SafeGraph Places provides baseline location information for every record in the SafeGraph product suite via the Places schema and polygon information when applicable via the Geometry schema. Company dataset can be used to ingest a list of business information such as phone number for lead generation. 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.

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.

SafeGraph Places provides baseline location information and addresses 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.

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.

SafeGraph Places provides baseline location information for every record in the SafeGraph product suite via the Places schema and polygon information when applicable via the Geometry schema. B2B marketing dataset can be used to ingest a list of companies and information such as phone number for lead generation. 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.

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.

Our Location Intelligence Data provides a detailed view of people’s movements across over 14 million physical locations worldwide. This aggregated and anonymized data is utilized to understand visit patterns and volumes at specific sites. Compiled from diverse global data sources, this information offers valuable context for analyzing foot traffic and location engagement.

Location Intelligence Data Reach

Our Location Intelligence Data delivers in-depth insights into Points of Interest (POIs), places, and Out-of-Home (OOH) advertising locations.By leveraging Factori's Mobility & People Graph data, which integrates information from numerous sources globally, we provide accurate foot-traffic attribution. For instance, to calculate foot traffic at a specific location, we combine attributes such as location ID, day of the week, and time of day, generating up to 40 distinct data records for each POI.

Data Export Methodology

We dynamically gather and update data, delivering the most current insights through methods tailored to your needs, whether daily, weekly, or monthly.

Use Cases

Our Location Intelligence Data is essential for credit scoring, retail analytics, market intelligence, and urban planning, offering businesses and organizations critical insights for strategic decision-making and planning.

Our building footprint dataset maps locations with precise polygons, enabling accurate foot traffic attribution, area analysis, and location-based strategy.

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 location is defined as any place humans can visit with the exception of single-family homes. This definition encompasses a diverse set of a location ranging from restaurants, grocery stores, and malls; to parks, hospitals, museums, offices, and industrial parks. Premium sets of Places include locations such as 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 location datasets on 51M+ physical locations globally. Hundreds of industry leaders like Mapbox, Verizon, Clear Channel, and Esri already rely on SafeGraph POI data and CPG to unlock business insights and drive innovation.

Our Location Intelligence Data connects people's movements to over 14M physical locations globally. These are aggregated and anonymized data that are only used to offer context for the volume and patterns of visits to certain locations. This data feed is compiled from different data sources around the world.

Location Intelligence Data Reach: Location Intelligence data brings the POI/Place/OOH level insights calculated based on Factori’s Mobility & People Graph data aggregated from multiple data sources globally. To achieve the desired foot-traffic attribution, specific attributes are combined to bring forward the desired reach data. For instance, to calculate the foot traffic for a specific location, a combination of location ID, day of the week, and part of the day can be combined to give specific location intelligence data. There can be a maximum of 40 data records possible for one POI based on the combination of these attributes.

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method at a suitable interval (daily/weekly/monthly).

Use Case:

Consumer Insights Gain a complete 360-degree view of the customer to detect behavioral changes, assess patterns, and forecast business effects.

Data Enrichment Leverage O2O consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment.

Sales Forecasting Analyze consumer behavior to predict sales and monitor performance of investments

Retail Analytics Analyze footfall trends in various locations and gain understanding of customer personas.

Geofencing: Geofencing involves creating virtual boundaries around physical locations, enabling businesses to trigger actions when users enter or exit these areas

Geo-Targeted Advertising: Utilizing location-based insights, businesses can deliver highly personalized advertisements to consumers based on their proximity to relevant POIs.

Marketing Campaign Strategy: Analyzing visitor demographics and behavior patterns around POIs, businesses can tailor their marketing strategies to effectively reach their target audience.

Site Selection: By assessing the proximity to relevant POIs such as competitors, customer demographics, and economic indicators, organizations can make informed decisions about opening new locations.

OOH/DOOH Campaign Planning: Identify high-traffic locations and understand consumer behavior in specific areas, to execute targeted advertising strategies effectively.

Data Attributes Included: Anonymous id poi_id name description category category_id full_address address city state zip country_code phone url domain rating price_level rating_distribution is_claimed photo_url attributes brand_name brand_id status total_photos popular_times places_topics people_also_search work_hours local_business_links contact_info reviews count naics_code naics_code_description sic_code sic_code_description shape_type shape_polugon geometry_location_type geometry_viewport_northeast_lat geometry_viewport_northeast_lng geometry_viewport_southwest_lat geometry_viewport_southwest_lng geometry_location_lat geometry_location_lng calculated_geo_hash_8 building_id building_name building_type id_type gender age carrier make model os os_version home_country home_geohash work_geohash affluence brands_visited places_categories geo_behaviour interests device_age device_price travelled_countries

This dataset provides granular, attribution-ready records of website leads, capturing detailed marketing channel parameters (UTM), user device and location data, and conversion values. It enables robust ROI analysis, campaign performance tracking, and optimization of marketing spend by linking every lead to its source and outcome.

Unify user profiles across devices with Redmob's Identity Graph Data. Built in-house for adtech, it links online and offline identifiers to enable precise targeting and attribution.

SafeGraph Places provides baseline location 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.

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.

Echo’s boundary dataset maps locations with precise polygons, enabling accurate foot traffic attribution, area analysis, and location-based strategy.

This metadata file describes a comma separated values (csv) file of data used to support attribution of trends and change points in annual peak streamflows observed at gages in the Northeast region. The file includes USGS gage identification and location information, developed land cover and basin storage data, correlation results between annual peak magnitudes and precipitation and Palmer Drought Severity Index (PDSI), and trend and change point results for precipitation and PDSI.

A geo-spatial layer depicting the approximate location of pathways as of November 2015 Please note that as part of the attribution of this data under the CC BY licence terms with which it is …Show full descriptionA geo-spatial layer depicting the approximate location of pathways as of November 2015 Please note that as part of the attribution of this data under the CC BY licence terms with which it is supplied, users should include the following statement: 'The information is provided to assist in field investigations. All locations, dimensions and depths shown are to be confirmed on site'. The City of Gold Coast is not a professional information provider and makes no representations or warranties about the accuracy, reliability, completeness or suitability for any particular purpose of the Data provided here. This Data is not provided with the intent that any person will rely on it for the purpose of making decisions with financial or legal implications. Persons who place such reliance on the Data do so at their own risk.

GIS feature classes of several key City data polygons used to identify a plat location for attribution for reporting and routing. A plat boundary is located and intersected with...

UA34 - Number and location (based on PPSN allocations data) of arrivals from Ukraine. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Number and location (based on PPSN allocations data) of arrivals from Ukraine...

Title: Attributing pedestrian networks with semantic information based on multi-source spatial dataAbstract: The lack of associating pedestrian networks, i.e., the paths and roads used for non-vehicular travel, with information about semantic attribution is a major weakness for many applications, especially those supporting accurate pedestrian routing. Researchers have developed various algorithms to generate pedestrian walkways based on datasets, including high-resolution images, existing map databases, and GPS data; however, the semantic attribution of pedestrian walkways is often ignored. The objective of our study is to automatically extract semantic information including incline values and the different categories of pedestrian paths from multi-source spatial data, such as crowdsourced GPS tracking data, land use data, and motor vehicle road (MVR) networks. Incline values for each pedestrian path were derived from tracking data through elevation filtering using wavelet theory and a similarity-based map-matching method. To automatically categorize pedestrian paths into five classes including sidewalk, crosswalk, entrance walkway, indoor path, and greenway, we developed a hierarchical strategy of spatial analysis using land use data and MVR networks. The effectiveness of our proposed method is demonstrated using real datasets including GPS tracking data collected by volunteers, land use data acquired from OpenStreetMap, and MVR network data downloaded from Gaode Map.

Our POI Data connects people's movements to over 14M physical locations globally. These are aggregated and anonymized data that are only used to offer context for the volume and patterns of visits to certain locations. This data feed is compiled from different data sources around the world. Reach: Location Intelligence data brings the POI/Place/OOH level insights calculated based on Factori’s Mobility & People Graph data aggregated from multiple data sources globally. To achieve the desired foot-traffic attribution, specific attributes are combined to bring forward the desired reach data. For instance, to calculate the foot traffic for a specific location, a combination of location ID, day of the week, and part of the day can be combined to give specific location intelligence data. There can be a maximum of 40 data records possible for one POI based on the combination of these attributes. Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method at a suitable interval (daily/weekly/monthly). Use Cases: Credit Scoring: Financial services can use alternative data to score an underbanked or unbanked customer by validating locations and persona. Retail Analytics: Analyze footfall trends in various locations and gain an understanding of customer personas. Market Intelligence: Study various market areas, the proximity of points or interests, and the competitive landscape Urban Planning: Build cases for urban development, public infrastructure needs, and transit planning based on fresh population data. Data Attributes: Location ID n_visitors day_of_week distance_from_home do_date month part_of_day travelled_countries Visitor_country_origin Visitor_home_origin Visitor_work_origin year

This data set provides industrial-scale onshore wind turbine locations in the United States through July 22, 2013, corresponding facility information, and turbine technical specifications. The database has more than 47,000 wind turbine records that have been collected, digitized, locationally verified, and internally quality controlled. Turbines from the Federal Aviation Administration Digital Obstacle File, through product release date July 22, 2013, were used as the primary source of turbine data points. Verification of the turbine positions was done by visual interpretation using high-resolution aerial imagery in ESRI ArcGIS Desktop. Turbines without Federal Aviation Administration Obstacle Repository System numbers were visually identified and point locations were added to the collection. We estimated a locational error of plus or minus 10 meters for turbine locations. Wind farm facility names were identified from publically available facility data sets. Facility names were then used in a web search of additional industry publications and press releases to attribute additional turbine information (such as manufacturer, model, and technical specifications of wind turbines). Wind farm facility location data from various wind and energy industry sources were used to search for and digitize turbines not in existing databases. Technical specifications for turbines were assigned based on the wind turbine make and model as described in literature, specifications listed in the Federal Aviation Administration Digital Obstacle File, and information on the turbine manufacturer’s website. Some facility and turbine information on make and model did not exist or was difficult to obtain. Thus, uncertainty may exist for certain turbine specifications. That uncertainty was rated and a confidence was recorded for both location and attribution data quality.

A companion table for the Crashes in DC layer. This is a related table linked by field attribution, CRASHID. These crash data are derived from the Metropolitan Police Department's (MPD) crash data management system (COBALT) and represent DDOT's attempt to summarize some of the most requested elements of the crash data. Further, DDOT has attempted to enhance this summary by locating each crash location along the DDOT roadway block line, providing a number of location references for each crash. In the event that location data is missing or incomplete for a crash, it is unable to be published within this dataset.Crash details related table,Type of participant (driver, occupant, bicyclist, pedestrian)Age of participantsIf injured, severity (minor, major, fatal)Type of vehicle (passenger car, large truck, taxi, government, bicycle, pedestrian, etc)If persons issued a ticketIf a vehicle, the state (jurisdiction) license plate was issued (not license plate number)Are any persons deemed ‘impaired’Was person in vehicle where speeding was indicatedRead more at https://ddotwiki.atlassian.net/wiki/spaces/GIS0225/pages/2053603429/Crash+Data. Questions on the contents of these layers should be emailed to Metropolitan Police Department or the DDOT Traffic Safety Division. Questions regarding the Open Data DC can be sent to @OpenDataDC.

A geo-spatial layer depicting the approximate location and construction of platforms as of November 2015\r \r Please note that as part of the attribution of this data under the CC BY licence terms with which it is supplied, users should include the following statement: 'The information is provided to assist in field investigations. All locations, dimensions and depths shown are to be confirmed on site'.\r \r The City of Gold Coast is not a professional information provider and makes no representations or warranties about the accuracy, reliability, completeness or suitability for any particular purpose of the Data provided here. This Data is not provided with the intent that any person will rely on it for the purpose of making decisions with financial or legal implications. Persons who place such reliance on the Data do so at their own risk.\r