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.

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.

Raczynski, K., Xavier, F., & Cartwright, J. H. (2025). GEO Tutorial: Joining Tables With Spatial Data. Mississippi State University: Geosystems Research Institute. [View Document]

GEO Tutorial Number of Pages: 6 Publication Date: 06/2025This work was supported through funding by the National Oceanic and Atmospheric Administration Regional Geospatial Modeling Grant, Award # NA19NOS4730207.

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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

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.

AT_2003_BACI_1 File Geodatabase Feature Class Thumbnail Not Available Tags There are no tags for this item. Summary There is no summary for this item. Description MD Property View 2003 A&T Database. For more information on the A&T Database refer to the enclosed documentation. This layer was edited to remove spatial outliers in the A&T Database. Spatial outliers are those points that were not geocoded and as a result fell outside of the Baltimore City Boundary; 416 spatial outliers were removed from this layer. The field BLOCKLOT2 can be used to join this layer with the Baltimore City parcel layer. Credits There are no credits for this item. Use limitations There are no access and use limitations for this item. Extent West -76.713418 East -76.526031 North 39.374429 South 39.197452

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.

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This shapefile was constructed …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This shapefile was constructed by combining crown TSR spatial data, information gathered from Rural Lands Protection Board (RLPB) rangers, and surveyed Conservation and Biodiversity data to compile a layer within 30 RLPB districts in NSW. The layer attempts to spatially reflect current TSRs as accurately as possible with conservation attributes for each one. Dataset History The initial process in production involved using the most up to date extract of TSR from the crown spatial layer as a base map, as this layer should reasonably accurately spatially reflect the location, size, and attributes of TSR in NSW. This crown spatial layer from which the TSR were extracted is maintained by the NSW Department of Lands. The TSR extract is comprised of approximately 25,000 polygons in the study area. These polygons were then attributed with names, IDs and other attributes from the Long Paddock (LP) points layer produced by the RLPB State Council, which contains approximately 4000 named reserves throughout the study area. This layer reflects the names and ID number by which the reserves were or are currently managed by the RLPB's. This layer was spatially joined with the TSR polygon layer by proximity to produce a polygon layer attributed with RLPB reserve names and ID numbers. This process was repeated for other small datasets in order to link data with the polygon layer and LP reserve names. The next and by far the most time consuming and laborious process in the project was transferring the data gathered from surveys undertaken with RLPB rangers about each reserve (location, spatial extent, name, currency conservation value and biodiversity). This spatial information was annotated on hard copy maps and referenced against the spatial join making manual edits where necessary. Edits were conducted manually as the reference information was only on hard copy paper maps. Any corrections were made to the merged layer to produce an accurate spatial reflection of the RLPB reserves by name and ID. This manual editing process composed the bulk of the time for layer production as all reserves in each RLPB district in the study area had to be checked manually. Any necessary changes had to then be made to correct the spatial location of the reserve and ensure the correct ID was assigned for attributing the conservation data. In approximately 80% of cases the spatial join was correct, although this figure would be less where long chains of TSR polygons exist. The majority of time was devoted to making the numerous additions that needed to be incorporated. A spreadsheet based on the LP point layer was attributed with the LP point [OBJECTID] in order to produce a unique reference for each reserve so that conservation and biodiversity value data could be attributed against each reserve in the spatial layer being produced. Any new reserves were allocated [OBJECTID] number both in the GIS and the spreadsheet in order to create this link. All relevant data was entered into the spreadsheet and then edited to a suitable level to be attached as an attribute table. Field names were chosen and appropriate an interpretable data formats each field. The completed spreadsheet was then linked to the shapefile to produce a polygon TSR spatial layer containing all available conservation and biodiversity information. Any additional attribute were either entered manually or obtained by merging with other layers. Attributes for the final layer were selected for usability by those wishing to query valuable Conservation Value (CV) data for each reserve, along with a number of administrative attributes for locating and querying certain aspects of each parcel. Constant error checking was conducted throughout the process to ensure minimal error being transferred to the production. This was done manually, and also by running numerous spatial and attribute based queries to identify potential errors in the spatial layer being produced. Follow up phone calls were made to the rangers to identify exact localities of reserves where polygons could not be allocated due to missing or ambiguous information. If precise location data was provided, polygons could be added in, either from other crown spatial layers or from cadastre. These polygons were also attributed with the lowest confindex rating, as their status as crown land is unknown or doubtful. In some cases existing GIS layers had been created for certain areas. Murray RLPB has data where 400+ polygons do not exist in the current crown TSR extract. According to the rangers interviewed it was determined the majority of these TSR exist. This data was incorporated in the TSR polygon by merging the two layers and then assigning attributes in the normal way, ie by being given a LP Name and ID and then updated from the marked up hard copy maps. In the confidence index these are given a rating of 1 (see confindex matrix) due to the unknown source of the data and no match with any other crown spatial data. A confidence index matrix (confindex) was produced in order to give the end user of the GIS product an idea as to how the data for each reserve was obtained, its purpose, and an indication to whether it is likely to be a current TSR. The higher the confindex, the more secure the user can be in the data. (See Confidence Index Matrix) This was necessary due to conflicting information from a number of datasets, usually the RLPB ranger (mark up on hard copy map) conflicting with the crown spatial data. If these conflicting reserves were to be deleted, this would lead to a large amount of information loss during the project. If additions were made without sufficient data to determine its crown status, currency, location, etc (which was not available in all cases) the end user may rely on data that has a low level of accuracy. The confindex was produced by determining the value of information and scoring it accordingly, compounding its value if data sources showed a correlation. Where an RLPB LP Name and ID point was not assigned to a polygon due to other points being in closer proximity these names and ID are effectively deleted from the polygon layer. In a number of cases this was correct due to land being revoked, relinquished and/or now freehold. In a number of cases where the TSR is thought to exist and a polygon could not be assigned due to no info available (Lot/DP, close proximity to a crown reserve, further ranger interview provided no info, etc etc). For these cases to ensure no information loss a points layer was compiled from the LP points layer with further info from the marked up hard copy maps to place the point in the most accurate approximate location to where the reserve is though to exist and then all CV data attached to the point. In many of these cases some further investigation could provide an exact location and inclusion in the TSR poly layer. The accuracy of the point is mentioned in the metadata, so that the location is not taken as an absolute location and is only to be used as a guide for the approximate location of the reserve. Topology checks were conducted to eliminate slivers in the layer and to remove duplicate polygons. Where two crown reserves existed on the same land parcel, the duplicate polygon was deleted and unique attributes (Crown Reserve Number, Type, and Purpose) were transferred. Once the polygon layer was satisfactorily completed, a list of the LP points not allocated to polygons was compiled. Any points (reserves) that were said to have been revoked or relinquished were then removed from this list to provide a list of those that are said to be current. An extract of the LP points layer was then produced with only the aforementioned points. These points were then attributed with the same conservation and biodiversity data as the polygon layer, in an attempt to minimise the amount of information loss. Dataset Citation "NSW Department of Environment, Climate Change and Water" (2010) Travelling Stock Route Conservation Values. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/198900d5-0d06-4bd0-832b-e30a7c4e8873.

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

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

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.

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

The 1885 UK parliamentary constituencies for Ireland were re-created in 2017 as part of a conference paper delivered at the Southern Irish Loyalism in Context conference at Maynooth University. The intial map only included the territory of the Irish Free State and was created by Martin Charlton and Jack Kavanagh. The remaining six counties of Ulster were completed by Eoin McLaughlin in 2018-19, the combined result is a GIS map of all the parliamentary constituecies across the island of Ireland for the period 1885-1918. The map is available in both ESRI Shapefile format and as a GeoPackage (GPKG). The methodology for creating the constituencies is outlined in detail below.

Methodology

A map showing the outlines of the 1855 – 1918 Constituency boundaries can be found on page 401 of Parliamentary Elections in Ireland, 1801-1922 (Dublin, 1978) by Brian Walker. This forms the basis for the creation of a set of digital boundaries which can then be used in a GIS. The general workflow involves allocating an 1885 Constituency identifier to each of the 309 Electoral Divisions present in the boundaries made available for the 2011 Census of Population data release by CSO. The ED boundaries are available in ‘shapefile’ format (a de facto standard for spatial data transfer). Once a Constituency identifier has been given to each ED, the GIS operation known as ‘dissolve’ is used to remove the boundaries between EDs in the same Constituency. To begin with Walker’s map was scanned at 1200 dots per inch in JPEG form. A scanned map cannot be linked to other spatial data without undergoing a process known as georeferencing. The CSO boundaries are available with spatial coordinates in the Irish National Grid system. The goal of georeferencing is to produce a rectified version of the map together with a world file. Rectification refers to the process of recomputing the pixel positions in the scanned map so that they are oriented with the ING coordinate system; the world file contains the extent in both the east-west and north-south directions of each pixel (in metres) and the coordinates of the most north-westerly pixel in the rectified image.

Georeferencing involves the identification of Ground Control Points – these are locations on the scanned map for which the spatial coordinates in ING are known. The Georeferencing option in ArcGIS 10.4 makes this a reasonably pain free task. For this map 36 GCPs were required for a local spline transformation. The Redistribution of Seats Act 1885 provides the legal basis for the constituencies to be used for future elections in England, Wales, Scotland and Ireland. Part III of the Seventh Schedule of the Act defines the Constituencies in terms of Baronies, Parishes (and part Parishes) and Townlands for Ireland. Part III of the Sixth Schedule provides definitions for the Boroughs of Belfast and Dublin.

The CSO boundary collection also includes a shapefile of Barony boundaries. This makes it possible code a barony in two ways: (i) allocated completely to a Division or (ii) split between two Divisions. For the first type, the code is just the division name, and for the second the code includes both (or more) division names. Allocation of these names to the data in the ED shapefile is accomplished by a spatial join operation. Recoding the areas in the split Baronies is done interactively using the GIS software’s editing option. EDs or groups of EDs can be selected on the screen, and the correct Division code updated in the attribute table. There are a handful of cases where an ED is split between divisions, so a simple ‘majority’ rule was used for the allocation. As the maps are to be used at mainly for displaying data at the national level, a misallocation is unlikely to be noticed. The final set of boundaries was created using the dissolve operation mentioned earlier. There were a dozen ED that had initially escaped being allocated a code, but these were quickly updated. Similarly, a few of the EDs in the split divisions had been overlooked; again updating was painless. This meant that the dissolve had to be run a few more times before all the errors have been corrected.

For the Northern Ireland districts, a slightly different methodology was deployed which involved linking parishes and townlands along side baronies, using open data sources from the OSM Townlands.ie project and OpenData NI.

OpenStreetMap (OSM) is a free, editable map & spatial database of the whole world. This dataset is an extract of OpenStreetMap data for 21 Pacific Island Countries, in a GIS-friendly format. The OSM data has been split into separate layers based on themes (buildings, roads, points of interest, etc), and it comes bundled with a QGIS project and styles, to help you get started with using the data in your maps. This OSM product will be updated weekly and contains data for Cook Islands, Federated States of Micronesia, Fiji, Kiribati, Republic of the Marshall Islands, Nauru, Niue, Palau, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, Vanuatu, Guam, Northern Mariana Islands, French Polynesia, Wallis and Futuna, Tokelau, American Samoa as well as data on the Pacific region. The goal is to increase awareness among Pacific GIS users of the richness of OpenStreetMap data in Pacific countries, as well as the gaps, so that they can take advantage of this free resource, become interested in contributing to OSM, and perhaps join the global OSM community.

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.

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.

This webinar will introduce the project on "the Workbook for Quantitative Methods and Socioconomic Application in GIS", and how to join the spatial data lab on those ongoing spacial social science research and development projects.

Aim: Fish assemblages –whether defined by taxonomy or functional traits—respond to regional and local habitat variation. We sampled rivers of Mongolia and the western United States (US) to determine the scale at which habitat could predict fish assemblage variation, classified by taxonomy or functional traits. Our hypothesis was that fish assemblages could be predicted using valley-scale hydrogeomorphology and reach-scale hydrology. We further predicted that if valley-scale variables explained high variation in fish assemblages then reach-scale variables would explain additional dimensions. Location: Mongolia, United States Methods: We evaluated reach- and valley-scale hydrogeomorphology of rivers in the US and Mongolia in each of three ecoregions, grassland, forest, and endorheic. Fishes were collected using backpack electrofisher following standard protocols. Results: Ordinations resulted in distinct assemblage patterns that corresponded with habitat variables at both valley- and reach-scales. Hydrogeomorphology differed for Mongolia and US rivers and likely contributed to different patterns that explained fish assemblage variation classified by taxonomy vs. traits. Ecoregions differed in factors contributing to fish assemblage patterns, likely a result of differences in hydrogeomorphology and historical influences, as well as effects of introduced species in the US. Main Conclusions: We found that fish assemblages were structured by hydrogeomorphic processes occurring at valley- and reach-scales, and that variables predicting fish assemblages vary with scale, ecoregion, and continent. We found a common pattern where if valley-scale variables provided high explanation of fish assemblages, then reach-scale variables frequently explained more ordination dimensions than valley-scale variables. This implies that reach-scale hydrology variables are always strong predictors of fish assemblage variation, and valley-scale geomorphology variables are sometimes strong predictors. We found evidence that introduced species or anthropogenic impacts modified our analyses predicting fish assemblage variation of Mongolia and US mountain steppe rivers. Although anthropogenic impacts were substantially higher for western US rivers than for Mongolia rivers, we were unable to detect strong differences in our ability to predict fish assemblage variation from reach- and valley-scale habitat variables. Methods 2.1 Study area and valley-scale habitat assessment We identified rivers in the US and Mongolia in three ecoregions, grassland (G), forest-steppe (F), and endorheic (E, Figures 1, 2) (Olson et al., 2001). Unique hydrogeomorphic patches were delineated into Functional Process Zones (FPZs, Thorp et al., 2006; 2008) using the GIS-based program RESonate (Williams et al., 2013) to extract valley-scale hydrogeomorphic and environmental variables from existing geospatial data. Maasri et al. (2019; 2021a) described details for data extraction using RESonate. We used the ten most influential variables for valley-scale hydrogeomorphology to delineate FPZs. These variables—which were extracted at 10 km stream intervals because of the size of these rivers--included elevation, mean annual precipitation, geology, valley width, valley floor (floodplain) width, valley width-to-valley width ratio, river channel sinuosity, right valley slope, left valley slope, and down valley slope. Data were normalized to a 0 to 1 scale for each river network, and a dissimilarity matrix was generated using a Gower dissimilarity transformation (Gower, 1971). A Gower transformation is recommended for non-biological data with range-standardization (Thoms & Parsons, 2003). The dissimilarity matrix was used in a hierarchical clustering following the Ward linkage method, as it resulted in the best partitioning of clusters (Murtagh & Legendre, 2014). We then used a Principal Components Analysis (PCA) to identify important contributive variables for group partitioning, and to describe cluster groups based on the ten variables described above. Cluster groups were later mapped to allow identification of sampling sites. We performed the clustering of FPZ groups using the cluster package (version 2.1.0) (Maechler et al., 2018) and the PCA using the FactoMineR package (version 1.42) (Lê et al., 2008) in R version 3.6.3 (R Core Team, 2020). We mapped the resulting groups using ArcGIS (version 10.5). We examined gradients in hydrogeomorphology with PCA using the ten influential valley-scale variables (above) in Minitab 18.1 (minitab.com) for all sites and by continent and ecoregion. 2.2 Reach-scale habitat assessment Each selected site was sampled following the Physical Habitat protocols from Environmental Monitoring and Assessment Program section 7 (Lazorchak et al., 1998) to provide a characterization of hydrogeomorphology at the reach-scale. Recorded field measurements were calculated into seven different metric sections (channel geometry, bank geometry, substrate, fish cover, human influence, riparian cover, flow) representing the habitat and dominant processes in the reach (Kaufmann et al., 1999). Sampling was conducted over a total reach length of 40 times the average wetted width, except where total reach length would have exceeded 5 km, where length was halved. Transects were taken at 0.1 intervals of the total reach length, while half transects were taken at 0.05 of the total reach length. Visual estimates of riparian cover were recorded as the amount and type of cover provided in a 10 m by 10 m area on the left and right banks centered on each transect. Visual estimates of the amount and type of fish cover were recorded representing an area 5 m upstream and 5 m downstream in and over the water at a transect. Human influence data were collected using a “presence metric” that also indicated closeness to the river at a given transect (P- Present > 10m away, C- Present within 10 m, B- present on the bank, 0- Not Present). Channel geometry data included five depth measurements across each transect, and wetted width at each transect and half transect. Bank geometry data were collected at each transect on both banks and included top-of-bank elevations and distances, bankfull elevations and distances, and bank angles. Substrate data were collected at the same spot as depth at transects, as well as at half transects. Additional reach-scale data were collected remotely in ArcGIS using digital elevation models and aerial photography to extract slope and sinuosity. In total, 120 characteristics and metrics were collected for each sampled site (Appendix 1). These variables were reduced by selecting only characteristics that were aggregates of multiple similar characteristics (i.e., PCT_FAST sums the percentages of falls cascades, rapids, and riffles). FPZ segment data were linked to sampled reaches through analysis in GIS, using a spatial join. The spatial join was conducted on the most downstream GPS point of a sampled site, joining one-to-one with the closest FPZ segment with a search radius to select a single FPZ line with a single sampled site. The spatial join was manually confirmed that each sampled site had an associated FPZ segment. In cases where reaches did not pair with an FPZ segment, manual connections were made by identifying the closest FPZ segment downstream. The FPZ dataset, originally representing over 4300 valley segments across FPZs (Costello et. al, in review), was reduced to 95 segments representing the FPZs that were sampled. The most downstream point of a sampled reach was used to delineate a contributing watershed area boundary. Using the watershed boundary, data were extracted from DEMs (SRTM 30-m Mongolia, 10-m US), land cover (IGBP Land Cover Classification, Mongolia; National Land Cover Dataset, US), and climate (WorldClim, 30 arc-second). Land cover characteristics were combined to provide consistent classifications between the United States and Mongolia. Land cover characteristics were divided by the contributing watershed area to allow for relative comparison across differing watershed sizes. A total of 29 characteristics were collected for each of the 96 contributing watersheds (Appendix 1). 2.3 Fish collections and traits We collected fishes from 94 sites that were identified as described above. Site distances for fish collections were 20 times the mean wetted width. We collected fishes by single-pass backpack electrofishing supplemented with angling (Ball State University IACUC #126193) following the American Fisheries Society standard collection protocols (Bonar et al., 2009). Fish abundances across sampled areas were standardized using CPUE fish-per-m. Fishes were collected during one-month expeditions in each of the river networks during summer or fall seasons from 2017-2019 (Maasri et al., 2021b). Species identifications, ecological and biological traits were from Mendsaikhan et al. (2017) for Mongolia, and from state fish guides for the US. Reproductive traits were reduced to four categories: nonguarder open substratum, nonguarder brood hiders, guarders, and viviparous based on Balon (1975). 2.4 Analyses Valley-scale geomorphology data and reach-scale hydrology data were reduced separately into fewer variables with minimal collinearity using Principal Components Analysis (PCA) in Minitab version 18. We used three PCA axes for the valley-scale data and five PCA axes for the reach-scale data. We then evaluated fish assemblage responses to valley-scale geomorphology and reach-scale hydrology variables using constrained ordinations with forward selection of environmental variables in CANOCO 5 software (canoco5.com). CANOCO evaluates length of the first ordination axis and recommends either a linear method (Redundancy Analysis, RDA) or a nonlinear method (Canonical Correspondence Analysis, CCA). RDA is a direct gradient technique for multifactorial analysis-of-variance models using ecologically relevant distance measures and

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.