Population and housing information extracted from decennial census Public Law 94-171 redistricting summary files for Washington state for years 2000 and 2010.
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This map layer shows the urban heat island effect in degrees Celsius. The urban heat island effect is the average air temperature difference between the urban and surrounding rural areas. This map layer displays a prediction based on several underlying map data: population density, wind speed, amount of green, blue and hardening. The urban heat island effect is greatest in an environment with little greenery and a lot of buildings. The effect with associated high (feeling) temperatures is detrimental to people's health.
My ArcGIS StoryMap is centered around The Green Book, an annual travel guide that allowed African Americans to travel safely during the height of the Jim Crow Era in the United States. More specifically, The Green Book listed establishments, such as hotels and restaurants, that would openly accept and welcome black customers into their businesses. As someone who is interested in the intersection between STEM and the humanities, I wanted to utilize The Science of Where to formulate a project that would reveal important historical implications to the public. Therefore, my overarching goal was to map each location in The Green Book in order to draw significant conclusions regarding racial segregation in one of the largest cities in the entire world.Although a more detailed methodology of my work can be found in the project itself, the following is a step by step walkthrough of my overall scientific process:Develop a question in relation to The Green Book to be solved through the completion of the project.Perform background research on The Green Book to gain a more comprehensive understanding of the subject matter.Formulate a hypothesis that answers the proposed question based on the background research.Transcribe names and addresses for each of the hotel listings in The Green Book into a comma separated values file.Transcribe names and addresses for each of the restaurants listings in The Green Book into a comma separated values file.Repeat Steps 4 and 5 for the 1940, 1950, 1960, and 1966 publications of The Green Book. In total, there should be eight unique database files (1940 New York City Hotels, 1940 New York City Restaurants, 1950 New York City Hotels, 1950 New York City Restaurants, 1960 New York City Hotels, 1960 New York City Restaurants, 1966 New York City Hotels, and 1966 New York City Restaurants.)Construct an address locator that references a New York City street base map to plot the information from the databases in Step 6 as points on a map.Manually plot locations that the address locator did not automatically match on the map.Repeat Steps 7 and 8 for all eight database files.Find and match the point locations for each listing in The Green Book with historical photographs.Generate a map tour using the geotagged images for each point from Step 10.Create a point density heat map for the locations in all eight database files.Research and obtain professional and historically accurate racial demographic data for New York City during the same time period as when The Green Book was published.Generate a hot spot map of the black population percentage using the demographic data.Analyze any geospatial trends between the point density heat maps for The Green Book and the black population percentage hot spot maps from the demographic data.Research and obtain professional and historically accurate redlining data for New York City during the same time period as when The Green Book was published.Overlay the points from The Green Book listings from Step 9 on top of the redlining shapefile.Count the number of point features completely located within each redlining zone ranking utilizing the spatial join tool.Plot the data recorded from Step 18 in the form of graphs.Analyze any geospatial trends between the listings for The Green Book and its location relative to the redlining ranking zones.Draw conclusions from the analyses in Steps 15 and 20 to present a justifiable rationale for the results._Student Generated Maps:New York City Pin Location Maphttps://arcg.is/15i4nj1940 New York City Hotels Maphttps://arcg.is/WuXeq1940 New York City Restaurants Maphttps://arcg.is/L4aqq1950 New York City Hotels Maphttps://arcg.is/1CvTGj1950 New York City Restaurants Maphttps://arcg.is/0iSG4r1960 New York City Hotels Maphttps://arcg.is/1DOzeT1960 New York City Restaurants Maphttps://arcg.is/1rWKTj1966 New York City Hotels Maphttps://arcg.is/4PjOK1966 New York City Restaurants Maphttps://arcg.is/1zyDTv11930s Manhattan Black Population Percentage Enumeration District Maphttps://arcg.is/1rKSzz1930s Manhattan Black Population Percentage Hot Spot Map (Same as Previous)https://arcg.is/1rKSzz1940 Hotels Point Density Heat Maphttps://arcg.is/jD1Ki1940 Restaurants Point Density Heat Maphttps://arcg.is/1aKbTS1940 Hotels Redlining Maphttps://arcg.is/8b10y1940 Restaurants Redlining Maphttps://arcg.is/9WrXv1950 Hotels Redlining Maphttps://arcg.is/ruGiP1950 Restaurants Redlining Maphttps://arcg.is/0qzfvC01960 Hotels Redlining Maphttps://arcg.is/1KTHLK01960 Restaurants Redlining Maphttps://arcg.is/0jiu9q1966 Hotels Redlining Maphttps://arcg.is/PXKn41966 Restaurants Redlining Maphttps://arcg.is/uCD05_Bibliography:Image Credits (In Order of Appearance)Header/Thumbnail Image:Student Generated Collage (Created Using Pictures from the Schomburg Center for Research in Black Culture, Manuscripts, Archives and Rare Books Division, The New York Public Library, https://digitalcollections.nypl.org/collections/the-green-book#/?tab=about.)Mob Violence Image:Kelley, Robert W. “A Mob Rocks an out of State Car Passing.” Life Magazine, www.life.com/history/school-integration-clinton-history, The Green Book Example Image:Schomburg Center for Research in Black Culture, Manuscripts, Archives and Rare Books Division, The New York Public Library Digital Collections, https://images.nypl.org/index.php?id=5207583&t=w. 1940s Borough of Manhattan Hotels and Restaurants Photographs:“Manhattan 1940s Tax Photos.” NYC Municipal Archives Collections, The New York City Department of Records & Information Services, https://nycma.lunaimaging.com/luna/servlet/NYCMA~5~5?cic=NYCMA~5~5.Figure 1:Student Generated GraphFigure 2:Student Generated GraphFigure 3:Student Generated GraphGIS DataThe Green Book Database:Student Generated (See Above)The Green Book Listings Maps:Student Generated (See Above)The Green Book Point Density Heat Maps:Student Generated (See Above)The Green Book Road Trip Map:Student GeneratedLION New York City Single Line Street Base Map:https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-lion.page 1930s Manhattan Census Data:https://s4.ad.brown.edu/Projects/UTP2/ncities.htm Mapping Inequality Redlining Data:https://dsl.richmond.edu/panorama/redlining/#loc=12/40.794/-74.072&city=manhattan-ny&text=downloads 1940 The Green Book Document:Schomburg Center for Research in Black Culture, Manuscripts, Archives and Rare Books Division, The New York Public Library. "The Negro Motorist Green-Book: 1940" The New York Public Library Digital Collections, 1940, https://digitalcollections.nypl.org/items/dc858e50-83d3-0132-2266-58d385a7b928. 1950 The Green Book Document:Schomburg Center for Research in Black Culture, Manuscripts, Archives and Rare Books Division, The New York Public Library. "The Negro Motorist Green-Book: 1950" The New York Public Library Digital Collections, 1950, https://digitalcollections.nypl.org/items/283a7180-87c6-0132-13e6-58d385a7b928. 1960 The Green Book Document:Schomburg Center for Research in Black Culture, Manuscripts, Archives and Rare Books Division, The New York Public Library. "The Travelers' Green Book: 1960" The New York Public Library Digital Collections, 1960, https://digitalcollections.nypl.org/items/a7bf74e0-9427-0132-17bf-58d385a7b928. 1966 The Green Book Document:Schomburg Center for Research in Black Culture, Manuscripts, Archives and Rare Books Division, The New York Public Library. "Travelers' Green Book: 1966-67 International Edition" The New York Public Library Digital Collections, 1966, https://digitalcollections.nypl.org/items/27516920-8308-0132-5063-58d385a7bbd0. Hyperlink Credits (In Order of Appearance)Referenced Hyperlink #1: Coen, Ross. “Sundown Towns.” Black Past, 23 Aug. 2020, blackpast.org/african-american-history/sundown-towns.Referenced Hyperlink #2: Foster, Mark S. “In the Face of ‘Jim Crow’: Prosperous Blacks and Vacations, Travel and Outdoor Leisure, 1890-1945.” The Journal of Negro History, vol. 84, no. 2, 1999, pp. 130–149., doi:10.2307/2649043. Referenced Hyperlink #3:Driskell, Jay. “An Atlas of Self-Reliance: The Negro Motorist's Green Book (1937-1964).” National Museum of American History, Smithsonian Institution, 30 July 2015, americanhistory.si.edu/blog/negro-motorists-green-book. Referenced Hyperlink #4:Kahn, Eve M. “The 'Green Book' Legacy, a Beacon for Black Travelers.” The New York Times, The New York Times, 6 Aug. 2015, www.nytimes.com/2015/08/07/arts/design/the-green-book-legacy-a-beacon-for-black-travelers.html. Referenced Hyperlink #5:Giorgis, Hannah. “The Documentary Highlighting the Real 'Green Book'.” The Atlantic, Atlantic Media Company, 25 Feb. 2019, www.theatlantic.com/entertainment/archive/2019/02/real-green-book-preserving-stories-of-jim-crow-era-travel/583294/. Referenced Hyperlink #6:Staples, Brent. “Traveling While Black: The Green Book's Black History.” The New York Times, The New York Times, 25 Jan. 2019, www.nytimes.com/2019/01/25/opinion/green-book-black-travel.html. Referenced Hyperlink #7:Pollak, Michael. “How Official Is Official?” The New York Times, The New York Times, 15 Oct. 2010, www.nytimes.com/2010/10/17/nyregion/17fyi.html. Referenced Hyperlink #8:“New Name: Avenue Becomes a Boulevard.” The New York Times, The New York Times, 22 Oct. 1987, www.nytimes.com/1987/10/22/nyregion/new-name-avenue-becomes-a-boulevard.html. Referenced Hyperlink #9:Norris, Frank. “Racial Dynamism in Los Angeles, 1900–1964.” Southern California Quarterly, vol. 99, no. 3, 2017, pp. 251–289., doi:10.1525/scq.2017.99.3.251. Referenced Hyperlink #10:Shertzer, Allison, et al. Urban Transition Historical GIS Project, 2016, https://s4.ad.brown.edu/Projects/UTP2/ncities.htm. Referenced Hyperlink #11:Mitchell, Bruce. “HOLC ‘Redlining’ Maps: The Persistent Structure Of Segregation And Economic Inequality.” National Community Reinvestment Coalition, 20 Mar. 2018,
The Kernel Density tool calculates the density of features in a neighborhood around those features.Kernel Density calculates the density of point features around each output raster cell. Conceptually, a smoothly curved surface is fitted over each point. The surface value is highest at the location of the point and diminishes with increasing distance from the point, reaching zero at the Search radius distance from the point. Only a circular neighborhood is possible. The volume under the surface equals the Population field value for the point, or 1 if NONE is specified. The density at each output raster cell is calculated by adding the values of all the kernel surfaces where they overlay the raster cell center. This layer is included in a storymap about the Panama City crayfish, a species listed as Threatened under the Endangered Species Act in 2022. Storymap link: https://fws.maps.arcgis.com/home/item.html?id=a791906fe3f8433eabadda5898184372
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The dataset is used to assess the urban heat risk in the city of Riyadh using proxy variables to evaluate the environmental, infrastructural, and social dimensions of the city.
The environmental component was evaluated using the mean values of land surface temperature (LST), air temperature (T2m), and discomfort index (DI) across the districts of Riyadh. These factors, derived from data like MODIS LST and available WRF simulations, represented the degree of heat exposure in different regions.
The infrastructural component of heat risk was evaluated by looking at the city's infrastructure, that is the building density per district. Buildings can act as "heat traps," thus higher building density suggests increased heat risk.
The social component considered demographic factors such as the percentage of the population over 65 old (OP) and under 14 years old (YP), which can indicate sensitivity to extreme heat conditions.
To map the heat risk, these components were combined into a composite heat risk indicator. For this to be achieved, each parameter was reclassified into three categories (1-less, 2-moderate, and 3-high) using the quantile classification which is a data classification method that distributes a set of values into groups that contain an equal number of values.
LST (°C) DI T2m (°C) <14 y.o. (%) >65 y.o (%) Buildings per sq. m.(BD)
1-Less risk <47.2 <28 <40.6 <23 <1 <66
2-Moderate risk 47.2 ≤ LST ≤ 47.9 28≤ DI ≤ 28.2 40.6 ≤ T2m ≤ 40.8 23≤ YP ≤28 1≤ OP ≤ 3 66≤ BD ≤ 109
3-High risk >47.9 >28.2 >40.8 >28 >3 >109
LST: Land Surface Temperature; DI: Discomfort Index; T2m: Air temperature at 2m height; YP<14 y.o.: People under 14 years old; OP y.o.: Older people over 65 years old;
Since the relative importance of each parameter is unknown, we considered that all parameters contributed equally to the composite heat risk index and the arithmetic values were aggregated. The final value for each district was then reclassified into three categories using the quantile classification method resulting in the final three categories of Urban Heat Risk (Less heat risk, Moderate heat risk, High heat risk)
All GHiGs datasets cover the whole of Scotland and have been derived by Greenspace Scotland over the project period of September 2020 to April 2021. Principal third party data suppliers include: - Ordnance Survey (greenspace and water body data) - Scottish Government (Scotland's Heat Map) - Energy Saving Trust (Home Analytics) Please reference the Data Guide and Methodology report (attached to the metadata record as an associated resource) and send any further queries on the quality/ accuracy of the data to parkpower@greenspacescotland.org.uk. GHiGs Settlements: A public summary of indicators for GHiGs analysis of low carbon heat based on data aggregated to Scotland's 516 settlements. Settlement boundaries are from 2012 derived from National Records of Scotland to be consistent with those used by Scotland's Heat Map v.2. Settlements are defined as places with populations greater than 500. Approximately 90% of Scotland's population lives in settlements. It is not clear why Scotland's Heat Map is using the NRS 2012 settlement boundaries rather than the more recent NRS 2016 settlement boundaries. Attributes were derived from Scotland's Heat Map with additional attributes from GHiGs analysis and EST Home Analytics GHiGs Settlements by LA: A more comprehensive spreadsheet of tables used for National Findings Report and all indicators for GHiGs analysis of low carbon heat based on data aggregated to Scotland's 516 settlements and, separately, the 32 Local Authorities. Settlement data aggregated to Local Authority geographies and presented based on OS BoundaryLine Local Authority boundaries. The data excludes areas outside settlements and therefore does NOT represent figures for complete local authorities. This is particularly evident for Local Authorities with more significant populations and businesses located outside of settlements. It includes most indicators used in the GHiGs National Findings report based on analysis of low carbon heat related data aggregated to Scotland's 516 settlements and then aggregated to 32 Local Authorities. GHiGs greenspaces: Boundaries derived from OS Mastermap Greenspace. Attributes derived from Scotland's Heat Map v.2 with additional attributes from GHiGs analysis (see our Methodology Report) and EST Home Analytics GHiGs strategic greenspaces: Subset of GHiGs Greenspaces based on selection criteria to identify the 3% (3,446) of national greenspace sites with high potential for supply of ground source heat (based on areal size / capacity) and have been classified as 'high' based on local linear heat density. These sites are likely to be the strongest candidates for larger scale ground source heat solutions, potentially storing and feeding low grade heat into low carbon district heat networks. The 'GSHP_Strategic_Importance' indicator category of 'VERY HIGH' was used to select this subset GHiGs static water bodies: Relatively static water bodies greater than 1000m2 in area in proximity to urban settlements including canals, lochs, lakes, flooded quarries/pits etc. derived largely from OS Mastermap Greenspace. This data does not include rivers. GHiGs DHN highest viability (Linear Heat Density 16000 kWh/m/yr): Linear Heat Density model created by Ramboll to highlight areas where District Heat Networks (DHNs) have highest viability based on heat demand from all buildings. Areas identifies have high levels of heat demand density and are therefore highly suitable for DHNs - source of heat demand data: Scotland's Heat Map v2. GHiGs DHN high viability (Linear Heat Density 8000 kWh/m/yr): Linear Heat Density model created by Ramboll to highlight areas where District Heat Networks (DHNs) have high viability based on heat demand from all buildings - source of heat demand data: Scotland's Heat Map v2. GHiGs DHN viable (Linear Heat Density 4000 kWh/m/yr): Linear Heat Density model created by Ramboll to highlight areas where District Heat Networks (DHNs) are viable based on heat demand from all buildings. Threshold of 4000 is widely used across the industry for Linear Heat Density modelling to identify areas with DHN viability. Polygons of area less than 250m2 were deleted which reduced the number of polygon features by 80% to cut file size. Source of heat demand data: Scotland's Heat Map v2. GHiGs DHN highest viability public buildings only (Linear Heat Density 16000 kWh/m/yr): Linear Heat Density model created by Ramboll based on a best estimate of public buildings to highlight areas where District Heat Networks have highest viable based on heat demand from only public buildings. Source of heat demand data: Scotland's Heat Map v2. GHiGs DHN high viability public buildings only (Linear Heat Density 8000 kWh/m/yr): Linear Heat Density model created by Ramboll based on a best estimate of public buildings to highlight areas where District Heat Networks have high viability based on heat demand from only public buildings. Source of heat demand data: Scotland's Heat Map v2. GHiGs DHN viable public buildings only (Linear Heat Density 4000 kWh/m/yr): Linear Heat Density model created by Ramboll based on a best estimate of public buildings to highlight areas where District Heat Networks are viable based on heat demand from only public buildings. Threshold of 4000 is widely used across the industry for Linear Heat Density modelling to identify areas with DHN viability - source of heat demand data: Scotland's Heat Map v2. GHiGs public buildings: Subset of Scotland's Heat Map at building level where buildings are assessed as likely to be publicly owned based on a selection of 125 OS AddressBase codes (see GHiGs Methodology report for details). This is the best available approximation of publicly owned buildings but will exclude those publicly owned buildings which are leased to third parties for more commercial-type services. This same identification method was the basis for creating the 3 Linear Heat Density map layers for public buildings only. GHiGs public buildings with heat demand greater than 50 MWh/year: Subset of 'GHiGs public buildings' dataset based on a filter for all those public buildings with an annual heat demand of 50 MWh or more. Multi-occupancy buildings like flatted properties are treated as separate buildings and therefore they are unlikely to appear in this dataset. GHiGs public buildings (>200 MWh) near greenspaces (>200 MWh): Subset of 'GHiGs public buildings' dataset where: (1) buildings are assessed as likely to be publicly owned based on a selection of 125 OS AddressBase codes and have a heat demand of at least 200 MWh; AND (2) they are located within 50m of a greenspace that, based on 20% space utilisation, could meet at least 200 MWh in terms of heat production from its available area. In effect this is a subset of public building locations that offers the strongest opportunities for larger scale GSHP projects based on use of nearby greenspace. Multi-occupancy buildings like flatted properties are treated as separate buildings and therefore examples such as high rise flats next to larger areas of greenspace are unlikely to appear in this dataset. GHiGs waste disposal sites: Potential sources of waste heat from waste disposal sites to feed into district heat networks - source: SEPA registered waste sites All GHiGs datasets cover Scotland and have been derived over the project period of September 2020 to April 2021. Principal third party data suppliers include: * Ordnance Survey (greenspace and water body data) * Scottish Government (Scotland's Heat Map) * Energy Saving Trust (Home Analytics)
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Urban Heat Island images:MorningAfternoonEveningTacoma Heat Island StudyData collected on 7/25/2018, collected by Dr. Vivek Shandas, Capa StrategiesWhat Earth Economics is working on:Through grant funding, Earth Economics is working on building out an approach and methodology using Urban Heat Island modeling (LANDSAT data) to assume health impacts (mortality rates) on a census tract level, using research on how demographics and UHI impact community health outcomes.Variables:Name: Census Block Group NamePop: Census Block Group populationIncome: Average individual Census Block Group level annual incomeOver 65: Population over age 65Under14: Population under age 14AF: Afternoon temperature (C), averaged to Census Block Group (July 25, 2018). Data collected by Dr. Vivek Shandas using this methodologyPm: Evening temperature (C), averaged to Census Block Group (July 25, 2018)Combtemp: Average of evening and afternoon temperatureHighRiskAgeGroup: Percent of population in a high risk age group for heat related illness (over age 65 and under age 14)Density: Population DensityCity of Tacoma Contact: Vanessa Simpson, Senior Technical GIS Analyst, Environmental Servicesvsimpson@cityoftacoma.org
The Discharge System subplan is one of three subplans (cf. Heat Reduction Subplan and Cold Air System subplan), which together form the plan for technical planning for heat reduction. Free space categories (discharge spaces) are shown. These are elements of the relief system. Open spaces should be accessible, for example, in a short distance or on pleasant climatic paths. This climate-optimised open space network (discharge system) has to be developed specifically for hotspots. Hotspots are areas that are exposed to high heat pollution due to their location and urban structure, with high population density and sensitive uses. From the point of view of specialist planning, the designated city-owned plants and properties have potential for future urban climatic optimisation. Sustainable solutions can be found in the following planning processes, especially in building and civil engineering. NOTE: In the context of STRB No 178/2020, the heat reduction technical planning, including sub-plans, supersedes the planning principles and recommendations adopted in the context of the KLAZ 2011 with STRB No 1384/2011. Purpose: The Discharge System sub-plan is aimed primarily at the public authorities, which should as far as possible produce the climate-optimised open space network over the coming years. Reference scale is 1:15 000.
Extreme heat is the most common climate-related hazard globally, with rising temperatures and more frequent heat waves affecting cities, ecosystems, and food production. Urban heat islands (UHIs), where city temperatures are higher than surrounding rural areas, are becoming more prevalent due to climate change. This occurs because urban structures like buildings and roads trap more heat than natural landscapes. To address this, creating a heat risk index (HRI) is essential for developing localized adaptation plans and prioritizing areas most at risk. This web map showing health risk index (HRI), temperature variations, population density, tree canopy cover across Toronto city. The inputs for this HRI was derived from multiple data sources from the ArcGIS Living Atlas of the World.
Created using ArcGIS Pro Geoprocessing tools (Create Space Time Cube, Emerging Hot Spot Analysis, and Enrich Layer) and the ArcGIS R Bridge. The EBest function, part of the spdep package was used to calculate an Empirical Bayes smoothed crime rate with 2016 population estimates. This procedure is presented as part of the R-ArcGIS Workflow Demo on GeoNet.Relative Burglary Risk is the natural log (Ln) of the kernel density of burglaries g(x) divided by the kernel density of households g(y) calculated using CrimeStat. Note: Ten months of burglary data (the minimum required) were used for this initial analysis. Also Note: These locations are one-half kilometer square polygons. It will be updated in the future as more data from the Albuquerque Police Department is obtained (see ABQ Data).Please see the web map for another similar way to present these results.More information at (http://www.unm.edu/~lspear/other_nm.html).
Extreme heat events, or heat waves, are on the rise and becoming more intense according to the U.S. Environmental Protection Agency (EPA). These events are more than just an annoyance and can lead to illness and death, particularly among vulnerable populations including seniors and young people. The EPA also states prolonged exposure to these heat events can lead to other impacts such as damaging crops or killing livestock. Climate resilience planning is one approach to preparing for and mitigating the effects of extreme heat. Climate resilience planning in local communities involves several steps including assessing vulnerability and risk.© 2024 Adobe Stock. All rights reserved.It is a fact that trees can lower the surrounding air temperature through evapotranspiration, providing shade, and taking up space that might otherwise be converted to pavement. Lots of pavement, blacktop roads, and concrete buildings absorb the sun's heat and radiate that heat into the surrounding air. This is especially evident in highly developed urban areas which can get up to 20 degrees warmer than surrounding vegetated areas. These hot zones are referred to as Urban Heat Islands. One way to reduce the warmer temperatures in urban areas is to plant trees and other vegetation. This layer displays census tracts that are ranked according to which would benefit most from tree planting. The ranking is based upon a composite index built with the following attributes:High Summer Average Surface Temperature (°F)Percent of Tract Covered by Tree Canopy (%)Population Density (ppl/km2)These attribute links take you to the original data sources. Preprocessing was needed to prepare many of these inputs for inclusion in our index. The links are provided for reference only.This layer is one of a series developed to support local climate resilience planning. Intended as planning tools for policy makers, climate resilience planners, and community members, these layers highlight areas of the community that are most likely to benefit from the resilience intervention the map supports. Each layer focuses on one specific heat resilience intervention intended to help mitigate against the climate hazard.Planting trees along streets and over dark surfaces in urban areas is proven to reduce air temperature which helps to mitigate the impacts of urban heat islands. For more resources on extreme heat visit heat.gov where you can learn about the impacts of tree planting campaigns. The heat resilience index (HRI) and methodology were developed in collaboration with the U.S. Centers for Disease Control and Prevention (CDC) and the UC Davis, Department of Public Health.Layers in the Extreme Heat hazard intervention series include Where Will a Buddy Program Improve Urban Heat Health?Where Will Tree Planting Improve Urban Heat Health? Where Will Cooling Centers Improve Urban Heat Health?Did you know you can build your own climate resilience index or use ours and customize it? The Customize a climate resilience index Tutorial provides more information on the index and also walks you through steps for taking our index and customizing it to your needs so you can create intervention maps better suited to your location and sourced from your own higher resolution data. For more information about how Esri enriched the census tracts with exposure, demographic, and environmental data to create composite indices called intervention indices, please read this technical reference.This feature layer was created from the Climate Resilience Planning Census Tracts hosted feature layer view and is one of 18 similar intervention layers, all of which can be found in ArcGIS Living Atlas of the World.
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Population and housing information extracted from decennial census Public Law 94-171 redistricting summary files for Washington state for years 2000 and 2010.