These data were used in research to evaluate the accuracy of selectivity estimation in multiway spatial joins. Five queries were executed, each consisting of ten real datasets.

There is no description for this dataset.

This data is utilized in the Lesson 1.1 What is Climate activity on the MI EnviroLearning Hub Climate Change page.Station data accessed was accessed from NOAA. Data was imported into ArcGIS Pro where Coordinate Table to Point was used to spatially enable the originating CSV. This feature service, which incorporates Census Designated Places from the U.S. Census Bureau’s 2020 Census Demographic and Housing Characteristics, was used to spatially join weather stations to the nearest incorporated area throughout Michigan.Email Egle-Maps@Michigan.gov for questions.Former name: MichiganStationswAvgs19912020_WithinIncoproatedArea_UpdatedName Display Name Field Name Description

STATION_ID MichiganStationswAvgs19912020_W Station ID where weather data is collected

STATION MichiganStationswAvgs19912020_1 Station name where weather data is collected

ELEVATION MichiganStationswAvgs19912020_6 Elevation above mean sea level-meters

MLY-PRCP-NORMAL MichiganStationswAvgs19912020_8 Long-term averages of monthly precipitation total-inches

MLY-TAVG-NORMAL MichiganStationswAvgs19912020_9 Long-term averages of monthly average temperature -F

OID MichiganStationswAvgs1991202_10 Object ID for weather dataset

Join_Count MichiganStationswAvgs1991202_11 Spatial join count of weather station data to specific weather station

TARGET_FID MichiganStationswAvgs1991202_12 Spatial Join ID

Current place ANSI code MichiganStationswAvgs1991202_13 Census codes for identification of geographic entities (used for join)

Geographic Identifier MichiganStationswAvgs1991202_14 Geographic identifier (used for join)

Current class code MichiganStationswAvgs1991202_15 Class (CLASSFP) code defines the current class of a geographic entity

Current functional status MichiganStationswAvgs1991202_16 Status of weather station

Area of Land (Square Meters) MichiganStationswAvgs1991202_17 Area of land in square meters

Area of Water (Square Meters) MichiganStationswAvgs1991202_18 Area of water in square meters

Current latitude of the internal point MichiganStationswAvgs1991202_19 Latitude

Current longitude of the internal point MichiganStationswAvgs1991202_20 Longitude

Name MichiganStationswAvgs1991202_21 Location name of weather station

Current consolidated city GNIS code MichiganStationswAvgs1991202_22 Geographic Names Information System for an incorporated area

OBJECTID MichiganStationswAvgs1991202_23 Object ID for point dataset

Number of incidents counted within census tracts based on different spatial join approaches.

This session will review the basic knowledge and skills that DLI contacts need to work with Census boundary files such as the differences between digital boundary files and cartographic boundary files, projections, feature selection, new layer creation, clipping & splitting, and spatial joins.

Initial Data Capture: Building were originally digitized using ESRI construction tools such as rectangle and polygon. Textron Feature Analyst was then used to digitize buildings using a semi-automated polygon capture tool as well as a fully automated supervised learning method. The method that proved to be most effective was the semi-automated polygon capture tool as the fully automated process produced polygons that required extensive cleanup. This tool increased the speed and accuracy of digitizing by 40%.Purpose of Data Created: To supplement our GIS viewers with a searchable feature class of structures within Ventura County that can aid in analysis for multiple agencies and the public at large.Types of Data Used: Aerial Imagery (Pictometry 2015, 9inch ortho/oblique, Pictometry 2018, 6inch ortho/oblique) Simi Valley Lidar Data (Q2 Harris Corp Lidar) Coverage of Data:Buildings have been collected from the aerial imageries extent. The 2015 imagery coverage the south county from the north in Ojai to the south in thousand oaks, to the east in Simi Valley, and to the West in the county line with Santa Barbara. Lockwood Valley was also captured in the 2015 imagery. To collect buildings for the wilderness areas we needed to use the imagery from 2007 when we last flew aerial imagery for the entire county. 2018 Imagery was used to capture buildings that were built after 2015.Schema: Fields: APN, Image Date, Image Source, Building Type, Building Description, Address, City, Zip, Data Source, Parcel Data (Year Built, Basement yes/no, Number of Floors) Zoning Data (Main Building, Out Building, Garage), First Floor Elevation, Rough Building Height, X/Y Coordinates, Dimensions. Confidence Levels/Methods:Address data: 90% All Buildings should have an address if they appear to be a building that would normally need an address (Main Residence). To create an address, we do a spatial join on the parcels from the centroid of a building polygon and extract the address data and APN. To collect the missing addresses, we can do a spatial join between the master address and the parcels and then the parcels back to the building polygons. Using a summarize to the APN field we will be able to identify the parcels that have multiple buildings and delete the address information for the buildings that are not a main residence.Building Type Data: 99% All buildings should have a building type according to the site use category code provided from the parcel table information. To further classify multiple buildings on parcels in residential areas, the shape area field was used to identify building polygons greater than 600 square feet as an occupied residence and all other buildings less than that size as outbuildings. All parcels, inparticular parcels with multiple buildings, are subject to classification error. Further defining could be possible with extensive quality control APN Data: 98% All buildings have received APN data from their associated parcel after a spatial join was performed. Building overlapping parcel lines had their centroid derived which allowed for an accurate spatial join.Troubleshooting Required: Buildings would sometimes overlap parcel lines making spatial joining inaccurate. To fix this you create a point from the centroid of the building polygon, join the parcel information to the point, then join the point with the parcel information back to the building polygon.

The CONtiguous United States (CONUS) “Flood Inundation Mapping Hydrofabric - ICESat-2 River Surface Slope” (FIM HF IRIS) dataset integrates river slopes from the global IRIS dataset for 117,357 spatially corresponding main-stream reaches within NOAA’s Office of Water Prediction operational FIM forecasting system, which utilizes the Height Above Nearest Drainage approach (OWP HAND-FIM) to help warn communities of floods. To achieve this, a spatial joining approach was developed to align FIM HF reaches with IRIS reaches, accounting for differences in reach flowline sources. When applied to OWP HAND-FIM, FIM HF IRIS improved flood map accuracy by an average of 31% (CSI) across eight flood events compared to the original FIM HF slopes. Using a common attribute, IRIS data were also transferred from FIM HF IRIS to the CONUS-scale Next Generation Water Resources Modeling Framework Hydrofabric (NextGen HF), creating the NextGen HF IRIS dataset. By referencing another common attribute, SWOT vector data (e.g., water surface elevation, slope, discharge) can be leveraged by OWP HAND-FIM and NextGen through the two resulting datasets. The spatial joining approach, which enables the integration of FIM HF with other hydrologic datasets via flowlines, is provided alongside the two resulting datasets.

The slope_iris_sword in FIM HF IRIS can be used with the Recalculate_Discharge_in_Hydrotable_useFIMHFIRIS.py script to regenerate the hydrotable for OWP HAND-FIM, where the discharge will be recalculated using slope_iris_sword. Consequently, the synthetic rating curves (SRCs) will be updated based on the new discharges (see more details in https://github.com/NOAA-OWP/inundation-mapping/wiki/3.-HAND-Methodology). The script can also be used to regenerate hydrotables using river slopes from other sources, such as NextGen HF, provided they are linked to the FIM HF flowlines.

The feature classes for FIMHF_IRIS and NextGenHF_IRIS are provided in formats of geopackage (.gpkg) and geodatabases (.gdb), which can be accessed using ArcGIS, QGIS, or relevant Python packages for inspection, visualization, or spatial analysis of slope_iris_sword.

More information can be found at: Chen, Y., Baruah, A., Devi, D., & Cohen, S. (2025). Improved River Slope Datasets for the United States Hydrofabrics [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15099149

AddressPointInfo (API) is a master address layer that contains City of Houston administrative boundary and service information. This feature class was original generated for Lagan 311 project. It is based on Planning & Development Departmet's AddressPoints feature class. Multiple spatial joins are performed to merge City of Houston administrative boundary and service related information. Enterprise GIS group updates this feature class monthly. This Address Points Layer was created as the foundation for the City of Houston's addressing team. This layer was developed by compiling all available known address information into one comprehensive data set. Due to its origins there is still a great deal of clean up that needs to occur with in the data. This clean up is on going. A note about the Status Field; An addresses with a status of preliminary is only a temporary address meant to serve as a 911 geocodeable location only. This address is not inhabitable or official and no permit may be issued to it with-out a recorded plat.

A. SUMMARY This dataset was first created as part of the SFMTA On Street Car Share Pilot Program (approved by the MTA Board in July 2013) to illustrate the _location of implemented and planned (various stages) spaces throughout the city. B. METHODOLOGY The locations were originally provided to the MTA as requests by the three car share organizations (CSOs). These were given as a .kml file, which was converted to a .shp. Additional fields were created using spatial joins (zipcode, supervisor district, CNN, etc). Use definition query tool to display those locations with a certain attribute. For example, query Existing = 1 to display those locations that are on street operating. 500 submissions were given by CSOs to the MTA, but only a portion of those were brought to the MTA Board for approval, and even fewer were implemented as operational on street spaces. With no definition query, you can see all spaces as features, with varying levels of data completion. C. UPDATE FREQUENCY During periods of implementation/construction, updates were as frequent as daily or weekly. However, as the frequency of newly implemented spaces slowed over the course of the pilot, updates occurred less frequently--weekly or monthly. Updates will be needed as new spaces are implemented--many of the spaces not taken past MTA Board approval have incomplete data. D. OTHER CRITICAL INFO Each feature (or each row, or point) represents a single car share parking space. Some parking spaces belong to a "pod" where there are two adjacent car share parking spaces, indicated by the "PodType" field. To summarize or analyze by pod, use the "POD" field.

A. SUMMARY Counts of publicly available, on-street parking for each street segment. B. METHODOLOGY Counts collected via field surveys from 2008-2014 assuming 17 feet per undemarcated parking space, with a few exceptions. Geoprocessing methodology involved a series of spatial joins between side of street points and each street segment. Full parking census methodology can be found at http://sfpark.org/resources/parking-census-data-context-and-map-april-2014/ C. UPDATE FREQUENCY Updated infrequently on a schedule TBD D. OTHER CRITICAL INFO Users should filter out segments with the value '5555' when aggregating parking census counts. This code is applied to some divided streets where the full parking census count for that street block was aggregated to one side of the divided street.

This project aims to identify areas in Los Angeles that are at high risk of crime in the future and to propose optimal locations for new police stations in those areas. By applying machine learning to post-COVID-19 crime data and various socioeconomic indicators, we predict crime risk at the ZIP Code level. Using a location-allocation model, we then determine suitable locations for new police stations to improve coverage of high-risk zones. The results of our analysis can support the efficient allocation of public safety resources in response to growing demand and budget constraints, helping city officials optimize law enforcement services. The content of the archive- Jupyter Notebook- Data (GeoJSON, CSV)- Summary report PDF FileThe platform on which the notebook should be run.This notebook is designed to run on Datahub.Project materials - Project Material we created on AGOL 1 Los Angeles Crime Hotspothttps://ucsdonline.maps.arcgis.com/home/item.html?id=4bddbae65c164f2d9b0285e09cb2820e 2 Choropleth Map of Predicted Crime Levels by ZIP Codehttps://ucsdonline.maps.arcgis.com/home/item.html?id=e47abb448f0a411ab77c6ac754ba0c34 3. Optimizing LA Police Station: A Location Allocation Analysishttps://ucsdonline.maps.arcgis.com/home/item.html?id=2409da85c3fe410e9578a0eaaed8471e - ArcGIS StoryMaphttps://ucsdonline.maps.arcgis.com/home/item.html?id=cfbd4fc27a3b400296e4e31555951d27 Software dependencies - pandas: Used for loading, formatting, and performing matrix operations on tabular data.- geopandas: Used for loading and processing spatial data, including spatial joins and coordinate transformations.- shapely.geometry.Point: Used to create spatial point objects from latitude and longitude coordinates.- arcgis.gis, arcgis.features, arcgis.geometry, arcgis.geoenrichment: Used to retrieve and manipulate geographic data from ArcGIS Online and to extract population statistics using the GeoEnrichment module.- numpy: Used for feature matrix formatting and numerical computations prior to model training.- IPython.display (display, Markdown, Image): Used to format and display Markdown text, data tables, and images within Jupyter Notebooks.- scikit-learn: Used for building and evaluating machine learning models. Specifically, it was used for data preprocessing (StandardScaler), splitting data (train_test_split), model selection and tuning (GridSearchCV, cross_val_score), training various regressors (e.g.,LinearRegression, RandomForestRegressor, KNeighborsRegressor), and assessing performance using metrics such as R², RMSE, and MAE.Other Components we used - ArcGIS Online: Used to create and host interactive web maps for spatial visualization and public presentation purposes.- Flourish: Used to create interactive graphs and charts for visualizing trends and supporting the analysis.

Anyone who has taught GIS using Census Data knows it is an invaluable data set for showing students how to take data stored in a table and join it to boundary data to transform this data into something that can be visualised and analysed spatially. Joins are a core GIS skill and need to be learnt, as not every data set is going to come neatly packaged as a shapefile or feature layer with all the data you need stored within. I don't know how many times I taught students to download data as a table from Nomis, load it into a GIS and then join that table data to the appropriate boundary data so they could produce choropleth maps to do some visual analysis, but it was a lot! Once students had gotten the hang of joins using census data they'd often ask why this data doesn't exist as a prepackaged feature layer with all the data they wanted within it. Well good news, now a lot off it is and it's accessible through the Living Atlas! Don't get me wrong I fully understand the importance of teaching students how to perform joins but once you have this understanding if you can access data that already contains all the information you need then you should be taking advantage of it to save you time. So in this exercise I am going to show you how to load English and Welsh Census Data from the 2021 Census into the ArcGIS Map Viewer from the Living Atlas and produce some choropleth maps to use to perform visual analysis without having to perform a single join.

The data for this analysis was obtained through a UC-Davis Coursera course as ElectionData2012.gdb, with polygon layers Counties and PrecinctVotingData. Both of those were loaded into a blank map document, followed by the World Light Grey Canvas basemap.

Then, the author conducted a Spatial Join of the PrecinctVotingData layers TO the Counties layer (target layer). A right click on the fields total_votes and proposition_37_yes_votes enabled the execution of a Sum merge operation for those fields.

After the spatial join, the author went into the Properties of the Join layer, selected Symbology, used the quantity gradient, selected sum_proposition_37_yes-votes as the field for symbology and normalized by the sum_total_votes field. Further, the author formatted the symbology such that the data was represented as a percentage (of the sum_total_votes) and used only 1 decimal place.

The author then went into the Label s tab of the Properties window, chose the County label style for the NAMES field, and edited the label to have a 1-pt. halo around the county names, centered on their feature.

From the attribute table of the Join, the author right-clicked the "sum_total_votes" and the "sum_proposition_37_yes_votes" fields and used the statistics function to gather the sum of the YES votes and the sum of the total votes for the state as a whole, for use in the final, shared map. Revisions were also made to layer names for the benefit of the final map.

This specialized location dataset delivers detailed information about marina establishments. Maritime industry professionals, coastal planners, and tourism researchers can leverage precise location insights to understand maritime infrastructure, analyze recreational boating landscapes, and develop targeted strategies.

How Do We Create Polygons?

-All our polygons are manually crafted using advanced GIS tools like QGIS, ArcGIS, and similar applications. This involves leveraging aerial imagery, satellite data, and street-level views to ensure precision. -Beyond visual data, our expert GIS data engineers integrate venue layout/elevation plans sourced from official company websites to construct highly detailed polygons. This meticulous process ensures maximum accuracy and consistency. -We verify our polygons through multiple quality assurance checks, focusing on accuracy, relevance, and completeness.

What's More?

-Custom Polygon Creation: Our team can build polygons for any location or category based on your requirements. Whether it’s a new retail chain, transportation hub, or niche point of interest, we’ve got you covered. -Enhanced Customization: In addition to polygons, we capture critical details such as entry and exit points, parking areas, and adjacent pathways, adding greater context to your geospatial data. -Flexible Data Delivery Formats: We provide datasets in industry-standard GIS formats like WKT, GeoJSON, Shapefile, and GDB, making them compatible with various systems and tools. -Regular Data Updates: Stay ahead with our customizable refresh schedules, ensuring your polygon data is always up-to-date for evolving business needs.

Unlock the Power of POI and Geospatial Data

With our robust polygon datasets and point-of-interest data, you can: -Perform detailed market and location analyses to identify growth opportunities. -Pinpoint the ideal locations for your next store or business expansion. -Decode consumer behavior patterns using geospatial insights. -Execute location-based marketing campaigns for better ROI. -Gain an edge over competitors by leveraging geofencing and spatial intelligence.

Why Choose LocationsXYZ?

LocationsXYZ is trusted by leading brands to unlock actionable business insights with our accurate and comprehensive spatial data solutions. Join our growing network of successful clients who have scaled their operations with precise polygon and POI datasets. Request your free sample today and explore how we can help accelerate your business growth.

A comprehensive dataset of 1,513 Pakistani cities, towns, tehsils, districts and places with latitude/longitude, administrative region, population (when available) and Wikidata IDs — ideal for mapping, geospatial analysis, enrichment, and location-based ML.

Why this dataset is valuable:

• Full geocoordinates for every entry (100% coverage) — ready for mapping and spatial joins.
• Wide geographic coverage across all 7 major regions of Pakistan (provinces / administrative regions).
• Wikidata IDs included for reliable cross-referencing and automatic enrichment from external knowledge bases.
• Useful for data scientists, GIS engineers, civic tech projects, academic research, and startups building Pakistan-focused location services.

Highlights (fetched from the data):

• Total rows: 1,513
• Unique places (city field): 1,497
• Rows with population > 0: 526 (≈34.8%)
• Coordinate coverage: 1513 / 1513 (100%) — directly usable with mapping libraries.

Column definitions (short):

• id — Internal numeric row id (unique integer).
• wikiDataId — Wikidata QID (e.g., Q####) for the place; use to fetch rich metadata.
• type — Administrative/place type (e.g., ADM1, ADM2, city, district, tehsil).
• city — Common/local city/place name (short label).
• name — Full name / official name of the place (may include “District”, “Tehsil”, etc.).
• country — Country name (Pakistan).
• countryCode — ISO country code (e.g., PK).
• region — Primary administrative region / province (e.g., Punjab, Sindh).
• regionCode — Short code for region (e.g., PB, KP depending on your encoding).
• regionWdId — Wikidata QID for the region.
• latitude — Latitude in decimal degrees (float).
• longitude — Longitude in decimal degrees (float).
• population — Integer population (0 or NA where unknown).

Typical & high-value use cases:

• Mapping & visualization: choropleth maps, point overlays, heatmaps of population or density.
• Geospatial analysis: distance calculations, nearest-neighbor queries, clustering of urban centers.
• Data enrichment: join with other datasets (OpenStreetMap, Wikidata, census data) using wikiDataId and coordinates.
• Machine learning & NLP: training geolocation models, geoparsing, toponym resolution, place name disambiguation.
• Urban planning & research: analyze distribution of population-ready places vs administrative units.
• Mobile / location-based apps: lookup & reverse geocoding fallback, seeding POI databases for Pakistan.
• Humanitarian & disaster response: baseline location lists for logistics and situational awareness.

This dataset contains daily temperature records (minimum, maximum, and average) for China from 1990 to 2022, processed from NOAA’s raw data. The data was spatially filtered to include only points within China’s administrative boundaries and converted from Fahrenheit to Celsius. The dataset is formatted for easy integration with geospatial analyses and climate studies.​​Key Features​​​​Data Source​​: Derived from NOAA’s global temperature records, filtered for China using spatial joins with administrative boundaries.​​Variables Included​​:TEMP_C: Daily average temperature (°C).MAX_C/MIN_C: Daily maximum/minimum temperatures (°C).LATITUDE/LONGITUDE: Geographic coordinates (WGS84).Additional metadata (e.g., station IDs, dates).​​Processing Workflow​​:​​Spatial Filtering​​: Data was clipped to China’s boundaries using geopandas spatial joins (EPSG:4326 CRS).​​Unit Conversion​​: Temperatures converted from Fahrenheit to Celsius.​​Format​​: Saved as .pkl (Pickle) files for efficient storage and Python compatibility.​​Code Availability​​: The Python script used for processing is included (see "Code" section), with dependencies listed below.​​Intended Use​​Climate trend analysis, regional temperature modeling, or validation of satellite-derived products.Integration with GIS platforms (e.g., QGIS, ArcGIS) or Python-based workflows.​​Technical Details​​​​Software​​: Processed using Python 3.x with pandas, geopandas, and pyarrow.​​Coordinate System​​: WGS84 (EPSG:4326).​​Temporal Coverage​​: January 1, 1990 – December 31, 2022.

• Spatial join of precintvotingdata in Counties *Change the symbology for quantities of normalization yes_votes.

Access APIGeocoded Addressing Theme - Address Point Please Note WGS 84 service aligned to GDA94 This dataset has spatial reference [WGS 84 ≈ GDA94] which may result in misalignments when viewed in …Show full description Access APIGeocoded Addressing Theme - Address Point Please Note WGS 84 service aligned to GDA94 This dataset has spatial reference [WGS 84 ≈ GDA94] which may result in misalignments when viewed in GDA2020 environments. A similar service with a ‘multiCRS’ suffix is available which can support GDA2020, GDA94 and WGS 84 ≈ GDA2020 environments. In due course, and allowing time for user feedback and testing, it is intended that the original service name will adopt the new multiCRS functionally. The Address point feature is a point feature class used to spatially locate an address / addressstring. The Address Point Layer includes the below subtypes: · Building · Homestead · Monument · Property · Unit/Strata · Other Metadata Type Esri Feature Service Update Frequency As required Contact Details Contact us via the Spatial Services Customer Hub Relationship to Themes and Datasets NSW Geocoded Addressing Theme of the Foundation Spatial Data Framework (FSDF) Accuracy The dataset maintains a positional relationship to, and alignment with, the Lot and Property digital datasets. This dataset was captured by digitising the best available cadastral mapping at a variety of scales and accuracies, ranging from 1:500 to 1:250 000 according to the National Mapping Council of Australia, Standards of Map Accuracy (1975). Therefore, the position of the feature instance will be within 0.5mm at map scale for 90% of the well-defined points. That is, 1:500 = 0.25m, 1:2000 = 1m, 1:4000 = 2m, 1:25000 = 12.5m, 1:50000 = 25m and 1:100000 = 50m. A program of positional upgrade (accuracy improvement) is currently underway. Spatial Reference System (dataset) Geocentric Datum of Australia 1994 (GDA94), Australian Height Datum (AHD) Spatial Reference System    (web service) EPSG 4326: WGS 84 Geographic 2D WGS 84 Equivalent To GDA94 Spatial Extent Full State Standards and Specifications Open Geospatial Consortium (OGC) implemented and compatible for consumption by common GIS platforms. Available as either cache or non-cache, depending on client use or requirement. Information about the “Feature Class” and “Domain Name” descriptions for the NSW Administrative Boundaries Theme can be found in the GURAS Delivery Model Data DictionarySome of Spatial Services Datasets are designed to work together for example “NSW Address Point” and “NSW Address String Table”, NSW Property (Polygon) and NSW Property Lot Table and NSW Lot (polygons). To do this you need to add a “Spatial Join”. A Spatial join is a GIS operation that affixes data from one feature layer’s attribute table to another from a spatial perspective. To see how Address, Property and Lot Geometry data and Tables can be joined together download the Data Model Document. This will show what attributes in the datasets can be linked. Distributors Service Delivery, DCS Spatial Services 346 Panorama Ave Bathurst NSW 2795 Dataset Producers and Contributors Administrative Spatial Programs, DCS Spatial Services 346 Panorama Ave Bathurst NSW 2795

A. SUMMARY This dataset is derived from parcels and several other overlay administrative boundaries (listed below). The dataset was developed by DataSF as a convenience for matching parcels to districts where appropriate. This can be simpler than running a geospatial process every time you want to join parcels to a boundary. The districts provided here run along streets and are non-overlapping so that the parcels will be contained within a single district.

The boundaries included are: 1. Analysis Neighborhoods 2. Supervisor Districts 3. Police Districts 4. Planning Districts

B. HOW THE DATASET IS CREATED A script runs daily that overlays parcels with each of the boundaries to produce the composite dataset.

C. UPDATE PROCESS Updated daily by a script based on the upstream parcels dataset which is also updated daily.

D. HOW TO USE THIS DATASET You can use this dataset to match to administrative districts provided here to datasets that contain a parcel number. This can be a simpler process than running these joins spatially.

In short, we pre-process the spatial overlays to make joins simpler and more performant.

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GIS Market Size 2025-2029

The GIS market size is forecast to increase by USD 24.07 billion, at a CAGR of 20.3% between 2024 and 2029.

The Global Geographic Information System (GIS) market is experiencing significant growth, driven by the increasing integration of Building Information Modeling (BIM) and GIS technologies. This convergence enables more effective spatial analysis and decision-making in various industries, particularly in soil and water management. However, the market faces challenges, including the lack of comprehensive planning and preparation leading to implementation failures of GIS solutions. Companies must address these challenges by investing in thorough project planning and collaboration between GIS and BIM teams to ensure successful implementation and maximize the potential benefits of these advanced technologies.
By focusing on strategic planning and effective implementation, organizations can capitalize on the opportunities presented by the growing adoption of GIS and BIM technologies, ultimately driving operational efficiency and innovation.

What will be the Size of the GIS Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The global Geographic Information Systems (GIS) market continues to evolve, driven by the increasing demand for advanced spatial data analysis and management solutions. GIS technology is finding applications across various sectors, including natural resource management, urban planning, and infrastructure management. The integration of Bing Maps, terrain analysis, vector data, Lidar data, and Geographic Information Systems enables precise spatial data analysis and modeling. Hydrological modeling, spatial statistics, spatial indexing, and route optimization are essential components of GIS, providing valuable insights for sectors such as public safety, transportation planning, and precision agriculture. Location-based services and data visualization further enhance the utility of GIS, enabling real-time mapping and spatial analysis.

The ongoing development of OGC standards, spatial data infrastructure, and mapping APIs continues to expand the capabilities of GIS, making it an indispensable tool for managing and analyzing geospatial data. The continuous unfolding of market activities and evolving patterns in the market reflect the dynamic nature of this technology and its applications.

How is this GIS Industry segmented?

The GIS industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Software
  Data
  Services


Type

  Telematics and navigation
  Mapping
  Surveying
  Location-based services


Device

  Desktop
  Mobile


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

The Global Geographic Information System (GIS) market encompasses a range of applications and technologies, including raster data, urban planning, geospatial data, geocoding APIs, GIS services, routing APIs, aerial photography, satellite imagery, GIS software, geospatial analytics, public safety, field data collection, transportation planning, precision agriculture, OGC standards, location intelligence, remote sensing, asset management, network analysis, spatial analysis, infrastructure management, spatial data standards, disaster management, environmental monitoring, spatial modeling, coordinate systems, spatial overlay, real-time mapping, mapping APIs, spatial join, mapping applications, smart cities, spatial data infrastructure, map projections, spatial databases, natural resource management, Bing Maps, terrain analysis, vector data, Lidar data, and geographic information systems.

The software segment includes desktop, mobile, cloud, and server solutions. Open-source GIS software, with its industry-specific offerings, poses a challenge to the market, while the adoption of cloud-based GIS software represents an emerging trend. However, the lack of standardization and interoperability issues hinder the widespread adoption of cloud-based solutions. Applications in sectors like public safety, transportation planning, and precision agriculture are driving market growth. Additionally, advancements in technologies like remote sensing, spatial modeling, and real-time mapping are expanding the market's scope.

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The Software segment was valued at USD 5.06 billion in 2019 and sho