Echo’s POI dataset delivers commercial point-of-interest data with high coverage and accuracy, empowering businesses with location intelligence for competitive benchmarking, site selection, and market analysis.

Curated with a strong focus on brand-level detail, our 82M+ POI dataset spans retail, food & dining, financial services, and other key sectors. Each POI includes rich metadata, such as opening hours, parent brand, and web presence — enabling a comprehensive view of the physical commercial landscape.

Key data points include: - POI name, category, and subcategory - Address, city, region, and coordinates - Opening hours, phone number, website - Parent brand and organization hierarchy - Over 100 business categories

POI data covering Europe, North America, and Latin America, this dataset supports use cases such as retail expansion, competitive intelligence, territory planning, and data enrichment for analytics platforms.

The data conclude two parts, i.e. shapefiles and excel files. The latter collected POI information of Beijing about the geographical coordinates, rating and other related description fields up to Aug 2017.

dataplor's Global Point of Interest (POI) Dataset provides expansive location and detailed geographic data used by businesses worldwide to enhance their operational strategies.

dataplor’s POI data offers a rich set of 55+ attributes that provide in-depth insights into each location. Key data attributes include:

• Business name
• Business Category
• Product Category
• Address
• Latitude and longitude
• NAIC
• SIC
• H3 Index
• Phone number
• Website
• Opening hours

This is a set of POI data sets of Shenzhen, Guangzhou, Beijing, and Shanghai cities, China.

Our dataset provides detailed and precise insights into the business, commercial, and industrial aspects of any given area in the USA (Including Point of Interest (POI) Data and Foot Traffic. The dataset is divided into 150x150 sqm areas (geohash 7) and has over 50 variables. - Use it for different applications: Our combined dataset, which includes POI and foot traffic data, can be employed for various purposes. Different data teams use it to guide retailers and FMCG brands in site selection, fuel marketing intelligence, analyze trade areas, and assess company risk. Our dataset has also proven to be useful for real estate investment.- Get reliable data: Our datasets have been processed, enriched, and tested so your data team can use them more quickly and accurately.- Ideal for trainning ML models. The high quality of our geographic information layers results from more than seven years of work dedicated to the deep understanding and modeling of geospatial Big Data. Among the features that distinguished this dataset is the use of anonymized and user-compliant mobile device GPS location, enriched with other alternative and public data.- Easy to use: Our dataset is user-friendly and can be easily integrated to your current models. Also, we can deliver your data in different formats, like .csv, according to your analysis requirements. - Get personalized guidance: In addition to providing reliable datasets, we advise your analysts on their correct implementation.Our data scientists can guide your internal team on the optimal algorithms and models to get the most out of the information we provide (without compromising the security of your internal data).Answer questions like: - What places does my target user visit in a particular area? Which are the best areas to place a new POS?- What is the average yearly income of users in a particular area?- What is the influx of visits that my competition receives?- What is the volume of traffic surrounding my current POS?This dataset is useful for getting insights from industries like:- Retail & FMCG- Banking, Finance, and Investment- Car Dealerships- Real Estate- Convenience Stores- Pharma and medical laboratories- Restaurant chains and franchises- Clothing chains and franchisesOur dataset includes more than 50 variables, such as:- Number of pedestrians seen in the area.- Number of vehicles seen in the area.- Average speed of movement of the vehicles seen in the area.- Point of Interest (POIs) (in number and type) seen in the area (supermarkets, pharmacies, recreational locations, restaurants, offices, hotels, parking lots, wholesalers, financial services, pet services, shopping malls, among others). - Average yearly income range (anonymized and aggregated) of the devices seen in the area.Notes to better understand this dataset:- POI confidence means the average confidence of POIs in the area. In this case, POIs are any kind of location, such as a restaurant, a hotel, or a library. - Category confidences, for example"food_drinks_tobacco_retail_confidence" indicates how confident we are in the existence of food/drink/tobacco retail locations in the area. - We added predictions for The Home Depot and Lowe's Home Improvement stores in the dataset sample. These predictions were the result of a machine-learning model that was trained with the data. Knowing where the current stores are, we can find the most similar areas for new stores to open.How efficient is a Geohash?Geohash is a faster, cost-effective geofencing option that reduces input data load and provides actionable information. Its benefits include faster querying, reduced cost, minimal configuration, and ease of use.Geohash ranges from 1 to 12 characters. The dataset can be split into variable-size geohashes, with the default being geohash7 (150m x 150m).

SafeGraph Places provides baseline Point of Interest (POI) 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 point of interests 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.

The size and share of the market is categorized based on Application (Commercial Use, Industrial Use, Public Utilities, Other) and Product (Database Platform, Modular Customized Reports) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

Massive POI dataset covering 3.5 million locations across multiple industries in US and Canada. Includes automotive, retail, food and dining, healthcare, education, and more. Essential for comprehensive market analysis and strategic planning across various sectors.

Point of Interest (POI) data is a critical source in urban geography for uncovering city structure and reflecting patterns of urban activity. Assessing and enhancing the effectiveness of POI data is fundamental. However, most current methods for evaluating POI validity rely on census data and overlook the dynamic nature of POIs, which greatly limits the effectiveness of evaluation methods and the accuracy of their results. This paper proposes a new approach to assess POI validity by leveraging the dynamic changes of POIs. To find changed POIs without relying on census data, the approach selected a sample of POIs, assumed to approximate the characteristics of the overall set, and conducted field research to track their dynamic changes and calculate their validity. Then the validity of the sample POIs is used to approximate the overall validity. The results of POI data validity assessment in Changzhou, China, show that considering dynamic changes increases the validity of POI data. The new sampling-based approach for collecting dynamic changes in POI, as well as insights on sampling priorities for collecting dynamic changes, not only overcome the limitations of existing research concerning reference data and research costs, but open a new direction for POI data validity studies.

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.

Our unparalleled combination of points-of-interest (POI) data enriched with Sentiment and Foot Traffic Data KPIs will empower your decision making. We provide the most accurate and comprehensive POI / Location data, enriched with Business Listings Data and customer insights.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) :

tags['amenity'] IS NOT NULL OR tags['man_made'] IS NOT NULL OR tags['shop'] IS NOT NULL OR tags['tourism'] IS NOT NULL

Features may have these attributes:

• name
• name:en
• amenity
• man_made
• shop
• tourism
• opening_hours
• beds
• rooms
• addr:full
• addr:housenumber
• addr:street
• addr:city
• source
• name:th

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This feature layer provides access to OpenStreetMap (OSM) point of interest (POI) data for North America, which is updated every 1-2 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. The layer includes POIs with a large number of tags, including amenity, shop, tourism, and several more.Zoom in to large scales (e.g. City level or 1:40k scale) to see the POI features display. You can click on the feature to get the name of the POI. The name of the POI 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 POI layer displaying just one or two 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. amenity is bar or shop is alcohol), 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 that are ready to use, but not for every type of amenity.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.

Xtract.io offers comprehensive POI and Polygon data, featuring 6 million locations across 11 industries. With global coverage and detailed geospatial data, get custom polygons drawn for the points of interest you choose.

This dataset contains the data presented in the paper GenUP: Generative User Profilers as In-Context Learners for Next POI Recommender Systems. Code: https://github.com/w11wo/GenUP

The Points of Interest (POI) web service provides the identification and location of a feature, service or activity that people may want to see, know about or visit. POI features for this service are primarily derived from features maintained within the Digital Topographic Database (DTDB). The POI feature class is maintained programmatically (automated) by sourcing spatial and aspatial attributes from other feature classes in the DTDB that contain POI features. The midpoint of a line or polygon features is used to define the POI. Points of Interest include features related to Community, Education, Recreation, Transportation, Utility, or Hydrography, Physiography and Place, and defined as a place with a prescribed name. The attribute information for an individual dataset may have been thinned or modifed to cater for the service. The service is available in a cached environment only. This dataset is compliant with the NSW FSDF and its specifications. For details information for each individual dataset contained in this web services.

NOTE: Please contact the Customer HUB https://customerhub.spatial.nsw.gov.au/ for advice on datasets access.

Connect with our experts to access accurate Global Point of Interest (POI) Data, enriched with everything you need to analyse POI distribution, customer sentiment, and footfall for any location around the globe. Explore Location Data and Map Data across 180+ countries. Coverage since 2019.

Techsalerator covers all POI's (Point of Interest) of businesses in Italy. Through our diligent local sourcing, we are able to provide location of businesses in Italy.

This include: address/Zip Code/ County / State/ Country + Lat/Long.

Companies utilize our POI's to enhance their publishing universe, do geo-mapping and to create identity graphs.

This repository contains the dataset from the paper GenUP: Generative User Profilers as In-Context Learners for Next POI Recommender Systems. This dataset is used for generating natural language user profiles from large-scale location-based social network check-ins. It can be used to improve POI (Point of Interest) prediction accuracy while offering enhanced transparency. Code: https://github.com/w11wo/GenUP

Understanding Places And Poi Apis
This dataset falls under the category Traffic Generating Parameters.
It contains the following data: POIs, which come with map tiles
This dataset was scouted on 01/21/2022 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://developer.tomtom.com/blog/decoded/understanding-places-and-poi-apis