to obtain the password: http://140.90.8.37/sloshPub/disclaim.php.The South Carolina Department of Health and Environmental Control’s Office of Ocean and Coastal Resource Management (DHEC-OCRM) is the source for the shoreline change data. The shoreline change data is based on three time steps: 1800s, 1930s, and 2000s. The shoreline change analysis was completed using the AMBUR tool and the change rate utilized with the HVA tool was the End Point Rate (EPR). The shoreline change data can be downloaded through DHEC’s HVA web application, located here: http://www.scdhec.gov/HomeAndEnvironment/maps/GIS/Applications/Flood data is based on FEMA’s DFIRM (Digital Flood Insurance Rate Map) data. The DFIRMS used in this analysis were preliminary. The issue dates for these preliminary DFIRMS are as follows: Charleston County (12/21/2017), Beaufort County (6/30/2017), Jasper County (1/16/2017), Berkeley County (2/12/2016), Dorchester County (11/13/2015), Georgetown County (11/13/2015), and Horry County (9/11/2015). For Colleton County, no DFIRM data were available; therefore, the National Flood Hazard Layer (NFHL) was used, with a latest study effective data of 12/21/2017. Current available GIS datasets for each county can be found here: https://msc.fema.gov/portal/advanceSearch#searchresultsanchorThe social and economic vulnerability component was represented by the Social Vulnerability Index (SoVI®), which measures social vulnerability to environmental hazards. This index is produced by the University of South Carolina’s Hazards & Vulnerability Research Institute (USC HVRI). This is a relative index which incorporates 29 socioeconomic variables. DHEC-OCRM contracted with the Institute to run a SoVI® analysis specific to the study area of this project. This product can be downloaded through DHEC’s HVA web application, located here: http://www.scdhec.gov/HomeAndEnvironment/maps/GIS/Applications/. Additional information about SoVI® can be obtained from the USC website: http://artsandsciences.sc.edu/geog/hvri/sovi%C2%AE-0HVA produces several combination products including the following: 1.) Inundation (surge + flooding), 2.) Inundation + SoVI®, and 3.) Shoreline Change (rate, plus temporal and spatial variations). Users can examine each hazard component to see which has the most impact in any given area. Products can be viewed and downloaded through DHEC’s HVA web application, located here: http://www.scdhec.gov/HomeAndEnvironment/maps/GIS/Applications

The Composite Exposure Index was generated following the methodology of the Tier 1 Risk Assessment cited in the USACE North Atlantic Coast Comprehensive Study (NACCS): https://www.nad.usace.army.mil/Portals/40/docs/NACCS/NACCS_Appendix_C.pdf. The Composite Exposure Index is created by summing the three Tier 1 Risk Assessment exposure indices on a percentage basis, Population and Infrastructure Exposure Index 60%, Environmental and Cultural Resources Exposure Index 30%, and Social Vulnerability Exposure Index 10%. The resulting grid is displayed with a stretch symbology, percent clip (min -.5 max .5), with 0 being the lowest exposure, and 1 being the highest exposure. The resolution of the grid is 30 meters. This Tier 1 dataset is available for download here:Tier 1 Risk Assessment Download