This dataset is about cities and is filtered where the country includes Italy, featuring 3 columns: city, country, and population. The preview is ordered by population (descending).

This dataset is about cities and is filtered where the country includes Italy, featuring 4 columns: city, country, latitude, and population. The preview is ordered by population (descending).

This dataset is about cities and is filtered where the country includes Italy, featuring 7 columns including city, continent, country, latitude, and longitude. The preview is ordered by population (descending).

Our analysis focuses on seven metropolitan cities across Italy. Here, we report the number of spatial cells of the mobile phone network and the population (in thousands) of each of these cities split across 6 age groups. Population data is retrieved from the 2011 Italian census and comprises all the census sections within the phone cells considered for each city. It is important to highlight that in each cell of the network there can be several mobile phone users, thus we cannot estimate the fraction of the census population included in our data set. Note that the age groups provided by the Italian census do not perfectly match those of the Telecom Italia dataset.

NB This datasets has restricted access due to GDPR considerations. Anna visited Swansea University, Wales, a recently built institution. Photos of the visit were taken by a friend, not by her. The experience was unusual for them coming from a small city in Italy. They were intrigued by modern dances like the Twist, which she hadn't seen before. They were surprised also by boys with long hair. While they were familiar with the Beatles, Italy had not yet seen young people organizing themselves into musical groups like we observed during our visit. In Italy, this type of musical band trend emerged later, perhaps a year or two after our visit to Swansea. In 1966, there was a second exchange involving the Italian group. The Welsh family she stayed with owned a grocery shop. The living quarters were located on the first floor above the shop, situated on the street. During her third exchange in Esslingen, they were taken to Stuttgart airport were they visited an airplane to explore its interior. This was Anna’s first time being inside an airplane, and everything about it was new and exciting for her. For Anna, art and cultural experiences were more important than discussions about economy and work. However, the most important aspect of these exchanges wasn't the places they visited or the activities we did—it was the opportunity to be together with boys and girls from different nationalities. Building connections and sharing experiences with peers from diverse cultures, speaking different languages, was the most enjoyable and valuable part of the exchange for Anna. 50 years have passed, a long time, according to Anna. She has forgotten many facts but remembers feelings. Remembers the feeling of great fun. Anna remembers how surprised she was about many things different from what she had seen till that moment. Friends in a Cold Climate: After the Second World War a number of friendship ties were established between towns in Europe. Citizens, council-officials and church representatives were looking for peace and prosperity in a still fragmented Europe. After a visit of the Royal Mens Choir Schiedam to Esslingen in 1963, representatives of Esslingen asked Schiedam to take part in friendly exchanges involving citizens and officials. The connections expanded and in 1970, in Esslingen, a circle of friends was established tying the towns Esslingen, Schiedam, Udine (IT) Velenje (SL) Vienne (F) and Neath together. Each town of this so called “Verbund der Ringpartnerstädte” had to keep in touch with at least 2 towns within the wider network. Friends in a Cold Climate looks primarily through the eyes the citizen-participant. Their motivation for taking part may vary. For example, is there a certain engagement with the European project? Did parents instil in their children a a message of fraternisation stemming from their experiences in WWII? Or did the participants only see youth exchange only as an opportunity for a trip to a foreign country? This latter motivation of taking part for other than Euro-idealistic reasons should however not be regarded as tourist or consumer-led behaviour. Following Michel de Certeau, Friends in a Cold Climate regards citizen-participants as a producers rather than as a consumers. A participant may "put to use" the Town Twinning facilities of travel and activities in his or her own way, regardless of the programme. Integration of West-Europe after the Second World War was driven by a broad movement aimed at peace, security and prosperity. Organised youth exchange between European cities formed an important part of that movement. This research focuses on young people who, from the 1960s onwards, participated in international exchanges organised by twinned towns, also called jumelage. Friends in a Cold Climate asks about the interactions between young people while taking into account the organisational structures on a municipal level, The project investigates the role of the ideology of a united West-Europe, individual desires for travel and freedom, the upcoming discourse about the Second World War and the influence of the prevalent “counterculture” of that period, thus shedding a light on the formative years of European integration.

This dataset contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes:
- macroregion (admin-1 including region)
- region (admin-2 including state, province, department, governorate)
- macrocounty (admin-3 including arrondissement)
- county (admin-4 including prefecture, sub-prefecture, regency, canton, commune)
- localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb)

The dataset also contains human settlement points and polygons for:
- localities (city, town, and village)
- neighbourhoods (borough, macrohood, neighbourhood, microhood)

The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the README.md file for complete data source metadata. Refer to our blog post for explanation of field names.

Data corrections can be proposed using Write Field, an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.

This dataset focuses on a sample of 105 Italian cities that have declared a climate emergency by the end of February 2021. It provides key data and information on the selected cities, the contents of their Climate Emergency Declarations (CEDs) and Local Climate Plans (LCPs), both mitigation and adaptation.

It is organised into four spreadsheets containing the following data respectively: 1. General key data on the city sample: List of cities, Province, Region, Latitude, Longitude, Population, Classes of population, Surface area, Adhesion to: C40 / Climate Allliance / Covenant of Mayors / Green City Network 2. Cities by macroregions / regions / provinces and by population classes
3. Climate Emergency Declarations (CEDs) of the sample cities (as of 28 February 2021). It includes the List of cities, CED date, Supporting documents/websites, and outcomes of the content analysis of CEDs, in terms of: references to national petitions, to the Friday for Future movement, to CEDAMIA, to the IPCC Report 2018, and to the Sustainable Development Goals, CO2/GHG targets, links/adhesions to transnational climate networks, references to LCP and their targets, mentions to Adaptation, Local air pollution, and support citizens' initiatives in favour of the climate, requests to local institutions (Regions) and to the government to take climate. 4. Local Climate Plans (LCPs) of the sample cities (as of 19 April 2021). It includes information on the availability of SEAP/SECAP within the Covenant of Mayors, Web source, Name of the MITIGATION plan, Approval date, and the outcomes of the content analysis of LCPs, in terms of: CO2/CO2eq emission target, baseline year, target year, carbon neutrality target and target year, web source, mentions to Local air pollution, adaptation plans (integrated or stand-alone), web source.

This dataset is about countries and is filtered where the country includes Italy, featuring 5 columns: capital city, continent, country, currency, and political leader. The preview is ordered by population (descending).

The Pavia University dataset is a hyperspectral image dataset which gathered by a sensor known as the reflective optics system imaging spectrometer (ROSIS-3) over the city of Pavia, Italy. The image consists of 610×340 pixels with 115 spectral bands. The image is divided into 9 classes with a total of 42,776 labelled samples, including the asphalt, meadows, gravel, trees, metal sheet, bare soil, bitumen, brick, and shadow.

Large Scale International Boundaries

Version 11.1 Release Date: August 22, 2022

Overview

The Office of the Geographer and Global Issues at the U.S. Department of State produces the Large Scale International Boundaries (LSIB) dataset. These data and their derivatives are the only international boundary lines approved for U.S. Government use. They reflect U.S. Government policy, and not necessarily de facto limits of control. This dataset is a National Geospatial Data Asset.

Details

Sources for these data include treaties, relevant maps, and data from boundary commissions and national mapping agencies. Where available, the dataset incorporates information from courts, tribunals, and international arbitrations. The research and recovery of the data involves analysis of satellite imagery and elevation data. Due to the limitations of source materials and processing techniques, most lines are within 100 meters of their true position on the ground.

Attributes

The dataset uses the following attributes: Attribute Name Explanation Country Code Country-level codes are from the Geopolitical Entities, Names, and Codes Standard (GENC). The Q2 code denotes a line representing a boundary associated with an area not in GENC. Country Names Names approved by the U.S. Board on Geographic Names (BGN). Names for lines associated with a Q2 code are descriptive and are not necessarily BGN-approved. Label Required text label for the line segment where scale permits Rank/Status Rank 1: International Boundary Rank 2: Other Line of International Separation Rank 3: Special Line Notes Explanation of any applicable special circumstances Cartographic Usage Depiction of the LSIB requires a visual differentiation between the three categories of boundaries: International Boundaries (Rank 1), Other Lines of International Separation (Rank 2), and Special Lines (Rank 3). Rank 1 lines must be the most visually prominent. Rank 2 lines must be less visually prominent than Rank 1 lines. Rank 3 lines must be shown in a manner visually subordinate to Ranks 1 and 2. Where scale permits, Rank 2 and 3 lines must be labeled in accordance with the “Label” field. Data marked with a Rank 2 or 3 designation does not necessarily correspond to a disputed boundary. Additional cartographic information can be found in Guidance Bulletins (https://hiu.state.gov/data/cartographic_guidance_bulletins/) published by the Office of the Geographer and Global Issues. Please direct inquiries to internationalboundaries@state.gov.

Credits

The lines in the LSIB dataset are the product of decades of collaboration between geographers at the Department of State and the National Geospatial-Intelligence Agency with contributions from the Central Intelligence Agency and the UK Defence Geographic Centre. Attribution is welcome: U.S. Department of State, Office of the Geographer and Global Issues.

Changes from Prior Release

This version of the LSIB contains changes and accuracy refinements for the following line segments. These changes reflect improvements in spatial accuracy derived from newly available source materials, an ongoing review process, or the publication of new treaties or agreements. Changes to lines include: • Akrotiri (UK) / Cyprus • Albania / Montenegro • Albania / Greece • Albania / North Macedonia • Armenia / Turkey • Austria / Czechia • Austria / Slovakia • Austria / Hungary • Austria / Slovenia • Austria / Germany • Austria / Italy • Austria / Switzerland • Azerbaijan / Turkey • Azerbaijan / Iran • Belarus / Latvia • Belarus / Russia • Belarus / Ukraine • Belarus / Poland • Bhutan / India • Bhutan / China • Bulgaria / Turkey • Bulgaria / Romania • Bulgaria / Serbia • Bulgaria / Romania • China / Tajikistan • China / India • Croatia / Slovenia • Croatia / Hungary • Croatia / Serbia • Croatia / Montenegro • Czechia / Slovakia • Czechia / Poland • Czechia / Germany • Finland / Russia • Finland / Norway • Finland / Sweden • France / Italy • Georgia / Turkey • Germany / Poland • Germany / Switzerland • Greece / North Macedonia • Guyana / Suriname • Hungary / Slovenia • Hungary / Serbia • Hungary / Romania • Hungary / Ukraine • Iran / Turkey • Iraq / Turkey • Italy / Slovenia • Italy / Switzerland • Italy / Vatican City • Italy / San Marino • Kazakhstan / Russia • Kazakhstan / Uzbekistan • Kosovo / north Macedonia • Kosovo / Serbia • Kyrgyzstan / Tajikistan • Kyrgyzstan / Uzbekistan • Latvia / Russia • Latvia / Lithuania • Lithuania / Poland • Lithuania / Russia • Moldova / Ukraine • Moldova / Romania • Norway / Russia • Norway / Sweden • Poland / Russia • Poland / Ukraine • Poland / Slovakia • Romania / Ukraine • Romania / Serbia • Russia / Ukraine • Syria / Turkey • Tajikistan / Uzbekistan

This release also contains topology fixes, land boundary terminus refinements, and tripoint adjustments.

Copyright Notice and Disclaimer

While U.S. Government works prepared by employees of the U.S. Government as part of their official duties are not subject to Federal copyright protection (see 17 U.S.C. § 105), copyrighted material incorporated in U.S. Government works retains its copyright protection. The works on or made available through download from the U.S. Department of State’s website may not be used in any manner that infringes any intellectual property rights or other proprietary rights held by any third party. Use of any copyrighted material beyond what is allowed by fair use or other exemptions may require appropriate permission from the relevant rightsholder. With respect to works on or made available through download from the U.S. Department of State’s website, neither the U.S. Government nor any of its agencies, employees, agents, or contractors make any representations or warranties—express, implied, or statutory—as to the validity, accuracy, completeness, or fitness for a particular purpose; nor represent that use of such works would not infringe privately owned rights; nor assume any liability resulting from use of such works; and shall in no way be liable for any costs, expenses, claims, or demands arising out of use of such works.

The dataset contains the final data of ISTAT relating to the General Census of Population and Housing for the year 2001 divided by Sub-municipal Areas (Neighborhoods) of the city of Matera. Speaking of censuses, it is good to trace a brief history of them. The first general census of the population in Italy dates back to 1861, the year of the unification of the country in the Kingdom of Italy and 26 million and three hundred thousand Italians were censused. From 1861 to 2011, the census sessions were held every 10 years with the exceptions of 1891, due to the financial difficulties of the country and 1941 due to the Second World War. Another exception is represented by the 1936 Census, carried out just 5 years after the previous one following a legislative reform of 1930 that had modified its periodicity, immediately after reported every ten years as still in force. At the present Census, the census population in our country was still less than 60 million.

The European Copernicus Coastal Flood Awareness System (ECFAS) project will contribute to the evolution of the Copernicus Emergency Monitoring Service by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS will provide a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.

The ECFAS Proof-of-Concept development will run from January 2021-December 2022. The ECFAS project is a collaboration between Istituto Universitario di Studi Superiori IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and is funded by the European Commission H2020 Framework Programme within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services.

This project has received funding from the European Union’s Horizon 2020 programme

Description of the containing files inside the Dataset.

The dataset was divided at European country level, except the Adriatic area which was extracted as a region and not on a country level due to the small size of the countries. The buffer zone of each data was 10km inland in order to be correlated with the new Copernicus product Coastal Zone LU/LC.

Specifically, the dataset includes the new Coastal LU/LC product which was implemented by the EEA and became available at the end of 2020. Additional information collected in relation to the location and characteristics of transport (road and railway) and utility networks (power plants), population density and time variability. Furthermore, some of the publicly available datasets that were used in CEMS related to the abovementioned assets were gathered such as OpenStreetMap (building footprints, road and railway network infrastructures), GeoNames (populated places but also names of administrative units, rivers and lakes, forests, hills and mountains, parks and recreational areas, etc.), the Global Human Settlement Layer (GHS) and Global Human Settlement Population Grid (GHS-POP) generated by JRC. Also, the dataset contains 2 layers with statistics information regarding the population of Europe per sex and age divided in administrative units at NUTS level 3. The first layers includes information fro the whole Europe and the second layer has only the information regaridng the population at the Coastal area. Finally, the dataset includes the global database of Floods protection standars. Below there are tables which present the dataset.

Disclaimer:

ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.

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

tags['aeroway'] IS NOT NULL OR tags['building'] = 'aerodrome' OR tags['emergency:helipad'] IS NOT NULL OR tags['emergency'] = 'landing_site'

Features may have these attributes:

• name
• name:en
• aeroway
• building
• emergency
• emergency:helipad
• operator:type
• capacity:persons
• addr:full
• addr:city
• source
• name:it

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.

Motivation

The data in this dataset is a spatial inventory of urban agriculture (UA) carried out in the city of Rome (Italy) (Grande Raccordo Anulare (GRA)). UA areas where identified with a multi-step and iterative procedure by using different web-mapping tools, especially multitemporal Google Earth images, and ancillary data such as Google Street View and Bing Maps.

License

Creative Commons CC-BY

Disclaimer

Despite our best efforts to validate the data, some information may be incorrect.

Description of the dataset

Typologies of UA

• Residential garden: Private parcel near single houses (e.g. backyard), villas, buildings, industrial and commercial activities, generally managed by property owners. Cultivation is diversified ranging from leafy vegetables to herbs and fruit trees. Production is intended for self-consumption and/or for hobby purposes.
• Community garden: A large area subdivided into multipleplots managed individually (i.e. allotment) or collectively by a group of people. Crop production is intended for self-consumption. Land is assigned by the Municipality; several cases of land cultivated without authorization are also common.
• Urban farm: Parcel managed by professional farmers with an intensive and an advanced cropping system. The cultivation can be specialized or oriented to high diversity vegetables. The production is intended for market. The mapping procedure focus exclusively on horticulture, vineyard, olive groves and orchard.
• Institutional garden: Parcel managed by institutions or organizations like schools, religious center, prisons and non-profit organizations. The production is generally intended for self-consumption and less frequently for trade. Several gardens in this category are intended for social purposes (e.g. recreation,education, etc.).
• Illegal garden: Parcel isolated, cultivated without authorization organized and managed individually or by a few people. Localization occurs on unused or abandoned areas owned by public bodies or private subjects. The production is intended for self-consumption.

Land use typologies

• Horticulture: annual crops generally seed sown in spring or summer (tomatoes, lettuce, zucchini, cucumbers, peppers).
• Vineyard: grape vines grown in order to produce wine or table grape.
• Olive groves: olive trees grown in order to produce olive oil or table olives.
• Orchards: mixed trees such as orange, stone fruit, pome fruit, olive trees.
• Mixed crops: an area grown with a mix of horticulture crops and fruit trees, not divisible.

Credit

Pulighe G., Lupia F. (2016) Mapping spatial patterns of urban agriculture in Rome (Italy) using Google Earth and web-mapping services. Land Use Policy 59(2016) 49-58.

www.sciencedirect.com/science/article/pii/S0264837716300059

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

tags['healthcare'] IS NOT NULL OR tags['amenity'] IN ('doctors', 'dentist', 'clinic', 'hospital', 'pharmacy')

Features may have these attributes:

• name
• name:en
• amenity
• building
• healthcare
• healthcare:speciality
• operator:type
• capacity:persons
• addr:full
• addr:city
• source
• name:it

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 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:it

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.

OpenStreetMap exports for use in GIS applications.

This theme includes all OpenStreetMap features in this area matching:

amenity IN ('mobile_money_agent','bureau_de_change','bank','microfinance','atm','sacco','money_transfer','post_office')

Features may have these attributes:

• name
• source
• addr:full
• network
• amenity
• operator
• addr:city

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

tags['amenity'] IN ('mobile_money_agent','bureau_de_change','bank','microfinance','atm','sacco','money_transfer','post_office')

Features may have these attributes:

• name
• name:en
• amenity
• operator
• network
• addr:full
• addr:city
• source
• name:it

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.

BRAINTEASER (Bringing Artificial Intelligence home for a better care of amyotrophic lateral sclerosis and multiple sclerosis) is a data science project that seeks to exploit the value of big data, including those related to health, lifestyle habits, and environment, to support patients with Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) and their clinicians. Taking advantage of cost-efficient sensors and apps, BRAINTEASER will integrate large, clinical datasets that host both patient-generated and environmental data.

As part of its activities, BRAINTEASER organized two open evaluation challenges on Intelligent Disease Progression Prediction (iDPP), iDPP@CLEF 2022 and iDPP@CLEF 2023, co-located with the Conference and Labs of the Evaluation Forum (CLEF).

The goal of iDPP@CLEF is to design and develop an evaluation infrastructure for AI algorithms able to:

• better describe disease mechanisms;
• stratify patients according to their phenotype assessed all over the disease evolution;
• predict disease progression in a probabilistic, time dependent fashion.

The iDPP@CLEF challenges relied on retrospective ALS and MS patient data made available by the clinical partners of the BRAINTEASER consortium. The datasets contain data about 2,204 ALS patients (static variables, ALSFRS-R questionnaires, spirometry tests, environmental/pollution data) and 1,792 MS patients (static variables, EDSS scores, evoked potentials, relapses, MRIs).

More in detail, the BRAINTEASER project retrospective datasets derived from the merging of already existing datasets obtained by the clinical centers involved in the BRAINTEASER Project.

• The ALS dataset was obtained by the merge and homogenisation of the Piemonte and Valle d’Aosta Registry for Amyotrophic Lateral Sclerosis (PARALS, Chiò et al., 2017) and the Lisbon ALS clinic (CENTRO ACADÉMICO DE MEDICINA DE LISBOA, Centro Hospitalar Universitário de Lisboa-Norte, Hospital de Santa Maria, Lisbon, Portugal,) dataset. Both datasets was initiated in 1995 and are currently maintained by researchers of the ALS Regional Expert Centre (CRESLA), University of Turin and of the CENTRO ACADÉMICO DE MEDICINA DE LISBOA-Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa. They include demographic and clinical data, comprehending both static and dynamic variables.
• The MS dataset was obtained from the Pavia MS clinical dataset, that was started in 1990 and contains demographic and clinical information that are continuously updated by the researchers of the Institute and the Turin MS clinic dataset (Department of Neurosciences and Mental Health, Neurology Unit 1, Città della Salute e della Scienza di Torino.
• Retrospective environmental data are accessible at various scales at the individual subject level. Thus, environmental data have been retrieved at different scales:
  • To gather macroscale air pollution data we’ve leveraged data coming from public monitoring stations that cover the whole extension of the involved countries, namely the European Air Quality Portal;
  • data from a network of air quality sensors (PurpleAir - Outdoor Air Quality Monitor / PurpleAir PA-II) installed in different points of the city of Pavia (Italy) were extracted as well. In both cases, environmental data were previously publicly available. In order to merge environmental data with individual subject location we leverage on postcodes (postcodes of the station for the pollutant detection and postcodes of subject address). Data were merged following an anonymization procedure based on hash keys. Environmental exposure trajectories have been pre-processed and aggregated in order to avoid fine temporal and spatial granularities. Thus, individual exposure information could not disclose personal addresses.

The datasets are shared in two formats:

• RDF (serialized in Turtle) modeled according to the BRAINTEASER Ontology (BTO);
• CSV, as shared during the iDPP@CLEF 2022 and 2023 challenges, split into training and test.

Each format corresponds to a specific folder in the datasets, where a dedicated README file provides further details on the datasets. Note that the ALS dataset is split into multiple ZIP files due to the size of the environmental data.

The BRAINTEASER Data Sharing Policy section below reports the details for requesting access to the datasets.

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 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 52M+ 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.