Data Driven Detroit created the data by selecting locations from NETS and ESRI business data with proper NAICS codes, then adding and deleting though local knowledge and confirmation with Google Streetview. These locations are Grocery stores which primarily sell food and don't include convenience stores. Visual confirmation cues included the existence of the word "grocery" in the name, or the presence of shopping carts.

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.

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 the 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.

This dataset was originally created in 2012 by the Office of the Chief Technology Officer. OCTO staff used the Alcoholic Beverage and Cannabis Administration’s (ABCA) definition of Full-Service Grocery Stores which outlines criteria for a business to obtain licenses to sell beer, wine, and spirits. Visit abca.dc.gov for full definition.OCTO staff then reviewed the Office of Planning DC Food Policy’s 2018 Food System Assessment listing grocery stores in Appendix D, and comparing these to the ABCA definition. This led to additional locations that meet, or come very close to, the full-service grocery store criteria. The criteria in section one of ABCA’s full-service grocery store determined the initial locations included in this dataset. View the full assessment at dcfoodpolicycouncil.org.Since the initial creation of this dataset, OCTO and the Deputy Mayor for Planning and Economic Development (DMPED) staff confirm grocery store operations by comparing datasets from DLCP, media outlets, commercially licensed datasets, and onsite visits.Please review supplemental metadata for more details.

GapMaps curates up-to-date and high-quality GIS Data tracking store openings and closures for leading retail brands across Asia and MENA. Get the insights you need to make more accurate and informed business decisions.

Spatial coverage index compiled by East View Geospatial of set "EVGmap 50 VMAP2-Compliant GIS Vector Data". Source data from EVG (publisher). Type: Topographic. Scale: 1:50,000. Region: World.

The dataset contains locations and attributes for Shopping Centers, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies.

GIS Data Management Market Outlook

The global GIS Data Management market size is projected to grow from USD 12.5 billion in 2023 to USD 25.6 billion by 2032, exhibiting a CAGR of 8.4% during the forecast period. This impressive growth is driven by the increasing adoption of geographic information systems (GIS) across various sectors such as urban planning, disaster management, and agriculture. The rising need for effective data management systems to handle the vast amounts of spatial data generated daily also significantly contributes to the market's expansion.

One of the primary growth factors for the GIS Data Management market is the burgeoning demand for spatial data analytics. Businesses and governments are increasingly leveraging GIS data to make informed decisions and strategize operational efficiencies. With the rapid urbanization and industrialization worldwide, there's an unprecedented need to manage and analyze geographic data to plan infrastructure, monitor environmental changes, and optimize resource allocation. Consequently, the integration of GIS with advanced technologies like artificial intelligence and machine learning is becoming more prominent, further fueling market growth.

Another significant factor propelling the market is the advancement in GIS technology itself. The development of sophisticated software and hardware solutions for GIS data management is making it easier for organizations to capture, store, analyze, and visualize geographic data. Innovations such as 3D GIS, real-time data processing, and cloud-based GIS solutions are transforming the landscape of geographic data management. These advancements are not only enhancing the capabilities of GIS systems but also making them more accessible to a broader range of users, from small enterprises to large governmental agencies.

The growing implementation of GIS in disaster management and emergency response activities is also a critical factor driving market growth. GIS systems play a crucial role in disaster preparedness, response, and recovery by providing accurate and timely geographic data. This data helps in assessing risks, coordinating response activities, and planning resource deployment. With the increasing frequency and intensity of natural disasters, the reliance on GIS data management systems is expected to grow, resulting in higher demand for GIS solutions across the globe.

Geospatial Solutions are becoming increasingly integral to the GIS Data Management landscape, offering enhanced capabilities for spatial data analysis and visualization. These solutions provide a comprehensive framework for integrating various data sources, enabling users to gain deeper insights into geographic patterns and trends. As organizations strive to optimize their operations and decision-making processes, the demand for robust geospatial solutions is on the rise. These solutions not only facilitate the efficient management of spatial data but also support advanced analytics and real-time data processing. By leveraging geospatial solutions, businesses and governments can improve their strategic planning, resource allocation, and environmental monitoring efforts, thereby driving the overall growth of the GIS Data Management market.

Regionally, North America holds a significant share of the GIS Data Management market, driven by high technology adoption rates and substantial investments in GIS technologies by government and private sectors. However, Asia Pacific is anticipated to witness the highest growth rate during the forecast period. The rapid urbanization, economic development, and increasing adoption of advanced technologies in countries like China and India are major contributors to this growth. Governments in this region are also focusing on smart city projects and infrastructure development, which further boosts the demand for GIS data management solutions.

Component Analysis

The GIS Data Management market is segmented by component into software, hardware, and services. The software segment is the largest and fastest-growing segment, driven by the continuous advancements in GIS software capabilities. GIS software applications enable users to analyze spatial data, create maps, and manage geographic information efficiently. The integration of GIS software with other enterprise systems and the development of user-friendly interfaces are key factors propelling the growth of this segment. Furthermore, the rise of mobile GIS applications, which allow field data collectio

Xtract.io's location data for home and electronics retailers delivers a comprehensive view of the retail sector. Retail analysts, industry researchers, and business developers can utilize this dataset to understand market distribution, identify potential opportunities, and develop strategic insights into home and electronics retail landscapes.

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

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

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

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

Dataset Description: Washington Grocery Store Locations

This dataset contains detailed information about the locations and operational status of grocery stores in Washington, spanning multiple years. It includes both spatial and temporal data, offering a comprehensive view of how grocery stores are distributed and have evolved over time. Below is a breakdown of the columns included in the dataset:

1. X, Y: Geographic coordinates (latitude and longitude) representing the store's location in the dataset.

2. STORENAME: The name of the grocery store.

3. ADDRESS: The physical address of the grocery store.

4. ZIPCODE: The ZIP code of the store’s location.

5. PHONE: The contact phone number for the store.

6. WARD: The local government ward in which the store is located.

7. SSL: A unique identifier or code related to the store, possibly referring to specific data collection attributes.

8. NOTES: Additional comments or information about the store.

9. PRESENT: Temporal indicators showing the presence (likely open or closed) of each store across various years. These columns provide insights into the longevity and temporal trends of grocery store operations.

10. GIS_ID: A unique identifier for geographic information system (GIS) data.

11. XCOORD, YCOORD: Coordinates (likely more specific) used for spatial data analysis, providing the exact location of the store.

12. MAR_ID: A unique identifier for marketing or regional analysis purposes.

13. GLOBALID: A global unique identifier for the store data.

14. CREATOR: The individual or system that created the data entry.

15. CREATED: Timestamp showing when the data entry was created.

16. EDITOR: The individual or system that edited the data entry.

17. EDITED: Timestamp showing when the data entry was last edited.

18. SE_ANNO_CAD_DATA: Specific annotation or data related to CAD (computer-aided design), possibly linked to store location details.

19. OBJECTID: A unique identifier for the object or record within the dataset.

Insights We Can Extract:

• Geographic Distribution: By analyzing the X and Y coordinates along with ZIP codes and wards, we can identify where grocery stores are concentrated and map areas with high or low store density.
• Temporal Trends: The data in the "PRESENT" columns helps us track the opening and closure patterns of grocery stores over time, providing insights into market trends and store longevity.
• Service Gaps: We can identify areas with no grocery stores, possibly indicating food deserts or underserved communities, by mapping the stores and comparing coverage across ZIP codes and wards.
• Operational Trends: By analyzing the temporal data and comparing store turnover, we can uncover patterns in the longevity or turnover of grocery stores.
• Urban Planning and Accessibility: This dataset could help us assess whether the location of grocery stores aligns with urban infrastructure like transportation routes or population density, which could inform policy decisions to improve grocery access.

This dataset is invaluable for urban planners, policymakers, and business stakeholders looking to improve food access and urban infrastructure.

Represents corner stores currently participating in DC Central Kitchen's (DCCK) Healthy Corners program. These corner stores sell fresh, healthy produce options.Layer compiled by the Office of Planning based on DCCK's list of stores. For the most up-to-date listing, visit DCCK's site at https://dccentralkitchen.org/healthy-corners/.

This dataset contains measures of the number and density of grocery stores, supermarkets, food stores, fruit and vegetable stores, meat and fish markets, and warehouse clubs (such as Costco and Sams Club) selling food per United States Census Tract or ZIP Code Tabulation Area (ZCTA) from 1990 through 2021. The dataset includes four separate files for four different geographic areas (GIS shapefiles from the United States Census Bureau). The four geographies include:● Census Tract 2010 ● Census Tract 2020● ZIP Code Tabulation Area (ZCTA) 2010 ● ZIP Code Tabulation Area (ZCTA) 2020Information about which dataset to use can be found in the Usage Notes section of this document.

Spatial coverage index compiled by East View Geospatial of set "Iran 1:100,000 Scale Geological GIS Vector Data". Source data from GSI (publisher). Type: Geoscientific - Geology. Scale: 1:100,000. Region: Middle East.

GapMaps Store Location Data uses known population data combined with billions of mobile device location points to provide highly accurate demographics insights at 150m grid levels across Asia and MENA. Understand who lives in a catchment, where they work and their spending potential.

Esri ArcGIS Online (AGOL) Hosted Feature Layer which provides access to the MDOT SHA Maintenance Shop Boundaries data product.MDOT SHA Maintenance Shop Boundaries data consists of polygon geometric features which represent the geographic area of responsibility for each MDOT SHA Maintenance Shop Facility throughout the State of Maryland. MDOT SHA Maintenance Shop Facility Boundaries data is used by various transportation business units throughout the Maryland Department of Transportation (MDOT), as well as many other Federal, State & local government agencies. This data is key to understanding and visualizing the geographic area of maintenance responsibility for MDOT SHA Maintenance Shop Facilities throughout the State of Maryland. MDOT SHA Maintenance Shop Boundaries data is owned by the MDOT SHA Office of Maintenance (OOM). This data is updated & published on an As-Needed basis, as it does not frequently or regularly change. However, as of February 2024, this data product was updated as part of a data quality assurance / quality control process in preparation for the MDOT SHA Asset Management Office (AMO) and their future management of MDOT asset data.For additional information, contact the MDOT SHA OIT Enterprise Information Services:Email: GIS@mdot.maryland.gov

Location of shopping centers 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, visit http://egis3.lacounty.gov/lms/.

Locations of Hardware Stores, which are deemed essential following hurricanes or other disaster scenarios.This dataset is fed from revenue with weekly updates

Xtract.io’s Pharmacy & Drug Store Location Data provides a complete geospatial view of pharmaceutical retail across the United States and Canada. This dataset includes handcrafted polygons and geocoded coordinates for each pharmacy location, making it a powerful resource for healthcare planners, market researchers, and retail strategists.

Organizations can leverage this dataset to:

• Conduct healthcare accessibility mapping and identify underserved areas.

• Evaluate market penetration and retail coverage across regions.

• Analyze the competitive landscape in pharmaceutical retail.

• Support site selection and expansion strategies.

How We Build Pharmacy Polygons

• Manually crafted polygons created using GIS tools like QGIS and ArcGIS, with aerial and street-level imagery.

• Integration of venue layouts and elevation plans from official sources for enhanced accuracy.

• Rigorous multi-stage quality checks ensure accuracy, completeness, and relevance.

What Else We Offer

• Custom polygon creation for any retail chain, healthcare facility, or point of interest.

• Enhanced metadata including entry/exit points, parking areas, and surrounding context.

• Flexible formats: WKT, GeoJSON, Shapefile, and GDB for smooth system integration.

• Regular updates tailored to client needs (30, 60, 90 days).

• Unlock the Power of Healthcare Geospatial Data

With detailed pharmacy polygon data and POI datasets, businesses can:

• Map healthcare service coverage and accessibility.

• Identify growth opportunities in underserved communities.

• Decode consumer behavior in the pharmaceutical retail space.

• Strengthen location-driven strategies with spatial intelligence.

Why Choose LocationsXYZ?

LocationsXYZ is trusted by enterprises worldwide to deliver 95% accurate, handcrafted POI and polygon data. With our pharma dataset, you gain actionable insights to support healthcare planning, retail expansion, and competitive benchmarking.

Point geometry with attributes displaying quick stop type businesses in East Baton Rouge Parish, Louisiana.Metadata