This dataset includes pre-processed geospatial data grids (.shp) and in high spatial resolution (50 m X 50 m) of 17 microscale built environment attributes. (in km per km2) The geospatial grids have been calculated using the Kernel density tool in ArcGIS Desktop, v.10.3 (ESRI, REDLANDS) applied to the raw GIS vectors (i.e., polylines), created during the street observation phase of our research. Please, also see the read_me.txt and desclaimer.txt files.

More information about the project can be seen: 1. on our website: http://geochoros.survey.ntua.gr/walkandthecitycenter/home 2. and our data article in 'Data in Brief' Journal: Bartzokas-Tsiompras, A., Photis, Y., Tsagkis, P., & Panagiotopoulos, G. (2021-under review). Microscale walkability indicators for fifty-nine European central urban areas: An open-access tabular dataset and geospatial web-based platform. Data in Brief.

This dataset is about book subjects. It has 3 rows and is filtered where the books is GIS in Italian urban planning. It features 4 columns: authors, books, and publication dates.

Definitive dataset framework of the Data Analysis on Sutri, HISMAGIS protocol.All the feature class, geodatabase and collector carpets have been translated in English.

Xverum’s Point of Interest (POI) Data is a comprehensive dataset containing 230M+ verified locations across 5000 business categories. Our dataset delivers structured geographic data, business attributes, location intelligence, and mapping insights, making it an essential tool for GIS applications, market research, urban planning, and competitive analysis.

With regular updates and continuous POI discovery, Xverum ensures accurate, up-to-date information on businesses, landmarks, retail stores, and more. Delivered in bulk to S3 Bucket and cloud storage, our dataset integrates seamlessly into mapping, geographic information systems, and analytics platforms.

🔥 Key Features:

Extensive POI Coverage: ✅ 230M+ Points of Interest worldwide, covering 5000 business categories. ✅ Includes retail stores, restaurants, corporate offices, landmarks, and service providers.

Geographic & Location Intelligence Data: ✅ Latitude & longitude coordinates for mapping and navigation applications. ✅ Geographic classification, including country, state, city, and postal code. ✅ Business status tracking – Open, temporarily closed, or permanently closed.

Continuous Discovery & Regular Updates: ✅ New POIs continuously added through discovery processes. ✅ Regular updates ensure data accuracy, reflecting new openings and closures.

Rich Business Insights: ✅ Detailed business attributes, including company name, category, and subcategories. ✅ Contact details, including phone number and website (if available). ✅ Consumer review insights, including rating distribution and total number of reviews (additional feature). ✅ Operating hours where available.

Ideal for Mapping & Location Analytics: ✅ Supports geospatial analysis & GIS applications. ✅ Enhances mapping & navigation solutions with structured POI data. ✅ Provides location intelligence for site selection & business expansion strategies.

Bulk Data Delivery (NO API): ✅ Delivered in bulk via S3 Bucket or cloud storage. ✅ Available in structured format (.json) for seamless integration.

🏆Primary Use Cases:

Mapping & Geographic Analysis: 🔹 Power GIS platforms & navigation systems with precise POI data. 🔹 Enhance digital maps with accurate business locations & categories.

Retail Expansion & Market Research: 🔹 Identify key business locations & competitors for market analysis. 🔹 Assess brand presence across different industries & geographies.

Business Intelligence & Competitive Analysis: 🔹 Benchmark competitor locations & regional business density. 🔹 Analyze market trends through POI growth & closure tracking.

Smart City & Urban Planning: 🔹 Support public infrastructure projects with accurate POI data. 🔹 Improve accessibility & zoning decisions for government & businesses.

💡 Why Choose Xverum’s POI Data?

• 230M+ Verified POI Records – One of the largest & most detailed location datasets available.
• Global Coverage – POI data from 249+ countries, covering all major business sectors.
• Regular Updates – Ensuring accurate tracking of business openings & closures.
• Comprehensive Geographic & Business Data – Coordinates, addresses, categories, and more.
• Bulk Dataset Delivery – S3 Bucket & cloud storage delivery for full dataset access.
• 100% Compliant – Ethically sourced, privacy-compliant data.

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

Tool and data set of road networks for 80 of the most populated urban areas in the world. The data consist of a graph edge list for each city and two corresponding GIS shapefiles (i.e., links and nodes).Make your own data with our ArcGIS, QGIS, and python tools available at: http://csun.uic.edu/codes/GISF2E.htmlPlease cite: Karduni,A., Kermanshah, A., and Derrible, S., 2016, "A protocol to convert spatial polyline data to network formats and applications to world urban road networks", Scientific Data, 3:160046, Available at http://www.nature.com/articles/sdata201646

This COVADIS data standard concerns local urban planning documents (PLUs) and land use plans (POS that are equivalent to PLU). This data standard provides a technical framework describing in detail how to dematerialise these urban planning documents into a geographical database that is exploitable by a GIS and interoperable tool. This data standard concerns both graphic zoning plans, the overlaying requirements and the regulations applicable to each type of zone.This COVADIS data standard was developed on the basis of the specifications for the dematerialisation of urban planning documents updated in 2012 by the CNIG, itself based on the consolidated version of the urban planning code dated 16 March 2012. The recommendations of these two documents are consistent even if their purpose is not the same. The COVADIS data standard offers definitions and a structure to organise and store existing PLU/POS geographical data in an infrastructure in digital form, while the CNIG specifications serve to frame the digitisation of this data. The ‘Data Structure’ section in this COVADIS standard provides additional recommendations for data file storage (see Part C). These are specific choices for the data infrastructure of the MAA and MEDDE that do not apply outside their context.The communal maps are subject to another COVADIS data standard.

This dataset contains all Euro-Mediterranean coastal municipalities located in EU Member States with a population of at least 20,000 inhabitants, based on the latest official data available. It was created to support comparative spatial analyses and benchmarking of coastal urban systems, with particular attention to city–sea interfaces, port functions, and the accessibility of public spaces. The dataset includes core indicators such as official resident population, port typology, and national demographic sources. Developed in the context of a scientific study currently under peer review, it supports a multidimensional evaluation framework focused on the spatial performance of coastal public spaces in relation to ecological transition and waterfront regeneration. Data were collected from national statistical agencies, port authority records, GIS analyses, and urban planning documents. The dataset is intended for urban planners, policy makers, and researchers working on coastal governance and planning, also in the frame of ICZM and MSP. Released under an open license, it encourages reuse for academic, institutional, and policy purposes.

Dataset Description: Washington Grocery Store Locations

This dataset contains detailed information about the locations and operational status of grocery stores in Washington, spanning multiple years. It includes both spatial and temporal data, offering a comprehensive view of how grocery stores are distributed and have evolved over time. Below is a breakdown of the columns included in the dataset:

1. X, Y: Geographic coordinates (latitude and longitude) representing the store's location in the dataset.

2. STORENAME: The name of the grocery store.

3. ADDRESS: The physical address of the grocery store.

4. ZIPCODE: The ZIP code of the store’s location.

5. PHONE: The contact phone number for the store.

6. WARD: The local government ward in which the store is located.

7. SSL: A unique identifier or code related to the store, possibly referring to specific data collection attributes.

8. NOTES: Additional comments or information about the store.

9. PRESENT: Temporal indicators showing the presence (likely open or closed) of each store across various years. These columns provide insights into the longevity and temporal trends of grocery store operations.

10. GIS_ID: A unique identifier for geographic information system (GIS) data.

11. XCOORD, YCOORD: Coordinates (likely more specific) used for spatial data analysis, providing the exact location of the store.

12. MAR_ID: A unique identifier for marketing or regional analysis purposes.

13. GLOBALID: A global unique identifier for the store data.

14. CREATOR: The individual or system that created the data entry.

15. CREATED: Timestamp showing when the data entry was created.

16. EDITOR: The individual or system that edited the data entry.

17. EDITED: Timestamp showing when the data entry was last edited.

18. SE_ANNO_CAD_DATA: Specific annotation or data related to CAD (computer-aided design), possibly linked to store location details.

19. OBJECTID: A unique identifier for the object or record within the dataset.

Insights We Can Extract:

• Geographic Distribution: By analyzing the X and Y coordinates along with ZIP codes and wards, we can identify where grocery stores are concentrated and map areas with high or low store density.
• Temporal Trends: The data in the "PRESENT" columns helps us track the opening and closure patterns of grocery stores over time, providing insights into market trends and store longevity.
• Service Gaps: We can identify areas with no grocery stores, possibly indicating food deserts or underserved communities, by mapping the stores and comparing coverage across ZIP codes and wards.
• Operational Trends: By analyzing the temporal data and comparing store turnover, we can uncover patterns in the longevity or turnover of grocery stores.
• Urban Planning and Accessibility: This dataset could help us assess whether the location of grocery stores aligns with urban infrastructure like transportation routes or population density, which could inform policy decisions to improve grocery access.

This dataset is invaluable for urban planners, policymakers, and business stakeholders looking to improve food access and urban infrastructure.

Public Open Space Geographic Information System data collection for Perth and Peel Metropolitan Areas

The public open space (POS) dataset contains polygon boundaries of areas defined as publicly available and open. This geographic information system (GIS) dataset was collected in 2011/2012 using ArcGIS software and aerial photography dated from 2010-2011. The data was collected across the Perth Metro and Peel Region.

POS refer to all land reserved for the provision of green space and natural environments (e.g. parks, reserves, bushland) that is freely accessible and intended for use for recreation purposes (active or passive) by the general public. Four types of “green and natural public open spaces” are distinguished: (1) Park; (2) Natural or Conservation Area; (3) School Grounds; and (4) Residual. Areas where the public are not permitted except on payment or which are available to limited and selected numbers by membership (e.g. golf courses and sports centre facilities) or setbacks and buffers required by legislation are not included.

Initially, potential POSs were identified from a combination of existing geographic information system (GIS) spatial data layers to create a generalized representation of ‘green space’ throughout the Perth metropolitan and Peel regions. Base data layers include: cadastral polygons, metropolitan and regional planning scheme polygons, school point locations, and reserve vesting polygons. The ‘green’ space layer was then visually updated and edited to represent the true boundaries of each POS using 2010-2011 aerial photography within the ArcGIS software environment. Each resulting ’green’ polygon was then classified using a decision tree into one of four possible categories: park, natural or conservation area, school grounds, or residual green space.

Following the classification process, amenity and other information about each POS was collected for polygons classified as “Park” following a protocol developed at the Centre for the Built Environment and Health (CBEH) called POSDAT (Public Open Space Desktop Auditing Tool). The parks were audited using aerial photography visualized using ArcGIS software. . The presence or absence of amenities such as sporting facilities (e.g. tennis courts, soccer fields, skate parks etc) were audited as well as information on the environmental quality (i.e. presence of water, adjacency to bushland, shade along paths, etc), recreational amenities (e.g. presence of BBQ’, café or kiosks, public access toilets) and information on selected features related to personal safety.

The data is stored in an ArcGIS File Geodatabase Feature Class (size 4MB) and has restricted access.

Data creation methodology, data definitions, and links to publications based on this data, accompany the dataset.

This dataset provides a complete and highly structured view of retail precincts across multiple regions, designed to support market analysis, location intelligence, retail expansion, and AI/ML modelling. It delivers information in multiple formats to accommodate a wide range of analytical, GIS, and business use cases, making it an essential resource for retail analysts, urban planners, investment teams, and data-driven decision-makers.

Included Data Files & Formats

1. Point File
2. All Precincts by Centroid (GeoJSON/Shapefile).
3. Each precinct is represented as a single point located at its geometric centroid.
4. Includes key attributes: id, precinct name. Ideal for quick visualisation, clustering, and spatial reference when boundary shapes are not required.

5. Supports applications such as proximity analysis, mapping, and location-based AI/ML models.

6. Polygon File – All Precincts by Polygon (GeoJSON/Shapefile)

7. Provides full precinct boundaries in polygon geometry for precise spatial representation.

8. Includes key attributes: id, precinct name.

9. Enables detailed GIS analysis, including area calculations, spatial overlays, and integration with mobility or demographic datasets.

10. Suitable for urban planning, retail network optimisation, trade area analysis, and catchment studies.

PDF – Precinct Reports (see attached sample) - Reports include comprehensive retail precinct insights across malls and high streets, showing retailer mix by category (F&B, Apparel, Fitness, Grocery, Health/Fitness, and more), catchment size, shopper origins, population, consuming class population and precinct ranking—designed to provide insights on store expansion opportunities. - Supports qualitative assessments, market research, and executive reporting.

1. Excel – Tabular Overview
2. Comprehensive spreadsheet with all precincts, including the following fields: ID, Precinct Name, Ranking, State, Country
3. Enables straightforward filtering, sorting, and integration with other datasets.
4. Useful for high-level analysis, reporting, and as a reference table for GIS mapping or AI models.

This COVADIS data standard concerns local urban planning documents (PLUs) and land use plans (POS that are equivalent to PLU). This data standard provides a technical framework describing in detail how to dematerialise these urban planning documents into a geographical database that is exploitable by a GIS and interoperable tool. This data standard concerns both graphic zoning plans, the overlaying requirements and the regulations applicable to each type of zone.This COVADIS data standard was developed on the basis of the specifications for the dematerialisation of urban planning documents updated in 2012 by the CNIG, itself based on the consolidated version of the urban planning code dated 16 March 2012. The recommendations of these two documents are consistent even if their purpose is not the same. The COVADIS data standard offers definitions and a structure to organise and store existing PLU/POS geographical data in an infrastructure in digital form, while the CNIG specifications serve to frame the digitisation of this data. The ‘Data Structure’ section in this COVADIS standard provides additional recommendations for data file storage (see Part C). These are specific choices for the data infrastructure of the MAA and MEDDE that do not apply outside their context.The communal maps are subject to another COVADIS data standard.

This dataset provides geospatial information on access to hospitals across all census tracts in Spain from 2010 to 2024. For each census tract and year, it includes travel time and distance to the nearest hospital (based on shortest driving time), both for walking and driving modes. Key variables include CUSEC (census tract ID), time_walk, dist_walk, time_car, and dist_car. Hospital locations were sourced from OpenStreetMap and routes were calculated using the OSRM engine. The data, available in CSV format, is part of a study on territorial inequalities in healthcare access and is openly available for reuse in public health and spatial analysis.

This dataset (JDD) is part of a data set that describes land use constraints related to anthropogenic risks, including, for each constraint, the government services or communities to be consulted. The initial target corresponds to those involved in the management of urban planning who wish to know these constraints in a given area of the Greater East region. In the context of the procedure “Treatment of requests relating to constraints of land use due to anthropogenic risks”, which oversees the consultations of the departmental units of DREAL and the Department of Anthropic Risk Prevention, the municipalities affected by constraints induced by a PPRT are identified. The induced constraints were brought to the attention of the mayors. DREAL does not need to be consulted on the constraints induced by these sites. The ‘technological risk prevention plan’ layer represents areas subject to constraints induced by these plans. For each of these areas, it shall specify: The name of the municipality The insee number of the municipality The name of the operator The Gaspar code of the site The date of approval of the PPRT Administrative follow-up of the PPRT (DREAL or Defence) The types of projects concerned by the utility easement(s) The service of the State concerned How to obtain further information The date of implementation of public utility easements Description of the perimeter of the constraints Regulatory sources of the perimeter of constraints Sources of the data The data scale The internal contact SPRA The date of the initial state of play The Layer Update Dates The objective of the layer and to make it possible to identify the municipalities impacted by a PPRT, to make available the information described above, and in particular to direct any consultations to the relevant interlocutors. The data is intended for all persons who may consult the UDs and the SPRA on subjects related to anthropogenic risks, including the local authorities’ teaching services, the other departments of DREAL and the State, the notaries. Contact point: The agent of the Anthropic Risk Prevention Service (SPRA) in charge of operational planning. Name of the GIS data: MU_PPRT_R44.shp

The City Parks dataset is a comprehensive geospatial resource that maps public parks within the City of La Mesa and surrounding areas. It provides accurate, up-to-date information to support urban planning, park management, and community engagement.

Each park feature includes attributes such as park name, jurisdiction, total area (in square feet and acres), perimeter, and unique park identifiers. This information facilitates land use planning, equity in park access, and recreational resource analysis.





The dataset is maintained and updated as needed, and is made publicly available through the City of La Mesa’s GIS Data Hub. It serves as an authoritative source for city staff, GIS professionals, and residents seeking to understand, plan for, or explore La Mesa’s network of public open spaces.





* Data Refreshed As-Needed

This dataset (JDD) is part of a data set that describes land use constraints related to anthropogenic risks, including, for each constraint, the government services or communities to be consulted. The initial target corresponds to those involved in the management of urban planning who wish to connect these constraints on a given territory of the Greater East region. The accuracy corresponds to the municipality. “Known polluted sites and soils” are sites which, according to the Basol database, have been subject to public easements and the constraints of which have been brought to the attention of mayors. DREAL does not need to be consulted on the constraints induced by these sites. The layer “known polluted sites and soils” represents municipalities on which at least one polluted site and soil has been subject to public easements. For each of these municipalities, it states: — The name of the municipality — The insee number of the municipality — The name of the polluted site(s) and soil present in the commune — The name(s) of the operators concerned — The address of the site — The Basol number of the site(s) — The S3IC number of the site(s) — The types of projects concerned by the public utility easement(s) — The service of the State concerned — The approach to obtain further information — The date of implementation of public easements — Description of the perimeter of the constraints — Regulatory sources of the perimeter of constraints — The sources of the data — The scale of the data — The internal contact SPRA — The date of the initial inventory — The dates of the update of the layer The objective of the layer and to enable the identification of known polluted sites and soils, to make available the information described above, and in particular to direct any consultations to the relevant interlocutors. The data is intended for all persons who may consult the UDs and the SPRA on subjects related to anthropogenic risks, including the local authorities’ teaching services, the other departments of DREAL and the State, the notaries. Contact point: The agent of the Anthropic Risk Prevention Service (SPRA) in charge of operational planning. Name of the GIS layer: MU_SSP_CONNU_DREAL_R44.shp

Study Objective and Design: A change vector analysis (CVA) was used to determine land cover changes and identify tree species that are best for urban greening based on carbon sequestration and air pollution. The study assessed land cover change in Kitwe, Zambia, from 1990 to 2015. This study identified the most planted urban tree species along Kitwe's main roads and highways and evaluated typical urban tree species' pH, RWC, total chlorophyll, ascorbic acid, and biomass.Place and Length of Study: The urban trees in Kitwe, Zambia, make up the study population. The city of Kitwe is a thriving centre for mining and commercial activities and is situated in Zambia's Copperbelt Province. The investigation took place between 2018 and 2019.Methodology: The NDVI and BSI indices were created using spectral indices created from Landsat images of Kitwe taken in 1990 and 2015, respectively. The size and direction of the land cover were then determined using change vector analysis, and a district database of land cover changes was constructed using GIS. Urban trees from the built-up area were utilised to create an inventory of common urban tree species based on the land cover classification. The Anticipated Performance Index (API), which measures the suitability of tree species for improving air quality, and the Air Pollution Tolerance Index (APTI), which measures the suitability of tree species for urban greening, are two of the three assessment methods that were employed. In addition, above-ground biomass (AGB) was employed to quantify thecarbon sequestration contribution of the current urban forest.Results: The study discovered that between 1990 and 2015, mining activity and urban growth in Kitwe both contributed to changes in the area's land cover. While the central business district still exhibits a persistent presence as a result of the town's age, having sprung up before the 1990s with more expansions in the new areas, areas being monitored showed low and medium change intensity, mostly in the northeast of the district. In the currentinvestigation, there was a significant difference in the relative abundance of species (p = 0.05). In the study site, Mangifera indica (RA = 12.3%) and Delonix regia (RA = 15.9%) were the two most prevalent species. According to the study, eleven species were found, and each has accumulated carbon in a unique way throughout time depending on its allometry and age. These distinctions in physiological response (tolerance) to air pollution are noteworthy. Bauhinia variegata, Toona ciliate, Gmelina arborea, Eucalyptus grandis, and Delonix regia were all identified as suitable tree species.Conclusion: Over the past 25 years, more than 50% of the land cover has changed, with the majority of that change occurring in regions that are now classified as built-up areas. The majority of Kitwe's urban forests are found in the populated areas and are made up of a variety of ornamental trees that are frequently cultivated for their aesthetic value, attractiveness, and shade. According to the research, this mixture also includes opportunistic urban trees (invasive species) and fruit-bearing trees intermingled with native species. Overall, this study suggests the following species: For urban trees suited for greening programmes aimed at improving air quality and providing shade and beauty in green areas, residences, and sidewalks that have a low air pollution environment, consider Bauhinia variegata, Toona ciliate, Gmelina arborea, Eucalyptus grandis, and Delonix regia.

This dataset (JDD) is part of a data set that describes land use constraints related to anthropogenic risks, including, for each constraint, the government services or communities to be consulted. The initial target corresponds to those involved in the management of urban planning who wish to connect these constraints on a given territory of the Greater East region. The accuracy corresponds to the municipality. “Known polluted sites and soils” are sites which, according to the Basol database, have been subject to public easements and the constraints of which have been brought to the attention of mayors. DREAL does not need to be consulted on the constraints induced by these sites. The layer “known polluted sites and soils” represents municipalities on which at least one polluted site and soil has been subject to public easements. For each of these municipalities, it states: — The name of the municipality — The insee number of the municipality — The name of the polluted site(s) and soil present in the commune — The name(s) of the operators concerned — The address of the site — The Basol number of the site(s) — The S3IC number of the site(s) — The types of projects concerned by the public utility easement(s) — The service of the State concerned — The approach to obtain further information — The date of implementation of public easements — Description of the perimeter of the constraints — Regulatory sources of the perimeter of constraints — The sources of the data — The scale of the data — The internal contact SPRA — The date of the initial inventory — The dates of the update of the layer The objective of the layer and to enable the identification of known polluted sites and soils, to make available the information described above, and in particular to direct any consultations to the relevant interlocutors. The data is intended for all persons who may consult the UDs and the SPRA on subjects related to anthropogenic risks, including the local authorities’ teaching services, the other departments of DREAL and the State, the notaries. Contact point: The agent of the Anthropic Risk Prevention Service (SPRA) in charge of operational planning. Name of the GIS layer: MU_SSP_CONNU_DREAL_R44.shp

This dataset repository includes input and output spatial data of urban microclimate simulations performed through QGIS and ENVI-met software used in the study "Urban microclimate simulations based on GIS data to mitigate thermal hot-spots: Tree design scenarios in an industrial area of Florence", published in the Building and Environment Journal, https://doi.org/10.1016/j.buildenv.2023.110854.

Unlock precise, high-quality GIS data covering 4.5M+ verified locations across Italy. 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 are distinguished from community gardens in that they are generally not intended for the public to use the space for their own growing activities, and in that many have a commercial focus. These were drawn by the Office of Planning based on ESRI satellite basemap imagery compared against the Urban Agriculture points layer. Note that, because many locations are small (or indoors) and could not be located through this satellite view, and because acreage as calculated by these polygons differs, sometimes significantly, from producers' self-reported acreage (indicating the presence of other, less visible growing space, or out-of-date satellite imagery), this layer should not be considered complete and should be used for internal purposes only.