Our Point Of Interest (POI) Data links people's movements to over 14 million physical locations worldwide. This aggregated and anonymized data provides context for visit volumes and patterns, compiled from diverse global sources.

Reach

We calculate POI, Place, and OOH level insights using Factori's Mobility & People Graph data from multiple sources. To attribute foot traffic accurately, we combine specific attributes such as location ID, day of the week, and time of day, yielding up to 40 possible data records for a single POI. This method ensures precise location intelligence data.

Data Export Methodology

Our dynamic data collection process ensures the most up-to-date information and insights are delivered at optimal intervals, whether daily, weekly, or monthly.

Use Cases

Point Of Interest (POI) Data is invaluable for credit scoring, retail analytics, market intelligence, and urban planning, providing a robust foundation for data-driven decision-making and strategic planning.

Xtract.io's massive 3.5M+ POI database represents a transformative resource for comprehensive location intelligence across the United States and Canada. Big data analysts, market researchers, and strategic planners can utilize these comprehensive places data insights to develop sophisticated market strategies, conduct advanced spatial analysis, and gain a deep understanding of regional geographical landscapes.

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 with comprehensive POI coverage.

LocationsXYZ, the POI data product from Xtract.io, offers a comprehensive POI 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 for Comprehensive Location Data? At LocationsXYZ, we: -Deliver 3.5M+ POI data with 95% accuracy -Refresh places data every 30, 60, or 90 days to ensure the most recent information -Create on-demand comprehensive POI datasets tailored to your specific needs -Handcraft boundaries (geofences) for locations to enhance accuracy -Provide multi-industry POI data and polygon data in multiple file formats

Unlock the Power of Places Data With our comprehensive location intelligence, you can: -Perform thorough market analyses across multiple industries -Identify the best locations for new stores using POI database insights -Gain insights into consumer behavior with places data -Achieve an edge with competitive intelligence using comprehensive coverage

LocationsXYZ has empowered businesses with geospatial insights and comprehensive 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 3.5M+ POI database.

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.

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).

ACCOMMODATION: 210012

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.

Introduction

The Global Points-Of-Interest (POI) Data Solutions Market is expected to reach USD 8.00 billion by 2034, growing from USD 3.03 billion in 2024, at a robust CAGR of 10.2% during the forecast period from 2025 to 2034. In 2024, North America held the largest market share, capturing more than 35%, with a revenue of USD 1.06 billion.

The increasing demand for location-based services, enhanced navigation, and personalized user experiences is are key factor driving this growth. POI data solutions, which provide critical information about specific locations, are becoming essential in sectors like retail, transportation, and tourism.

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.

Global Points Of Interest Poi Data Solution market size 2025 was XX Million. Points Of Interest Poi Data Solution Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

By 2034, the Points-Of-Interest (POI) Data Solutions Market is expected to reach a valuation of USD 8 bn, expanding at a healthy CAGR of 10%.

Points-of-Interest (POI) Data Solutions Market Outlook

The Points-of-Interest (POI) Data Solutions market is experiencing robust growth, with a market size valued at approximately $2.3 billion in 2023. This market is expected to expand at a compound annual growth rate (CAGR) of 11.5% from 2024 to 2032, reaching an estimated value of $5.9 billion by 2032. The key drivers of this growth include the increasing adoption of location-based services, advancements in geospatial analytics, and the rising demand for personalized customer experiences across various industries.

One of the primary growth factors in the POI data solutions market is the rapid proliferation of mobile devices and the subsequent demand for location-based services. As consumers increasingly rely on smartphones and other mobile devices for navigation, shopping, and social interaction, businesses are investing heavily in POI data to enhance their services. The ability to offer real-time location-based information and personalized experiences is becoming a crucial differentiator for companies looking to engage consumers more effectively and gain a competitive edge. Furthermore, the integration of artificial intelligence and machine learning technologies with POI data is enabling more accurate predictions and improved decision-making, further driving market growth.

Another significant factor contributing to the market's expansion is the growing need for advanced navigation and mapping solutions. With the advent of autonomous vehicles and smart city initiatives, the demand for precise and comprehensive POI data is on the rise. Governments and private sector companies are increasingly investing in geospatial data infrastructure to support these initiatives, thereby fueling the demand for POI data solutions. Additionally, in industries such as transportation, logistics, and real estate, the ability to leverage detailed POI data for route optimization, asset tracking, and location analysis is enhancing operational efficiencies and driving market growth.

The marketing and advertising sector also plays a pivotal role in the expansion of the POI data solutions market. Businesses are leveraging POI data to create targeted marketing campaigns and gain insights into consumer behavior. By understanding the demographic and behavioral patterns associated with specific locations, companies can tailor their advertising strategies to reach their target audience more effectively. This trend is particularly pronounced in the retail sector, where location-based marketing is becoming increasingly prevalent. As businesses continue to recognize the value of hyper-local marketing and the role of POI data in driving customer engagement, the market is poised for further growth.

Regionally, the Points-of-Interest Data Solutions market is witnessing varying levels of growth across different geographies. North America currently holds the largest market share, owing to the early adoption of advanced technologies and the presence of major industry players. However, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period. The increasing penetration of smartphones, coupled with the rapid development of smart city projects in countries like China and India, is driving the demand for POI data solutions. Meanwhile, Europe and Latin America are also showing steady growth, fueled by advancements in geospatial technology and increasing investments in digital infrastructure.

Component Analysis

The Points-of-Interest (POI) Data Solutions market is broadly segmented into software and services components. On the software side, the increasing demand for advanced analytics and data management solutions is a significant growth driver. Businesses are adopting sophisticated software platforms that enable them to efficiently gather, process, and analyze POI data. These platforms often incorporate features like real-time data integration, machine learning algorithms, and predictive analytics, allowing companies to derive actionable insights from their data. The software component is further boosted by the rise of cloud computing, which offers scalable solutions that can handle large volumes of data with ease.

In contrast, the services segment encompasses a wide array of offerings, including data collection, data integration, consulting, and support services. As organizations strive to leverage POI data more effectively, there is a growing need for expert guidance and support. Service providers are playing a crucial role in helping companies navigate the complexities of data integration and management. The

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.

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

This core point of interest dataset consists of 1M location information of retail stores in the US and Canada. The POI database includes electronic stores, supermarkets and groceries, specialty retailers, home improvement and convenience stores, and apparel and accessories shops.

Official POI (points of interest) from Editus Luxembourg. The points of interest are split into 14 categories, that can be selected in the data catalog. They are imported from the database of Editus Luxembourg, and are updated in the geoportal several times a year. Editus daily works to update its database, but as synchronisation with the geoportal only happens several times a year, it is possible that some information displayed may be lacking in actuality when you consult the geoportal. It is important to note that some entities from the database are NOT represented in the map due to imcomplete or unprecise spatial reference, as for example in the case of imcomplete addresses or P.O.Boxes. If you notice points that are not correctly placed, do not hesitate to give us feedback on support.geoportail@act.etat.lu

Massive 3.5M+ POI database covering extensive places data across multiple industries in the US and Canada. Includes automotive, retail, food and dining, healthcare, education, and more. Essential for thorough multi-industry market research and strategic planning across various sectors.

The global Points-of-Interest (POI) Data Solutions market is expected to exhibit a remarkable growth trajectory, with a CAGR of XX% during the forecast period of 2025-2033. This market is projected to reach a substantial value of XXX million by 2033, indicating its promising growth prospects. The expansion of this market is primarily driven by the increasing demand for accurate and comprehensive POI data from various industries, including retail, real estate, and transportation. The rise of location-based services and the proliferation of mobile devices have further spurred the demand for reliable POI data to enhance user experiences and provide customized recommendations. Key growth drivers for the POI Data Solutions market include the increasing adoption of smartphones and location-based technologies, the rising demand for data-driven decisions in businesses, and the need for real-time information for navigation and exploration. However, the market may face challenges such as data privacy concerns, the availability of free and open-source POI data, and the dependence on third-party data sources for accuracy and completeness. The market is expected to be dominated by North America and Europe, with a significant presence of major players such as Google Cloud, Factual, and HERE Technologies. Asia Pacific is projected to experience notable growth due to the increasing smartphone penetration and the rapid development of smart cities in the region.

The automated valuation model (AVM) has been widely used by real estate stakeholders to provide accurate property value estimations automatically. Traditional valuation models are subjective and inaccurate, and previous studies have shown that machine learning (ML) approaches perform better in real estate valuation. These valuation models are based on structured tabular data, and few consider integrating multi-source unstructured data such as images. Most previous studies use fixed feature space for model training without considering the model performance variation brought by various feature configuration parameters. To fill these gaps, this study uses Hong Kong as a case study and proposes an enhanced ML-based real estate valuation framework with feature configuration and multi-source image data fusion, including exterior housing photos, street view and remote sensing images. ‌‌Eight ML regressors, namely, Random Forest, Extra Tree, XGBoost, Light Gradient Boosting Machine (LightGBM), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Multiple Linear Regression (MLR) are used to formulate ML pipelines for training. The SHapley Additive exPlanations (SHAP) method is used to examine the effects of images on housing prices. The experimental results show that the model performances using different feature configuration parameters are significantly different, indicating the necessity of feature configuration to obtain more accurate and reliable predictions. Extra Tree performs significantly better than other models. Half of the top 10 significant features are image features, and incorporating multi-source image features can improve property valuation accuracy. Nonlinear associations exist between image features and housing prices, and the spatial distribution patterns of image feature values and corresponding SHAP main effects vary significantly from the city centre to the suburbs. These findings contribute to a better understanding of AVM development with image fusion and the nonlinear associations between image features and housing prices for public authorities, urban planners, and real estate developers.

POI and OpenStreetmap data used in the analysis for the publication in Plos one titled " A City-Wide Examination of Fine-Grained Human Emotions through Social Media Analysis"

The following files are included

SanFranciscoOSM.tsv: Contains the POI dataset from San Francisco

LondonOSM.tsv: Contains the POI dataset from London

LondonPOIWithMatchedEmotions.tsv: Contains the list of each POI location in the Greater London area with information about the averaged emotions of the tweets that were matched with them

SanFranciscoPOIWithMatchedEmotions.tsv: Contains the list of each POI location in SanFrancisco with information about the averaged emotions of the tweets that were matched with them

LondonDaysEmotions.tsv: The list of days with the average of different emotions in Greater London

SanFrancisco-DaysEmotions.tsv: The list of days with the average of different emotions in San Francisco

SanFrancisco_TweetsNearbyPlaceCats10m.tsv: The list of tweets in San Francisco with the number of POI categories within 10m and the different emotions detected on that tweet

SanFrancisco_TweetsNearbyPlaceCats20m.tsv: The list of tweets in San Francisco with the number of POI categories within 20m and the different emotions detected on that tweet

SanFrancisco_TweetsNearbyPlaceCats30m.tsv: The list of tweets in San Francisco with the number of POI categories within 30m and the different emotions detected on that tweet

London_TweetsNearbyPlaceCats10m.tsv: The list of tweets in London with the number of POI categories within 10m and the different emotions detected on that tweet

London _TweetsNearbyPlaceCats20m.tsv: The list of tweets in London with the number of POI categories within 20m and the different emotions detected on that tweet

London _TweetsNearbyPlaceCats30m.tsv: The list of tweets in London with the number of POI categories within 30m and the different emotions detected on that tweet

The data conclude two parts