To this day, the Geodatindustry database is the world's most complete and accurate in the retail, commercial and industry area, with 25 years of experience and a qualified teams.

Geodatindustry Database is the perfect tool to lead your decision making, market analytics, strategy building, prospecting, advertizing compaigns, etc.

By purchasing this dataset, you gain access to more than 18,000 shopping malls all over the World, hosting millions of stores and welcoming millions of visitors each year.

Included Points of Interest in this dataset : -Shopping Malls and Centers -Outlets -Big Supermakets and Hypermarkets.

Information (if known) : shopping mall's name, physical address, number of shops, x,y coordinates, annual visitors counts (in millions), owner and managers, global area and GLA (in ranges), the website.

Global area and GLA Ranges : A = 0-2 500 m² B = 2 500-5 000 m² C = 5 000-10 000 m² D = 10 000-25 000 m²
E = 25 000-50 000 m² F = 50 000-75 000 m² G = 75 000-100 000 m² H = 100 000-1M m² I = 1M-10M m² J = 10M m² and +

Prices depend on the amount of Shopping Malls for each country. It goes from 59€ to 3990€ per country.

Comprehensive dataset of 5,979 Shopping malls in California, United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 1,368 Shopping malls in Virginia, United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 714 Shopping malls in Alabama, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This location dataset offers a detailed geographical representation of shopping centers across North America focusing on Canada. Retail strategists, real estate investors, and market researchers can leverage precise location information to analyze retail landscapes, identify market trends, and develop targeted strategies for shopping center markets.

Point of Interest (POI) data, also known as places data, provides the exact location of buildings, stores, or specific places. It has become essential for businesses to make smarter, geography-driven decisions in today's competitive landscape.

LocationsXYZ, the POI data product from Xtract.io, offers a comprehensive database of 6 million locations across the US, UK, and Canada, spanning 11 diverse industries, including:

-Retail -Restaurants -Healthcare -Automotive -Public utilities (e.g., ATMs, park-and-ride locations) -Shopping malls, and more

Why Choose LocationsXYZ? At LocationsXYZ, we: -Deliver POI data with 95% accuracy -Refresh POIs every 30, 60, or 90 days to ensure the most recent information -Create on-demand POI datasets tailored to your specific needs -Handcraft boundaries (geofences) for locations to enhance accuracy -Provide POI and polygon data in multiple file formats

Unlock the Power of POI Data With our point-of-interest data, you can: -Perform thorough market analyses -Identify the best locations for new stores -Gain insights into consumer behavior -Achieve an edge with competitive intelligence

LocationsXYZ has empowered businesses with geospatial insights, helping them scale and make informed decisions. Join our growing list of satisfied customers and unlock your business's potential with our cutting-edge POI data.

This comprehensive retail point-of-interest (POI) dataset provides a detailed map of retail establishments across the United States and Canada. Retail strategists, market researchers, and business developers can leverage precise store location data to analyze market distribution, identify emerging trends, and develop targeted expansion strategies.

Point of Interest (POI) data, also known as places data, provides the exact location of buildings, stores, or specific places. It has become essential for businesses to make smarter, geography-driven decisions in today's competitive retail landscape of location intelligence.

LocationsXYZ, the POI data product from Xtract.io, offers a comprehensive retail store data database of 6 million locations across the US, UK, and Canada, spanning 11 diverse industries, including: -Retail store locations -Restaurants -Healthcare -Automotive -Public utilities (e.g., ATMs, park-and-ride locations) -Shopping centers and malls, and more

Why Choose LocationsXYZ for Your Retail POI Data Needs? At LocationsXYZ, we: -Deliver POI data with 95% accuracy for reliable store location data -Refresh POIs every 30, 60, or 90 days to ensure the most recent retail location information -Create on-demand POI datasets tailored to your specific retail data requirements -Handcraft boundaries (geofences) for shopping center locations to enhance accuracy -Provide retail POI data and polygon data in multiple file formats

Unlock the Power of Retail Location Intelligence With our point-of-interest data for retail stores, you can: -Perform thorough market analyses using comprehensive store location data -Identify the best locations for new retail stores -Gain insights into consumer behavior and shopping patterns -Achieve an edge with competitive intelligence in retail markets

LocationsXYZ has empowered businesses with geospatial insights and retail location data, helping them scale and make informed decisions. Join our growing list of satisfied customers and unlock your business's potential with our cutting-edge retail POI data and shopping center location intelligence.

Xtract.io's comprehensive location data for European malls provides international retail strategists. Researchers, investors, and business developers can utilize this dataset to analyze retail landscape, identify market trends, and develop sophisticated strategies for European shopping center markets.

Point of Interest (POI) data, also known as places data, provides the exact location of buildings, stores, or specific places. It has become essential for businesses to make smarter, geography-driven decisions in today's competitive landscape.

LocationsXYZ, the POI data product from Xtract.io, offers a comprehensive database of 6 million locations across the US, UK, and Canada, spanning 11 diverse industries, including:

-Retail -Restaurants -Healthcare -Automotive -Public utilities (e.g., ATMs, park-and-ride locations) -Shopping malls, and more

Why Choose LocationsXYZ? At LocationsXYZ, we: -Deliver POI data with 95% accuracy -Refresh POIs every 30, 60, or 90 days to ensure the most recent information -Create on-demand POI datasets tailored to your specific needs -Handcraft boundaries (geofences) for locations to enhance accuracy -Provide POI and polygon data in multiple file formats

Unlock the Power of POI Data With our point-of-interest data, you can: -Perform thorough market analyses -Identify the best locations for new stores -Gain insights into consumer behavior -Achieve an edge with competitive intelligence

LocationsXYZ has empowered businesses with geo-spatial insights, helping them scale and make informed decisions. Join our growing list of satisfied customers and unlock your business's potential with our cutting-edge POI data.

The purpose of this project was to assess the level of security in large indoor shopping malls. The researchers administered surveys to the security directors of the nation's largest indoor retail malls. The researchers sent letters with surveys attached, to 1,372 security directors of enclosed retail malls across the country, which were at least 250,000 square feet in size. A total of 120 completed surveys were returned. The survey instrument was divided into the following nine sections: (1) Hiring Standards, (2) Training, (3) Security Budget, (4) Human Surveillance Strategies, (5) Access Control Plans, (6) Technological Enhancements, (7) Emergency Preparedness Procedures, (8) Relations with the Public Sector, and (9) Opinions about Terrorist Threat.

Comprehensive dataset of 1,650 Shopping malls in Pennsylvania, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Graph and download economic data for Sales by Retail Stores for United States (M0689BUSM144NNBR) from Jan 1946 to Sep 1974 about retail trade, sales, retail, and USA.

Xtract.io's comprehensive location data for restaurants and food stores offers a detailed view of the retail food landscape. Retail strategists, market researchers, and business developers can utilize this dataset to analyze market distribution, identify emerging trends, and develop targeted expansion strategies across the food retail sector.

Point of Interest (POI) data, also known as places data, provides the exact location of buildings, stores, or specific places. It has become essential for businesses to make smarter, geography-driven decisions in today's competitive landscape.

LocationsXYZ, the POI data product from Xtract.io, offers a comprehensive database of 6 million locations across the US, UK, and Canada, spanning 11 diverse industries, including:

-Retail -Restaurants -Healthcare -Automotive -Public utilities (e.g., ATMs, park-and-ride locations) -Shopping malls, and more

Why Choose LocationsXYZ? At LocationsXYZ, we: -Deliver POI data with 95% accuracy -Refresh POIs every 30, 60, or 90 days to ensure the most recent information -Create on-demand POI datasets tailored to your specific needs -Handcraft boundaries (geofences) for locations to enhance accuracy -Provide POI and polygon data in multiple file formats

Unlock the Power of POI Data With our point-of-interest data, you can: -Perform thorough market analyses -Identify the best locations for new stores -Gain insights into consumer behavior -Achieve an edge with competitive intelligence

LocationsXYZ has empowered businesses with geospatial insights, helping them scale and make informed decisions. Join our growing list of satisfied customers and unlock your business's potential with our cutting-edge POI data.

United States Retail Sales: Book Stores data was reported at 1.320 USD bn in Aug 2018. This records an increase from the previous number of 661.000 USD mn for Jul 2018. United States Retail Sales: Book Stores data is updated monthly, averaging 1.022 USD bn from Jan 1992 (Median) to Aug 2018, with 320 observations. The data reached an all-time high of 2.425 USD bn in Aug 2008 and a record low of 523.000 USD mn in Apr 1992. United States Retail Sales: Book Stores data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.H001: Retail Sales: By NAIC System.

United States Retail Sales: Miscellaneous Stores Retail data was reported at 11.376 USD bn in Jun 2018. This records a decrease from the previous number of 12.174 USD bn for May 2018. United States Retail Sales: Miscellaneous Stores Retail data is updated monthly, averaging 8.662 USD bn from Jan 1992 (Median) to Jun 2018, with 318 observations. The data reached an all-time high of 12.350 USD bn in Dec 1999 and a record low of 3.642 USD bn in Jan 1992. United States Retail Sales: Miscellaneous Stores Retail data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.H001: Retail Sales: By NAIC System.

Massive POI database covering 6 million locations across 11 industries in the US and Canada. Includes 40+ rich data attributes for each location. Empowers data-driven decision-making across various sectors, from retail to healthcare, with high-quality, diverse location intelligence.

This massive global point of interest database represents a transformative resource for location intelligence. Global businesses, researchers, and analysts can leverage these comprehensive location insights to conduct advanced spatial analysis, develop market strategies, and gain understanding of global geographical dynamics.

Point of Interest (POI) data, also known as places data, provides the exact location of buildings, stores, or specific places. It has become essential for businesses to make smarter, geography-driven decisions in today's competitive landscape.

LocationsXYZ, the POI data product from Xtract.io, offers a comprehensive database of 6 million locations across the US, UK, and Canada, spanning 11 diverse industries, including:

-Retail -Restaurants -Healthcare -Automotive -Public utilities (e.g., ATMs, park-and-ride locations) -Shopping malls, and more

Why Choose LocationsXYZ? At LocationsXYZ, we: -Deliver POI data with 95% accuracy -Refresh POIs every 30, 60, or 90 days to ensure the most recent information -Create on-demand POI datasets tailored to your specific needs -Handcraft boundaries (geofences) for locations to enhance accuracy -Provide POI and polygon data in multiple file formats

Unlock the Power of POI Data With our point-of-interest data, you can: -Perform thorough market analyses -Identify the best locations for new stores -Gain insights into consumer behavior -Achieve an edge with competitive intelligence

LocationsXYZ has empowered businesses with geo-spatial insights, helping them scale and make informed decisions. Join our growing list of satisfied customers and unlock your business's potential with our cutting-edge POI data.

Comprehensive dataset of 268 Shopping malls in Iowa, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

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.

A listing of all retail food stores which are licensed by the Department of Agriculture and Markets.

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

Potential Applications of the Dataset:

1. Geospatial Information: Precise geographical coordinates for each Walgreens store, enabling accurate mapping and spatial analysis. State-wise and city-wise breakdown of store locations for a comprehensive overview.

2. Store Details: Store addresses, including street name, city, state, and zip code, facilitating easy identification and location-based analysis. Contact information, such as phone numbers, providing a direct link to store management.

3. Operational Attributes: Store opening and closing hours, aiding businesses in strategic planning and market analysis. Services and amenities are available at each location, offering insights into the diverse offerings of Walgreens stores.

4. Historical Data: Historical data on store openings and closures, providing a timeline perspective on Walgreens' expansion and market presence.

5. Demographic Insights: Demographic information of the areas surrounding each store, empowering users to understand the local customer base.

6. Comprehensive and Up-to-Date: Regularly updated to ensure the dataset reflects the latest information on Walgreens store locations and attributes. Detailed data quality checks and verification processes for accuracy and reliability.

The dataset is structured in a flexible format, allowing users to tailor their queries and analyses based on specific criteria and preferences.