This is a map provided by the Walmart Emergency Operations Center (EOC) to view store/club status.

This map shows the average amount spent on clothing and shoes in Spain in a multiscale map (Autonomous Community, Province, Municipality, Census District, Census Section). Spending on clothes and shoes is measured in Euro (€). The pop-up is configured to show the following information at each geography level:Total householdsAverage household spending on clothing and shoesBreakdown of clothing and shoes expendituresData Note: Certain regions are historical areas belonging to more than one municipality, and are considered deserted. These areas have no official names or data associated with them. Due to this, these areas will appear on the map as "no data collected".The source of this data is AIS, Instituto Nacional de Estadística (INE), and Esri Spain. The vintage of the data is 2023.For more information about Esri demographics including geography levels, click here.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

Locations of offices providing clothing services in Los Angeles CountyThis dataset is maintained through the County of Los Angeles Location Management System. The Location Management System is used by the County of Los Angeles GIS Program to maintain a single, comprehensive geographic database of locations countywide. For more information on the Location Management System, visithttp://egis3.lacounty.gov/lms/.

The Spatiotemporal Big Data Store Tutorial introduces you the the capabilities of the spatiotemporal big data store in ArcGIS Data Store, available with ArcGIS Enterprise. Observation data can be moving objects, changing attributes of stationary sensors, or both. The spatiotemporal big data store enables archival of high volume observation data, sustains high velocity write throughput, and can run across multiple machines (nodes). Adding additional machines adds capacity, enabling you to store more data, implement longer retention policies of your data, and support higher data write throughput.

After completing this tutorial you will:

Understand the concepts and best practices for working with the spatiotemporal big data store available with ArcGIS Data Store. Have configured the appropriate security settings and certificates on a enterprise server, real-time server, and a data server which are necessary for working with the spatiotemporal big data store. Have learned how to process and archive large amounts of observational data in the spatiotemporal big data store. Have learned how to visualize the observational data that is stored in the spatiotemporal big data store.

Releases

Each release contains a tutorial compatible with the version of GeoEvent Server listed. The release of the component you deploy does not have to match your version of ArcGIS GeoEvent Server, so long as the release of the component is compatible with the version of GeoEvent Server you are using. For example, if the release contains a tutorial for version 10.6; this tutorial is compatible with ArcGIS GeoEvent Server 10.6 and later. Each release contains a Release History document with a compatibility table that illustrates which versions of ArcGIS GeoEvent Server the component is compatible with.

NOTE: The release strategy for ArcGIS GeoEvent Server components delivered in the ArcGIS GeoEvent Server Gallery has been updated. Going forward, a new release will only be created when

  a component has an issue,
  is being enhanced with new capabilities,
  or is not compatible with newer versions of ArcGIS GeoEvent Server.

This strategy makes upgrades of these custom
components easier since you will not have to
upgrade them for every version of ArcGIS GeoEvent Server
unless there is a new release of
the component. The documentation for the
latest release has been
updated and includes instructions for updating
your configuration to align with this strategy.

Latest

Release 4 - February 2, 2017 - Compatible with ArcGIS GeoEvent Server 10.5 and later.

Previous

Release 3 - July 7, 2016 - Compatible with ArcGIS GeoEvent Server 10.4 thru 10.8.

Release 2 - May 17, 2016 - Compatible with ArcGIS GeoEvent Server 10.4 thru 10.8.

Release 1 - March 18, 2016 - Compatible with ArcGIS GeoEvent Server 10.4 thru 10.8.

SafeGraph Places provides baseline information for every record in the SafeGraph product suite via the Places schema and polygon information when applicable via the Geometry schema. The current scope of a place is defined as any location humans can visit with the exception of single-family homes. This definition encompasses a diverse set of places ranging from restaurants, grocery stores, and malls; to parks, hospitals, museums, offices, and industrial parks. Premium sets of Places include apartment buildings, Parking Lots, and Point POIs (such as ATMs or transit stations).

SafeGraph Places is a point of interest (POI) data offering with varying coverage depending on the country. Note that address conventions and formatting vary across countries. SafeGraph has coalesced these fields into the Places schema.

SafeGraph provides clean and accurate geospatial datasets on 51M+ physical places/points of interest (POI) globally. Hundreds of industry leaders like Mapbox, Verizon, Clear Channel, and Esri already rely on SafeGraph POI data to unlock business insights and drive innovation.

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. 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. Methodology Every 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

This layer contains grocery store locations 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. You must zoom in to neighborhood scale to see the stores. 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. The layer was uploaded as a hosted feature layer, from which this vector tile layer was created. When you add this layer to your web map, along with the walkable access layer, the drivable access layer, and the Census blocks with counts of accessible stores, 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 this layer to a web map 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?

This is a layer provided by the Walmart Emergency Operations Center (EOC) to view store/club status.

This layer shows the market opportunity for clothing and accessories stores in the U.S. in 2016 in a multiscale map (by country, state, county, ZIP Code, tract, and block group). The map uses the Leakage/Surplus Factor, an indexed value that represents opportunity (leakage), saturation (surplus), or balance within a market. This map focuses on the opportunity for clothing/accessories stores (NAICS 448) The pop-up is configured to include the following information for each geography level:Count of clothing/accessory stores - NAICS 448 Total annual NAICS 448 sales (supply)Total annual NAICS 448 sales potential (demand)Market Opportunity for NAICS 448 (expressed as an index)Total annual supply and demand for various food industriesClothing Stores - NAICS 4481Shoe Stores - NAICS 4482Jewelry/Luggage/Leather Goods Stores - NAICS 4483Esri's Leakage/Surplus Factor measures the balance between the volume of retail sales (supply) generated by retail businesses and the volume of retail potential (demand) produced by household spending on retail goods within the same industry. The factor enables a one-step comparison of supply against demand, and a simple way to identify business opportunity. Leakage implies that potential sales are "leaking" from an area, while surplus implies a saturation within a given area. The values range from -100 to +100, with a value of 0 representing a balanced market. See the Leakage/Surplus Factor Data Note for more information. Esri's 2016 Retail MarketPlace (RMP) database provides a direct comparison between retail sales and consumer spending by industry and measures the gap between supply and demand. This database includes retail sales by industry to households and retail potential or spending by households. The Retail MarketPlace data helps organizations accurately measure retail activity by trade area and to compare retail sales to consumer spending by NAICS industry classification. See Retail MarketPlace Database to view the methodology statement, supported geography levels, and complete variable list. Additional Esri Resources:Esri DemographicsU.S. 2016/2021 Esri Updated DemographicsEssential demographic vocabularyEsri's arcgis.com demographic map layers

Grocery Store AccessHow do people get to a grocery store in your city?The option to travel quickly to a grocery store varies by location. Explore grocery store access in your neighborhood. Enter your ZIP code, city, or point of interest into the app’s search to see how many stores people there can reach in a 10-minute walk or drive. Interactive charts update as you move around the map. How does grocery access differ with neighboring areas, states, or across the US? This Esri map estimates 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. How does your city compare? Learn more about this map

As Esri’s commercial partner for parcel data, Regrid invites you to enjoy this free tile layer of parcel boundaries covering 100% of the United States. Complete parcel attributes are also available from an integrated Data Store."I think it’s fantastic that this layer exists. It's really helpful for my staff to see parcel boundaries in a quick and accessible layer."- Kate Berg, Geographic Information Systems (GIS) Manager | Department of Environment, Great Lakes, and EnergyVisit the Regrid Data Store for the ArcGIS User CommunityHassle-Free Parcel Data for Esri UsersWhen you click a parcel in the tile layer, you will see its address, size, and parcel ID number, along with a convenient link to purchase additional parcel attributes in The Regrid Data Store for the ArcGIS User Community. Once in the Data Store, you can purchase and download parcel files with attributes by the county and state for use in ArcGIS, as well as our add-on datasets like standardized zoning, matched building footprints, and matched secondary addresses.See regrid.com/esri for all of Regrid’s parcel products for the Esri ecosystem, including Feature Service delivery for ongoing parcel updates at scale.Key Features of Regrid's Parcel DataSourced & Standardized: Data combines authoritative public sources & third-party enrichments, aggregated, standardized, and matched by the Regrid team.158+ Million Parcel Records: Covering all 3,200+ US counties and territories.143+ Standardized Data Fields: Including geometry, ownership, buildings, secondary addresses, land use, and zoning.Universal Parcel ID & Placekey Location Identifier: Ensuring precise identification and integration.Detailed Attributes: Tax assessments, building counts, square footage, stacked parcels (condos), right-of-way, vacancy indicators and USPS deliverability. Comprehensive Coverage: 100% land parcel coverage across the US.Parcel Data Resources & DocumentationRegrid Data Dictionary / Parcel Data SchemaRegrid Coverage ReportParcel Data FAQsThank you to all the GIS professionals, state, county and federal officials, assessors, recorders, and public officials across the country who maintain the nation's parcel data and infrastructure.

Point locations of churches, cemeteries, post offices, libraries, recreational facilities, and the like within the 16-county NCTCOG region. Data can be viewed in the Development Monitoring in North Central Texas web mapping application. For the program overview, visit NCTCOG Development Monitoring Program Overview.pdf

Information Lookup is a configurable web application template that can be used to provide the general public, internal staff and other interested parties with information about a location. If no features are found at that location, a general message is displayed. Optionally, the location entered can be stored in a point layer.Configurable OptionsThe template can be configured using the following options:Lookup Layers: One or more polygon layers queried by the location specified. The pop-up defined in these layers combined into a single pop-up and displayed to the user. The layers can either be a feature service layer or a layer that is part of a dynamic map service. Use a vertical bar or pipe (|) to separate this list of layers. It is recommended that these layers visibility is turned off.Pop-up Title: The title of the pop-up when results are returned from one or more of the Lookup Layers.Pop-up Width: The width of the pop-up. pop-up Max Height: The maximum height title of the pop-up.Unavailable pop-up Title: The title of the pop-up when no results are returned from the Lookup Layers.Unavailable pop-up Message: The message to display in the pop-up when no results are returned from the Lookup Layers.Zoom Level for Location: The scale to set the map at when a location is specified.Store Location: Option to store the location specified in a point layer, if checked on, fill out the remaining parameters.Application Title: Enter a custom title for the application.Storage Layer Name: Name of the point feature service layer in the map to store the location. Editing must be enabled on this layer.Storage Layer Field: Field in the Storage Layer to store a value if a result was returned from the Lookup Layers.Yes Value: The value to store in the Storage Layer Field specified above when a result is returned from the Lookup Layers.No Value: The value to store in the Storage Layer Field specified above when no results are returned from the Lookup Layers.Display Splash Screen on Startup: Option to show a splash screen when the app loads.Splash Screen message: The message to display in the splash screen.Splash Screen Theme: The color scheme for the splash screen.Supported DevicesThis application is responsively designed to support use in browsers on desktops, mobile phones, and tablets.Data RequirementsThis web app includes the capability to edit a hosted feature service or an ArcGIS Server feature service. Creating hosted feature services requires an ArcGIS Online organizational subscription or an ArcGIS Developer account. Get Started This application can be created in the following ways:Click the Create a Web App button on this pageShare a map and choose to Create a Web AppOn the Content page, click Create - App - From Template Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.

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. 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. Methodology Every 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

Important Note: This item is in mature support as of June 2022 and will be retired in December 2025.This shows the market opportunity for electronics and appliances stores in the U.S. in 2017 (in 2021 geography) in a multiscale map (by country, state, county, ZIP Code, tract, and block group). The map uses the Leakage/Surplus Factor, an indexed value that represents opportunity (leakage), saturation (surplus), or balance within a market. This map focuses on the opportunity for electronics and appliances stores (NAICS 443). The pop-up is configured to include the following information for each geography level:Count of electronics and appliances stores - NAICS 443 Total annual NAICS 443 sales (supply)Total annual NAICS 443 sales potential (demand)Market Opportunity for NAICS 443 (expressed as an index)Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

This feature layer provides access to OpenStreetMap (OSM) shops data for North America, which is updated every 5 minutes with the latest edits. This hosted feature layer view is referencing a hosted feature layer of OSM point (node) data in ArcGIS Online that is updated with minutely diffs from the OSM planet file. This feature layer view includes shop features defined as a query against the hosted feature layer (i.e. shop is not blank).In OSM, a shop is a place selling retail products or services, such as a supermarket, bakery, or florist. These features are identified with a shop tag. There are thousands of different tag values for shop used in the OSM database. In this feature layer, unique symbols are used for several of the most popular shop types, while lesser used types are grouped in an "other" category.Zoom in to large scales (e.g. Neighborhood level or 1:80k scale) to see the shop features display. You can click on a feature to get the name of the shop. The name of the shop will display by default at very large scales (e.g. Building level of 1:2k scale). Labels can be turned off in your map if you prefer.Create New LayerIf you would like to create a more focused version of this shop layer displaying just one or two shop types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. shop is jewelry), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like. Esri may publish a few such layers (e.g. supermarket or convenience shop) that are ready to use, but not for every type of shop.Important Note: if you do create a new layer, it should be provided under the same Terms of Use and include the same Credits as this layer. You can copy and paste the Terms of Use and Credits info below in the new Item page as needed.

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.

This map shows which areas have concentrations of high risk businesses and potential loss of sales revenue in the event of an economic downturn. Areas in yellow have a higher concentration of sales revenue in one or more of the five categories (by NAICS code): Clothing/Accessory stores, General Merchandise stores, Arts/Entertainment/Recreation, Accommodation, and Food Service/Drinking Places. The popup breaks down total sales revenue by area, sales revenue as a percentage of total by area, percent of businesses for the area, and sales revenue by category. Data is 2019 vintage and available by county, tract, and block group. Overall, in the US, these 5 categories make up 7.11% of total sales revenue.Esri's Business Summary Data: Esri's Business Locations data is extracted from a comprehensive list of businesses licensed from Infogroup. It summarizes the comprehensive list of businesses from Infogroup for select NAICS and SIC summary categories by geography and includes total number of businesses, total sales, and total number of employees for a trade area.Esri's U.S. 2019 Data: Population, age, income, race, home value, spending, business, and market potential are among the topics included in the data suite. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies. To browse, all data variables available within Esri's demographics explore the Data Browser. Additional Esri Resources:Get StartedEsri DemographicsU.S. 2019 Esri Updated DemographicsBusiness Summary DataMethodologies

Laatste update: 03 februari 2025Terug naar Esri Nederland Support HubEen Personal Use-licentie is een licentie die aangeschaft kan worden voor niet commerciële activiteiten. Een Personal Use-licentie is een jaar geldig en kan daarna steeds met een jaar verlengd worden.Een Personal Use-licentie aanschaffen (of verlengen) kan via de Esri Store. Hier is een creditcard voor nodig.