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.

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

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

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

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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

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.

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.

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

For every address in the City of Kitchener, a GIS spatial join has been created to select the closest Park, Playground, Elementary School, etc

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 dataset is one of several segments of a regional high detailed stream flowpath dataset. The data was separated using the TOPO 50 map series extents.The stream network was originally created for the purpose of high detailed work along rivers and streams in the Wellington region. It was started as a pilot study for the Mangatarere subcatchment of the Waiohine River for the Environmental Sciences department who was attempting to measure riparian vegetation. The data was sourced from a modelled stream network created using the 2013 LiDAR digital elevation model. Once the Mangatarere was complete the process was expanded to cover the entire region on an as needed basis for each whaitua. This dataset is one of several that shows the finished stream datasets for the Wairarapa region.The base stream network was created using a mixture of tools found in ArcGIS Spatial Analyst under Hydrology along with processes located in the Arc Hydro downloadable add-on for ArcGIS. The initial workflow for the data was based on the information derived from the help files provided at the Esri ArcGIS 10.1 online help files. The updated process uses the core Spatial Analyst tools to generate the streamlines while digital dams are corrected using the DEM Reconditioning tool provided by the Arc Hydro toolset. The whaitua were too large for processing separated into smaller units according to the subcatchments within it. In select cases like the Taueru subcatchment of the Ruamahanga these subcatchments need to be further defined to allow processing. The catchment boundaries available are not as precise as the LiDAR information which causes overland flows that are on edges of the catchments to become disjointed from each other and required manual correction.Attributes were added to the stream network using the River Environment Classification (REC) stream network from NIWA. The Spatial Join tool in Arcmap was used to add the Reach ID to each segment of the generated flow path. This ID was used to join a table which had been created by intersecting stream names (generated from a point feature class available from LINZ) with the REC subcatchment dataset. Both of the REC datasets are available from NIWA's website.

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

Export Data Access API Geocoded 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 functionality. Metadata Portal Metadata InformationContent TitleNSW Geocoded Addressing Theme - Address PointContent TypeHosted Feature LayerDescriptionThe Geocoded Urban and Rural Addressing System (GURAS) is a ‘property’ based address database. The GURAS database is commonly used by all levels of government for emergency services, computer aided dispatch systems, postal and delivery services, and to identify location.Address points are generally system generated points and do not always have a direct correlation to the dwelling location. In circumstances where there are multiple disparate lots for one property, particularly in rural addresses, the system generated address points may not reside within the correct property polygon. Owner's names are not part of the GURAS database, nor does GURAS contain any personal information.The Geocoded Addressing Theme is a single source of truth for address information in NSW, GURAS eliminates the costly duplication of effort where all local councils, Australia Post, emergency service organisations and other agencies and businesses maintained individual address databases with different creation and distribution regimes.Address Point - A point feature class used to spatially locate an address / address stringThe Address Point Layer includes the below sub types:BuildingHomesteadMonumentPropertyUnit/StrataOtherInitial Publication Date14/02/2020Data Currency01/01/3000Data Update FrequencyOtherContent SourceData provider filesFile TypeESRI File Geodatabase (*.gdb)Attribution© State of New South Wales (Spatial Services, a business unit of the Department of Customer Service NSW). For current information go to spatial.nsw.gov.auData Theme, Classification or Relationship to other DatasetsNSW Geocoded Addressing Theme of the Foundation Spatial Data FrameworkAccuracyThis dataset was captured by utilising the best available source 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 to upgrade the spatial location and accuracy of data is ongoing.Spatial Reference System (dataset)GDA94Spatial Reference System (web service)EPSG:3857WGS84 Equivalent ToGDA94Spatial ExtentFull StateContent LineageFor additional information, please contact us via the Spatial Services Customer HubData ClassificationUnclassifiedData Access PolicyOpenData QualityFor additional information, please contact us via the Spatial Services Customer HubTerms and ConditionsCreative CommonsStandard and SpecificationOpen 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.Data CustodianDCS Spatial Services346 Panorama AveBathurst NSW 2795Point of ContactPlease contact us via the Spatial Services Customer HubData AggregatorDCS Spatial Services346 Panorama AveBathurst NSW 2795Data DistributorDCS Spatial Services346 Panorama AveBathurst NSW 2795Additional Supporting InformationData DictionariesTRIM Number

Data Source: The primary data source used for this analysis are point-level business establishment data from InfoUSA. This commercial database produced by InfoGroup provides a comprehensive list of businesses in the SCAG region, including their industrial classification, number of employees, and several additional fields. Data have been post-processed for accuracy by SCAG staff and have an effective date of 2016. Locally-weighted regression: First, the SCAG region is overlaid with a grid, or fishnet, of 1km, 2km, and ½-km per cell. At the 1km cell size, there are 16,959 cells covering the SCAG region. Using the Spatial Join feature in ArcGIS, a sum total of business establishments and total employees (i.e., not separated by industrial classification) were joined to each grid cell. Note that since cells are of a standard size, the employment total in a cell is the equivalent of the employment density. A locally-weighted regression (LWR) procedure was developed using the R Statistical Software package in order to identify subcenters. The below procedure is described for 1km grid cells, but was repeated for 2km and 1/2km cells. 1.) Identify local maxima candidates. Using R’s lwr package, each cell’s 120 nearest neighbors, corresponding to roughly 5.5 km in each direction, was explored to identify high outliers or local maxima based on the total employment field. Cells with a z-score of above 2.58 were considered local maxima candidates. 2.) Identify local maxima. LWR can result in local maxima existing within close proximity. This step used a .dbf-format spatial weights matrix (knn=120 nearest neighbors) to identify only cells which are higher than all of their 120 nearest neighbors. At the 1km scale, 84 local maxima were found, which will form the “peak” of each individual subcenter. 3.) Search adjacent cells to include as part of each subcenter. In order to find which cells also are part of each local maximum’s subcenter, we use a queen (adjacency) contiguity matrix to search adjacent cells up to 120 nearest neighbors, adding cells if they are also greater than the average density in their neighborhood. A total of 695 cells comprise subcenters at the 1km scale. A video from Kane et al. (2018) demonstrates the above aspects of the methodology (please refer to 0:35 through 2:35 of https://youtu.be/ylTWnvCCO54), with the following differences: - Different years and slightly different post-processing steps for InfoUSA data - Video study covers 5-county region (Imperial county not included) - Limited to 1km scale subcenters - Due to these differences, the final map of subcenters is different. A challenge arises in that using 1km grid cells may fail to identify the correct local maximum for a particularly large employment center whose experience of high density occurs over a larger area. The process was repeated at a 2km scale, resulting in 54 “coarse scaled” subcenters. Similarly, some centers may exist with a particularly tightly-packed area of dense employment which is not detectable at the medium, 1km scale. The process was repeated again with ½-km grid cells, resulting in 95 “fine scaled” subcenters. In many instances, boundaries of fine, medium, and coarse scaled subcenters were similar, but differences existed. The final step involved qualitatively comparing results at each scale to create the final map of 69 job centers across the region. Most centers are medium scale, but some known areas of especially employment density were better captured at the 2km scale while . Giuliano and Small’s (1991) “ten jobs per acre” threshold was used as a rough guide to test for reasonableness when choosing a larger or smaller scale. For example, in some instances, a 1km scale included much additional land which reduced job density well below 10 jobs per acre. In this instance, an overlapping or nearby 1/2km scaled center provided a better reflection of the local employment peak. Ultimately, the goal was to identify areas where job density is distinct from nearby areas.

This feature class was derived from the GIS polygon dataset BLM Grazing Allotments which was downloaded from the Geospatial Gateway in April 2025. Fields were added to the feature classes and calculated as needed to allow the Rangeland Administration System (RAS) tabular data to be joined to the GIS datasets. RAS tabular data for Authorized allotments and pastures (as of April 2025) was provided by BLM Rangeland Management Specialist Josh Robbins in April 2025 and processed as dbfs, with fields added and calculated as needed to match the BLM GIS Grazing Allotments feature class. RAS tables and BLM GIS data for allotments were joined using the State Allotment Number, a concatenation of allotment number and BLM Administrative State for allotments (ST_ALLOT_NUM). RAS records for Authorized Allotments that did not match during a join operation were tracked in a separate excel sheet from the matching records. Matching records were then joined back to the BLM GIS Allotments grazing feature class and Allotment name fields were edited as necessary. A Status field was added to indicate if the data are either Billed or Authorized and a Source field was added to indicate that the data came from Allotments or Trailing Allotments. An additional field, TR_ALLOT_NUM, was added to designate any Trailing Allotments in the data. Trailing allotments were identified and processed separately for Nevada, since these allotments overlap portions of other allotments. Any overlaps in the data were removed via dissolve and Spatial Join.Input BLM GIS Grazing data:BLM Grazing Pastures and BLM Grazing Allotments are areas of land designated and managed for grazing of livestock. It may include private, state, and public lands under the jurisdiction of the Bureau of Land Management and/or other federal agencies. An allotment is derived from its pastures, where the grazing of livestock is occurring. The attributes of the BLM Grazing Allotment features may be duplicated in RAS, but are considered to be minimum information for unique identification and cartographic purposes.Input RAS Data:The Rangeland Administration System (RAS) provides grazing administrative support and management reports for the BLM and the public. The Rangeland Administration system serves as an electronic calendar for issuance of applications and grazing authorizations, including Permits, Leases, and Exchange-of-Use Agreements. The Authorized data is current as of April 2025 and was provided by BLM Rangeland Management Specialist Josh Robbins in April 2025.

Author: Titus, Maxwell (mtitus@esri.com)Last Updated: 3/4/2025Intended Environment: ArcGIS ProPurpose: This Notebook was designed to automate updates for Hosted Feature Services hosted in ArcGIS Online (or ArcGIS Portal) from ArcGIS Pro and a spatial join of two live datasets.Description: This Notebook was designed to automate updates for Hosted Feature Services hosted in ArcGIS Online (or ArcGIS Portal) from ArcGIS Pro. An associated ArcGIS Dashboard would then reflect these updates. Specifically, this Notebook would:First, pull two datasets - National Weather Updates and Public Schools - from the Living Atlas and add them to an ArcGIS Pro map.Then, the Notebook would perform a spatial join on two layers to give Public Schools features information on whether they fell within an ongoing weather event or alert. Next, the Notebook would truncate the Hosted Feature Service in ArcGIS Online - that is, delete all the data - and then append the new data to the Hosted Feature ServiceAssociated Resources: This Notebook was used as part of the demo for FedGIS 2025. Below are the associated resources:Living Atlas Layer: NWS National Weather Events and AlertsLiving Atlas Layer: U.S. Public SchoolsArcGIS Demo Dashboard: Demo Impacted Schools Weather DashboardUpdatable Hosted Feature Service: HIFLD Public Schools with Event DataNotebook Requirements: This Notebook has the following requirements:This notebook requires ArcPy and is meant for use in ArcGIS Pro. However, it could be adjusted to work with Notebooks in ArcGIS Online or ArcGIS Portal with the advanced runtime.If running from ArcGIS Pro, connect ArcGIS Pro to the ArcGIS Online or ArcGIS Portal environment.Lastly, the user should have editable access to the hosted feature service to update.

Connecticut Erosion Susceptibility a 1:24,000-scale, polygon feature-based layer that was developed as a predictive tool to show areas most susceptible to terrace escarpment type erosion. The layer compiled from the soils and quaternary geology data layers and was field tested during October-December, 2005. The Erosion Susceptilibity layer was developed as part of Project #03-02 Statewide GIS Analysis and Mapping of the Geologic Conditions Contributing to Eroding Terrace Escarpments. The layer does not represent eroding conditions at any one particular point in time, but rather base or general conditions which can be accounted for during planning or management strategies. The layer includes 4 types of areas susceptible to erosion, ranked 1 (most susceptible) through 4, and their descriptive attribute. Areas outside of the mapped polygons can be considered less susceptible to erosion. Data is compiled at 1:24,000 scale. This data is not updated.

Connecticut Erosion Sites is a site specific, point feature-based layer developed at 1:24,000-scale that includes decriptive information regarding the character of the erosion (severity, slope, geologic factors) at selected locations through out the state. The layer is based on information collected and compiled during October-December, 2005 while field testing the applicability of the Erosion Susceptilibity layer developed as part of Project #03-02 Statewide GIS Analysis and Mapping of the Geologic Conditions Contributing to Eroding Terrace Escarpments. The layer represents conditions at a particular point in time. The layer includes 83 locations and descriptive attributes (site name, severity of erosion, description, etc) as well as attributes from a spatial join with merged soils and quaternary geology layers. Features are point locations that represent the selected study areas within the state; it is NOT a comprehensive inventory of erosion locations. Data is compiled at 1:24,000 scale. This data is not updated.

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.

This database represents structures impacted by wildland fire that are inside or within 100 meters of the fire perimeter. Information such as structure type, construction features, and some defensible space attributes are determined as best as possible even when the structure is completely destroyed. Some attributes may have a null value when they could not be determined.Fire damage and poor access are major limiting factors for damage inspectors. All inspections are conducted using a systematic inspection process, however not all structures impacted by the fire may be identified due to these factors. Therefore, a small margin of error is expected. Two address fields are included in the database. The street number, street name, and street type fields are “field determined.” The inspector inputs this information based on what they see in the field. The Address (parcel) and APN (parcel) fields are added through a spatial join after data collection is complete. Additional fields such as Category and Structure Type are based off fields needed in the Incident Status Summary (ICS 209).Please review the DINS database dictionary for additional information. Damage PercentageDescription>0-10%Affected Damage10-25%Minor Damage25-50%Major Damage50-100%DestroyedNo DamageNo Damage

Export DataAccess APIAdministrative Boundaries Theme – Local Government AreaPlease 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 functionality.Metadata Portal Metadata InformationContent TitleNSW Administrative Boundaries Theme - Local Government AreaContent TypeHosted Feature LayerDescriptionNSW Local Government Area is a dataset within the Administrative Boundaries Theme (FSDF). It depicts polygons of gazetted boundaries defining the Local Government Area. It contains all of the cadastral line data or topographic features which are used to define the boundaries between adjoining shires, municipalities, cities (Local Government Act) and the unincorporated areas of NSW.The dataset also contains Council Names, ABS Codes, Ito Codes, Vg Codes, and Wb Codes. Any changes that occur to the dataset should have a reference in the authority of reference feature class in the Land Parcel and Property.Features are positioned in topological alignment within the extents of the land parcel and property polygons for each Local Government Area and are held in alignment, including changes resulting cadastral maintenance and upgrades.Initial Publication Date05/05/2020Data Currency01/01/3000Data Update FrequencyDailyContent SourceData provider filesFile TypeESRI File Geodatabase (*.gdb)Attribution© State of New South Wales (Spatial Services, a business unit of the Department of Customer Service NSW). For current information go to spatial.nsw.gov.auData Theme, Classification or Relationship to other DatasetsNSW Administrative Boundaries Theme of the Foundation Spatial Data Framework (FSDF)AccuracyThe 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 to upgrade the spatial location and accuracy of data is ongoing.Spatial Reference System (dataset)GDA94Spatial Reference System (web service)EPSG:4326WGS84 Equivalent ToGDA94Spatial ExtentFull StateContent LineageFor additional information, please contact us via the Spatial Services Customer HubData ClassificationUnclassifiedData Access PolicyOpenData QualityFor additional information, please contact us via the Spatial Services Customer HubTerms and ConditionsCreative CommonsStandard and SpecificationOpen 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 NSW Cadastral Data Dictionary.Some 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 NSW Address, Property, Lot Geometry data and tables can be spatially joined, download the Data Model Document. Data CustodianDCS Spatial Services346 Panorama AveBathurst NSW 2795Point of ContactPlease contact us via the Spatial Services Customer HubData AggregatorDCS Spatial Services346 Panorama AveBathurst NSW 2795Data DistributorDCS Spatial Services346 Panorama AveBathurst NSW 2795Additional Supporting InformationData DictionariesData Model Document. TRIM Number