This is a collection of maps, layers, apps and dashboards that show population access to essential retail locations, such as grocery stores. Data sourcesPopulation data is from the 2010 U.S. Census blocks. Each census block has a count of stores within a 10 minute walk, and a count of stores within a ten minute drive. Census blocks known to be unpopulated are given a score of 0. The layer is available as a hosted feature layer.Grocery store locations are from SafeGraph, reflecting what was in the data as of October 2020. Access to the layer was obtained from the SafeGraph offering in ArcGIS Marketplace. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it already has the necessary attributes on each street segment to identify which streets are considered walkable, and supports a wide variety of driving parameters.The walkable access layer and drivable access layers are rasters, whose colors were chosen to allow the drivable access layer to serve as backdrop to the walkable access layer. Alternative versions of these layers are available. These pairs use different colors but are otherwise identical in content.Data PreparationArcGIS Network Analyst was used to set up a network street layer for analysis. ArcGIS StreetMap Premium was installed to a local hard drive and selected in the Origin-Destination workflow as the network data source. This allows the origins (Census block centroids) and destinations (SafeGraph grocery stores) to be connected to that network, to allow origin-destination analysis.The Census blocks layer contains the centroid of each Census block. The data allows a simple popup to be created. This layer's block figures can be summarized further, to tract, county and state levels.The SafeGraph grocery store locations were created by querying the SafeGraph source layer based on primary NAICS code. After connecting to the layer in ArcGIS Pro, a definition query was set to only show records with NAICS code 445110 as an initial screening. The layer was exported to a local disk drive for further definition query refinement, to eliminate any records that were obviously not grocery stores. The final layer used in the analysis had approximately 53,600 records. In this map, this layer is included as a vector tile layer.MethodologyEvery census block in the U.S. was assigned two access scores, whose numbers are simply how many grocery stores are within a 10 minute walk and a 10 minute drive of that census block. Every census block has a score of 0 (no stores), 1, 2 or more stores. The count of accessible stores was determined using Origin-Destination Analysis in ArcGIS Network Analyst, in ArcGIS Pro. A set of Tools in this ArcGIS Pro package allow a similar analysis to be conducted for any city or other area. The Tools step through the data prep and analysis steps. Download the Pro package, open it and substitute your own layers for Origins and Destinations. Parcel centroids are a suggested option for Origins, for example. Origin-Destination analysis was configured, using ArcGIS StreetMap Premium as the network data source. Census block centroids with population greater than zero were used as the Origins, and grocery store locations were used as the Destinations. A cutoff of 10 minutes was used with the Walk Time option. Only one restriction was applied to the street network: Walkable, which means Interstates and other non-walkable street segments were treated appropriately. You see the results in the map: wherever freeway overpasses and underpasses are present near a grocery store, the walkable area extends across/through that pass, but not along the freeway.A cutoff of 10 minutes was used with the Drive Time option. The default restrictions were applied to the street network, which means a typical vehicle's access to all types of roads was factored in.The results for each analysis were captured in the Lines layer, which shows which origins are within the cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle).The Lines layer was then summarized by census block ID to capture the Maximum value of the Destination_Rank field. A census block within 10 minutes of 3 stores would have 3 records in the Lines layer, but only one value in the summarized table, with a MAX_Destination_Rank field value of 3. This is the number of stores accessible to that census block in the 10 minutes measured, for walking and driving. These data were joined to the block centroids layer and given unique names. At this point, all blocks with zero population or null values in the MAX_Destination_Rank fields were given a store count of 0, to help the next step.Walkable and Drivable areas are calculated into a raster layer, using Nearest Neighbor geoprocessing tool on the count of stores within a 10 minute walk, and a count of stores within a ten minute drive, respectively. This tool uses a 200 meter grid and interpolates the values between each census block. A census tracts layer containing all water polygons "erased" from the census tract boundaries was used as an environment setting, to help constrain interpolation into/across bodies of water. The same layer use used to "shoreline" the Nearest Neighbor results, to eliminate any interpolation into the ocean or Great Lakes. This helped but was not perfect.Notes and LimitationsThe map provides a baseline for discussing access to grocery stores in a city. It does not presume local population has the desire or means to walk or drive to obtain groceries. It does not take elevation gain or loss into account. It does not factor time of day nor weather, seasons, or other variables that affect a person's commute choices. Walking and driving are just two ways people get to a grocery store. Some people ride a bike, others take public transit, have groceries delivered, or rely on a friend with a vehicle. Thank you to Melinda Morang on the Network Analyst team for guidance and suggestions at key moments along the way; to Emily Meriam for reviewing the previous version of this map and creating new color palettes and marker symbols specific to this project. Additional ReadingThe methods by which access to food is measured and reported have improved in the past decade or so, as has the uses of such measurements. Some relevant papers and articles are provided below as a starting point.Measuring Food Insecurity Using the Food Abundance Index: Implications for Economic, Health and Social Well-BeingHow to Identify Food Deserts: Measuring Physical and Economic Access to Supermarkets in King County, WashingtonAccess to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their ConsequencesDifferent Measures of Food Access Inform Different SolutionsThe time cost of access to food – Distance to the grocery store as measured in minutes

To create this layer, OCTO staff used ABCA's definition of “Full-Service Grocery Stores” (https://abca.dc.gov/page/full-service-grocery-store#gsc.tab=0)– pulled from the Food System Assessment below), and using those criteria, determined locations that fulfilled the categories in section 1 of the definition.Then, staff reviewed the Office of Planning’s Food System Assessment (https://dcfoodpolicycouncilorg.files.wordpress.com/2019/06/2018-food-system-assessment-final-6.13.pdf) list in Appendix D, comparing that to the created from the ABCA definition, which led to the addition of a additional examples that meet, or come very close to, the full-service grocery store criteria. The explanation from Office of Planning regarding how the agency created their list:“To determine the number of grocery stores in the District, we analyzed existing business licenses in the Department of Consumer and Regulatory Affairs (2018) Business License Verification system (located at https://eservices.dcra.dc.gov/BBLV/Default.aspx). To distinguish grocery stores from convenience stores, we applied the Alcohol Beverage and Cannabis Administration’s (ABCA) definition of a full-service grocery store. This definition requires a store to be licensed as a grocery store, sell at least six different food categories, dedicate either 50% of the store’s total square feet or 6,000 square feet to selling food, and dedicate at least 5% of the selling area to each food category. This definition can be found at https://abca.dc.gov/page/full-service-grocery-store#gsc.tab=0. To distinguish small grocery stores from large grocery stores, we categorized large grocery stores as those 10,000 square feet or more. This analysis was conducted using data from the WDCEP’s Retail and Restaurants webpage (located at https://wdcep.com/dc-industries/retail/) and using ARCGIS Spatial Analysis tools when existing data was not available. Our final numbers differ slightly from existing reports like the DC Hunger Solutions’ Closing the Grocery Store Gap and WDCEP’s Grocery Store Opportunities Map; this difference likely comes from differences in our methodology and our exclusion of stores that have closed.”Staff also conducted a visual analysis of locations and relied on personal experience of visits to locations to determine whether they should be included in the list.

Executive Summary of the 2024 Retail Market Analysis. This report examines the retail market to identify new opportunities for advancing the retail services available in the community.

Snapshot img

Geographic Information System Analytics Market Size 2024-2028

The geographic information system analytics market size is forecast to increase by USD 12 billion at a CAGR of 12.41% between 2023 and 2028.

The GIS Analytics Market analysis is experiencing significant growth, driven by the increasing need for efficient land management and emerging methods in data collection and generation. The defense industry's reliance on geospatial technology for situational awareness and real-time location monitoring is a major factor fueling market expansion. Additionally, the oil and gas industry's adoption of GIS for resource exploration and management is a key trend. Building Information Modeling (BIM) and smart city initiatives are also contributing to market growth, as they require multiple layered maps for effective planning and implementation. The Internet of Things (IoT) and Software as a Service (SaaS) are transforming GIS analytics by enabling real-time data processing and analysis.
Augmented reality is another emerging trend, as it enhances the user experience and provides valuable insights through visual overlays. Overall, heavy investments are required for setting up GIS stations and accessing data sources, making this a promising market for technology innovators and investors alike.

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

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The geographic information system analytics market encompasses various industries, including government sectors, agriculture, and infrastructure development. Smart city projects, building information modeling, and infrastructure development are key areas driving market growth. Spatial data plays a crucial role in sectors such as transportation, mining, and oil and gas. Cloud technology is transforming GIS analytics by enabling real-time data access and analysis. Startups are disrupting traditional GIS markets with innovative location-based services and smart city planning solutions. Infrastructure development in sectors like construction and green buildings relies on modern GIS solutions for efficient planning and management. Smart utilities and telematics navigation are also leveraging GIS analytics for improved operational efficiency.
GIS technology is essential for zoning and land use management, enabling data-driven decision-making. Smart public works and urban planning projects utilize mapping and geospatial technology for effective implementation. Surveying is another sector that benefits from advanced GIS solutions. Overall, the GIS analytics market is evolving, with a focus on providing actionable insights to businesses and organizations.

How is this Geographic Information System Analytics Industry segmented?

The geographic information system analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Retail and Real Estate
  Government
  Utilities
  Telecom
  Manufacturing and Automotive
  Agriculture
  Construction
  Mining
  Transportation
  Healthcare
  Defense and Intelligence
  Energy
  Education and Research
  BFSI


Components

  Software
  Services


Deployment Modes

  On-Premises
  Cloud-Based


Applications

  Urban and Regional Planning
  Disaster Management
  Environmental Monitoring Asset Management
  Surveying and Mapping
  Location-Based Services
  Geospatial Business Intelligence
  Natural Resource Management


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    South Korea


  Middle East and Africa

    UAE


  South America

    Brazil


  Rest of World

By End-user Insights

The retail and real estate segment is estimated to witness significant growth during the forecast period.

The GIS analytics market analysis is witnessing significant growth due to the increasing demand for advanced technologies in various industries. In the retail sector, for instance, retailers are utilizing GIS analytics to gain a competitive edge by analyzing customer demographics and buying patterns through real-time location monitoring and multiple layered maps. The retail industry's success relies heavily on these insights for effective marketing strategies. Moreover, the defense industries are integrating GIS analytics into their operations for infrastructure development, permitting, and public safety. Building Information Modeling (BIM) and 4D GIS software are increasingly being adopted for construction project workflows, while urban planning and designing require geospatial data for smart city planning and site selection.

The oil and gas industry is leveraging satellite imaging and IoT devices for land acquisition and mining operations. In the public sector,

This layer shows which parts of the United States and Puerto Rico fall within ten minutes' walk of one or more grocery stores. It is estimated that 20% of U.S. population live within a 10 minute walk of a grocery store, and 92% of the population live within a 10 minute drive of a grocery store. The layer is suitable for looking at access at a neighborhood scale.When you add this layer to your web map, along with the drivable access layer and the SafeGraph grocery store layer, it becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. Add the Census block points layer to show a popup with the count of stores within 10 minutes' walk and drive. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or don't own a car?How to Use This Layer in a Web MapUse this layer in a web map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying. See this example web map which you can use in your projects, storymaps, apps and dashboards.The layer was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access.Lastly, this layer can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population's grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples).The layer is a useful visual resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved.Data sourcesPopulation data is from the 2010 U.S. Census blocks. Each census block has a count of stores within a 10 minute walk, and a count of stores within a ten minute drive. Census blocks known to be unpopulated are given a score of 0. The layer is available as a hosted feature layer.Grocery store locations are from SafeGraph, reflecting what was in the data as of October 2020. Access to the layer was obtained from the SafeGraph offering in ArcGIS Marketplace. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it already has the necessary attributes on each street segment to identify which streets are considered walkable, and supports a wide variety of driving parameters.The walkable access layer and drivable access layers are rasters, whose colors were chosen to allow the drivable access layer to serve as backdrop to the walkable access layer. Alternative versions of these layers are available. These pairs use different colors but are otherwise identical in content.Data PreparationArcGIS Network Analyst was used to set up a network street layer for analysis. ArcGIS StreetMap Premium was installed to a local hard drive and selected in the Origin-Destination workflow as the network data source. This allows the origins (Census block centroids) and destinations (SafeGraph grocery stores) to be connected to that network, to allow origin-destination analysis.The Census blocks layer contains the centroid of each Census block. The data allows a simple popup to be created. This layer's block figures can be summarized further, to tract, county and state levels.The SafeGraph grocery store locations were created by querying the SafeGraph source layer based on primary NAICS code. After connecting to the layer in ArcGIS Pro, a definition query was set to only show records with NAICS code 445110 as an initial screening. The layer was exported to a local disk drive for further definition query refinement, to eliminate any records that were obviously not grocery stores. The final layer used in the analysis had approximately 53,600 records. In this map, this layer is included as a vector tile layer.MethodologyEvery census block in the U.S. was assigned two access scores, whose numbers are simply how many grocery stores are within a 10 minute walk and a 10 minute drive of that census block. Every census block has a score of 0 (no stores), 1, 2 or more stores. The count of accessible stores was determined using Origin-Destination Analysis in ArcGIS Network Analyst, in ArcGIS Pro. A set of Tools in this ArcGIS Pro package allow a similar analysis to be conducted for any city or other area. The Tools step through the data prep and analysis steps. Download the Pro package, open it and substitute your own layers for Origins and Destinations. Parcel centroids are a suggested option for Origins, for example. Origin-Destination analysis was configured, using ArcGIS StreetMap Premium as the network data source. Census block centroids with population greater than zero were used as the Origins, and grocery store locations were used as the Destinations. A cutoff of 10 minutes was used with the Walk Time option. Only one restriction was applied to the street network: Walkable, which means Interstates and other non-walkable street segments were treated appropriately. You see the results in the map: wherever freeway overpasses and underpasses are present near a grocery store, the walkable area extends across/through that pass, but not along the freeway.A cutoff of 10 minutes was used with the Drive Time option. The default restrictions were applied to the street network, which means a typical vehicle's access to all types of roads was factored in.The results for each analysis were captured in the Lines layer, which shows which origins are within the cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle).The Lines layer was then summarized by census block ID to capture the Maximum value of the Destination_Rank field. A census block within 10 minutes of 3 stores would have 3 records in the Lines layer, but only one value in the summarized table, with a MAX_Destination_Rank field value of 3. This is the number of stores accessible to that census block in the 10 minutes measured, for walking and driving. These data were joined to the block centroids layer and given unique names. At this point, all blocks with zero population or null values in the MAX_Destination_Rank fields were given a store count of 0, to help the next step.Walkable and Drivable areas are calculated into a raster layer, using Nearest Neighbor geoprocessing tool on the count of stores within a 10 minute walk, and a count of stores within a ten minute drive, respectively. This tool uses a 200 meter grid and interpolates the values between each census block. A census tracts layer containing all water polygons "erased" from the census tract boundaries was used as an environment setting, to help constrain interpolation into/across bodies of water. The same layer use used to "shoreline" the Nearest Neighbor results, to eliminate any interpolation into the ocean or Great Lakes. This helped but was not perfect.Notes and LimitationsThe map provides a baseline for discussing access to grocery stores in a city. It does not presume local population has the desire or means to walk or drive to obtain groceries. It does not take elevation gain or loss into account. It does not factor time of day nor weather, seasons, or other variables that affect a person's commute choices. Walking and driving are just two ways people get to a grocery store. Some people ride a bike, others take public transit, have groceries delivered, or rely on a friend with a vehicle. Thank you to Melinda Morang on the Network Analyst team for guidance and suggestions at key moments along the way; to Emily Meriam for reviewing the previous version of this map and creating new color palettes and marker symbols specific to this project. Additional ReadingThe methods by which access to food is measured and reported have improved in the past decade or so, as has the uses of such measurements. Some relevant papers and articles are provided below as a starting point.Measuring Food Insecurity Using the Food Abundance Index: Implications for Economic, Health and Social Well-BeingHow to Identify Food Deserts: Measuring Physical and Economic Access to Supermarkets in King County, WashingtonAccess to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their ConsequencesDifferent Measures of Food Access Inform Different SolutionsThe time cost of access to food – Distance to the grocery store as measured in minutes

Then, we reviewed the Office of Planning’s Food System Assessment (https://dcfoodpolicycouncilorg.files.wordpress.com/2019/06/2018-food-system-assessment-final-6.13.pdf) list in Appendix D, comparing that to the created from the ABCA definition, which led to the addition of a few more examples that meet or come very close to the full-service grocery store criteria. Here’s the explanation from OP regarding how they came to create their list:“To determine the number of grocery stores in the District, we analyzed existing business licenses in the Department of Consumer and Regulatory Affairs (2018) Business License Verification system (located at https://eservices.dcra.dc.gov/BBLV/Default.aspx). To distinguish grocery stores from convenience stores, we applied the Alcohol Beverage and Cannabis Administration’s (ABCA) definition of a full-service grocery store. This definition requires a store to be licensed as a grocery store, sell at least six different food categories, dedicate either 50% of the store’s total square feet or 6,000 square feet to selling food, and dedicate at least 5% of the selling area to each food category. This definition can be found at https://abca.dc.gov/page/full-service-grocery-store#gsc.tab=0. To distinguish small grocery stores from large grocery stores, we categorized large grocery stores as those 10,000 square feet or more. This analysis was conducted using data from the WDCEP’s Retail and Restaurants webpage (located at https://wdcep.com/dc-industries/retail/) and using ARCGIS Spatial Analysis tools when existing data was not available. Our final numbers differ slightly from existing reports like the DC Hunger Solutions’ Closing the Grocery Store Gap and WDCEP’s Grocery Store Opportunities Map; this difference likely comes from differences in our methodology and our exclusion of stores that have closed.”We also conducted a visual analysis of locations and relied on personal experience of visits to locations to determine whether they should be included in the list.

This Map Journal storymap and the maps within it, was entirely built using ArcGIS Online spatial analysis tools. It describes how retailers can develop new insights and understand by using the lens of location to add new information about their customers.CRM data for anonymous customers in the San Francisco Bay Area is aggregated, geoenriched and analyzed to look at the similarities and differences between individual customers, groups of customers and their neighbors. These variations can explain shopping patterns and observed behaviors as well as enable predictive analysis to identify customer activity and improve store performance.Developed by the Esri Commercial Marketing Team

The global digital indoor map market is experiencing robust growth, driven by the increasing adoption of indoor navigation systems in various sectors. The market size is projected to reach $XXX million by 2033, expanding at a CAGR of XX% over the forecast period of 2025-2033. Key drivers include the rising demand for indoor mapping solutions in public sector agencies and retail establishments, the integration of advanced technologies such as augmented reality and artificial intelligence, and the growing emphasis on indoor safety and security measures. Segmentation of the digital indoor map market reveals distinct application areas including automotive navigation, mobile and the internet, public sector agencies and enterprises, and others. Types of digital indoor maps available in the market include retail indoor maps, airport indoor maps, and others. Geographic regions considered in the analysis include North America, South America, Europe, Middle East & Africa, and Asia Pacific. Leading companies operating in the digital indoor map market include WoNoBo, Bing Maps, GeoMapserver, MapQuest, ArcGIS Online, and Yahoo! Maps, among others.

The Indoor Location-based Services Market size was valued at USD 17.99 billion in 2023 and is projected to reach USD 45.29 billion by 2032, exhibiting a CAGR of 14.1 % during the forecasts period. The Indoor Location-based Service (ILBS) market comprises range technologies that are used for location detection and for navigation of indoor infrastructure through services like Wi-Fi, Bluetooth, RFID and GPS. They are employed in various industries including retail, healthcare, third-party logistics and transportation with the view of improving on customer experience, operations and safety respectively. Uses include location-based service and optimization; embracing use of indoor navigation, identification of assets, location of analytics, and proximity marketing to enable timely offering of services as well as efficiency in service delivery. Some of the trends in the ILBS market currently are enhanced by the use of augmented reality, especially for way-finding, the growth in the use of IoT devices, and the improvement of machine learning algorithms for precise positioning. With the growth of urbanisation, demand for Indoor Navigation System solutions will be very high. Recent developments include: In December 2022, Oriient New Media Ltd, a software-based IndoorGPS provider that utilizes Earth's magnetic field, launched its solution on the Google Cloud Marketplace. It enables customers with existing commitments on the platform to procure and receive consolidated billing through a single channel conveniently. The availability of Oriient's solution on the marketplace aims to assist retailers in enhancing the in-store shopping experience by incorporating additional digital touchpoints and leveraging customer journey analytics for operational improvements and increased profitability. , In September 2022, Inpixon, an indoor intelligence company, joined forces with Schauenburg Systems, an OEM specializing in mine safety systems and equipment. The collaboration aims to promote Inpixon's real-time location technologies to mining companies operating in South Africa. The agreement focuses on selling numerous nanoLOC chips and other core technologies from Inpixon that provide collision avoidance, offering real-time tracking and proximity applications for the mining industry. , In July 2022, Esri, a geographic information system company, launched ArcGIS IPS, an indoor positioning system designed for facility wayfinding. This innovative solution empowers organizations to incorporate indoor location services throughout their facilities and campuses. By utilizing ArcGIS IPS, maintenance workers can efficiently review service requests and promptly identify and navigate to specific locations inside buildings where repairs are required. , In January 2022, HERE, a location data and technology platform, launched Indoor Map as a service. This comprehensive solution for indoor mapping is made possible through a partnership with Navenio, a software company. By leveraging this technology, businesses can create high-definition, 3D indoor maps of their buildings, accurately representing real-world data. .

This layer shows which parts of the United States and Puerto Rico fall within ten minutes' walk of one or more grocery stores. It is estimated that 20% of U.S. population live within a 10 minute walk of a grocery store, and 92% of the population live within a 10 minute drive of a grocery store. The layer is suitable for looking at access at a neighborhood scale.When you add this layer to your web map, along with the drivable access layer and the SafeGraph grocery store layer, it becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. Add the Census block points layer to show a popup with the count of stores within 10 minutes' walk and drive. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or don't own a car?How to Use This Layer in a Web MapUse this layer in a web map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying. See this example web map which you can use in your projects, storymaps, apps and dashboards.The layer was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access.Lastly, this layer can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population's grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples).The layer is a useful visual resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved.Data sourcesPopulation data is from the 2010 U.S. Census blocks. Each census block has a count of stores within a 10 minute walk, and a count of stores within a ten minute drive. Census blocks known to be unpopulated are given a score of 0. The layer is available as a hosted feature layer.Grocery store locations are from SafeGraph, reflecting what was in the data as of October 2020. Access to the layer was obtained from the SafeGraph offering in ArcGIS Marketplace. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it already has the necessary attributes on each street segment to identify which streets are considered walkable, and supports a wide variety of driving parameters.The walkable access layer and drivable access layers are rasters, whose colors were chosen to allow the drivable access layer to serve as backdrop to the walkable access layer. Alternative versions of these layers are available. These pairs use different colors but are otherwise identical in content.Data PreparationArcGIS Network Analyst was used to set up a network street layer for analysis. ArcGIS StreetMap Premium was installed to a local hard drive and selected in the Origin-Destination workflow as the network data source. This allows the origins (Census block centroids) and destinations (SafeGraph grocery stores) to be connected to that network, to allow origin-destination analysis.The Census blocks layer contains the centroid of each Census block. The data allows a simple popup to be created. This layer's block figures can be summarized further, to tract, county and state levels.The SafeGraph grocery store locations were created by querying the SafeGraph source layer based on primary NAICS code. After connecting to the layer in ArcGIS Pro, a definition query was set to only show records with NAICS code 445110 as an initial screening. The layer was exported to a local disk drive for further definition query refinement, to eliminate any records that were obviously not grocery stores. The final layer used in the analysis had approximately 53,600 records. In this map, this layer is included as a vector tile layer.MethodologyEvery census block in the U.S. was assigned two access scores, whose numbers are simply how many grocery stores are within a 10 minute walk and a 10 minute drive of that census block. Every census block has a score of 0 (no stores), 1, 2 or more stores. The count of accessible stores was determined using Origin-Destination Analysis in ArcGIS Network Analyst, in ArcGIS Pro. A set of Tools in this ArcGIS Pro package allow a similar analysis to be conducted for any city or other area. The Tools step through the data prep and analysis steps. Download the Pro package, open it and substitute your own layers for Origins and Destinations. Parcel centroids are a suggested option for Origins, for example. Origin-Destination analysis was configured, using ArcGIS StreetMap Premium as the network data source. Census block centroids with population greater than zero were used as the Origins, and grocery store locations were used as the Destinations. A cutoff of 10 minutes was used with the Walk Time option. Only one restriction was applied to the street network: Walkable, which means Interstates and other non-walkable street segments were treated appropriately. You see the results in the map: wherever freeway overpasses and underpasses are present near a grocery store, the walkable area extends across/through that pass, but not along the freeway.A cutoff of 10 minutes was used with the Drive Time option. The default restrictions were applied to the street network, which means a typical vehicle's access to all types of roads was factored in.The results for each analysis were captured in the Lines layer, which shows which origins are within the cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle).The Lines layer was then summarized by census block ID to capture the Maximum value of the Destination_Rank field. A census block within 10 minutes of 3 stores would have 3 records in the Lines layer, but only one value in the summarized table, with a MAX_Destination_Rank field value of 3. This is the number of stores accessible to that census block in the 10 minutes measured, for walking and driving. These data were joined to the block centroids layer and given unique names. At this point, all blocks with zero population or null values in the MAX_Destination_Rank fields were given a store count of 0, to help the next step.Walkable and Drivable areas are calculated into a raster layer, using Nearest Neighbor geoprocessing tool on the count of stores within a 10 minute walk, and a count of stores within a ten minute drive, respectively. This tool uses a 200 meter grid and interpolates the values between each census block. A census tracts layer containing all water polygons "erased" from the census tract boundaries was used as an environment setting, to help constrain interpolation into/across bodies of water. The same layer use used to "shoreline" the Nearest Neighbor results, to eliminate any interpolation into the ocean or Great Lakes. This helped but was not perfect.Notes and LimitationsThe map provides a baseline for discussing access to grocery stores in a city. It does not presume local population has the desire or means to walk or drive to obtain groceries. It does not take elevation gain or loss into account. It does not factor time of day nor weather, seasons, or other variables that affect a person's commute choices. Walking and driving are just two ways people get to a grocery store. Some people ride a bike, others take public transit, have groceries delivered, or rely on a friend with a vehicle. Thank you to Melinda Morang on the Network Analyst team for guidance and suggestions at key moments along the way; to Emily Meriam for reviewing the previous version of this map and creating new color palettes and marker symbols specific to this project. Additional ReadingThe methods by which access to food is measured and reported have improved in the past decade or so, as has the uses of such measurements. Some relevant papers and articles are provided below as a starting point.Measuring Food Insecurity Using the Food Abundance Index: Implications for Economic, Health and Social Well-BeingHow to Identify Food Deserts: Measuring Physical and Economic Access to Supermarkets in King County, WashingtonAccess to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their ConsequencesDifferent Measures of Food Access Inform Different SolutionsThe time cost of access to food – Distance to the grocery store as measured in minutes

Measure and Map Access to Grocery StoresFrom the perspective of the people living in each neighborhoodHow do people in your city get to the grocery store? The answer to that question depends on the person and where they live. This web map helps answer the question in this app.Some live in cities and stop by a grocery store within a short walk or bike ride of home or work. Others live in areas where car ownership is more prevalent, and so they drive to a store. Some do not own a vehicle, and rely on a friend or public transit. Others rely on grocery delivery for their needs. And, many live in rural areas far from town, so a trip to a grocery store is an infrequent event involving a long drive.This map from Esri shows which areas are within a ten minute walk or ten minute drive of a grocery store in the United States and Puerto Rico. Darker color indicates access to more stores. The chart shows how many people can walk to a grocery store if they wanted to or needed to.It is estimated that 20% of U.S. population live within a 10 minute walk of a grocery store, and 92% of the population live within a 10 minute drive of a grocery store.Look up your city to see how the numbers change as you move around the map. Or, draw a neighborhood boundary on the map to get numbers for that area.Every census block is scored with a count of walkable and drivable stores nearby, making this a map suitable for a dashboard for any city, or any of the 50 states, DC and Puerto Rico. Two colorful layers visualize this definition of access, one for walkable access (suitable for looking at a city neighborhood by neighborhood) and one for drivable access (suitable for looking across a city, county, region or state).On the walkable layer, shades of green define areas within a ten minute walk of one or more grocery stores. The colors become more intense and trend to a blue-green color for the busiest neighborhoods, such as downtown San Francisco. As you zoom in, a layer of Census block points visualizes the local population with or without walkable access.As you zoom out to see the entire city, the map adds a light blue - to dark blue layer, showing which parts of the region fall within ten minutes' drive of one or more grocery stores. As a result, the map is useful at all scales, from national to regional, state and local levels. It becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or don't own a car?How to Use This MapUse this map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying.The map was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access.There is data behind the map, which can be summarized to show how many people have walkable access to local grocery stores. The map includes a feature layer of population in Census block points, which are visible when you zoom in far enough. This feature layer can be plugged into an app like this one that summarizes the population with/without walkable or drivable access.Lastly, this map can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population's grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples).The map is a useful visual and analytic resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved.Data sourcesPopulation data is from the 2010 U.S. Census blocks. Each census block has a count of stores within a 10 minute walk, and a count of stores within a ten minute drive. Census blocks known to be unpopulated are given a score of 0. The layer is available as a hosted feature layer.Grocery store locations are from SafeGraph, reflecting what was in the data as of October 2020. Access to the layer was obtained from the SafeGraph offering in ArcGIS Marketplace. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it already has the necessary attributes on each street segment to identify which streets are considered walkable, and supports a wide variety of driving parameters.The walkable access layer and drivable access layers are rasters, whose colors were chosen to allow the drivable access layer to serve as backdrop to the walkable access layer. Alternative versions of these layers are available. These pairs use different colors but are otherwise identical in content.Data PreparationArcGIS Network Analyst was used to set up a network street layer for analysis. ArcGIS StreetMap Premium was installed to a local hard drive and selected in the Origin-Destination workflow as the network data source. This allows the origins (Census block centroids) and destinations (SafeGraph grocery stores) to be connected to that network, to allow origin-destination analysis.The Census blocks layer contains the centroid of each Census block. The data allows a simple popup to be created. This layer's block figures can be summarized further, to tract, county and state levels.The SafeGraph grocery store locations were created by querying the SafeGraph source layer based on primary NAICS code. After connecting to the layer in ArcGIS Pro, a definition query was set to only show records with NAICS code 445110 as an initial screening. The layer was exported to a local disk drive for further definition query refinement, to eliminate any records that were obviously not grocery stores. The final layer used in the analysis had approximately 53,600 records. In this map, this layer is included as a vector tile layer.MethodologyEvery census block in the U.S. was assigned two access scores, whose numbers are simply how many grocery stores are within a 10 minute walk and a 10 minute drive of that census block. Every census block has a score of 0 (no stores), 1, 2 or more stores. The count of accessible stores was determined using Origin-Destination Analysis in ArcGIS Network Analyst, in ArcGIS Pro. A set of Tools in this ArcGIS Pro package allow a similar analysis to be conducted for any city or other area. The Tools step through the data prep and analysis steps. Download the Pro package, open it and substitute your own layers for Origins and Destinations. Parcel centroids are a suggested option for Origins, for example. Origin-Destination analysis was configured, using ArcGIS StreetMap Premium as the network data source. Census block centroids with population greater than zero were used as the Origins, and grocery store locations were used as the Destinations. A cutoff of 10 minutes was used with the Walk Time option. Only one restriction was applied to the street network: Walkable, which means Interstates and other non-walkable street segments were treated appropriately. You see the results in the map: wherever freeway overpasses and underpasses are present near a grocery store, the walkable area extends across/through that pass, but not along the freeway.A cutoff of 10 minutes was used with the Drive Time option. The default restrictions were applied to the street network, which means a typical vehicle's access to all types of roads was factored in.The results for each analysis were captured in the Lines layer, which shows which origins are within the cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle).The Lines layer was then summarized by census block ID to capture the Maximum value of the Destination_Rank field. A census block within 10 minutes of 3 stores would have 3 records in the Lines layer, but only one value in the summarized table, with a MAX_Destination_Rank field value of 3. This is the number of stores accessible to that census block in the 10 minutes measured, for walking and driving. These data were joined to the block centroids layer and given unique names. At this point, all blocks with zero population or null values in the MAX_Destination_Rank fields were given a store count of 0, to help the next step.Walkable and Drivable areas are calculated into a raster layer, using Nearest Neighbor geoprocessing tool on the count of stores within a 10 minute walk, and a count of stores within a ten minute drive, respectively. This tool uses a 200 meter grid and interpolates the values between each census block. A census tracts layer containing all water polygons "erased" from the census tract boundaries was used as an environment setting, to help constrain interpolation into/across bodies of water. The same layer use used to "shoreline" the Nearest Neighbor results, to eliminate any interpolation into the ocean or Great Lakes. This helped but was not perfect.Notes and LimitationsThe map provides a baseline for discussing access to grocery stores in a city. It does not presume local population has the desire or means to walk or drive to obtain groceries. It does not take elevation gain or loss into account. It does not factor time of day nor weather, seasons, or other variables that affect a

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This layer shows which parts of the United States and Puerto Rico fall within ten minutes' walk of one or more grocery stores. It is estimated that 20% of U.S. population live within a 10 minute walk of a grocery store, and 92% of the population live within a 10 minute drive of a grocery store. The layer is suitable for looking at access at a neighborhood scale.When you add this layer to your web map, along with the drivable access layer and the SafeGraph grocery store layer, it becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. Add the Census block points layer to show a popup with the count of stores within 10 minutes' walk and drive. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or don't own a car?How to Use This Layer in a Web MapUse this layer in a web map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying. See this example web map which you can use in your projects, storymaps, apps and dashboards.The layer was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access.Lastly, this layer can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population's grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples).The layer is a useful visual resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved.Data sourcesPopulation data is from the 2010 U.S. Census blocks. Each census block has a count of stores within a 10 minute walk, and a count of stores within a ten minute drive. Census blocks known to be unpopulated are given a score of 0. The layer is available as a hosted feature layer.Grocery store locations are from SafeGraph, reflecting what was in the data as of October 2020. Access to the layer was obtained from the SafeGraph offering in ArcGIS Marketplace. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it already has the necessary attributes on each street segment to identify which streets are considered walkable, and supports a wide variety of driving parameters.The walkable access layer and drivable access layers are rasters, whose colors were chosen to allow the drivable access layer to serve as backdrop to the walkable access layer. Alternative versions of these layers are available. These pairs use different colors but are otherwise identical in content.Data PreparationArcGIS Network Analyst was used to set up a network street layer for analysis. ArcGIS StreetMap Premium was installed to a local hard drive and selected in the Origin-Destination workflow as the network data source. This allows the origins (Census block centroids) and destinations (SafeGraph grocery stores) to be connected to that network, to allow origin-destination analysis.The Census blocks layer contains the centroid of each Census block. The data allows a simple popup to be created. This layer's block figures can be summarized further, to tract, county and state levels.The SafeGraph grocery store locations were created by querying the SafeGraph source layer based on primary NAICS code. After connecting to the layer in ArcGIS Pro, a definition query was set to only show records with NAICS code 445110 as an initial screening. The layer was exported to a local disk drive for further definition query refinement, to eliminate any records that were obviously not grocery stores. The final layer used in the analysis had approximately 53,600 records. In this map, this layer is included as a vector tile layer.MethodologyEvery census block in the U.S. was assigned two access scores, whose numbers are simply how many grocery stores are within a 10 minute walk and a 10 minute drive of that census block. Every census block has a score of 0 (no stores), 1, 2 or more stores. The count of accessible stores was determined using Origin-Destination Analysis in ArcGIS Network Analyst, in ArcGIS Pro. A set of Tools in this ArcGIS Pro package allow a similar analysis to be conducted for any city or other area. The Tools step through the data prep and analysis steps. Download the Pro package, open it and substitute your own layers for Origins and Destinations. Parcel centroids are a suggested option for Origins, for example. Origin-Destination analysis was configured, using ArcGIS StreetMap Premium as the network data source. Census block centroids with population greater than zero were used as the Origins, and grocery store locations were used as the Destinations. A cutoff of 10 minutes was used with the Walk Time option. Only one restriction was applied to the street network: Walkable, which means Interstates and other non-walkable street segments were treated appropriately. You see the results in the map: wherever freeway overpasses and underpasses are present near a grocery store, the walkable area extends across/through that pass, but not along the freeway.A cutoff of 10 minutes was used with the Drive Time option. The default restrictions were applied to the street network, which means a typical vehicle's access to all types of roads was factored in.The results for each analysis were captured in the Lines layer, which shows which origins are within the cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle).The Lines layer was then summarized by census block ID to capture the Maximum value of the Destination_Rank field. A census block within 10 minutes of 3 stores would have 3 records in the Lines layer, but only one value in the summarized table, with a MAX_Destination_Rank field value of 3. This is the number of stores accessible to that census block in the 10 minutes measured, for walking and driving. These data were joined to the block centroids layer and given unique names. At this point, all blocks with zero population or null values in the MAX_Destination_Rank fields were given a store count of 0, to help the next step.Walkable and Drivable areas are calculated into a raster layer, using Nearest Neighbor geoprocessing tool on the count of stores within a 10 minute walk, and a count of stores within a ten minute drive, respectively. This tool uses a 200 meter grid and interpolates the values between each census block. A census tracts layer containing all water polygons "erased" from the census tract boundaries was used as an environment setting, to help constrain interpolation into/across bodies of water. The same layer use used to "shoreline" the Nearest Neighbor results, to eliminate any interpolation into the ocean or Great Lakes. This helped but was not perfect.Notes and LimitationsThe map provides a baseline for discussing access to grocery stores in a city. It does not presume local population has the desire or means to walk or drive to obtain groceries. It does not take elevation gain or loss into account. It does not factor time of day nor weather, seasons, or other variables that affect a person's commute choices. Walking and driving are just two ways people get to a grocery store. Some people ride a bike, others take public transit, have groceries delivered, or rely on a friend with a vehicle. Thank you to Melinda Morang on the Network Analyst team for guidance and suggestions at key moments along the way; to Emily Meriam for reviewing the previous version of this map and creating new color palettes and marker symbols specific to this project. Additional ReadingThe methods by which access to food is measured and reported have improved in the past decade or so, as has the uses of such measurements. Some relevant papers and articles are provided below as a starting point.Measuring Food Insecurity Using the Food Abundance Index: Implications for Economic, Health and Social Well-BeingHow to Identify Food Deserts: Measuring Physical and Economic Access to Supermarkets in King County, WashingtonAccess to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their ConsequencesDifferent Measures of Food Access Inform Different SolutionsThe time cost of access to food – Distance to the grocery store as measured in minutes

In this sales info layer, you will find all types of sale from the beginning of January 2018. Information contained in this layer include:Property LocationProperty's Parcel Identification NumberThe current Digest Class (whether Agricultural, residential, commercial, etc.)Land acres included in sale along with the price of the landHeated area if there was an improvement (either residential structure or commercial structure) along with the price per square footStructure type ( whether it's a single family residence, condo, etc if residential, or building type (medical office, retail store, etc) if commercial.Year the structure was builtThe grade of the structure is also stated ( this is a rating of the current condition of the structure) Grades can range from 0 - 250The sale reason type is also included (this will tell whether the sale was a Fair Market/bona fide sale, foreclosure, bank sale, HUD, etc)The Current Value of the property is also included Total Sale Price - the price listed as being what the property in question was sold forNet Sale Price - very often, there may be included in the total sale price, items (such as furniture or timber**), extra parcels/properties, etc that have nothing to do with the actual parcel/property in question. These additional items are deducted from the total sale price to get the actual price for the property itself.**Timber is not to be included in the sale price, since timber is taxed at the time it is cut/harvested.The Sales Ratio is stated on the sale. In the State of Georgia, property is to be assessed(taxed) at 40% of its Fair Market Value (100% value). Sales ratio studies are done to see how well the County is valuing property, to notice any trends that may warrant re-appraising properties due to changes in the market, and measure equity so that valuations are fair and equal among property owners and types within Dougherty County. A ratio is obtained by dividing the Current Assessed (40%) Value by the recent "Net Sale Price". The goal is to be as close to 40% as possible. This study will include many different types of sales, however, the only sales that will be used for final analysis will be sales deemed to be Bona fide sales. Georgia Code 45-5-2 (.1) defines a bona fide sale as: "A transaction which has occurred in good faith without fraud or deceit carried out by unrelated or unaffiliated parties, as by a willing buyer and a willing seller, each acting in his or her own self-interest, including but not limited to a distress sale, short sale, bank sale, or sale at public auction".Georgia Code 48-5-2 (3) defines a Fair Market sale as: "The amount a knowledgeable buyer would pay for the property and a willing seller would accept for the property at an arm's length, bona fide sale."