BestPlace is an innovative retail data and analytics tool created explicitly for medium and enterprise-level CPG/FMCG companies. It's designed to revolutionize your retail data analysis approach by adding a strategic location-based perspective to your existing database. This perspective enriches your data landscape and allows your business to understand better and cater to shopping behavior. An In-Depth Approach to Retail Analytics Unlike conventional analytics tools, BestPlace delves deep into each store location details, providing a comprehensive analysis of your retail database. We leverage unique tools and methodologies to extract, analyze, and compile data. Our processes have been accurately designed to provide a holistic view of your business, equipping you with the information you need to make data-driven data-backed decisions. Amplifying Your Database with BestPlace At BestPlace, we understand the importance of a robust and informative retail database design. We don't just add new stores to your database; we enrich each store with vital characteristics and factors. These enhancements come from open cartographic sources such as Google Maps and our proprietary GIS database, all carefully collected and curated by our experienced data analysts. Store Features We enrich your retail database with an array of store features, which include but are not limited to: Number of reviews Average ratings Operational hours Categories relevant to each point Our attention to detail ensures your retail database becomes a powerful tool for understanding customer interactions and preferences.

Extensive Use Cases BestPlace's capabilities stretch across various applications, offering value in areas such as: Competition Analysis: Identify your competitors, analyze their performance, and understand your standing in the market with our extensive POI database and retail data analytics capabilities. New Location Search: Use our rich retail store database to identify ideal locations for store expansions based on foot traffic data, proximity to key points, and potential customer demographics.

Xverum’s Point of Interest (POI) Data is a comprehensive dataset of 230M+ verified locations, covering businesses, commercial properties, and public places across 5000+ industry categories. Our dataset enables retailers, investors, and GIS professionals to make data-driven decisions for business expansion, location intelligence, and geographic analysis.

With regular updates and continuous POI discovery, Xverum ensures your mapping and business location models have the latest data on business openings, closures, and geographic trends. Delivered in bulk via S3 Bucket or cloud storage, our dataset integrates seamlessly into geospatial analysis, market research, and navigation platforms.

🔥 Key Features:

📌 Comprehensive POI Coverage ✅ 230M+ global business & location data points, spanning 5000+ industry categories. ✅ Covers retail stores, corporate offices, hospitality venues, service providers & public spaces.

🌍 Geographic & Business Location Insights ✅ Latitude & longitude coordinates for accurate mapping & navigation. ✅ Country, state, city, and postal code classifications. ✅ Business status tracking – Open, temporarily closed, permanently closed.

🆕 Continuous Discovery & Regular Updates ✅ New business locations & POIs added continuously. ✅ Regular updates to reflect business openings, closures & relocations.

📊 Rich Business & Location Data ✅ Company name, industry classification & category insights. ✅ Contact details, including phone number & website (if available). ✅ Consumer review insights, including rating distribution (optional feature).

📍 Optimized for Business & Geographic Analysis ✅ Supports GIS, navigation systems & real estate site selection. ✅ Enhances location-based marketing & competitive analysis. ✅ Enables data-driven decision-making for business expansion & urban planning.

🔐 Bulk Data Delivery (NO API) ✅ Delivered in bulk via S3 Bucket or cloud storage. ✅ Available in structured formats (.csv, .json, .xml) for seamless integration.

🏆 Primary Use Cases:

📈 Business Expansion & Market Research 🔹 Identify key business locations & competitors for strategic growth. 🔹 Assess market saturation & regional industry presence.

📊 Geographic Intelligence & Mapping Solutions 🔹 Enhance GIS platforms & navigation systems with precise POI data. 🔹 Support smart city & infrastructure planning with location insights.

🏪 Retail Site Selection & Consumer Insights 🔹 Analyze high-traffic locations for new store placements. 🔹 Understand customer behavior through business density & POI patterns.

🌍 Location-Based Advertising & Geospatial Analytics 🔹 Improve targeted marketing with location-based insights. 🔹 Leverage geographic data for precision advertising & customer segmentation.

💡 Why Choose Xverum’s POI Data? - 230M+ Verified POI Records – One of the largest & most structured business location datasets available. - Global Coverage – Spanning 249+ countries, covering all major business categories. - Regular Updates & New POI Discoveries – Ensuring accuracy. - Comprehensive Geographic & Business Data – Coordinates, industry classifications & category insights. - Bulk Dataset Delivery (NO API) – Direct access via S3 Bucket or cloud storage. - 100% GDPR & CCPA-Compliant – Ethically sourced & legally compliant.

Access Xverum’s 230M+ POI Data for business location intelligence, geographic analysis & market research. Request a free sample or contact us to customize your dataset today!

This is a graphical polygon dataset which depicts concentrated downtown retail, service office and mixed uses in the existing downtown business district. Major/regional shopping centers are permitted, but urban design standards are required in order to maintain a neighborhood commercial scale, to promote pedestrian activity and to maintain the unique character of the center.

Built-Up Areas are man-made land cover features, ranging from small hamlets at rural cross roads to large cities.

This product requires the use of GIS software.

*[GIS]: geographic information system

More Metadata

Data updated daily.

Address points mark the location of each addressable structure and its access point. The access point is the place where a driveway intersects the road. The building point is where the structure is located. An addressable structure is one where people live, work, or gather. A 1 to 5-digit number designates an address. Purpose: The access point is used to assign an address to the structure. Addresses are also assigned to assist in the provision of emergency services; they can be queried at all Fire and Rescue stations and by E-911 dispatchers. Supplemental Information: Data are stored in the corporate ArcSDE Geodatabase as a feature class. The coordinate system is Virginia State Plane (North), Zone 4501, datum NAD83 HARN. Maintenance and Update Frequency: Daily Completeness Report: Features may have been eliminated or generalized due to scale and intended use. To assist Loudoun County, Virginia in the maintenance of the data, please provide any information concerning discovered errors, omissions, or other discrepancies found in the data.

Data Owner: Office of Mapping and Geographic Information

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.

Database file was created to support The Office of Economic Development ArcGIS.com application and to use in conjunction with ESRI Business Online Analyst. The data was provided by Data Axle in February 2023. The purpose of the map is to view business data in relation to local zoning and land use, aerial photography, and relevant demographic data.By mining multiple data sources, Data Axle delivers a database that is that is the most robust and comprehensive available. We compile our databases from a number of sources including: GIS projects require the use of comprehensive data sets in order to perform a detailed analysis. Data Axle is widely recognized as providing the most comprehensive solution in the market – with more than 160+ business data elements to select from. A sample listing of the type of attributes collected are listed below.• Hundreds of county-level public sources, publications of record and Secretaries of State for new business registrations• Utility connects and disconnects nationwide• Industry & tourism directories• User generated feedback• Postal processing (NCOALink®, DPV®, LACSLink®, DSF®)• 4,000+ U.S. Yellow and White Page directories• Business name• Full address (Location, mailing, and landmark)• Type of business (Yellow Page heading, SIC and NAICS codes)• ZIP Code™ (including ZIP + 4®)• Telephone number• Fax number (where available)• Website addresses• Number of employees• Sales volume• Name, title, and gender of key executives• Franchise and brand information• Year the business was established• Headquarters, branch, and subsidiary information• Stock exchange and ticker symbol• Latitude, longitude and parcel-level geocodes• News headlines• UCC filings and bankruptcy notices (where available)• Square footage of business campus

In March 2020, Mayor Carter announced the Saint Paul Bridge Fund to provide emergency relief for families and small businesses most vulnerable to the economic impacts of the COVID-19 pandemic. The program was funded through $3.25 million dollars from the Saint Paul Housing and Redevelopment Authority along with contributions from philanthropic, corporate and individual donors. Through these additional contributions, the fund provided $4.1 million to families and small businesses in Saint Paul.More than 5,200 applications applied for a family grant of $1,000• 64% were from ACP50 areas (Areas of Concentrated Poverty where 50% or more of the residents are people of color)The applications were reviewed in order of a random number assigned at application close. Of these applications:• 1,265 families were awarded a $1000 grant- 63% were from ACP50 areas- 66% indicated they are renters- 37% cited layoff or furlough as contributing to their economic hardship- 22% cited reduced hours as contributing to their economic hardship- 19% are unbankedThis is a de-identified dataset of the families who applied for the Bridge Fund and includes:• Self-reported survey responses• Award information• Geographic informationAdditional information about the Saint Paul Bridge Fund may be found at stpaul.gov/bridge-fund

More MetadataThis layer identifies existing structures within Loudoun County and their current Land Use. The existing structures data source is Loudoun County VA, Office of Mapping & Geographic Information's (OMAGI) addressable structure layer for all of Loudoun County, VA.All residential uses, which includes Single Family, Multi-Family and Group Quarter uses, and Commercial structures (Offices, Retail, Medical Offices, Data Centers, etc) are speciified. The other uses specifically identified are Vacant (Address point assigned but no building permit issued), Miscellaneous (no employment generating), and Multi-Use (2 different uses).

Querying BigQuery tables You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.github_repos.[TABLENAME].

• Project: "bigquery-public-data"
• Table: "utility_us"

Fork this kernel to get started to learn how to safely manage analyzing large BigQuery datasets.

If you're using Python, you can start with this code:

import pandas as pd
from bq_helper import BigQueryHelper
bq_assistant = BigQueryHelper("bigquery-public-data", "utility_us")

HUBZone is a United States Small Business Administration (SBA) program for small companies that operate and employ people in Historically Underutilized Business Zones (HUBZones). The HUBZone program was created in response to the HUBZone Empowerment Act created by the US Congress in 1998. These areas offer incentives to new small businesses.

The USGS National Hydrography Dataset (NHD) Downloadable Data Collection from The National Map (TNM) is a comprehensive set of digital spatial data that encodes information about naturally occurring and constructed bodies of surface water (lakes, ponds, and reservoirs), paths through which water flows (canals, ditches, streams, and rivers), and related entities such as point features (springs, wells, stream gages, and dams). The information encoded about these features includes classification and other characteristics, delineation, geographic name, position and related measures, a "reach code" through which other information can be related to the NHD, and the direction of water flow. The network of reach codes delineating water and transported material flow allows users to trace movement in upstream and downstream directions. In addition to this geographic information, the dataset contains metadata that supports the exchange of future updates and improvements to the data. The NHD supports many applications, such as making maps, geocoding observations, flow modeling, data maintenance, and stewardship. For additional information on NHD, go to https://www.usgs.gov/core-science-systems/ngp/national-hydrography.

DWR was the steward for NHD and Watershed Boundary Dataset (WBD) in California. We worked with other organizations to edit and improve NHD and WBD, using the business rules for California. California's NHD improvements were sent to USGS for incorporation into the national database. The most up-to-date products are accessible from the USGS website. Please note that the California portion of the National Hydrography Dataset is appropriate for use at the 1:24,000 scale.

For additional derivative products and resources, including the major features in geopackage format, please go to this page: https://data.cnra.ca.gov/dataset/nhd-major-features Archives of previous statewide extracts of the NHD going back to 2018 may be found at https://data.cnra.ca.gov/dataset/nhd-archive.

In September 2022, USGS officially notified DWR that the NHD would become static as USGS resources will be devoted to the transition to the new 3D Hydrography Program (3DHP). 3DHP will consist of LiDAR-derived hydrography at a higher resolution than NHD. Upon completion, 3DHP data will be easier to maintain, based on a modern data model and architecture, and better meet the requirements of users that were documented in the Hydrography Requirements and Benefits Study (2016). The initial releases of 3DHP will be the NHD data cross-walked into the 3DHP data model. It will take several years for the 3DHP to be built out for California. Please refer to the resources on this page for more information.

The FINAL,STATIC version of the National Hydrography Dataset for California was published for download by USGS on December 27, 2023. This dataset can no longer be edited by the state stewards.

The first public release of the 3D Hydrography Program map service may be accessed at https://hydro.nationalmap.gov/arcgis/rest/services/3DHP_all/MapServer.

Questions about the California stewardship of these datasets may be directed to nhd_stewardship@water.ca.gov.

The Business Locations service depicts the approximate location of many of the businesses within the city of Alpharetta, however this is not a comprehensive list of all businesses. The service is used in the AlphaGIS public GIS Mapping Application.

All businesses operating within the City of Long Beach must obtain a business license, including out-of-city or home-based businesses. The City tracks business license records in a database system called Infor, previously known as Hansen. The symbology for this layer uses a field called 'Milestone simple' which is based on a query of 'Milestone' and 'License Status' fields. If the Milestone is 'Closed,' the business is likely closed and the record is present for historical purposes. This hosted view layer is updated nightly by a script. For more information about business licenses, please see the City of Long Beach Financial Management website: www.longbeach.gov/finance/business-license/

Unlock precise, high-quality GIS data covering 164M+ verified locations across 220+ countries. With 50+ enriched attributes including coordinates, building structures, and spatial geometry our dataset provides the granularity and accuracy needed for in-depth spatial analysis. Powered by AI-driven enrichment and deduplication, and backed by 30+ years of expertise, our GIS solutions support industries ranging from mapping and navigation to urban planning and market analysis, helping businesses and organizations make smarter, data-driven decisions.

Key use cases of GIS Data helping our customers :

1. Optimize Mapping & Spatial Analysis : Use GIS data to analyse landscapes, urban infrastructure, and competitor locations, ensuring data-driven planning and decision-making.
2. Enhance Navigation & Location-Based Services : Improve real-time route planning, asset tracking, and EV charging station discovery for seamless location-based experiences.
3. Identify Strategic Sites for Business Expansion : Leverage GIS intelligence to select optimal retail sites, franchise locations, and warehouses with precision.
4. Improve Logistics & Address Accuracy : Streamline delivery networks, validate addresses, and optimize courier routes to boost efficiency and customer satisfaction.
5. Support Environmental & Urban Development Initiatives : Utilize GIS insights for disaster preparedness, sustainable city planning, and land-use management.

We started with 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, 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.

Unlock precise, high-quality GIS data covering 46M+ verified locations across North America. With 50+ enriched attributes including coordinates, building structures, and spatial geometry our dataset provides the granularity and accuracy needed for in-depth spatial analysis. Powered by AI-driven enrichment and deduplication, and backed by 30+ years of expertise, our GIS solutions support industries ranging from mapping and navigation to urban planning and market analysis, helping businesses and organizations make smarter, data-driven decisions.

Key use cases of GIS Data helping our customers :

1. Optimize Mapping & Spatial Analysis : Use GIS data to analyse landscapes, urban infrastructure, and competitor locations, ensuring data-driven planning and decision-making.
2. Enhance Navigation & Location-Based Services : Improve real-time route planning, asset tracking, and EV charging station discovery for seamless location-based experiences.
3. Identify Strategic Sites for Business Expansion : Leverage GIS intelligence to select optimal retail sites, franchise locations, and warehouses with precision.
4. Improve Logistics & Address Accuracy : Streamline delivery networks, validate addresses, and optimize courier routes to boost efficiency and customer satisfaction.
5. Support Environmental & Urban Development Initiatives : Utilize GIS insights for disaster preparedness, sustainable city planning, and land-use management.

These data include the individual responses for the City of Tempe Annual Business Survey conducted by ETC Institute. These data help determine priorities for the community as part of the City's on-going strategic planning process. Averaged Business Survey results are used as indicators for city performance measures. The performance measures with indicators from the Business Survey include the following (as of 2023):1. Financial Stability and Vitality5.01 Quality of Business ServicesThe location data in this dataset is generalized to the block level to protect privacy. This means that only the first two digits of an address are used to map the location. When they data are shared with the city only the latitude/longitude of the block level address points are provided. This results in points that overlap. In order to better visualize the data, overlapping points were randomly dispersed to remove overlap. The result of these two adjustments ensure that they are not related to a specific address, but are still close enough to allow insights about service delivery in different areas of the city.Additional InformationSource: Business SurveyContact (author): Adam SamuelsContact E-Mail (author): Adam_Samuels@tempe.govContact (maintainer): Contact E-Mail (maintainer): Data Source Type: Excel tablePreparation Method: Data received from vendor after report is completedPublish Frequency: AnnualPublish Method: ManualData DictionaryMethods:The survey is mailed to a random sample of businesses in the City of Tempe. Follow up emails and texts are also sent to encourage participation. A link to the survey is provided with each communication. To prevent people who do not live in Tempe or who were not selected as part of the random sample from completing the survey, everyone who completed the survey was required to provide their address. These addresses were then matched to those used for the random representative sample. If the respondent’s address did not match, the response was not used.To better understand how services are being delivered across the city, individual results were mapped to determine overall distribution across the city.Processing and Limitations:The location data in this dataset is generalized to the block level to protect privacy. This means that only the first two digits of an address are used to map the location. When they data are shared with the city only the latitude/longitude of the block level address points are provided. This results in points that overlap. In order to better visualize the data, overlapping points were randomly dispersed to remove overlap. The result of these two adjustments ensure that they are not related to a specific address, but are still close enough to allow insights about service delivery in different areas of the city.The data are used by the ETC Institute in the final published PDF report.

Using the coronavirus infographic template in Business/Community Analyst Web (ArcGIS Blog).

DCCED certified population counts annually from 2011 to present for communities in the State of Alaska. Population counts are noted as DCCED Certified or DOWLD Estimates.Note on use for analysis: This data set mixes scale. It includes rows for boroughs, which contain multiple CDP's and cities from this same data set in many cases. The current naming conventions reflect the most recent data. Boundaries and names for boroughs and CDP's have changed over time. Contact dcraresearchandanalysis@alaska.gov with questions.Source: State of Alaska, Department of Commerce, Community, and Economic Development/Department of Labor and Workforce Development