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.

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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

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.

Visit this website for an explanation of the parcel's assessing info: https://www.mass.gov/info-details/massgis-data-property-tax-parcels#attributes-Through a series of joins, spatial joins and select by location with various datasets, the following key attribute fields were populated in the Municipal Properties dataset.Open Space/Conservation Land Attributes are: OS_ID OS_ID is a unique ID for polygons in the open space/conservation land database, [Fee_Owner], [Level_Protection], OLI_1_INT. For an explanation of the coded values used in these fields, visit: https://www.mass.gov/info-details/massgis-data-protected-and-recreational-openspace#attributes-Zoning info for the parcel is contained within [ZONECODE], [MinLot_ac], and [SubStd_Sz]. Zonecode assigned to a parcel is based on the location of the center point of the parcel. The minimum lot size is per the Town's zoning bylaws. Parcel's smaller than the bylaws minimum lot size were assigned a 'yes' value in the Substandard Size attribute column.The attribute [vacant] was assigned a 'yes' value if the assessor's Building Value > $0.00 for the parcel OR the parcel contained one or more structures per the MassGIS structures dataset.The attribute [conserved] was assigned a 'yes' value if the parcel's center point coincided with a parcel in the Dukes County Open Space & Conservation Land dataset.The attribute [AbutPot] Abutter Potential is assigned a 'yes' value if any of the following attributes contain a 'yes' value: [AbutMuni], [AbutOS], or [AbutVacPrv].The attribute [Notes] were manually added by the GIS staff based on local knowledge.Attributes dealing with Abutters: [AbutMuni] indicates if the municipal owned parcel abuts other municipally owned parcels. 'Abuts' are any parcels that thouch (share a boundary) or are within 40ft of each other. [AbutOS] indicates if the municipal owned parcel abuts a parcel which is open space/conservation land. [AbutVacPrv] indicates if the municipal owned parcel abuts a parcel which is vacant residential land. "Vacant Residential Land" was identified by the assessor's Use Code = 1300 or 1310 for the parcel.Identifying Neighbors: All municipal parcels were buffered 40ft and dissolved together. Then that resulting multi-part dataset was 'exploded' so each distinct polygon was represented by a distinct record in the attribute table. Each polygon was assigned an ID number. This output is the "Municipal Property Clusters".Via a Spatial Join, the respective Cluster (aka group ID) was assigned to the respective municipal parcel. Similarly, by finding the (a) Vacant Residential properties and (b) Conservation Land properties that intersected with the Municipal Property Clusters, the Cluster/Group ID was assigned to the respective vacant residential properties and conservation land properties. A & B each have a distinct dataset which is included in this bundle of data.By having the Group ID in the Municipal Properties dataset and the Vacant Residential and Conservation Land datasets ...let's say a parcel has a Group ID = 3 --> then you can find the abutters by finding the other Municipal Parcels with a Group ID = 3 AND look in the Vacant Residential attribute table for Group ID = 3 AND look in the Conservation Land attribute table for Group ID = 3 --AND then you have tons of info at your fingertips regarding that municipally owned parcel and its abutting vacant properties.

This layer contains Census Tracts that have been designated as Qualified Opportunity Zones and contains additional data determined by the EPA to be of interest to users who are seeking revitalization-oriented information about these tracts. Based on nominations of eligible census tracts by the Chief Executive Officers of each State, Treasury has completed its designation of Qualified Opportunity Zones. Each State nominated the maximum number of eligible tracts, per statute, and these designations are final. The statute and legislative history of the Opportunity Zone designations, under IRC § 1400Z, do not contemplate an opportunity for additional or revised designations after the maximum number of zones allowable have been designated in a State or Territory. The data in this layer was updated in January 2021. For more information on Opportunity Zones, please visit: https://www.cdfifund.gov/Pages/Opportunity-Zones.aspx

EPA has added these indicators to the QOZ tracts list:

1. Count of Superfund facilities from EPA National Priorities List (NPL). Count was generated by performing spatial join of Tract boundaries to NPL points—yielding per tract counts. Spatial Extent: all US states and territories. Source: https://www.epa.gov/superfund/superfund-data-and-reports

2. Count of Brownfields properties from EPA Assessment, Cleanup and Redevelopment Exchange System (ACRES). Count was generated by performing spatial join of Tract boundaries to ACRES points--yielding per tract counts. Spatial Extent: all US states and territories. Source: https://edap-oei-data-commons.s3.amazonaws.com/EF/GIS/EF_ACRES.csv

3. Technical Assistance Communities from EPA Office of Community Revitalization (OCR). 13 layers were merged into one; count was generated by performing spatial join of Tract boundaries to combined point layer—yielding per tract counts. Please note that technical assistance communities are often serving areas larger than a single Census tract. Please contact OCR with questions. Spatial Extent: all US states and territories. Source: https://epa.maps.arcgis.com/home/item.html?id=b8795575db194340a4ad1c251e4d6ca1

4. Lead Paint Index from Environmental Justice Screening and Mapping Tool (EJSCREEN). Block group-level values were population weighted and summed to produce a tract-level estimate. The “raw” values were converted to tract-level percentiles. Spatial Extent: all US states and Puerto Rico. Source: https://gaftp.epa.gov/EJSCREEN/2019/

5. Air Toxics Respiratory Index from EJSCREEN. Block group-level values were population weighted and summed to produce a tract-level estimate. The “raw” values were converted to tract-level percentiles. Spatial Extent: all US states and Puerto Rico. Source: https://gaftp.epa.gov/EJSCREEN/2019/

6. Demographic Index Indicator from EJSCREEN. Block group-level values were population weighted and summed to produce a tract-level estimate. The “raw” values were converted to tract-level percentiles. Spatial Extent: all US states and Puerto Rico. Source: https://gaftp.epa.gov/EJSCREEN/2019/

7. Estimated Floodplain Indicator from EPA EnviroAtlas. Floodplain raster was converted to polygon feature class; Y/N indicator was generated by performing a spatial join of Tract boundaries to the Floodplain polygons. Spatial Extent: Continental US. Source: https://gaftp.epa.gov/epadatacommons/ORD/EnviroAtlas/Estimated_floodplain_CONUS.zip

8. National Walkability Index from EPA Smart Location Tools. The National Walkability Index is a nationwide geographic data resource that ranks block groups according to their relative walkability. Tract values assigned by averaging values from block group-level table. Spatial Extent: all US states and territories. Source: EPA Office of Policy—2020 NWI update

9. Impaired Waters Indicator from EPA Office of Water (OW). Y/N indicator was generated by performing spatial joins of Tract boundaries to 3 separate impaired waters layers (point, line and polygon). Y was assigned for all intersected geographies. Extent: all US states and Puerto Rico. Source: https://watersgeo.epa.gov/GEOSPATIALDOWNLOADS/rad_303d_20150501_fgdb.zip

10. Tribal Areas Indicator from EPA. Y/N indicator was generated by performing spatial joins of Tract boundaries to 4 separate Tribal areas layers (Alaska Native Villages, Alaska Allotments, Alaska Reservations, Lower 48 Tribes). Y as assigned for all intersected geographies. Spatial Extent: Alaska and Continental US. Source: https://edg.epa.gov/data/PUBLIC/OEI/OIAA/TRIBES/EPAtribes.zip

11. Count of Resource Conservation and Recovery Act (RCRA) Corrective Action facilities. Count was generated by performing spatial join of Tract boundaries to Corrective Action points—yielding per tract counts. Spatial Extent: all US states and territories. Source: https://www.epa.gov/cleanups/cimc-web-map-service-and-more

12. Count of Toxics Release Inventory facilities from EPA. Count was generated by performing spatial join of Tract boundaries to TRI points—yielding per tract counts. Spatial Extent: all US states and territories. Source: https://edap-oei-data-commons.s3.amazonaws.com/EF/GIS/EF_TRI.csv

13. Social Vulnerability Index (SVI) Housing/Transportation Index from CDC, published in 2018. The Housing/Transportation Index includes ACS 2014-2018 data on crowding in housing and no access to vehicle, among others. County values assigned to tracts by joining Tracts to county-level table. For detailed documentation: https://svi.cdc.gov/Documents/Data/2018_SVI_Data/SVI2018Documentation.pdfSpatial Extent: all US states. Source: https://epa.maps.arcgis.com/home/item.html?id=cbd68d9887574a10bc89ea4efe2b8087

14. Low Access to Food Store Indicator from USDA Food Access Atlas. Y/N indicator was generated by performing a table join of Tracts to the Food Access table records meeting the test criteria. Spatial Extent: all US states. Source: https://www.ers.usda.gov/data-products/food-access-research-atlas/download-the-data/

15. Overall Social Vulnerability Index (SVI) from CDC. Values (RPL_THEMES) assigned by joining the Tract boundaries to source Tract-level table. Spatial Extent: All US states. Source: https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html

16. Rural Communities Indicator from USDA Economic Research Service (ERS). Source tract-level table was flagged as rural where RUCA Codes in 4-10 or 2 and 3 where area >= 400 sq. miles and pop density

The Referrals Spatial Database - Public records locations of referrals submitted to the Department under the Environment Protection and Biodiversity Conservation (EPBC Act) 1999. A proponent (those who are proposing a development) must supply the maximum extent (location) of any proposed activities that need to be assessed under the EPBC Act through an application process.Referral boundaries should not be misinterpreted as development footprints but where referrals have been received by the Department. It should be noted that not all referrals captured within the Referrals Spatial Database, are assessed and approved by the Minister for the Environment, as some are withdrawn before assessment can take place. For more detailed information on a referral a URL is provided to the EPBC Act Public notices pages. Status and detailed planning documentation is available on the EPBC Act Public notices (http://epbcnotices.environment.gov.au/referralslist/).Post September 2019, this dataset is updated using a spatial data capture tool embedded within the Referral form on the department’s website. Users are able to supply spatial data in multiple formats, review spatial data online and submitted with the completed referral form automatically. Nightly processes update this dataset that are then available for internal staff to use (usually within 24 hours). Prior to September 2019, a manual process was employed to update this dataset. In the first instance where a proponent provides GIS data, this is loaded as the polygons for a referral. Where this doesn't exist other means to digitize boundaries are employed to provide a relatively accurate reflection of the maximum extent for which the referral may impact (it is not a development footprint). This sometimes takes the form of heads up digitizing planning documents, sourcing from other state databases (such as PSMA Australia) features and coordinates supplied through the application forms.Any variations to boundaries after the initial referral (i.e. during the assessment, approval or post-approval stages) are processed on an ad hoc basis through a manual update to the dataset. The REFERRALS_PUBLIC_MV layer is a materialized view that joins the spatial polygon data with the business data (e.g. name, case id, type etc.) about a referral. This layer is available for use by the public and is available via a web service and spatial data download. The data for the web service is updated weekly, while the data download is updated quarterly.

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 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 detailed indoor polygons. This meticulous process ensures higher accuracy and consistency. -We verify our polygons through multiple quality 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 specific 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 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 analyses to identify growth opportunities. -Pinpoint the ideal location for your next store or business expansion. -Decode consumer behavior patterns using geospatial insights. -Execute targeted, location-driven 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 spatial data solutions. Join our growing network of successful clients who have scaled their operations with precise polygon and POI data. Request your free sample today and explore how we can help accelerate your business growth.

Data Use: An enriched layer of City Council Districts (as of 2023) with metrics for supporting urban agriculture such as need (e.g, the SVI and dietary health), community assets (e.g., places of faith, meal assistance programs, public housing), land opportunities (e.g., vacant parcels), and existing urban agriculture projects. These metrics were aggregated from other features within the City Council Districts such as tract centroids and point locations. The pop-up for this layer provide a broad overview for urban agriculture potential within a District. The attribute table can be used to build District-level charts and tables. The layer was first downloaded by the provider from its source and enriched with spatial joins in ArcGISPro before being uploaded back into the AGOL environment. The layer is designed as a contextual layer in the webmap, COD Social Health UA. Data source: City of Dallas GIS Services, Current Council Districts - Polygon, COD_DistrictBoundariesFullDataYear: 2023Provider: FHEED, LLCRelated: COD Social Health UA, ID: ed08af809e9f4a968713fcb0e8cf8750

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 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 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.

Several previously published reports and geographic information system (GIS) data layers were used to code information on site attributes for each assessment plot using the spatial join tool in ArcMap. This information was used for an analysis of dieback and non-dieback habitat characteristics. The results of this analysis are presented in this table which depicts the probability of heavy to severe canopy dieback occurring at some time at a particular 30 x 30 m pixel location within the study area.

The Referrals Spatial Database - Public records locations of referrals submitted to the Department under the Environment Protection and Biodiversity Conservation (EPBC Act) 1999. A proponent (those …Show full descriptionThe Referrals Spatial Database - Public records locations of referrals submitted to the Department under the Environment Protection and Biodiversity Conservation (EPBC Act) 1999. A proponent (those who are proposing a development) must supply the maximum extent (location) of any proposed activities that need to be assessed under the EPBC Act through an application process. Referral boundaries should not be misinterpreted as development footprints but where referrals have been received by the Department. It should be noted that not all referrals captured within the Referrals Spatial Database, are assessed and approved by the Minister for the Environment, as some are withdrawn before assessment can take place. For more detailed information on a referral a URL is provided to the EPBC Act Public notices pages. Status and detailed planning documentation is available on the EPBC Act Public notices (http://epbcnotices.environment.gov.au/referralslist/). Post September 2019, this dataset is updated using a spatial data capture tool embedded within the Referral form on the department’s website. Users are able to supply spatial data in multiple formats, review spatial data online and submitted with the completed referral form automatically. Nightly processes update this dataset that are then available for internal staff to use (usually within 24 hours). Prior to September 2019, a manual process was employed to update this dataset. In the first instance where a proponent provides GIS data, this is loaded as the polygons for a referral. Where this doesn't exist other means to digitize boundaries are employed to provide a relatively accurate reflection of the maximum extent for which the referral may impact (it is not a development footprint). This sometimes takes the form of heads up digitizing planning documents, sourcing from other state databases (such as PSMA Australia) features and coordinates supplied through the application forms. Any variations to boundaries after the initial referral (i.e. during the assessment, approval or post-approval stages) are processed on an ad hoc basis through a manual update to the dataset. The REFERRALS_PUBLIC_MV layer is a materialized view that joins the spatial polygon data with the business data (e.g. name, case id, type etc.) about a referral. This layer is available for use by the public and is available via a web service and spatial data download. The data for the web service is updated weekly, while the data download is updated quarterly.

Data in this Address_Points layer was loaded from the interim Building_Points_Joined layer, the result of a spatial join performed between Building_Points (centroids) and Parcels.The Building_Points layer was created on 11/ 08/2013 using the Feature to Point geoprocessing tool; input feature class was Building_Footprints_2010.Process outline:Create a copy of Building_Footprints_2010 in a local, file geodatabasePerform Feature to Point on copy of Building_Footprints_2010; result - Building_PointsPerform spatial join of Parcels to Building_Points; give each point the attribute values of the parcel within which it fallsAdd fields to Building_Points_Joined (FULLNAME, ADDRNUMSUF, ADDRCLASS) and calculate based on values in other attribute fieldsFULLNAME values are the result of a function which concacatenated, then trimmed, using STREET_DIR, STREET_NAM, STREET_TYP from the Parcels table.ADDRNUMSUF values are the result of a parsing operation in which '1/2' addresses were split from applicable HOUSE values. ADDRCLASS values resulted from a calculation based on the Parcels' BLDG_TYPE field.Data loaded to SiteAddressPoint feature class in a local gdb, sourced by Building_Points_Joined.SiteAddressPoint feature class copied to vector.GIS sdeSITEADDID calculated by GENERATE_ID value method using the DynamicValue table.On_Create rule established for SITEADDID when new features are created.Municipality calculated by INTERSECTING_FEATURE value method using the Dynamic Value table.CAPTUREMETH populated with value 'Other' to match the PointCollectionMethoddomain for that field.ZIP will require calculation when a reliable ZIP Codes layer has been secured. Questions? Contact Us

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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)

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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

This dataset represents the base (ground-level) outline, or footprint, of buildings and other man-made structures in Fulton County, Georgia. The original data were produced by digitizing structures from 1988 aerial ortho-photography. Updates to the data are made from various aerial ortho-photography. In 2010, the data table structures was modified to include a number of attributes derived from tax assessment data through a spatial join of structures with tax parcels. The attributes include feature type (residential or commercial), structure form (conventional, ranch, colonial, etc.), number of stories, and the year built. In 2012, updates to features began using building sketch data collected by the Fulton County Tax Assessors. The building sketch data consist of turtle graphics type descriptors defining (in ungeoreferenced space) the ground-level outline of each structure in the County. These descriptors were converted to an ESRI SDE feature class using Python, georeferencing each structure by placing it in the center of its associated tax parcel. Each structure shape was is then manually translated and rotated into position using aerial imagery as a reference. As of May 2014, this update process was still in progress.This dataset is used in large-scale mapping to show the location of individual buildings and other man-made structures and in smaller-scale mapping to show general patterns of development. May also be used to estimate human population for very small areas. Other applications include the computation of impervious surfaces in stormwater studies and the development of 3-D urban models.

Title of reference article:Nine Days of Naptown Arrests: How and Why Spatial Data Should Discomfort UsAuthor J. Kevin ByrneDate authored: August 23, 2020Abstract: During nine successive days in 2019 Indianapolis (IN) police made arrests across six districts. Exploratory spatial data analysis (ESDA) revealed how variables of arrests, race, aggressive use of force (UOF), injuries, and their location interact with each other. Scatterplots with R-squared values > 0.6 suggested aggressive UOF contributed to injuries of arrested residents across all races, Caucasian officers may have excessively injured arrested residents, and aggressive UOF correlated with arrests of African-Americans. Findings for parallel-coordinate-plots dove deeper in terms of spatial implications and ethical considerations (e.g., by visually demonstrating presence of a cluster of observed residents’ arrests as coinciding with African-American census geodemographics). This “small-sample” can surprise the reader. My conclusion proposed two aims: 1) solidify hypotheses (for further ESDA) that may induce ethical discomfort (a good thing) pertaining to the subject of structural racism, and 2) use findings to usher civic policymakers down more strident paths to sociocultural change.Indianapolis (IN) police districts and zones shapefiles that were made public by ESRI were used by way of my ESDA. Path to shapefiles’ source:http://data.indy.gov/datasets/indianapolis-police-zonesN.B.: Safari web-browser not recommended. Shapefile metadata are here: https://www.arcgis.com/home/item.html?id=b59421675f2a40fda9b00beeb875996fUsing GeoDa I did a spatial join that permitted my ESDA to analyze variables with scatterplots, PCPs, and datamaps. My final GeoDa file – titled NapWorksProj.gda – is herewith.Also herewith are my GeoDa's shapefiles – created natively – titled as follows:· NapWorks.cpg· NapWorks.dbf· NapWorks.prj· NapWorks.shp· NapWorks.shx