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.

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

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.

Vermont E911 Site locations (ESITEs) including buildings, facilities, and development sites; locations are represented by points. Points are attributed with addresses--composing an address points layer. Dataset is updated weekly.Field Descriptions:OBJECTID: Internal feature number. Auto-generated by Esri software.SEGMENTID: Unique segment ID.ESITEID: Unique ESITE ID.GEONAMEID: Ties ESITE to GEONAMEID (unique ID for each road name) in VT E911 Road Centerlines.PD: Prefix Direction, previously name PRE.DIR.PT: Prefix Type.SN: Street Name. Previously named STREET.ST: Street Type.SD: Suffix Direction, i.e., W for West, E for East etc.PRIMARYNAME: A concatenation of the street-name parts (PD, PT, SN, ST, SD).ALIAS1: Alternate road name.ALIAS2: Alternate road name.ALIAS3: Alternate road name.ALIAS4: Alternate road name.ALIAS5: Alternate road name.PRIMARYADDRESS: A concatenation of house number and street-name parts (PD, PT, SN, ST, SD).SITETYPE: Type of site. Uses SiteTypes domain*.TOWNNAME: Town name.MCODE: Municpal code.ESN: Emergency Service Number. Developed for each town that indicates a unique town code for each law, fire, and EMS provider. These providers are compared against the master list to determine if they are already present. If they are, the existing state code is used. If the provider is new, they are added to the state master list with the next unique provider number.ZIP: Zip code.PARCELNUM: Parcel number.GPSX: GPS X coordinate.GPSY: GPS Y coordinate.MAPYEAR: Date added to E911 data.UPDATEDATE: Update date.STATE: US State.FIPS8: Federal information processing standards codes.SPAN: Pulled from the VCGI parcel dataset via spatial join 1-3 times per year; NOT MAINTAINED DAILY.SUBTYPE: Field not in use.GlobalID_1: System-generated ID.UNITCOUNT: For commercial and residential, number of units in the site.PRIMARYADD1: Concatenation of house number, full street name, and E911 town. E911 TOWN (AKA E911 JBOUND) IS NOT ALWAYS THE SAME AS POSTAL TOWN NOR IS IT ALWAYS THE SAME AS TOWN DEFINED BY MUNICIPAL BOUNDARY. E911 TOWN (E911 JBOUND) was originally defined for the Master Street Address Guide (MSAG) Community; E911 JBOUND contains names chosen by towns for representing town names for 911 purposes.PRIMARYADD2: Concatenation of PRIMARYADD1 plus zip code.SITETYPE_MULTI1: Additional SITETYPE--if applicable. For development sites, contains the main use the site is to become. Uses SiteTypes domain*.SITETYPE_MULTI2: Additional SITETYPE--if applicable. For development sites, contains the main use the site is to become. Uses SiteTypes domain*.SITETYPE_MULTI3: Additional SITETYPE--if applicable. For development sites, contains the main use the site is to become. Uses SiteTypes domain*.SITETYPE_MULTI4: Additional SITETYPE--if applicable. For development sites, contains the main use the site is to become. Uses SiteTypes domain*.SITETYPE_MULTI5: Additional SITETYPE--if applicable. For development sites, contains the main use the site is to become. Uses SiteTypes domain*.COUNTY: County.COUNTRY: Country.SOURCEOFDATA: Source of data.DRIVEWAYID: Field not in use.ESZ: Emergency Service Zone--a defined area covered by four primary-response agencies.HOUSE_NUMBER: House number.HOUSE_NUMBERSUFFIX: For addresses not in compliance with standards (typically in urbanized areas where otherwise renumbering needs to occur). For example, a new house between 8 and 10 is built and the town calls it 8 1/2 or 8A instead of renumbering; the 1/2 or A would be in this field; there are approximately less than 300-400 of these cases.HOUSE_NUMBERPREFIX: For the three streets where alpha characters come before the house number (e.g., A20 or B12).FIPS: County FIPS number.Shape: Feature geometry.*SiteTypes Domain:ABANDONEDACCESS POINTACCESSORY BUILDINGAIR SUPPORT / MAINTENANCE FACILITYAIR TRAFFIC CONTROL CENTER / COMMAND CENTERAIRPORT TERMINALAMBULANCE SERVICEAUDITORIUM / CONCERT HALL / THEATER / OPERA HOUSEBANKBOAT RAMP / DOCKBORDER CROSSINGBORDER PATROLBUS STATION / DISPATCH FACILITYCAMPCAMPGROUNDCEMETERYCITY / TOWN HALLCOAST GUARDCOLLEGE / UNIVERSITYCOMMERCIALCOMMERCIAL CONSTRUCTION SERVICECOMMERCIAL FARMCOMMERCIAL GARAGECOMMERCIAL W/RESIDENCECOMMUNICATION BOXCOMMUNICATION TOWERCOMMUNITY / RECREATION FACILITYCOURT HOUSECULTURALCUSTOMS SERVICEDAY CARE FACILITYDEVELOPMENT SITEEBS TOWEREDUCATIONALEMERGENCY PHONE / CALLBOXFAIR / EXHIBITION/ RODEO GROUNDSFERRY TERMINAL / DISPATCH FACILITYFIRE STATIONFISH FARM / HATCHERYFITNESS FACILITYFOOD DISTRIBUTION CENTERGAS STATIONGATED W/BUILDINGGATED W/O BUILDINGGOLF COURSEGOVERNMENTGRAVEL PITGREENHOUSE / NURSERYGROCERY STOREHARBOR / MARINAHAZARDOUS MATERIALS FACILITYHAZARDOUS STORAGE FACILITYHEALTH CLINICHELIPAD / HELIPORT / HELISPOTHISTORIC SITE / POINT OF INTERESTHOSPITAL / MEDICAL CENTERHOUSE OF WORSHIPHYDROELECTRIC FACILITYICE ARENAINDUSTRIALINSTITUTIONAL RESIDENCE / DORM / BARRACKSLANDFILLLAW ENFORCEMENTLIBRARYLODGINGLOOKOUT TOWERLUMBER MILL / SAW MILLMANUFACTURING FACILITYMINEMOBILE HOMEMORGUEMULTI-FAMILY DWELLINGMUSEUMNATIONAL GUARD / ARMORYNUCLEAR FACILITYNURSING HOME / LONG TERM CAREOFFICE BUILDINGOFFICE OF EMERGENCY MANAGEMENTOIL / GAS FACILITYOTHEROTHER COMMERCIALOTHER RESIDENTIALOUTPATIENT CLINICPARK AND RIDE / COMMUTER LOTPHARMACYPICNIC AREAPOST OFFICEPRISON / CORRECTIONAL FACILITYPRIVATE AND EXPRESS SHIPPING FACILITYPSAPPUBLIC BEACHPUBLIC GATHERINGPUBLIC TELEPHONEPUBLIC WATER SUPPLY INTAKEPUBLIC WATER SUPPLY WELLPUMP STATIONRACE TRACK / DRAGSTRIPRADIO / TV BROADCAST FACILITYRAILROAD STATIONRESIDENTIAL FARMREST STOP / ROADSIDE PARKRESTAURANTRETAIL FACILITYRV HOOKUPSCHOOLSEASONAL HOMESINGLE FAMILY DWELLINGSKI AREA / ALPINE RESORTSOLAR FACILITYSPORTS ARENA / STADIUMSTATE CAPITOLSTATE GARAGESTATE GOVERNMENT FACILITYSTATE PARKSTORAGE UNITSSUBSTATIONSUGARHOUSETEMPORARY STRUCTURETOWN GARAGETOWN OFFICETRAILHEADTRANSFER STATIONUNKNOWNUS FOREST FACILITYUS GOVERNMENT FACILITYUTILITYUTILITY POLE W/PHONEVETERINARY HOSPITAL / CLINICVISITOR / INFORMATION CENTERWAREHOUSEWASTE / BIOMASS FACILITYWASTEWATER TREATMENT PLANTWATER TANKWATER TOWERWIND FACILITY / WIND TOWERYOUTH CAMP

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 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.

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

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.

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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

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

This service contains data on both spwaning and nursery grounds. These can be described as follows:

Fish Spawning Grounds: This layer is the nominal spawning distribution as gauged from the distribution and relative abundance of egg and/or larval stages from contemporary data and Coull et al. (1998). Areas with higher concentrations of eggs and/or larvae that are considered to relate to more important spawning grounds are designated as high intensity. This data is focused on species that are considered to be of conservation importance because it was developed for the Marine Conservation Zones project. This may be insufficient for other applications.

Fish Nursery Grounds: This layer provides the nominal nursery grounds for 17 highly mobile species distributed at a half ICES statistical rectangle resolution (0.5 by 0.5 degrees), Data were obtained from sampling surveys and Coull et al. (1998). Areas of higher concetrations of juveniles are considered to relate to more important nursery grounds. High intensity nursery grounds are those deemed as a main nursery ground with high relative abundance of juveniles.

Known Limitations: There are several data quality issues, especially regarding taxonomic identification (Section 2.3), that need to be considered when interpreting the data layers. For other elements of data quality, it is recommended by CEFAS that the users of the data layers refer to the associated report on the distribution of highly mobile species (Defra Project MB0102, Report No. 15, Task 2B). More intensive surveys have been undertaken in the Irish Sea, and higher resolution (HR) maps are provided for Cod, Sole and Plaice

Nominal spawning grounds for Ling are for a fringe of the distribution. The main distribution of Ling is not sampled appropriately for eggs & larvae, and no shapefile is provided.

Data for the North Sea population of mackerel were not available during this study.

For those areas (e.g. NW Scotland, parts of the English Channel and Irish Coasts) where there are no recent egg/larval data we recommend the user consult Coull et al. (1998) until more recent data become available.

In many cases, available data are limited or are of questionable quality (e.g. due to taxonomic problems, sampling artefacts etc.). Hence, there was a need to attach a measure of confidence for the data available (see Table 4 om ).

To enhance the way the data can be classified the Marine Management Organisation (MMO) carried out additional analysis to identify areas that were high or low intensity for multiple species. This allows the data to be classified by High/Low intensity and by the number of species. Each record contains attribution on the intensity, number of species and species type. Analysis was carried out using Geoprocessing tools in ArcGIS and ET Geowizard (Union, Advanced Edit, Dissolve and Spatial Join).

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 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

Summary of category 3 water pollution incidents reported to the Environment Agency are held on the National Incident Reporting System. Sum of incidents reported between 2001 and 2020 summarised by WFD Operational Catchment.Extracted from NIRS for Closed Category 3 and 4 Incidents classified as 3 and 4 in the Water Environmental Level code field from 01/01/2020 until date of extraction 20/05/2024. This data includes grid references for each incident. These Grid references were then used to map each Incident within ArcMap and analyse using the Spatial Join Tool how many incidents are located within each WFD Operational. Within the data tab shows a table of Counts of Category 3 and 4 Incidents within each WFD Operational Catchments from 01/01/2020 to data extraction date (20/05/2024).

This is a collaboration between City of Los Angeles Mayor's Office, StreetsLA, and USC. To consolidate / aggregate many datasets for Street Sweeping. Task 2: to perform spatial join between Centerlines and Biweekly Posted Routes.

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.

This intersection points feature class represents current intersections in the City of Los Angeles. Few intersection points, named pseudo nodes, are used to split the street centerline at a point that is not a true intersection at the ground level. The Mapping and Land Records Division of the Bureau of Engineering, Department of Public Works provides the most current geographic information of the public right of way. The right of way information is available on NavigateLA, a website hosted by the Bureau of Engineering, Department of Public Works.Intersection layer was created in geographical information systems (GIS) software to display intersection points. Intersection points are placed where street line features join or cross each other and where freeway off- and on-ramp line features join street line features. The intersection points layer is a feature class in the LACityCenterlineData.gdb Geodatabase dataset. The layer consists of spatial data as a point feature class and attribute data for the features. The intersection points relates to the intersection attribute table, which contains data describing the limits of the street segment, by the CL_NODE_ID field. The layer shows the location of the intersection points on map products and web mapping applications, and the Department of Transportation, LADOT, uses the intersection points in their GIS system. The intersection attributes are used in the Intersection search function on BOE's web mapping application NavigateLA. The intersection spatial data and related attribute data are maintained in the Intersection layer using Street Centerline Editing application. The City of Los Angeles Municipal code states, all public right-of-ways (roads, alleys, etc) are streets, thus all of them have intersections. List of Fields:Y: This field captures the georeferenced location along the vertical plane of the point in the data layer that is projected in Stateplane Coordinate System NAD83. For example, Y = in the record of a point, while the X = .CL_NODE_ID: This field value is entered as new point features are added to the edit layer, during Street Centerline application editing process. The values are assigned automatically and consecutively by the ArcGIS software first to the street centerline spatial data layer, then the intersections point spatial data layer, and then the intersections point attribute data during the creation of new intersection points. Each intersection identification number is a unique value. The value relates to the street centerline layer attributes, to the INT_ID_FROM and INT_ID_TO fields. One or more street centerline features intersect the intersection point feature. For example, if a street centerline segment ends at a cul-de-sac, then the point feature intersects only one street centerline segment.X: This field captures the georeferenced location along the horizontal plane of the point in the data layer that is projected in Stateplane Coordinate System NAD83. For example, X = in the record of a point, while the Y = .ASSETID: User-defined feature autonumber.USER_ID: The name of the user carrying out the edits.SHAPE: Feature geometry.LST_MODF_DT: Last modification date of the polygon feature.LAT: This field captures the Latitude in deciaml degrees units of the point in the data layer that is projected in Geographic Coordinate System GCS_North_American_1983.OBJECTID: Internal feature number.CRTN_DT: Creation date of the polygon feature.TYPE: This field captures a value for intersection point features that are psuedo nodes or outside of the City. A pseudo node, or point, does not signify a true intersection of two or more different street centerline features. The point is there to split the line feature into two segments. A pseudo node may be needed if for example, the Bureau of Street Services (BSS) has assigned different SECT_ID values for those segments. Values: • S - Feature is a pseudo node and not a true intersection. • null - Feature is an intersection point. • O - Intersection point is outside of the City of LA boundary.LON: This field captures the Longitude in deciaml degrees units of the point in the data layer that is projected in Geographic Coordinate System GCS_North_American_1983.

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.

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

Access APIGeocoded Addressing Theme 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 …Show full description Access APIGeocoded Addressing Theme 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 Geocoded Urban and Rural Addressing System (GURAS) is a ‘property’ based address database. Each property polygon captured within GURAS has a unique numeric identifier and contains at least one authoritative address which is sourced from local councils via the valuation of land database, also managed by LPI-Valnet. Properties may contain more than one address sourced from various other organisations. 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. Owners 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.Geocoded Addressing Data Theme includes the following feature classes:Waypoint - A WayPoint is a point located on the RoadSegment feature class for an address where the road naming attributes from both the AddressString and the RoadSegment classes are identical. Indicates the approximate entry point of for an address.Address Point - A point feature class used to spatially locate an address / address stringThe Address Point Layer includes the below subtypes:· Building· Homestead· Monument· Property· Unit/Strata· OtherPro Way - A Proway is a line that spatially connects the AddressPoint and WayPoint.The Pro Way Layer includes the following subtypes:· Right· Left· OtherMetadata Type Esri Map ServiceUpdate 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 ToGDA94 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 2795Dataset Producers and Contributors Administrative Spatial Programs, DCS Spatial Services 346 Panorama Ave Bathurst NSW 2795