Raczynski, K., Xavier, F., & Cartwright, J. H. (2025). GEO Tutorial: Joining Tables With Spatial Data. Mississippi State University: Geosystems Research Institute. [View Document]

GEO Tutorial Number of Pages: 6 Publication Date: 06/2025This work was supported through funding by the National Oceanic and Atmospheric Administration Regional Geospatial Modeling Grant, Award # NA19NOS4730207.

Author: Titus, Maxwell (mtitus@esri.com)Last Updated: 3/4/2025Intended Environment: ArcGIS ProPurpose: This Notebook was designed to automate updates for Hosted Feature Services hosted in ArcGIS Online (or ArcGIS Portal) from ArcGIS Pro and a spatial join of two live datasets.Description: This Notebook was designed to automate updates for Hosted Feature Services hosted in ArcGIS Online (or ArcGIS Portal) from ArcGIS Pro. An associated ArcGIS Dashboard would then reflect these updates. Specifically, this Notebook would:First, pull two datasets - National Weather Updates and Public Schools - from the Living Atlas and add them to an ArcGIS Pro map.Then, the Notebook would perform a spatial join on two layers to give Public Schools features information on whether they fell within an ongoing weather event or alert. Next, the Notebook would truncate the Hosted Feature Service in ArcGIS Online - that is, delete all the data - and then append the new data to the Hosted Feature ServiceAssociated Resources: This Notebook was used as part of the demo for FedGIS 2025. Below are the associated resources:Living Atlas Layer: NWS National Weather Events and AlertsLiving Atlas Layer: U.S. Public SchoolsArcGIS Demo Dashboard: Demo Impacted Schools Weather DashboardUpdatable Hosted Feature Service: HIFLD Public Schools with Event DataNotebook Requirements: This Notebook has the following requirements:This notebook requires ArcPy and is meant for use in ArcGIS Pro. However, it could be adjusted to work with Notebooks in ArcGIS Online or ArcGIS Portal with the advanced runtime.If running from ArcGIS Pro, connect ArcGIS Pro to the ArcGIS Online or ArcGIS Portal environment.Lastly, the user should have editable access to the hosted feature service to update.

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

Initial Data Capture: Building were originally digitized using ESRI construction tools such as rectangle and polygon. Textron Feature Analyst was then used to digitize buildings using a semi-automated polygon capture tool as well as a fully automated supervised learning method. The method that proved to be most effective was the semi-automated polygon capture tool as the fully automated process produced polygons that required extensive cleanup. This tool increased the speed and accuracy of digitizing by 40%.Purpose of Data Created: To supplement our GIS viewers with a searchable feature class of structures within Ventura County that can aid in analysis for multiple agencies and the public at large.Types of Data Used: Aerial Imagery (Pictometry 2015, 9inch ortho/oblique, Pictometry 2018, 6inch ortho/oblique) Simi Valley Lidar Data (Q2 Harris Corp Lidar) Coverage of Data:Buildings have been collected from the aerial imageries extent. The 2015 imagery coverage the south county from the north in Ojai to the south in thousand oaks, to the east in Simi Valley, and to the West in the county line with Santa Barbara. Lockwood Valley was also captured in the 2015 imagery. To collect buildings for the wilderness areas we needed to use the imagery from 2007 when we last flew aerial imagery for the entire county. 2018 Imagery was used to capture buildings that were built after 2015.Schema: Fields: APN, Image Date, Image Source, Building Type, Building Description, Address, City, Zip, Data Source, Parcel Data (Year Built, Basement yes/no, Number of Floors) Zoning Data (Main Building, Out Building, Garage), First Floor Elevation, Rough Building Height, X/Y Coordinates, Dimensions. Confidence Levels/Methods:Address data: 90% All Buildings should have an address if they appear to be a building that would normally need an address (Main Residence). To create an address, we do a spatial join on the parcels from the centroid of a building polygon and extract the address data and APN. To collect the missing addresses, we can do a spatial join between the master address and the parcels and then the parcels back to the building polygons. Using a summarize to the APN field we will be able to identify the parcels that have multiple buildings and delete the address information for the buildings that are not a main residence.Building Type Data: 99% All buildings should have a building type according to the site use category code provided from the parcel table information. To further classify multiple buildings on parcels in residential areas, the shape area field was used to identify building polygons greater than 600 square feet as an occupied residence and all other buildings less than that size as outbuildings. All parcels, inparticular parcels with multiple buildings, are subject to classification error. Further defining could be possible with extensive quality control APN Data: 98% All buildings have received APN data from their associated parcel after a spatial join was performed. Building overlapping parcel lines had their centroid derived which allowed for an accurate spatial join.Troubleshooting Required: Buildings would sometimes overlap parcel lines making spatial joining inaccurate. To fix this you create a point from the centroid of the building polygon, join the parcel information to the point, then join the point with the parcel information back to the building polygon.

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GIS Market Size 2025-2029

The GIS market size is forecast to increase by USD 24.07 billion, at a CAGR of 20.3% between 2024 and 2029.

The Global Geographic Information System (GIS) market is experiencing significant growth, driven by the increasing integration of Building Information Modeling (BIM) and GIS technologies. This convergence enables more effective spatial analysis and decision-making in various industries, particularly in soil and water management. However, the market faces challenges, including the lack of comprehensive planning and preparation leading to implementation failures of GIS solutions. Companies must address these challenges by investing in thorough project planning and collaboration between GIS and BIM teams to ensure successful implementation and maximize the potential benefits of these advanced technologies.
By focusing on strategic planning and effective implementation, organizations can capitalize on the opportunities presented by the growing adoption of GIS and BIM technologies, ultimately driving operational efficiency and innovation.

What will be the Size of the GIS Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The global Geographic Information Systems (GIS) market continues to evolve, driven by the increasing demand for advanced spatial data analysis and management solutions. GIS technology is finding applications across various sectors, including natural resource management, urban planning, and infrastructure management. The integration of Bing Maps, terrain analysis, vector data, Lidar data, and Geographic Information Systems enables precise spatial data analysis and modeling. Hydrological modeling, spatial statistics, spatial indexing, and route optimization are essential components of GIS, providing valuable insights for sectors such as public safety, transportation planning, and precision agriculture. Location-based services and data visualization further enhance the utility of GIS, enabling real-time mapping and spatial analysis.

The ongoing development of OGC standards, spatial data infrastructure, and mapping APIs continues to expand the capabilities of GIS, making it an indispensable tool for managing and analyzing geospatial data. The continuous unfolding of market activities and evolving patterns in the market reflect the dynamic nature of this technology and its applications.

How is this GIS Industry segmented?

The GIS industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Software
  Data
  Services


Type

  Telematics and navigation
  Mapping
  Surveying
  Location-based services


Device

  Desktop
  Mobile


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

The Global Geographic Information System (GIS) market encompasses a range of applications and technologies, including raster data, urban planning, geospatial data, geocoding APIs, GIS services, routing APIs, aerial photography, satellite imagery, GIS software, geospatial analytics, public safety, field data collection, transportation planning, precision agriculture, OGC standards, location intelligence, remote sensing, asset management, network analysis, spatial analysis, infrastructure management, spatial data standards, disaster management, environmental monitoring, spatial modeling, coordinate systems, spatial overlay, real-time mapping, mapping APIs, spatial join, mapping applications, smart cities, spatial data infrastructure, map projections, spatial databases, natural resource management, Bing Maps, terrain analysis, vector data, Lidar data, and geographic information systems.

The software segment includes desktop, mobile, cloud, and server solutions. Open-source GIS software, with its industry-specific offerings, poses a challenge to the market, while the adoption of cloud-based GIS software represents an emerging trend. However, the lack of standardization and interoperability issues hinder the widespread adoption of cloud-based solutions. Applications in sectors like public safety, transportation planning, and precision agriculture are driving market growth. Additionally, advancements in technologies like remote sensing, spatial modeling, and real-time mapping are expanding the market's scope.

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The Software segment was valued at USD 5.06 billion in 2019

For every address in the City of Kitchener, a GIS spatial join has been created to select the closest Park, Playground, Elementary School, etc

This data is utilized in the Lesson 1.1 What is Climate activity on the MI EnviroLearning Hub Climate Change page.Station data accessed was accessed from NOAA. Data was imported into ArcGIS Pro where Coordinate Table to Point was used to spatially enable the originating CSV. This feature service, which incorporates Census Designated Places from the U.S. Census Bureau’s 2020 Census Demographic and Housing Characteristics, was used to spatially join weather stations to the nearest incorporated area throughout Michigan.Email Egle-Maps@Michigan.gov for questions.Former name: MichiganStationswAvgs19912020_WithinIncoproatedArea_UpdatedName Display Name Field Name Description

STATION_ID MichiganStationswAvgs19912020_W Station ID where weather data is collected

STATION MichiganStationswAvgs19912020_1 Station name where weather data is collected

ELEVATION MichiganStationswAvgs19912020_6 Elevation above mean sea level-meters

MLY-PRCP-NORMAL MichiganStationswAvgs19912020_8 Long-term averages of monthly precipitation total-inches

MLY-TAVG-NORMAL MichiganStationswAvgs19912020_9 Long-term averages of monthly average temperature -F

OID MichiganStationswAvgs1991202_10 Object ID for weather dataset

Join_Count MichiganStationswAvgs1991202_11 Spatial join count of weather station data to specific weather station

TARGET_FID MichiganStationswAvgs1991202_12 Spatial Join ID

Current place ANSI code MichiganStationswAvgs1991202_13 Census codes for identification of geographic entities (used for join)

Geographic Identifier MichiganStationswAvgs1991202_14 Geographic identifier (used for join)

Current class code MichiganStationswAvgs1991202_15 Class (CLASSFP) code defines the current class of a geographic entity

Current functional status MichiganStationswAvgs1991202_16 Status of weather station

Area of Land (Square Meters) MichiganStationswAvgs1991202_17 Area of land in square meters

Area of Water (Square Meters) MichiganStationswAvgs1991202_18 Area of water in square meters

Current latitude of the internal point MichiganStationswAvgs1991202_19 Latitude

Current longitude of the internal point MichiganStationswAvgs1991202_20 Longitude

Name MichiganStationswAvgs1991202_21 Location name of weather station

Current consolidated city GNIS code MichiganStationswAvgs1991202_22 Geographic Names Information System for an incorporated area

OBJECTID MichiganStationswAvgs1991202_23 Object ID for point dataset

This dataset is one of several segments of a regional high detailed stream flowpath dataset. The data was separated using the TOPO 50 map series extents.The stream network was originally created for the purpose of high detailed work along rivers and streams in the Wellington region. It was started as a pilot study for the Mangatarere subcatchment of the Waiohine River for the Environmental Sciences department who was attempting to measure riparian vegetation. The data was sourced from a modelled stream network created using the 2013 LiDAR digital elevation model. Once the Mangatarere was complete the process was expanded to cover the entire region on an as needed basis for each whaitua. This dataset is one of several that shows the finished stream datasets for the Wairarapa region.The base stream network was created using a mixture of tools found in ArcGIS Spatial Analyst under Hydrology along with processes located in the Arc Hydro downloadable add-on for ArcGIS. The initial workflow for the data was based on the information derived from the help files provided at the Esri ArcGIS 10.1 online help files. The updated process uses the core Spatial Analyst tools to generate the streamlines while digital dams are corrected using the DEM Reconditioning tool provided by the Arc Hydro toolset. The whaitua were too large for processing separated into smaller units according to the subcatchments within it. In select cases like the Taueru subcatchment of the Ruamahanga these subcatchments need to be further defined to allow processing. The catchment boundaries available are not as precise as the LiDAR information which causes overland flows that are on edges of the catchments to become disjointed from each other and required manual correction.Attributes were added to the stream network using the River Environment Classification (REC) stream network from NIWA. The Spatial Join tool in Arcmap was used to add the Reach ID to each segment of the generated flow path. This ID was used to join a table which had been created by intersecting stream names (generated from a point feature class available from LINZ) with the REC subcatchment dataset. Both of the REC datasets are available from NIWA's website.

Data Source: The primary data source used for this analysis are point-level business establishment data from InfoUSA. This commercial database produced by InfoGroup provides a comprehensive list of businesses in the SCAG region, including their industrial classification, number of employees, and several additional fields. Data have been post-processed for accuracy by SCAG staff and have an effective date of 2016. Locally-weighted regression: First, the SCAG region is overlaid with a grid, or fishnet, of 1km, 2km, and ½-km per cell. At the 1km cell size, there are 16,959 cells covering the SCAG region. Using the Spatial Join feature in ArcGIS, a sum total of business establishments and total employees (i.e., not separated by industrial classification) were joined to each grid cell. Note that since cells are of a standard size, the employment total in a cell is the equivalent of the employment density. A locally-weighted regression (LWR) procedure was developed using the R Statistical Software package in order to identify subcenters. The below procedure is described for 1km grid cells, but was repeated for 2km and 1/2km cells. 1.) Identify local maxima candidates. Using R’s lwr package, each cell’s 120 nearest neighbors, corresponding to roughly 5.5 km in each direction, was explored to identify high outliers or local maxima based on the total employment field. Cells with a z-score of above 2.58 were considered local maxima candidates. 2.) Identify local maxima. LWR can result in local maxima existing within close proximity. This step used a .dbf-format spatial weights matrix (knn=120 nearest neighbors) to identify only cells which are higher than all of their 120 nearest neighbors. At the 1km scale, 84 local maxima were found, which will form the “peak” of each individual subcenter. 3.) Search adjacent cells to include as part of each subcenter. In order to find which cells also are part of each local maximum’s subcenter, we use a queen (adjacency) contiguity matrix to search adjacent cells up to 120 nearest neighbors, adding cells if they are also greater than the average density in their neighborhood. A total of 695 cells comprise subcenters at the 1km scale. A video from Kane et al. (2018) demonstrates the above aspects of the methodology (please refer to 0:35 through 2:35 of https://youtu.be/ylTWnvCCO54), with the following differences: - Different years and slightly different post-processing steps for InfoUSA data - Video study covers 5-county region (Imperial county not included) - Limited to 1km scale subcenters - Due to these differences, the final map of subcenters is different. A challenge arises in that using 1km grid cells may fail to identify the correct local maximum for a particularly large employment center whose experience of high density occurs over a larger area. The process was repeated at a 2km scale, resulting in 54 “coarse scaled” subcenters. Similarly, some centers may exist with a particularly tightly-packed area of dense employment which is not detectable at the medium, 1km scale. The process was repeated again with ½-km grid cells, resulting in 95 “fine scaled” subcenters. In many instances, boundaries of fine, medium, and coarse scaled subcenters were similar, but differences existed. The final step involved qualitatively comparing results at each scale to create the final map of 69 job centers across the region. Most centers are medium scale, but some known areas of especially employment density were better captured at the 2km scale while . Giuliano and Small’s (1991) “ten jobs per acre” threshold was used as a rough guide to test for reasonableness when choosing a larger or smaller scale. For example, in some instances, a 1km scale included much additional land which reduced job density well below 10 jobs per acre. In this instance, an overlapping or nearby 1/2km scaled center provided a better reflection of the local employment peak. Ultimately, the goal was to identify areas where job density is distinct from nearby areas.

This dataset represents the base (ground-level) outline, or footprint, of buildings and other man-made structures in Fulton County, Georgia. The original data were produced by digitizing structures from 1988 aerial ortho-photography. Updates to the data are made from various aerial ortho-photography. In 2010, the data table structures was modified to include a number of attributes derived from tax assessment data through a spatial join of structures with tax parcels. The attributes include feature type (residential or commercial), structure form (conventional, ranch, colonial, etc.), number of stories, and the year built. In 2012, updates to features began using building sketch data collected by the Fulton County Tax Assessors. The building sketch data consist of turtle graphics type descriptors defining (in ungeoreferenced space) the ground-level outline of each structure in the County. These descriptors were converted to an ESRI SDE feature class using Python, georeferencing each structure by placing it in the center of its associated tax parcel. Each structure shape was is then manually translated and rotated into position using aerial imagery as a reference. As of May 2014, this update process was still in progress.This dataset is used in large-scale mapping to show the location of individual buildings and other man-made structures and in smaller-scale mapping to show general patterns of development. May also be used to estimate human population for very small areas. Other applications include the computation of impervious surfaces in stormwater studies and the development of 3-D urban models.

Several previously published reports and geographic information system (GIS) data layers were used to code information on site attributes for each assessment plot using the spatial join tool in ArcMap. This information was used for an analysis of dieback and non-dieback habitat characteristics. The results of this analysis are presented in this table which depicts the probability of heavy to severe canopy dieback occurring at some time at a particular 30 x 30 m pixel location within the study area.

This specialized location dataset delivers detailed information about marina establishments. Maritime industry professionals, coastal planners, and tourism researchers can leverage precise location insights to understand maritime infrastructure, analyze recreational boating landscapes, and develop targeted strategies.

How Do We Create Polygons? -All our polygons are manually crafted using advanced GIS tools like QGIS, ArcGIS, and similar applications. This involves leveraging aerial imagery and street-level views to ensure precision. -Beyond visual data, our expert GIS data engineers integrate venue layout/elevation plans sourced from official company websites to construct detailed indoor polygons. This meticulous process ensures higher accuracy and consistency. -We verify our polygons through multiple quality checks, focusing on accuracy, relevance, and completeness.

What's More? -Custom Polygon Creation: Our team can build polygons for any location or category based on your specific requirements. Whether it’s a new retail chain, transportation hub, or niche point of interest, we’ve got you covered. -Enhanced Customization: In addition to polygons, we capture critical details such as entry and exit points, parking areas, and adjacent pathways, adding greater context to your geospatial data. -Flexible Data Delivery Formats: We provide datasets in industry-standard formats like WKT, GeoJSON, Shapefile, and GDB, making them compatible with various systems and tools. -Regular Data Updates: Stay ahead with our customizable refresh schedules, ensuring your polygon data is always up-to-date for evolving business needs.

Unlock the Power of POI and Geospatial Data With our robust polygon datasets and point-of-interest data, you can: -Perform detailed market analyses to identify growth opportunities. -Pinpoint the ideal location for your next store or business expansion. -Decode consumer behavior patterns using geospatial insights. -Execute targeted, location-driven marketing campaigns for better ROI. -Gain an edge over competitors by leveraging geofencing and spatial intelligence.

Why Choose LocationsXYZ? LocationsXYZ is trusted by leading brands to unlock actionable business insights with our spatial data solutions. Join our growing network of successful clients who have scaled their operations with precise polygon and POI data. Request your free sample today and explore how we can help accelerate your business growth.

This is a collaboration between City of Los Angeles Mayor's Office, StreetsLA, and USC. To consolidate / aggregate many datasets for Street Sweeping. Task 2: to perform spatial join between Centerlines and Tracts in order to get the Median HHI from 2018 Demographics.

Title of reference article:Nine Days of Naptown Arrests: How and Why Spatial Data Should Discomfort UsAuthor J. Kevin ByrneDate authored: August 23, 2020Abstract: During nine successive days in 2019 Indianapolis (IN) police made arrests across six districts. Exploratory spatial data analysis (ESDA) revealed how variables of arrests, race, aggressive use of force (UOF), injuries, and their location interact with each other. Scatterplots with R-squared values > 0.6 suggested aggressive UOF contributed to injuries of arrested residents across all races, Caucasian officers may have excessively injured arrested residents, and aggressive UOF correlated with arrests of African-Americans. Findings for parallel-coordinate-plots dove deeper in terms of spatial implications and ethical considerations (e.g., by visually demonstrating presence of a cluster of observed residents’ arrests as coinciding with African-American census geodemographics). This “small-sample” can surprise the reader. My conclusion proposed two aims: 1) solidify hypotheses (for further ESDA) that may induce ethical discomfort (a good thing) pertaining to the subject of structural racism, and 2) use findings to usher civic policymakers down more strident paths to sociocultural change.Indianapolis (IN) police districts and zones shapefiles that were made public by ESRI were used by way of my ESDA. Path to shapefiles’ source:http://data.indy.gov/datasets/indianapolis-police-zonesN.B.: Safari web-browser not recommended. Shapefile metadata are here: https://www.arcgis.com/home/item.html?id=b59421675f2a40fda9b00beeb875996fUsing GeoDa I did a spatial join that permitted my ESDA to analyze variables with scatterplots, PCPs, and datamaps. My final GeoDa file – titled NapWorksProj.gda – is herewith.Also herewith are my GeoDa's shapefiles – created natively – titled as follows:· NapWorks.cpg· NapWorks.dbf· NapWorks.prj· NapWorks.shp· NapWorks.shx

The Community Business Enterprise (CBE) Program encourages business owners who are minorities, women, disabled veterans, or disadvantaged to capitalize on opportunities in government and private-sector procurement programs. For more information, please see the LA County Department of Consumer & Business Affairs website. This layer is an extract of the CBE dataset in August 2021. In default settings, the layer is symbolized to highlight minority business enterprises (MBE). This layer is distinct from similar upload in that this layer has supervisor district (sup_dist) and service planning area (spa) as attributes, based on "center in" spatial join.

For more information about this dataset, please contact egis@isd.lacounty.gov

Summary of category 3 water pollution incidents reported to the Environment Agency are held on the National Incident Reporting System. Sum of incidents reported between 2001 and 2020 summarised by WFD Operational Catchment.Extracted from NIRS for Closed Category 3 and 4 Incidents classified as 3 and 4 in the Water Environmental Level code field from 01/01/2020 until date of extraction 20/05/2024. This data includes grid references for each incident. These Grid references were then used to map each Incident within ArcMap and analyse using the Spatial Join Tool how many incidents are located within each WFD Operational. Within the data tab shows a table of Counts of Category 3 and 4 Incidents within each WFD Operational Catchments from 01/01/2020 to data extraction date (20/05/2024).

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

For more information about LA County efforts to provide housing and tenant protections, see the DCBA website.This layer is distinct from similar upload in that this layer has supervisor district (sup_dist) and service planning area (spa) as attributes, based on "center in" spatial join.For more information about this dataset, please contact egis@isd.lacounty.gov

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This shapefile was constructed …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This shapefile was constructed by combining crown TSR spatial data, information gathered from Rural Lands Protection Board (RLPB) rangers, and surveyed Conservation and Biodiversity data to compile a layer within 30 RLPB districts in NSW. The layer attempts to spatially reflect current TSRs as accurately as possible with conservation attributes for each one. Dataset History The initial process in production involved using the most up to date extract of TSR from the crown spatial layer as a base map, as this layer should reasonably accurately spatially reflect the location, size, and attributes of TSR in NSW. This crown spatial layer from which the TSR were extracted is maintained by the NSW Department of Lands. The TSR extract is comprised of approximately 25,000 polygons in the study area. These polygons were then attributed with names, IDs and other attributes from the Long Paddock (LP) points layer produced by the RLPB State Council, which contains approximately 4000 named reserves throughout the study area. This layer reflects the names and ID number by which the reserves were or are currently managed by the RLPB's. This layer was spatially joined with the TSR polygon layer by proximity to produce a polygon layer attributed with RLPB reserve names and ID numbers. This process was repeated for other small datasets in order to link data with the polygon layer and LP reserve names. The next and by far the most time consuming and laborious process in the project was transferring the data gathered from surveys undertaken with RLPB rangers about each reserve (location, spatial extent, name, currency conservation value and biodiversity). This spatial information was annotated on hard copy maps and referenced against the spatial join making manual edits where necessary. Edits were conducted manually as the reference information was only on hard copy paper maps. Any corrections were made to the merged layer to produce an accurate spatial reflection of the RLPB reserves by name and ID. This manual editing process composed the bulk of the time for layer production as all reserves in each RLPB district in the study area had to be checked manually. Any necessary changes had to then be made to correct the spatial location of the reserve and ensure the correct ID was assigned for attributing the conservation data. In approximately 80% of cases the spatial join was correct, although this figure would be less where long chains of TSR polygons exist. The majority of time was devoted to making the numerous additions that needed to be incorporated. A spreadsheet based on the LP point layer was attributed with the LP point [OBJECTID] in order to produce a unique reference for each reserve so that conservation and biodiversity value data could be attributed against each reserve in the spatial layer being produced. Any new reserves were allocated [OBJECTID] number both in the GIS and the spreadsheet in order to create this link. All relevant data was entered into the spreadsheet and then edited to a suitable level to be attached as an attribute table. Field names were chosen and appropriate an interpretable data formats each field. The completed spreadsheet was then linked to the shapefile to produce a polygon TSR spatial layer containing all available conservation and biodiversity information. Any additional attribute were either entered manually or obtained by merging with other layers. Attributes for the final layer were selected for usability by those wishing to query valuable Conservation Value (CV) data for each reserve, along with a number of administrative attributes for locating and querying certain aspects of each parcel. Constant error checking was conducted throughout the process to ensure minimal error being transferred to the production. This was done manually, and also by running numerous spatial and attribute based queries to identify potential errors in the spatial layer being produced. Follow up phone calls were made to the rangers to identify exact localities of reserves where polygons could not be allocated due to missing or ambiguous information. If precise location data was provided, polygons could be added in, either from other crown spatial layers or from cadastre. These polygons were also attributed with the lowest confindex rating, as their status as crown land is unknown or doubtful. In some cases existing GIS layers had been created for certain areas. Murray RLPB has data where 400+ polygons do not exist in the current crown TSR extract. According to the rangers interviewed it was determined the majority of these TSR exist. This data was incorporated in the TSR polygon by merging the two layers and then assigning attributes in the normal way, ie by being given a LP Name and ID and then updated from the marked up hard copy maps. In the confidence index these are given a rating of 1 (see confindex matrix) due to the unknown source of the data and no match with any other crown spatial data. A confidence index matrix (confindex) was produced in order to give the end user of the GIS product an idea as to how the data for each reserve was obtained, its purpose, and an indication to whether it is likely to be a current TSR. The higher the confindex, the more secure the user can be in the data. (See Confidence Index Matrix) This was necessary due to conflicting information from a number of datasets, usually the RLPB ranger (mark up on hard copy map) conflicting with the crown spatial data. If these conflicting reserves were to be deleted, this would lead to a large amount of information loss during the project. If additions were made without sufficient data to determine its crown status, currency, location, etc (which was not available in all cases) the end user may rely on data that has a low level of accuracy. The confindex was produced by determining the value of information and scoring it accordingly, compounding its value if data sources showed a correlation. Where an RLPB LP Name and ID point was not assigned to a polygon due to other points being in closer proximity these names and ID are effectively deleted from the polygon layer. In a number of cases this was correct due to land being revoked, relinquished and/or now freehold. In a number of cases where the TSR is thought to exist and a polygon could not be assigned due to no info available (Lot/DP, close proximity to a crown reserve, further ranger interview provided no info, etc etc). For these cases to ensure no information loss a points layer was compiled from the LP points layer with further info from the marked up hard copy maps to place the point in the most accurate approximate location to where the reserve is though to exist and then all CV data attached to the point. In many of these cases some further investigation could provide an exact location and inclusion in the TSR poly layer. The accuracy of the point is mentioned in the metadata, so that the location is not taken as an absolute location and is only to be used as a guide for the approximate location of the reserve. Topology checks were conducted to eliminate slivers in the layer and to remove duplicate polygons. Where two crown reserves existed on the same land parcel, the duplicate polygon was deleted and unique attributes (Crown Reserve Number, Type, and Purpose) were transferred. Once the polygon layer was satisfactorily completed, a list of the LP points not allocated to polygons was compiled. Any points (reserves) that were said to have been revoked or relinquished were then removed from this list to provide a list of those that are said to be current. An extract of the LP points layer was then produced with only the aforementioned points. These points were then attributed with the same conservation and biodiversity data as the polygon layer, in an attempt to minimise the amount of information loss. Dataset Citation "NSW Department of Environment, Climate Change and Water" (2010) Travelling Stock Route Conservation Values. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/198900d5-0d06-4bd0-832b-e30a7c4e8873.