Download In State Plane Projection Here The 2024 Parcel Fabric Data is a copy of the Lake County Chief Assessor's Office spatial dataset, consisting of separate layers which represent the boundaries for Tax Parcels, Lots, Units, Subs, Condos, Rights of Way, and Encumbrance parcels, along with points, lines, and PLSS townships for reference, which have all been captured for the 2024 Tax Year.This data is spatial in nature and does not include extensive fields of attributes to which each layer may be associated. This data is provided for use to individuals or entities with an understanding of Esri's ArcGIS Pro (specifically the Parcel Fabric), and those with access to ArcGIS Pro, which is necessary to view or manipulate the data.Casual users can find the standalone Tax Parcel Boundary Data here and Parcel Attribute Data here. Update Frequency: This dataset is updated on a yearly basis.

Performing parcel merge with Parcel Fabric in ArcGIS Pro is simple!Don't believe it? Watch the video by clicking the "Open" button on the top right of this page.Editing in Parcel Fabric is maintained and tracked by the record associated to the parcels, thanks to ArcGIS being used as a system of record to maintain parcel data.Check out ArcGIS Parcel Fabric Community Page on Esri GeoNet for other videos and resources about Parcel Fabric.

Performing parcel split with Parcel Fabric in ArcGIS Pro is simple!Don't believe it? Watch the video by clicking the "Open" button on the top right of this page.Editing in Parcel Fabric is maintained and tracked by the record associated to the parcels, thanks to ArcGIS being used as a system of record to maintain parcel data.Check out ArcGIS Parcel Fabric Community Page on Esri GeoNet for other videos and resources about Parcel Fabric.

The 2023 Parcel Fabric Data is a copy of the Lake County Chief Assessor's Office spatial dataset, consisting of separate layers which represent the boundaries for Tax Parcels, Lots, Units, Subs, Condos, Rights of Way, and Encumbrance parcels, along with points, lines, and PLSS townships for reference, which have all been captured for the 2023 Tax Year.This data is spatial in nature and does not include extensive fields of attributes to which each layer may be associated. This data is provided for use to individuals or entities with an understanding of Esri's ArcGIS Pro (specifically the Parcel Fabric), and those with access to ArcGIS Pro, which is necessary to view or manipulate the data.Casual users can find the standalone Tax Parcel Boundary Data here and Parcel Attribute Data here. Update Frequency: This dataset is updated on a yearly basis.

The GeologicSurficial_SURFICIAL data consists of surficial geologic features as digitized from the 1:62,500 15 minute series USGS quadrangle map sheets, compiled by The Vermont Geological Survey 1956-1970. Surficial geologic features are represented as polygon, line and point features.This layer is not symbolized. You can access fully symbolized layer files of point, polygon and line features relating to the surficial geologic features 1:62500 layer here:https://anrmaps.vermont.gov/websites/Geology/Surficial62kLayerFiles/

The Conveyance Division is the primary division of the survey system. Examples are lots, units, or common elements. These are nested within the simultaneous conveyance and do not cross boundaries. The Conveyance Division may not tessellate or uniquely divide the entire simultaneous conveyance. The Conveyance Division feature class is updated nightly extracting information from the Parcel Fabric into the feature class.

Download In State Plane Projection Here.

The 2017 Parcel Fabric Data is a copy of the Lake County Chief Assessor's Office spatial dataset, consisting of separate layers which represent the boundaries for Tax Parcels, Lots, Units, Subs, Condos, Rights of Way, and Encumbrance parcels, along with points, lines, and PLSS townships for reference, which have all been captured for the 2017 Tax Year.

This data is spatial in nature and does not include extensive fields of attributes to which each layer may be associated. This data is provided for use to individuals or entities with an understanding of Esri's ArcMap (specifically the Parcel Fabric), and those with access to ArcMap, which is necessary to view or manipulate the data.

Casual users can find the standalone Tax Parcel Boundary Data above and Parcel Attribute Data here.

Update Frequency: Yearly

PLSSQuarterSections_GCDB is the third level of a hierarchical breakdown of the Public Land Survey System Rectangular surveys. This data is Version 2.3 2020 of the Utah PLSS Fabric. This data set represents the GIS Version of the Public Land Survey System. Updates are expected annually as horizontal control positions from published sources and global positioning system (GPS) observations are added. The primary source for the data is cadastral survey records housed by the BLM supplemented with local records and geographic control coordinates from states, counties as well as other federal agencies such as the USGS and USFS. This data was originally published on 1/3/2017. This is no longer a standard and is now published as a Reference layer. This data is derived from the Second Division of the PLSS which includes quarter, quarter-quarter, sixteenth or government lot division of the PLSS.Updates were made to Quarter Sections in Utah County to add quarter sections to areas that were not broken down less than the Section level. They were added using information from points collected by county surveyors, that is in the data and extrapolating information from adjoining sections wherever possible. They are still areas that could not be interpreted well enough and they were left empty, beyond the section level.Quarter Sections were consolidated from the Quarter Quarter Sections to better represent the four quarters in all Aliquot Part areas. Areas with Lots or Special Surveys remain as is. Updated 4/8/2022

Access through GeoBC ParcelMap BC Parcel Fabric - Datasets - Data Catalogue (gov.bc.ca)ParcelMap BC is the single, complete, trusted and sustainable electronic map of active titled parcels and surveyed provincial Crown land parcels in British Columbia. This particular dataset is a subset of the complete ParcelMap BC data and is comprised of the parcel fabric and attributes for over two million parcels published under the Open Government Licence - British Columbia.

Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the... Visit https://dataone.org/datasets/sha256%3A3e3f055bf6281f979484f847d0ed5eeb96143a369592149328c370fe5776742b for complete metadata about this dataset.

Market Overview: The global geospatial solutions market is projected to reach a valuation of $438.15 billion by 2033, growing at a robust CAGR of 14.6% from 2025 to 2033. This growth is driven by increasing urbanization, rising demand for real-time location-based services, advancements in sensor technologies, and government initiatives promoting smart infrastructure. The market is segmented based on technology (GIS/Geospatial Analytics, Remote Sensing, GPS, 3D Scanning), component (Hardware, Software, Services), application (Surveying & Mapping, Geovisualization, Planning & Analysis, Land Management), end use (Utility, Business, Transportation, Defense & Intelligence, Infrastructure Development, Natural Resources), and region. Key Trends and Challenges: The adoption of cloud-based geospatial solutions is a major trend, enabling cost-effective access to data and services. Artificial intelligence (AI) and machine learning (ML) are also gaining prominence, enhancing data analysis and decision-making. However, challenges include data interoperability, data security concerns, and the need for skilled professionals. The market is fragmented, with established vendors such as Microsoft, Google, and Trimble coexisting with innovative startups. Government initiatives and partnerships with private companies are expected to foster market growth. Key regions include North America, Europe, and Asia Pacific, with emerging markets in Latin America and the Middle East offering significant growth potential. Recent developments include: In July 2024, Esri collaborated with Microsoft Corporation to integrate its spatial analytics technology with Fabric, Microsoft's unified analytics SaaS platform. Data experts, including analysts, data scientists, engineers, and executives, could seamlessly use Esri's advanced spatial analytics tools and visualizations within Microsoft Fabric through this integration. This collaboration enabled powerful spatial analytics to be easily shared across organizational tools such as Microsoft Fabric, Power BI, and Esri's ArcGIS environment. , In February 2024, GE Vernova announced the release of Proficy for Sustainability Insights, a new AI-based software solution aimed at helping manufacturers achieve sustainability goals while maximizing productivity and profitability. The software integrates operational and sustainability data, enabling industrial companies to use resources more efficiently and manage climate metrics required for regulatory compliance across plants or enterprises. .

PLSSQuarterQuarterSections_GCDB is the fourth level of a hierarchical break down of the Public Land Survey System Rectangular surveys. This data is Version 2.3 2020 of the Utah PLSS Fabric. This data set represents the GIS Version of the Public Land Survey System. Updates are expected annually as horizontal control positions from published sources and global positioning system (GPS) observations are added. The primary source for the data is cadastral survey records housed by the BLM supplemented with local records and geographic control coordinates from states, counties as well as other federal agencies such as the USGS and USFS. This data was orginally published on 1/3/2017. Updated 12/15/2020Updates were made to Quarter Quarter Sections in Utah County to add quarter-quarter sections to areas that were not broken down less than the Section level. These are labeled as SECDIVTYP = Z and SECDIVTXT = Unsurveyed Unprotracted. They were added using information from points collected by county surveyors, that is in the data and extrapolating information from adjoining sections wherever possible. They are still areas that could not be interpreted well enough and they were left empty, beyond the section level.

For use in the FloridaCommerce Office of Broadband BEAD Eligibility by Address Application.

The published representation of real property areas, combined with assessing and tax information from CAMA and Tax systems, and organized for consumption in desktop and web applications. This feature class contains redundant geometry in cases where there are multiple condominium units on a given tax parcel. The Tax Parcel feature class is updated nightly extracting information from the Parcel Fabric into the feature class.

Sensitivity indicator. Volume constructed / urban surface area. The urban surface area is calculated as the sum of the areas with coverage comprising SIOSE 811, 812 and 813. The volume constructed is calculated as the product of the surface area with buildings within the urban surface area, multiplied by the average height of the buildings. Sources: Land registry. Buildings data and SIOSE (urban surface area). IDENA (municipalities). Methodology Municipal system of sustainability indicators Date: 2019 (land registry) and 2014 (SIOSE).Monitoring: The volume constructed can be monitored annually using land registry data, but the urban surface area (SIOSE) is not updated annually.

Production Parcel Fabric Layers

Authoritative as of 4/30/2025. Served-Funded Locations and their respective probable enforceable commitments are still subject to change at this time and might not be reflective of the most up-to-date project plans.This dataset was created to provide a comprehensive representation of broadband locations in Vermont by merging and reconciling data from Vermont's Emergency 911 (E911) site locations, dated 1/3/2024, and Version 4 of the FCC National Broadband Fabric. Since E911 data is widely regarded as the most accurate representation of address locations in Vermont and is frequently used by Vermont providers, the Vermont Community Broadband Board (VCBB) developed a robust process to align these datasets for use in mapping and reporting.Key processes include identifying overlaps between E911 and FCC data, reconciling unmatched locations, and assigning a principal location for duplicated or overlapping data. Principal locations are determined through a prioritization framework that evaluates factors such as initial matching accuracy, E911 site occupancy status, and proximity to matched FCC locations to ensure the most representative data points. Unique E911 sites and unmatched FCC locations are independently recognized as principal locations. This approach ensures that the dataset provides an accurate and unified representation of broadband service locations across Vermont.For more information about this dataset, visit explore-vcbb.hub.arcgis.com/pages/documentation-and-training.Availability data for cable, copper, fiber to the premises and licensed fixed wireless for Vermont was extracted from the FCC Broadband Map (https://broadbandmap.fcc.gov/data-download/nationwide-data?version=jun2023) on 1/22/2024 and is dated 1/9/2024.The statuses in the Status field are defined as follows, with speeds reported as (download speed)/(upload speed), in mbps:Served: Served by 100/20 broadband speeds or more.Served - Funded: Funded by an enforceable commitment and are considered served.Underserved: Locations with broadband speeds greater than or equal to 25/3 and less than 100/20.Underserved - Modified: Only served by wireless, DSL or only wireless and DSL.Unserved: Locations with broadband speeds less than 25/3 or are not served by broadband at all.Direct any questions to vcbb_tech_support@stone-env.com.

Data in this layer is compiled from a variety of sources. Attributes have been added to distinguish the sources."LeePA Building Footprints" are created and maintained by the Lee County Property Appraiser's GIS. The geometry and attributes are extracted from their databases and combined based on the unique building key."LeePA Condo Buildings" are created from features in the Lee County Property Appraiser's parcel fabric. The geometry and attributes are extracted from their databases and combined using a variety of methods.Other buildings have been added by Lee County GIS. These are typically mobile/manufactured homes or time shares. Most mobile/manufactured homes were created using Esri's Building Footprint Extraction deep learning package and Regularize Building Footprint geoprocessing tool from 2024 aerial imagery. Additional attributes were added by Lee County GIS.

This map/feature service shows the components of the parcel fabric; easements, rights-of-way, tax parcels, subdivisions, planning zones, council wards, annexations, city limits and PLSS sections.The service only contains simple feature, not the parcel fabric itself. It is not editable and is updated nightly.