Introduction

Geographical scale, in terms of spatial extent, provide a basis for other branches of science. This dataset contains newly proposed geographical and geological GIS boundaries for the Pan-Tibetan Highlands (new proposed name for the High Mountain Asia), based on geological and geomorphological features. This region comprises the Tibetan Plateau and three adjacent mountain regions: the Himalaya, Hengduan Mountains and Mountains of Central Asia, and boundaries are also given for each subregion individually. The dataset will benefit quantitative spatial analysis by providing a well-defined geographical scale for other branches of research, aiding cross-disciplinary comparisons and synthesis, as well as reproducibility of research results.

The dataset comprises three subsets, and we provide three data formats (.shp, .geojson and .kmz) for each of them. Shapefile format (.shp) was generated in ArcGIS Pro, and the other two were converted from shapefile, the conversion steps refer to 'Data processing' section below. The following is a description of the three subsets:

(1) The GIS boundaries we newly defined of the Pan-Tibetan Highlands and its four constituent sub-regions, i.e. the Tibetan Plateau, Himalaya, Hengduan Mountains and the Mountains of Central Asia. All files are placed in the "Pan-Tibetan Highlands (Liu et al._2022)" folder.

(2) We also provide GIS boundaries that were applied by other studies (cited in Fig. 3 of our work) in the folder "Tibetan Plateau and adjacent mountains (Others’ definitions)". If these data is used, please cite the relevent paper accrodingly. In addition, it is worthy to note that the GIS boundaries of Hengduan Mountains (Li et al. 1987a) and Mountains of Central Asia (Foggin et al. 2021) were newly generated in our study using Georeferencing toolbox in ArcGIS Pro.

(3) Geological assemblages and characters of the Pan-Tibetan Highlands, including Cratons and micro-continental blocks (Fig. S1), plus sutures, faults and thrusts (Fig. 4), are placed in the "Pan-Tibetan Highlands (geological files)" folder.

Note: High Mountain Asia: The name ‘High Mountain Asia’ is the only direct synonym of Pan-Tibetan Highlands, but this term is both grammatically awkward and somewhat misleading, and hence the term ‘Pan-Tibetan Highlands’ is here proposed to replace it. Third Pole: The first use of the term ‘Third Pole’ was in reference to the Himalaya by Kurz & Montandon (1933), but the usage was subsequently broadened to the Tibetan Plateau or the whole of the Pan-Tibetan Highlands. The mainstream scientific literature refer the ‘Third Pole’ to the region encompassing the Tibetan Plateau, Himalaya, Hengduan Mountains, Karakoram, Hindu Kush and Pamir. This definition was surpported by geological strcture (Main Pamir Thrust) in the western part, and generally overlaps with the ‘Tibetan Plateau’ sensu lato defined by some previous studies, but is more specific.

More discussion and reference about names please refer to the paper. The figures (Figs. 3, 4, S1) mentioned above were attached in the end of this document.

Data processing

We provide three data formats. Conversion of shapefile data to kmz format was done in ArcGIS Pro. We used the Layer to KML tool in Conversion Toolbox to convert the shapefile to kmz format. Conversion of shapefile data to geojson format was done in R. We read the data using the shapefile function of the raster package, and wrote it as a geojson file using the geojson_write function in the geojsonio package.

Version

Version 2022.1.

Acknowledgements

This study was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDB31010000), the National Natural Science Foundation of China (41971071), the Key Research Program of Frontier Sciences, CAS (ZDBS-LY-7001). We are grateful to our coauthors insightful discussion and comments. We also want to thank professors Jed Kaplan, Yin An, Dai Erfu, Zhang Guoqing, Peter Cawood, Tobias Bolch and Marc Foggin for suggestions and providing GIS files.

Citation

Liu, J., Milne, R. I., Zhu, G. F., Spicer, R. A., Wambulwa, M. C., Wu, Z. Y., Li, D. Z. (2022). Name and scale matters: Clarifying the geography of Tibetan Plateau and adjacent mountain regions. Global and Planetary Change, In revision

Jie Liu & Guangfu Zhu. (2022). Geographical and geological GIS boundaries of the Tibetan Plateau and adjacent mountain regions (Version 2022.1). https://doi.org/10.5281/zenodo.6432940

Contacts

Dr. Jie LIU: E-mail: liujie@mail.kib.ac.cn;

Mr. Guangfu ZHU: zhuguangfu@mail.kib.ac.cn

Institution: Kunming Institute of Botany, Chinese Academy of Sciences

Address: 132# Lanhei Road, Heilongtan, Kunming 650201, Yunnan, China

Copyright

This dataset is available under the Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).

Data OriginThe dataset provided by Ofwat is rooted in legal records. The dataset is digitised from the official appointments of companies as water and sewage undertakers, which include legally binding documents and maps. These documents establish the specific geographic areas each water company is responsible for. The dataset was sourced from Constituency information: Water companiesData TriageAnonymisation is not required for this dataset, since the data is publicly available and focuses on geographical boundaries of water companies rather than individual or sensitive information. The shapefile serves a specific purpose related to geospatial analysis and regulatory compliance, offering transparent information about the service areas of different water companies as designated by Ofwat.Further ReadingBelow is a curated selection of links for additional reading, which provide a deeper understanding of the water company boundaries datasetOfwat (The Water Services Regulation Authority): As the regulatory body for water and wastewater services in England and Wales, Ofwat's website is a primary source for detailed information about the water industry, including company boundaries.Data.gov.uk: This site provides access to national datasets, including the Water Resource Zone GIS Data (WRMP19), which covers all water resource zones in England. This dataset is crucial for understanding geographical boundaries related to water management.Water UK: As a trade body representing UK water and wastewater service providers, Water UK's website offers insights into the industry's workings, including aspects related to geographical boundaries.Specifications and CaveatsWhen compiling the dataset, the following specifications and caveats were made:This shapefile is intended solely for geospatial analysis. The authoritative legal delineation of areas is maintained in the maps and additional details specified in the official appointments of companies as water and/or sewerage undertakers, along with any alterations to their areas.The shapefile does not encompass data on any structures or properties that, despite being outside the designated boundary, are included in the area, or those within the boundary yet excluded from the area.In terms of geospatial analysis and visual representation, the Mean High Water Line has been utilized to define any boundary extending into the sea, though it's more probable that the actual boundary aligns with the low water mark. Furthermore, islands that are incorporated into the area might not be included in this representation.Ofwat’s data was last updated on 25th May 2022Contact Details If you have a query about this dataset, please email foi@ofwat.gov.uk

Public Open Space Geographic Information System data collection for Perth and Peel Metropolitan Areas

The public open space (POS) dataset contains polygon boundaries of areas defined as publicly available and open. This geographic information system (GIS) dataset was collected in 2011/2012 using ArcGIS software and aerial photography dated from 2010-2011. The data was collected across the Perth Metro and Peel Region.

POS refer to all land reserved for the provision of green space and natural environments (e.g. parks, reserves, bushland) that is freely accessible and intended for use for recreation purposes (active or passive) by the general public. Four types of “green and natural public open spaces” are distinguished: (1) Park; (2) Natural or Conservation Area; (3) School Grounds; and (4) Residual. Areas where the public are not permitted except on payment or which are available to limited and selected numbers by membership (e.g. golf courses and sports centre facilities) or setbacks and buffers required by legislation are not included.

Initially, potential POSs were identified from a combination of existing geographic information system (GIS) spatial data layers to create a generalized representation of ‘green space’ throughout the Perth metropolitan and Peel regions. Base data layers include: cadastral polygons, metropolitan and regional planning scheme polygons, school point locations, and reserve vesting polygons. The ‘green’ space layer was then visually updated and edited to represent the true boundaries of each POS using 2010-2011 aerial photography within the ArcGIS software environment. Each resulting ’green’ polygon was then classified using a decision tree into one of four possible categories: park, natural or conservation area, school grounds, or residual green space.

Following the classification process, amenity and other information about each POS was collected for polygons classified as “Park” following a protocol developed at the Centre for the Built Environment and Health (CBEH) called POSDAT (Public Open Space Desktop Auditing Tool). The parks were audited using aerial photography visualized using ArcGIS software. . The presence or absence of amenities such as sporting facilities (e.g. tennis courts, soccer fields, skate parks etc) were audited as well as information on the environmental quality (i.e. presence of water, adjacency to bushland, shade along paths, etc), recreational amenities (e.g. presence of BBQ’, café or kiosks, public access toilets) and information on selected features related to personal safety.

The data is stored in an ArcGIS File Geodatabase Feature Class (size 4MB) and has restricted access.

Data creation methodology, data definitions, and links to publications based on this data, accompany the dataset.

Attribute field definitions for data created by Forest Practice GIS on plans and notices for timber harvesting either submitted to, approved, or accepted by, the California Department of Forestry and Fire Protection. Includes roads and hydrology within and adjacent to harvest areas.

This is a GIS dataset of the vegetation of the Windmill Islands. Interpretation was done by Rod Seppelt (Australian Antarctic Division) based on his field work, Zeiss aerial photography flown in January 1994 and a paper: Melick, D.R., Hovenden, M.J., Seppelt, R.D. (1994) Phytogeography of bryophyte and lichen vegetation in the Windmill Islands, Wilkes Land, Continental Antarctica. Vegetatio 111. 71-87 The data have been formatted according to the SCAR Feature Catalogue (see link below).

All data associated with the Town of Young Floodplain Risk Management Study and Plan. GIS Data Outputs, Hydraulics, Hydrology, Reporting, Survey. Data and Resources Data associated with Town of Young Floodplain Risk Management Study and PlanZIP (11.5 GB) All Data and GIS data associated with the Town of Young Floodplain Risk Management Study and Plan. Explore More information Download More info Creative Commons Attribution 4.0 International Public License By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution 4.0 International Public License (“Public License”). 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“Ecumene” is a term used by geographers, meaning “inhabited lands.” Populated places in the ecumene database are referenced using natural boundaries, as opposed to administrative or census boundaries, and provide a more suitable means for integrating socio-economic data with ecological and environmental data in a region. The Canadian Ecumene GeoDatabase 3.0 includes the custom boundaries for more than 3,000 populated areas across Canada, many of which were derived from remote-sensing “night-lights” imagery. Each ecumene place has a corresponding set of attributes pertaining to place name, province, ecozone, indigenous communities, and other descriptive information, as well as an initial custom set of demographic variables derived from Statistics Canada Census and National Household Survey data for 2001, 2006, 2011 and 2016. A number of additional layers are also included that map the extents of Canada's ecumene in alternate ways, using transportation and utility networks, nightlights imagery, and population density. (NOTE: In the list below, the V2 Shape, KML, and TIFF files have not changed for the CanEcumene 3.0) Provided layer: The Canadian Ecumene (CanEcumene) 3.0 GIS Database ============================================================================================ Database Citation (Update): Eddy, B.G., Muggridge, M., LeBlanc, R., Osmond, J., Kean, C., and Boyd, E. 2023. The CanEcumene 3.0 GIS Database. Federal Geospatial Platform (FGP), Natural Resources Canada. https://open.canada.ca Methods Publication Citation: Eddy B, Muggridge M, LeBlanc R, Osmond J, Kean C, Boyd E (2020) An Ecological Approach for Mapping Socio-Economic Data in Support of Ecosystems Analysis: Examples in Mapping Canada’s Forest Ecumene. One Ecosystem 5: e55881. https://doi.org/10.3897/oneeco.5.e55881

Oklahoma Tribal Statistical AreasThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau (USCB), displays Oklahoma Tribal Statistical Areas (OTSA). Per USCB, “OTSAs are statistical areas that were identified and delineated by the Census Bureau in consultation with federally recognized American Indian tribes based in Oklahoma. An OTSA is intended to represent the former American Indian reservation that existed in Indian and Oklahoma territories prior to Oklahoma statehood in 1907. OTSAs are intended to provide geographic entities comparable to the former Oklahoma reservations so that statistical data can be viewed over time.”Cherokee OTSAData currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Oklahoma Tribal Statistical Areas) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 64 (Series Information for American Indian/Alaska Native/Native Hawaiian Areas (AIANNH) National TIGER/Line Shapefiles, Current)OGC API Features Link: (Oklahoma Tribal Statistical Areas - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: Definitions of the American Indian and Alaska Native Geographic AreasFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."For other NGDA Content: Esri Federal Datasets

This is the current Medical Service Study Area. California Medical Service Study Areas are created by the California Department of Health Care Access and Information (HCAI).Check the Data Dictionary for field descriptions.Search for the Medical Service Study Area data on the CHHS Open Data Portal.Checkout the California Healthcare Atlas for more Medical Service Study Area information.This is an update to the MSSA geometries and demographics to reflect the new 2020 Census tract data. The Medical Service Study Area (MSSA) polygon layer represents the best fit mapping of all new 2020 California census tract boundaries to the original 2010 census tract boundaries used in the construction of the original 2010 MSSA file. Each of the state's new 9,129 census tracts was assigned to one of the previously established medical service study areas (excluding tracts with no land area), as identified in this data layer. The MSSA Census tract data is aggregated by HCAI, to create this MSSA data layer. This represents the final re-mapping of 2020 Census tracts to the original 2010 MSSA geometries. The 2010 MSSA were based on U.S. Census 2010 data and public meetings held throughout California.Source of update: American Community Survey 5-year 2006-2010 data for poverty. For source tables refer to InfoUSA update procedural documentation. The 2010 MSSA Detail layer was developed to update fields affected by population change. The American Community Survey 5-year 2006-2010 population data pertaining to total, in households, race, ethnicity, age, and poverty was used in the update. The 2010 MSSA Census Tract Detail map layer was developed to support geographic information systems (GIS) applications, representing 2010 census tract geography that is the foundation of 2010 medical service study area (MSSA) boundaries. ***This version is the finalized MSSA reconfiguration boundaries based on the US Census Bureau 2010 Census. In 1976 Garamendi Rural Health Services Act, required the development of a geographic framework for determining which parts of the state were rural and which were urban, and for determining which parts of counties and cities had adequate health care resources and which were "medically underserved". Thus, sub-city and sub-county geographic units called "medical service study areas [MSSAs]" were developed, using combinations of census-defined geographic units, established following General Rules promulgated by a statutory commission. After each subsequent census the MSSAs were revised. In the scheduled revisions that followed the 1990 census, community meetings of stakeholders (including county officials, and representatives of hospitals and community health centers) were held in larger metropolitan areas. The meetings were designed to develop consensus as how to draw the sub-city units so as to best display health care disparities. The importance of involving stakeholders was heightened in 1992 when the United States Department of Health and Human Services' Health and Resources Administration entered a formal agreement to recognize the state-determined MSSAs as "rational service areas" for federal recognition of "health professional shortage areas" and "medically underserved areas". After the 2000 census, two innovations transformed the process, and set the stage for GIS to emerge as a major factor in health care resource planning in California. First, the Office of Statewide Health Planning and Development [OSHPD], which organizes the community stakeholder meetings and provides the staff to administer the MSSAs, entered into an Enterprise GIS contract. Second, OSHPD authorized at least one community meeting to be held in each of the 58 counties, a significant number of which were wholly rural or frontier counties. For populous Los Angeles County, 11 community meetings were held. As a result, health resource data in California are collected and organized by 541 geographic units. The boundaries of these units were established by community healthcare experts, with the objective of maximizing their usefulness for needs assessment purposes. The most dramatic consequence was introducing a data simultaneously displayed in a GIS format. A two-person team, incorporating healthcare policy and GIS expertise, conducted the series of meetings, and supervised the development of the 2000-census configuration of the MSSAs.MSSA Configuration Guidelines (General Rules):- Each MSSA is composed of one or more complete census tracts.- As a general rule, MSSAs are deemed to be "rational service areas [RSAs]" for purposes of designating health professional shortage areas [HPSAs], medically underserved areas [MUAs] or medically underserved populations [MUPs].- MSSAs will not cross county lines.- To the extent practicable, all census-defined places within the MSSA are within 30 minutes travel time to the largest population center within the MSSA, except in those circumstances where meeting this criterion would require splitting a census tract.- To the extent practicable, areas that, standing alone, would meet both the definition of an MSSA and a Rural MSSA, should not be a part of an Urban MSSA.- Any Urban MSSA whose population exceeds 200,000 shall be divided into two or more Urban MSSA Subdivisions.- Urban MSSA Subdivisions should be within a population range of 75,000 to 125,000, but may not be smaller than five square miles in area. If removing any census tract on the perimeter of the Urban MSSA Subdivision would cause the area to fall below five square miles in area, then the population of the Urban MSSA may exceed 125,000. - To the extent practicable, Urban MSSA Subdivisions should reflect recognized community and neighborhood boundaries and take into account such demographic information as income level and ethnicity. Rural Definitions: A rural MSSA is an MSSA adopted by the Commission, which has a population density of less than 250 persons per square mile, and which has no census defined place within the area with a population in excess of 50,000. Only the population that is located within the MSSA is counted in determining the population of the census defined place. A frontier MSSA is a rural MSSA adopted by the Commission which has a population density of less than 11 persons per square mile. Any MSSA which is not a rural or frontier MSSA is an urban MSSA. Last updated December 6th 2024.

This public GIS dataset comes from the Alaska GAP project, and it is part of the final project report (Gotthard, Pyare, Huettmann et al. 2013). Here we present a copy of the original data set as a value-added product for basic use and training purposes. It consists of 53 environmental layers for all of Alaska in an ArcGIS 10 format and usually with a pixel size of 60m. These layers were compiled from various sources, and authorships should be fully honoured as stated in the details of this metadata. Output maps were clipped using a state of Alaska coastline in the Alaska Albers NAD83 projection; very small islands are excluded.The data layers were initially compiled for ecological niche models of Alaska's terrestrial biodiversity using Maxent and other Machine Learning algorithms. However, they can also be used for many other purposes, e.g. strategic conservation planning and individual information and assessments. The datasets are a snapshot in space and time (2012) but likely remain valid for years to come. It is appreciated that these data layers are 'living products', and it is hoped that this public data publication here will progress and trigger many updates and data quality improvements for Alaska and its public high-quality data over time. The following variables are included in this dataset: Boundaries Coastline, Climate Precipitation January til December Average monthly precipitation (mm), Climate Precipitation Average annual precipitation (mm), Climate Temperature January til December Average monthly temperature (deg C), Climate Temperature annual temperature (dec C), Climate First day of thaw (Julian date), Climate First day of freeze (Julian date), Climate Length of growing season Number of days, Disturbance Insect history (Year), Distance to Disturbance Insect location (m), Disturbance Fire history Year of fire (1942 til 2007), distance to Disturbance Fire location (m), Soils Grid (category), Surfacial Geology Grid values, Glacial Distance (m), Distance(m) to lotic water, Distance (m) to permafrost boundary, Distance(m) to lentic water, Saltwater Presence, Distance (m) to Sea Ice Extent 2003-2007 December, Distance (m) to Sea Ice Extent 2003-2007 July, Distance to Development Infrastructure, Landcover Vegetation (Landfire), Landcover nlcd60, Elevation (m), Slope (%), Aspect (Degrees from due south), Terrain Ruggedness index, Extent nullgrid 9999, Coast raster.

This GIS dataset consists of polygons that represent the footprints of solar powered electric generation facilities and related infrastructure in California called Solar Footprints. The location of solar footprints was identified using other existing solar footprint datasets from various sources along with imagery interpretation. CEC staff reviewed footprints identified with imagery and digitized polygons to match the visual extent of each facility. Previous datasets of existing solar footprints used to locate solar facilities include: GIS Layers: (1) California Solar Footprints, (2) UC Berkeley Solar Points, (3) Kruitwagen et al. 2021, (4) BLM Renewable Project Facilities, (5) Quarterly Fuel and Energy Report (QFER)Imagery Datasets: Esri World Imagery, USGS National Agriculture Imagery Program (NAIP), 2020 SENTINEL 2 Satellite Imagery, 2023Solar facilities with large footprints such as parking lot solar, large rooftop solar, and ground solar were included in the solar footprint dataset. Small scale solar (approximately less than 0.5 acre) and residential footprints were not included. No other data was used in the production of these shapes. Definitions for the solar facilities identified via imagery are subjective and described as follows: Rooftop Solar: Solar arrays located on rooftops of large buildings. Parking lot Solar: Solar panels on parking lots roughly larger than 1 acre, or clusters of solar panels in adjacent parking lots. Ground Solar: Solar panels located on ground roughly larger than 1 acre, or large clusters of smaller scale footprints. Once all footprints identified by the above criteria were digitized for all California counties, the features were visually classified into ground, parking and rooftop categories. The features were also classified into rural and urban types using the 42 U.S. Code § 1490 definition for rural. In addition, the distance to the closest substation and the percentile category of this distance (e.g. 0-25th percentile, 25th-50th percentile) was also calculated. The coverage provided by this data set should not be assumed to be a complete accounting of solar footprints in California. Rather, this dataset represents an attempt to improve upon existing solar feature datasets and to update the inventory of "large" solar footprints via imagery, especially in recent years since previous datasets were published. This procedure produced a total solar project footprint of 150,250 acres. Attempts to classify these footprints and isolate the large utility-scale projects from the smaller rooftop solar projects identified in the data set is difficult. The data was gathered based on imagery, and project information that could link multiple adjacent solar footprints under one larger project is not known. However, partitioning all solar footprints that are at least partly outside of the techno-economic exclusions and greater than 7 acres yields a total footprint size of 133,493 acres. These can be approximated as utility-scale footprints. Metadata: (1) CBI Solar FootprintsAbstract: Conservation Biology Institute (CBI) created this dataset of solar footprints in California after it was found that no such dataset was publicly available at the time (Dec 2015-Jan 2016). This dataset is used to help identify where current ground based, mostly utility scale, solar facilities are being constructed and will be used in a larger landscape intactness model to help guide future development of renewable energy projects. The process of digitizing these footprints first began by utilizing an excel file from the California Energy Commission with lat/long coordinates of some of the older and bigger locations. After projecting those points and locating the facilities utilizing NAIP 2014 imagery, the developed area around each facility was digitized. While interpreting imagery, there were some instances where a fenced perimeter was clearly seen and was slightly larger than the actual footprint. For those cases the footprint followed the fenced perimeter since it limits wildlife movement through the area. In other instances, it was clear that the top soil had been scraped of any vegetation, even outside of the primary facility footprint. These footprints included the areas that were scraped within the fencing since, especially in desert systems, it has been near permanently altered. Other sources that guided the search for solar facilities included the Energy Justice Map, developed by the Energy Justice Network which can be found here:https://www.energyjustice.net/map/searchobject.php?gsMapsize=large&giCurrentpageiFacilityid;=1&gsTable;=facility&gsSearchtype;=advancedThe Solar Energy Industries Association’s “Project Location Map” which can be found here: https://www.seia.org/map/majorprojectsmap.phpalso assisted in locating newer facilities along with the "Power Plants" shapefile, updated in December 16th, 2015, downloaded from the U.S. Energy Information Administration located here:https://www.eia.gov/maps/layer_info-m.cfmThere were some facilities that were stumbled upon while searching for others, most of these are smaller scale sites located near farm infrastructure. Other sites were located by contacting counties that had solar developments within the county. Still, others were located by sleuthing around for proposals and company websites that had images of the completed facility. These helped to locate the most recently developed sites and these sites were digitized based on landmarks such as ditches, trees, roads and other permanent structures.Metadata: (2) UC Berkeley Solar PointsUC Berkeley report containing point location for energy facilities across the United States.2022_utility-scale_solar_data_update.xlsm (live.com)Metadata: (3) Kruitwagen et al. 2021Abstract: Photovoltaic (PV) solar energy generating capacity has grown by 41 per cent per year since 2009. Energy system projections that mitigate climate change and aid universal energy access show a nearly ten-fold increase in PV solar energy generating capacity by 2040. Geospatial data describing the energy system are required to manage generation intermittency, mitigate climate change risks, and identify trade-offs with biodiversity, conservation and land protection priorities caused by the land-use and land-cover change necessary for PV deployment. Currently available inventories of solar generating capacity cannot fully address these needs. Here we provide a global inventory of commercial-, industrial- and utility-scale PV installations (that is, PV generating stations in excess of 10 kilowatts nameplate capacity) by using a longitudinal corpus of remote sensing imagery, machine learning and a large cloud computation infrastructure. We locate and verify 68,661 facilities, an increase of 432 per cent (in number of facilities) on previously available asset-level data. With the help of a hand-labelled test set, we estimate global installed generating capacity to be 423 gigawatts (−75/+77 gigawatts) at the end of 2018. Enrichment of our dataset with estimates of facility installation date, historic land-cover classification and proximity to vulnerable areas allows us to show that most of the PV solar energy facilities are sited on cropland, followed by arid lands and grassland. Our inventory could aid PV delivery aligned with the Sustainable Development GoalsEnergy Resource Land Use Planning - Kruitwagen_etal_Nature.pdf - All Documents (sharepoint.com)Metadata: (4) BLM Renewable ProjectTo identify renewable energy approved and pending lease areas on BLM administered lands. To provide information about solar and wind energy applications and completed projects within the State of California for analysis and display internally and externally. This feature class denotes "verified" renewable energy projects at the California State BLM Office, displayed in GIS. The term "Verified" refers to the GIS data being constructed at the California State Office, using the actual application/maps with legal descriptions obtained from the renewable energy company. https://www.blm.gov/wo/st/en/prog/energy/renewable_energy

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .

Introduction The GIS (Geographic Information System) vectorisation project will deliver the incremental digital conversion of our legacy geospatial network records. This dataset defines the sub-areas which will be incrementally delivered, detailing corresponding current status and planned completion dates. This allows users to understand the current and future coverage of digital geospatial network records as the project progresses.

Methodological Approach

Progress against a defined project plan is captured and updated throughout the day. A script is run to convert into a shapefile. This shapefile is then uploaded onto the Open Data Portal.

Quality Control Statement

This dataset is provided "as is".

Assurance Statement The Open Data Team has checked outputs to validate.

Other Download dataset information: Metadata (JSON)Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: https://ukpowernetworks.opendatasoft.com/pages/glossary/

U.S. Census BlocksThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau (USCB), displays Census Blocks in the United States. A brief description of Census Blocks, per USCB, is that "Census blocks are statistical areas bounded by visible features such as roads, streams, and railroad tracks, and by nonvisible boundaries such as property lines, city, township, school district, county limits and short line-of-sight extensions of roads." Also, "the smallest level of geography you can get basic demographic data for, such as total population by age, sex, and race."Census Block 1007Data currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Census Blocks) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 69 (Series Information for 2020 Census Block State-based TIGER/Line Shapefiles, Current)OGC API Features Link: (U.S. Census Blocks - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: What are census blocksFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."For other NGDA Content: Esri Federal Datasets

This dataset contains the street centerlines of roadways in Hennepin County in the Metro Regional Centerline (MRCC) standard format. It is primarily used for geocoding purposes. The attribute table includes road names and geocoding ranges for each road feature. The associated street alias table can be joined to this dataset using the UNIQUE_ID field and the JOIN_ID field from the alias table. More recently, the dataset has been enhanced to better support routing, including the conversion of single line segments to dual carriageway for divided highways and boulevards and the addition of access ramps. Speed, elevation, and one way attributes are also provided, allowing this dataset to be built into a routable network dataset

Attribute information is defined by the MRCC standard. Information on the standard, including attribute definitions, is available on the MetroGIS MRCC project site. While this dataset includes only Hennepin County roadways, it is intended to align with neighboring counties to provide a standard, multi-purpose regional street centerline dataset. The full regional dataset is available for download from MN Geospatial Commons website.

Update information can be found within the layer’s attributes and in a table on the Utah Parcel Data webpage under LIR Parcels.In Spring of 2016, the Land Information Records work group, an informal committee organized by the Governor’s Office of Management and Budget’s State Planning Coordinator, produced recommendations for expanding the sharing of GIS-based parcel information. Participants in the LIR work group included representatives from county, regional, and state government, including the Utah Association of Counties (County Assessors and County Recorders), Wasatch Front Regional Council, Mountainland and Bear River AOGs, Utah League of Cities and Towns, UDOT, DNR, AGRC, the Division of Emergency Management, Blue Stakes, economic developers, and academic researchers. The LIR work group’s recommendations set the stage for voluntary sharing of additional objective/quantitative parcel GIS data, primarily around tax assessment-related information. Specifically the recommendations document establishes objectives, principles (including the role of local and state government), data content items, expected users, and a general process for data aggregation and publishing. An important realization made by the group was that ‘parcel data’ or ‘parcel record’ products have a different meaning to different users and data stewards. The LIR group focused, specifically, on defining a data sharing recommendation around a tax year parcel GIS data product, aligned with the finalization of the property tax roll by County Assessors on May 22nd of each year. The LIR recommendations do not impact the periodic sharing of basic parcel GIS data (boundary, ID, address) from the County Recorders to AGRC per 63F-1-506 (3.b.vi). Both the tax year parcel and the basic parcel GIS layers are designed for general purpose uses, and are not substitutes for researching and obtaining the most current, legal land records information on file in County records. This document, below, proposes a schedule, guidelines, and process for assembling county parcel and assessment data into an annual, statewide tax parcel GIS layer. gis.utah.gov/data/sgid-cadastre/It is hoped that this new expanded parcel GIS layer will be put to immediate use supporting the best possible outcomes in public safety, economic development, transportation, planning, and the provision of public services. Another aim of the work group was to improve the usability of the data, through development of content guidelines and consistent metadata documentation, and the efficiency with which the data sharing is distributed.GIS Layer Boundary Geometry:GIS Format Data Files: Ideally, Tax Year Parcel data should be provided in a shapefile (please include the .shp, .shx, .dbf, .prj, and .xml component files) or file geodatabase format. An empty shapefile and file geodatabase schema are available for download at:At the request of a county, AGRC will provide technical assistance to counties to extract, transform, and load parcel and assessment information into the GIS layer format.Geographic Coverage: Tax year parcel polygons should cover the area of each county for which assessment information is created and digital parcels are available. Full coverage may not be available yet for each county. The county may provide parcels that have been adjusted to remove gaps and overlaps for administrative tax purposes or parcels that retain these expected discrepancies that take their source from the legally described boundary or the process of digital conversion. The diversity of topological approaches will be noted in the metadata.One Tax Parcel Record Per Unique Tax Notice: Some counties produce an annual tax year parcel GIS layer with one parcel polygon per tax notice. In some cases, adjacent parcel polygons that compose a single taxed property must be merged into a single polygon. This is the goal for the statewide layer but may not be possible in all counties. AGRC will provide technical support to counties, where needed, to merge GIS parcel boundaries into the best format to match with the annual assessment information.Standard Coordinate System: Parcels will be loaded into Utah’s statewide coordinate system, Universal Transverse Mercator coordinates (NAD83, Zone 12 North). However, boundaries stored in other industry standard coordinate systems will be accepted if they are both defined within the data file(s) and documented in the metadata (see below).Descriptive Attributes:Database Field/Column Definitions: The table below indicates the field names and definitions for attributes requested for each Tax Parcel Polygon record.FIELD NAME FIELD TYPE LENGTH DESCRIPTION EXAMPLE SHAPE (expected) Geometry n/a The boundary of an individual parcel or merged parcels that corresponds with a single county tax notice ex. polygon boundary in UTM NAD83 Zone 12 N or other industry standard coordinates including state plane systemsCOUNTY_NAME Text 20 - County name including spaces ex. BOX ELDERCOUNTY_ID (expected) Text 2 - County ID Number ex. Beaver = 1, Box Elder = 2, Cache = 3,..., Weber = 29ASSESSOR_SRC (expected) Text 100 - Website URL, will be to County Assessor in most all cases ex. webercounty.org/assessorBOUNDARY_SRC (expected) Text 100 - Website URL, will be to County Recorder in most all cases ex. webercounty.org/recorderDISCLAIMER (added by State) Text 50 - Disclaimer URL ex. gis.utah.gov...CURRENT_ASOF (expected) Date - Parcels current as of date ex. 01/01/2016PARCEL_ID (expected) Text 50 - County designated Unique ID number for individual parcels ex. 15034520070000PARCEL_ADD (expected, where available) Text 100 - Parcel’s street address location. Usually the address at recordation ex. 810 S 900 E #304 (example for a condo)TAXEXEMPT_TYPE (expected) Text 100 - Primary category of granted tax exemption ex. None, Religious, Government, Agriculture, Conservation Easement, Other Open Space, OtherTAX_DISTRICT (expected, where applicable) Text 10 - The coding the county uses to identify a unique combination of property tax levying entities ex. 17ATOTAL_MKT_VALUE (expected) Decimal - Total market value of parcel's land, structures, and other improvements as determined by the Assessor for the most current tax year ex. 332000LAND _MKT_VALUE (expected) Decimal - The market value of the parcel's land as determined by the Assessor for the most current tax year ex. 80600PARCEL_ACRES (expected) Decimal - Parcel size in acres ex. 20.360PROP_CLASS (expected) Text 100 - Residential, Commercial, Industrial, Mixed, Agricultural, Vacant, Open Space, Other ex. ResidentialPRIMARY_RES (expected) Text 1 - Is the property a primary residence(s): Y'(es), 'N'(o), or 'U'(nknown) ex. YHOUSING_CNT (expected, where applicable) Text 10 - Number of housing units, can be single number or range like '5-10' ex. 1SUBDIV_NAME (optional) Text 100 - Subdivision name if applicable ex. Highland Manor SubdivisionBLDG_SQFT (expected, where applicable) Integer - Square footage of primary bldg(s) ex. 2816BLDG_SQFT_INFO (expected, where applicable) Text 100 - Note for how building square footage is counted by the County ex. Only finished above and below grade areas are counted.FLOORS_CNT (expected, where applicable) Decimal - Number of floors as reported in county records ex. 2FLOORS_INFO (expected, where applicable) Text 100 - Note for how floors are counted by the County ex. Only above grade floors are countedBUILT_YR (expected, where applicable) Short - Estimated year of initial construction of primary buildings ex. 1968EFFBUILT_YR (optional, where applicable) Short - The 'effective' year built' of primary buildings that factors in updates after construction ex. 1980CONST_MATERIAL (optional, where applicable) Text 100 - Construction Material Types, Values for this field are expected to vary greatly by county ex. Wood Frame, Brick, etc Contact: Sean Fernandez, Cadastral Manager (email: sfernandez@utah.gov; office phone: 801-209-9359)

The protected area shapefile was compiled by the Nature Conservancy (TNC) with substantial submissions and assistance from 21 different governments with sometimes multiple representatives within a government in the insular Caribbean. The data represents a core dataset in conservation for the region. The Nature Conservancy works to keep this file as up to date as possible and uses it heavily in representing what is protected across the insular Caribbean. The origins of the data are from the World Database on Protected Areas (WDPA). The WDPA is the most inclusive spatial dataset on marine and terrestrial protected areas globally. Since 1981, UNEP-WCMC, through its Protected Areas Program, has been gathering aspatial (tabular) information and making it available to the global community. In the late 1980s, the WDPA started to include spatial data and in 2003 the WDPA was formally incorporating the UN list of protected areas. Today, the WDPA is a joint project of UNEP and IUCN, produced by UNEP-WCMC and the IUCN World Commission on Protected Areas and works with governments and collaborating NGOs, like TNC. Although TNC began working in the insular Caribbean in 1974, its work was largely site based and limited in regional scope depending on previously existing projects or donations. This changed with the Conservation Assessment of the Insular Caribbean (Huggins et al. 2007). Protected areas were a key component in the conservation assessment, thus the need for a spatial (GIS) dataset of boundaries along with tabular attributes for all protected across the insular Caribbean. The Conservation Assessment of the Insular Caribbean primarily used the WDPA Consortium 2003 version, with addition local information. With the launch of the Caribbean Challenge in 2008 and the pledges made by participating countries, the need for an accurate and reliable spatial dataset of existing and proposed protected areas materialized. Protected area representation is most useful when it accurately reflects a changing dynamic. In particular, the areas of research to greatly benefit from a dynamic dataset is MPA cluster analysis related to international connectivity and deeper MPAs that meet the conservation needs of pelagic biodiversity on the high seas (Game et al. 2009). But maintaining this accuracy requires appropriate effort and capacity not yet observed. As intended, the WDPA represents the authority and foundation of protected areas for the many uses of this information. However, the current model for updating the WDPA seems problematic for the insular Caribbean given the discrepancies between this dataset and WDPAs current version. Along with the Caribbean Marine Protected Area Managers network and forum (CaMPAM)and TNC, these three data depositories have contributed in parts to the insular Caribbean protected area GIS dataset, but better collaboration must be formed to ensure timely data flows and that all representations of the insular Caribbean protected area dataset are the sameThe majority of Caribbean governments lack the necessary resources to track and update protected area boundaries and attributes. These databases require continual custodial stewardship as new data becomes available and updates are needed. Consequently, strategic partnerships where resources and talent can be shared are necessary to fill the technical and resource capacity gaps that exist for island governments. We recommend using a regional approach via partner collaboration, and national expert review and validation to sustain the protected area dataset. This process will facilitate regular updates to the World Database on Protected Areas (WDPA). To date, the associated Caribbean regional protected area dataset represents the most accurate baseline on which country-level MPA statistics can be reported. As efforts to fulfill commitments to regional and global biodiversity goals continue to ramp up (e.g. Caribbean Challenge Initiative, CDB Aichi Biodiversity Targets), this data will serve as an important baseline and resource to Caribbean governments and regional entities seeking to assess current levels of marine protection and understand the remaining gap that needs to be filled.The protected areas in this file are locations which receive some sort of protection or designated as a particular managed area under the law related to natural, ecological and/or cultural values. Overlap does occur in this dataset.All attributes have associated definitions in the fields section of the metadata. However notable fields include:SOURCE - Main source of the particular data for respective record.CF - Confidence in record accuracy from TNC's perspectivePROTDATE - Established date of protected areaCOUNTRY - Country or government nameMOD_DATE - Date that the record in this dataset was added or updatedEDITOR - Name of the editor performing the updates or modifications to this dataSTATUS - Status of the area as designated or proposedWDPA_ID - Protected area ID as assigned in the WDPACAMPAM_ID - Protected area ID as assigned in the CaMPAM databaseON_WATER - Used to designate what is a marine protected area (MPA) and what is a terrestrial protected area. "MPA"s were primarily designated by applying the IUCN MPA definition to the insular Caribbean protected area GIS shapefile. Essentially, any boundary (i.e. “area… which has been reserved by law or other effective means to protect part or all of the enclosed environment”) which overlaps the shoreline representing “intertidal or subtidal terrain, together with its overlying water and associated flora, fauna, historical, and cultural features” was initially selected as a MPA. This preliminary output is refined by the intent of the protected area, determined through local knowledge, the protected area name and/or legal definition. In some cases, although a boundary might include “intertidal or subtidal terrain,” the true intent of the law or means to protect the environment excluded the “marine” section. SBIS_ID - Corresponds to ID number for streaming data into The Bahamas Spatial Biodiversity Information SystemNotable Comments by Government:USVI - includes lands owned by TNC which are split out by parcel. This means that this dataset does not represent a one off list of protected areas by record unless these are removed and merged. Also includes Areas of Particular ConcernReferencesGame, Edward T., Hedley S. Grantham, Alistair J. Hobday, Robert L. Pressey, Amanda T. Lombard, Lynnath E. Beckley, Kristina Gjerde, Rodrigo Bustamante, Hugh P. Possingham, and Anthony J. Richardson. 2009. “Pelagic Protected Areas: The Missing Dimension in Ocean Conservation.” Trends in Ecology & Evolution 24 (7): 360 – 369. doi:http://dx.doi.org/10.1016/j.tree.2009.01.011.Huggins, A.E., S. Keel, P. Kramer, F. Núñez, S. Schill, R. Jeo, A. Chatwin, K. Thurlow, M. McPherson, M. Libby, R. Tingey, M. Palmer and R. Seybert 2007. Biodiversity Conservation Assessment of the Insular Caribbean Using the Caribbean Decision Support System, Technical Report, The Nature Conservancy - See more at: http://www.conservationgateway.org/ConservationByGeography/NorthAmerica/Caribbean/science/planning

Update information can be found within the layer’s attributes and in a table on the Utah Parcel Data webpage under LIR Parcels.In Spring of 2016, the Land Information Records work group, an informal committee organized by the Governor’s Office of Management and Budget’s State Planning Coordinator, produced recommendations for expanding the sharing of GIS-based parcel information. Participants in the LIR work group included representatives from county, regional, and state government, including the Utah Association of Counties (County Assessors and County Recorders), Wasatch Front Regional Council, Mountainland and Bear River AOGs, Utah League of Cities and Towns, UDOT, DNR, AGRC, the Division of Emergency Management, Blue Stakes, economic developers, and academic researchers. The LIR work group’s recommendations set the stage for voluntary sharing of additional objective/quantitative parcel GIS data, primarily around tax assessment-related information. Specifically the recommendations document establishes objectives, principles (including the role of local and state government), data content items, expected users, and a general process for data aggregation and publishing. An important realization made by the group was that ‘parcel data’ or ‘parcel record’ products have a different meaning to different users and data stewards. The LIR group focused, specifically, on defining a data sharing recommendation around a tax year parcel GIS data product, aligned with the finalization of the property tax roll by County Assessors on May 22nd of each year. The LIR recommendations do not impact the periodic sharing of basic parcel GIS data (boundary, ID, address) from the County Recorders to AGRC per 63F-1-506 (3.b.vi). Both the tax year parcel and the basic parcel GIS layers are designed for general purpose uses, and are not substitutes for researching and obtaining the most current, legal land records information on file in County records. This document, below, proposes a schedule, guidelines, and process for assembling county parcel and assessment data into an annual, statewide tax parcel GIS layer. gis.utah.gov/data/sgid-cadastre/ It is hoped that this new expanded parcel GIS layer will be put to immediate use supporting the best possible outcomes in public safety, economic development, transportation, planning, and the provision of public services. Another aim of the work group was to improve the usability of the data, through development of content guidelines and consistent metadata documentation, and the efficiency with which the data sharing is distributed.GIS Layer Boundary Geometry:GIS Format Data Files: Ideally, Tax Year Parcel data should be provided in a shapefile (please include the .shp, .shx, .dbf, .prj, and .xml component files) or file geodatabase format. An empty shapefile and file geodatabase schema are available for download at:At the request of a county, AGRC will provide technical assistance to counties to extract, transform, and load parcel and assessment information into the GIS layer format.Geographic Coverage: Tax year parcel polygons should cover the area of each county for which assessment information is created and digital parcels are available. Full coverage may not be available yet for each county. The county may provide parcels that have been adjusted to remove gaps and overlaps for administrative tax purposes or parcels that retain these expected discrepancies that take their source from the legally described boundary or the process of digital conversion. The diversity of topological approaches will be noted in the metadata.One Tax Parcel Record Per Unique Tax Notice: Some counties produce an annual tax year parcel GIS layer with one parcel polygon per tax notice. In some cases, adjacent parcel polygons that compose a single taxed property must be merged into a single polygon. This is the goal for the statewide layer but may not be possible in all counties. AGRC will provide technical support to counties, where needed, to merge GIS parcel boundaries into the best format to match with the annual assessment information.Standard Coordinate System: Parcels will be loaded into Utah’s statewide coordinate system, Universal Transverse Mercator coordinates (NAD83, Zone 12 North). However, boundaries stored in other industry standard coordinate systems will be accepted if they are both defined within the data file(s) and documented in the metadata (see below).Descriptive Attributes:Database Field/Column Definitions: The table below indicates the field names and definitions for attributes requested for each Tax Parcel Polygon record.FIELD NAME FIELD TYPE LENGTH DESCRIPTION EXAMPLE SHAPE (expected) Geometry n/a The boundary of an individual parcel or merged parcels that corresponds with a single county tax notice ex. polygon boundary in UTM NAD83 Zone 12 N or other industry standard coordinates including state plane systemsCOUNTY_NAME Text 20 - County name including spaces ex. BOX ELDERCOUNTY_ID (expected) Text 2 - County ID Number ex. Beaver = 1, Box Elder = 2, Cache = 3,..., Weber = 29ASSESSOR_SRC (expected) Text 100 - Website URL, will be to County Assessor in most all cases ex. webercounty.org/assessorBOUNDARY_SRC (expected) Text 100 - Website URL, will be to County Recorder in most all cases ex. webercounty.org/recorderDISCLAIMER (added by State) Text 50 - Disclaimer URL ex. gis.utah.gov...CURRENT_ASOF (expected) Date - Parcels current as of date ex. 01/01/2016PARCEL_ID (expected) Text 50 - County designated Unique ID number for individual parcels ex. 15034520070000PARCEL_ADD (expected, where available) Text 100 - Parcel’s street address location. Usually the address at recordation ex. 810 S 900 E #304 (example for a condo)TAXEXEMPT_TYPE (expected) Text 100 - Primary category of granted tax exemption ex. None, Religious, Government, Agriculture, Conservation Easement, Other Open Space, OtherTAX_DISTRICT (expected, where applicable) Text 10 - The coding the county uses to identify a unique combination of property tax levying entities ex. 17ATOTAL_MKT_VALUE (expected) Decimal - Total market value of parcel's land, structures, and other improvements as determined by the Assessor for the most current tax year ex. 332000LAND _MKT_VALUE (expected) Decimal - The market value of the parcel's land as determined by the Assessor for the most current tax year ex. 80600PARCEL_ACRES (expected) Decimal - Parcel size in acres ex. 20.360PROP_CLASS (expected) Text 100 - Residential, Commercial, Industrial, Mixed, Agricultural, Vacant, Open Space, Other ex. ResidentialPRIMARY_RES (expected) Text 1 - Is the property a primary residence(s): Y'(es), 'N'(o), or 'U'(nknown) ex. YHOUSING_CNT (expected, where applicable) Text 10 - Number of housing units, can be single number or range like '5-10' ex. 1SUBDIV_NAME (optional) Text 100 - Subdivision name if applicable ex. Highland Manor SubdivisionBLDG_SQFT (expected, where applicable) Integer - Square footage of primary bldg(s) ex. 2816BLDG_SQFT_INFO (expected, where applicable) Text 100 - Note for how building square footage is counted by the County ex. Only finished above and below grade areas are counted.FLOORS_CNT (expected, where applicable) Decimal - Number of floors as reported in county records ex. 2FLOORS_INFO (expected, where applicable) Text 100 - Note for how floors are counted by the County ex. Only above grade floors are countedBUILT_YR (expected, where applicable) Short - Estimated year of initial construction of primary buildings ex. 1968EFFBUILT_YR (optional, where applicable) Short - The 'effective' year built' of primary buildings that factors in updates after construction ex. 1980CONST_MATERIAL (optional, where applicable) Text 100 - Construction Material Types, Values for this field are expected to vary greatly by county ex. Wood Frame, Brick, etc Contact: Sean Fernandez, Cadastral Manager (email: sfernandez@utah.gov; office phone: 801-209-9359)

Update information can be found within the layer’s attributes and in a table on the Utah Parcel Data webpage under LIR Parcels.In Spring of 2016, the Land Information Records work group, an informal committee organized by the Governor’s Office of Management and Budget’s State Planning Coordinator, produced recommendations for expanding the sharing of GIS-based parcel information. Participants in the LIR work group included representatives from county, regional, and state government, including the Utah Association of Counties (County Assessors and County Recorders), Wasatch Front Regional Council, Mountainland and Bear River AOGs, Utah League of Cities and Towns, UDOT, DNR, AGRC, the Division of Emergency Management, Blue Stakes, economic developers, and academic researchers. The LIR work group’s recommendations set the stage for voluntary sharing of additional objective/quantitative parcel GIS data, primarily around tax assessment-related information. Specifically the recommendations document establishes objectives, principles (including the role of local and state government), data content items, expected users, and a general process for data aggregation and publishing. An important realization made by the group was that ‘parcel data’ or ‘parcel record’ products have a different meaning to different users and data stewards. The LIR group focused, specifically, on defining a data sharing recommendation around a tax year parcel GIS data product, aligned with the finalization of the property tax roll by County Assessors on May 22nd of each year. The LIR recommendations do not impact the periodic sharing of basic parcel GIS data (boundary, ID, address) from the County Recorders to AGRC per 63F-1-506 (3.b.vi). Both the tax year parcel and the basic parcel GIS layers are designed for general purpose uses, and are not substitutes for researching and obtaining the most current, legal land records information on file in County records. This document, below, proposes a schedule, guidelines, and process for assembling county parcel and assessment data into an annual, statewide tax parcel GIS layer. gis.utah.gov/data/sgid-cadastre/ It is hoped that this new expanded parcel GIS layer will be put to immediate use supporting the best possible outcomes in public safety, economic development, transportation, planning, and the provision of public services. Another aim of the work group was to improve the usability of the data, through development of content guidelines and consistent metadata documentation, and the efficiency with which the data sharing is distributed.GIS Layer Boundary Geometry:GIS Format Data Files: Ideally, Tax Year Parcel data should be provided in a shapefile (please include the .shp, .shx, .dbf, .prj, and .xml component files) or file geodatabase format. An empty shapefile and file geodatabase schema are available for download at:At the request of a county, AGRC will provide technical assistance to counties to extract, transform, and load parcel and assessment information into the GIS layer format.Geographic Coverage: Tax year parcel polygons should cover the area of each county for which assessment information is created and digital parcels are available. Full coverage may not be available yet for each county. The county may provide parcels that have been adjusted to remove gaps and overlaps for administrative tax purposes or parcels that retain these expected discrepancies that take their source from the legally described boundary or the process of digital conversion. The diversity of topological approaches will be noted in the metadata.One Tax Parcel Record Per Unique Tax Notice: Some counties produce an annual tax year parcel GIS layer with one parcel polygon per tax notice. In some cases, adjacent parcel polygons that compose a single taxed property must be merged into a single polygon. This is the goal for the statewide layer but may not be possible in all counties. AGRC will provide technical support to counties, where needed, to merge GIS parcel boundaries into the best format to match with the annual assessment information.Standard Coordinate System: Parcels will be loaded into Utah’s statewide coordinate system, Universal Transverse Mercator coordinates (NAD83, Zone 12 North). However, boundaries stored in other industry standard coordinate systems will be accepted if they are both defined within the data file(s) and documented in the metadata (see below).Descriptive Attributes:Database Field/Column Definitions: The table below indicates the field names and definitions for attributes requested for each Tax Parcel Polygon record.FIELD NAME FIELD TYPE LENGTH DESCRIPTION EXAMPLE SHAPE (expected) Geometry n/a The boundary of an individual parcel or merged parcels that corresponds with a single county tax notice ex. polygon boundary in UTM NAD83 Zone 12 N or other industry standard coordinates including state plane systemsCOUNTY_NAME Text 20 - County name including spaces ex. BOX ELDERCOUNTY_ID (expected) Text 2 - County ID Number ex. Beaver = 1, Box Elder = 2, Cache = 3,..., Weber = 29ASSESSOR_SRC (expected) Text 100 - Website URL, will be to County Assessor in most all cases ex. webercounty.org/assessorBOUNDARY_SRC (expected) Text 100 - Website URL, will be to County Recorder in most all cases ex. webercounty.org/recorderDISCLAIMER (added by State) Text 50 - Disclaimer URL ex. gis.utah.gov...CURRENT_ASOF (expected) Date - Parcels current as of date ex. 01/01/2016PARCEL_ID (expected) Text 50 - County designated Unique ID number for individual parcels ex. 15034520070000PARCEL_ADD (expected, where available) Text 100 - Parcel’s street address location. Usually the address at recordation ex. 810 S 900 E #304 (example for a condo)TAXEXEMPT_TYPE (expected) Text 100 - Primary category of granted tax exemption ex. None, Religious, Government, Agriculture, Conservation Easement, Other Open Space, OtherTAX_DISTRICT (expected, where applicable) Text 10 - The coding the county uses to identify a unique combination of property tax levying entities ex. 17ATOTAL_MKT_VALUE (expected) Decimal - Total market value of parcel's land, structures, and other improvements as determined by the Assessor for the most current tax year ex. 332000LAND _MKT_VALUE (expected) Decimal - The market value of the parcel's land as determined by the Assessor for the most current tax year ex. 80600PARCEL_ACRES (expected) Decimal - Parcel size in acres ex. 20.360PROP_CLASS (expected) Text 100 - Residential, Commercial, Industrial, Mixed, Agricultural, Vacant, Open Space, Other ex. ResidentialPRIMARY_RES (expected) Text 1 - Is the property a primary residence(s): Y'(es), 'N'(o), or 'U'(nknown) ex. YHOUSING_CNT (expected, where applicable) Text 10 - Number of housing units, can be single number or range like '5-10' ex. 1SUBDIV_NAME (optional) Text 100 - Subdivision name if applicable ex. Highland Manor SubdivisionBLDG_SQFT (expected, where applicable) Integer - Square footage of primary bldg(s) ex. 2816BLDG_SQFT_INFO (expected, where applicable) Text 100 - Note for how building square footage is counted by the County ex. Only finished above and below grade areas are counted.FLOORS_CNT (expected, where applicable) Decimal - Number of floors as reported in county records ex. 2FLOORS_INFO (expected, where applicable) Text 100 - Note for how floors are counted by the County ex. Only above grade floors are countedBUILT_YR (expected, where applicable) Short - Estimated year of initial construction of primary buildings ex. 1968EFFBUILT_YR (optional, where applicable) Short - The 'effective' year built' of primary buildings that factors in updates after construction ex. 1980CONST_MATERIAL (optional, where applicable) Text 100 - Construction Material Types, Values for this field are expected to vary greatly by county ex. Wood Frame, Brick, etc Contact: Sean Fernandez, Cadastral Manager (email: sfernandez@utah.gov; office phone: 801-209-9359)

Update information can be found within the layer’s attributes and in a table on the Utah Parcel Data webpage under LIR Parcels.In Spring of 2016, the Land Information Records work group, an informal committee organized by the Governor’s Office of Management and Budget’s State Planning Coordinator, produced recommendations for expanding the sharing of GIS-based parcel information. Participants in the LIR work group included representatives from county, regional, and state government, including the Utah Association of Counties (County Assessors and County Recorders), Wasatch Front Regional Council, Mountainland and Bear River AOGs, Utah League of Cities and Towns, UDOT, DNR, AGRC, the Division of Emergency Management, Blue Stakes, economic developers, and academic researchers. The LIR work group’s recommendations set the stage for voluntary sharing of additional objective/quantitative parcel GIS data, primarily around tax assessment-related information. Specifically the recommendations document establishes objectives, principles (including the role of local and state government), data content items, expected users, and a general process for data aggregation and publishing. An important realization made by the group was that ‘parcel data’ or ‘parcel record’ products have a different meaning to different users and data stewards. The LIR group focused, specifically, on defining a data sharing recommendation around a tax year parcel GIS data product, aligned with the finalization of the property tax roll by County Assessors on May 22nd of each year. The LIR recommendations do not impact the periodic sharing of basic parcel GIS data (boundary, ID, address) from the County Recorders to AGRC per 63F-1-506 (3.b.vi). Both the tax year parcel and the basic parcel GIS layers are designed for general purpose uses, and are not substitutes for researching and obtaining the most current, legal land records information on file in County records. This document, below, proposes a schedule, guidelines, and process for assembling county parcel and assessment data into an annual, statewide tax parcel GIS layer. gis.utah.gov/data/sgid-cadastre/ It is hoped that this new expanded parcel GIS layer will be put to immediate use supporting the best possible outcomes in public safety, economic development, transportation, planning, and the provision of public services. Another aim of the work group was to improve the usability of the data, through development of content guidelines and consistent metadata documentation, and the efficiency with which the data sharing is distributed.GIS Layer Boundary Geometry:GIS Format Data Files: Ideally, Tax Year Parcel data should be provided in a shapefile (please include the .shp, .shx, .dbf, .prj, and .xml component files) or file geodatabase format. An empty shapefile and file geodatabase schema are available for download at:At the request of a county, AGRC will provide technical assistance to counties to extract, transform, and load parcel and assessment information into the GIS layer format.Geographic Coverage: Tax year parcel polygons should cover the area of each county for which assessment information is created and digital parcels are available. Full coverage may not be available yet for each county. The county may provide parcels that have been adjusted to remove gaps and overlaps for administrative tax purposes or parcels that retain these expected discrepancies that take their source from the legally described boundary or the process of digital conversion. The diversity of topological approaches will be noted in the metadata.One Tax Parcel Record Per Unique Tax Notice: Some counties produce an annual tax year parcel GIS layer with one parcel polygon per tax notice. In some cases, adjacent parcel polygons that compose a single taxed property must be merged into a single polygon. This is the goal for the statewide layer but may not be possible in all counties. AGRC will provide technical support to counties, where needed, to merge GIS parcel boundaries into the best format to match with the annual assessment information.Standard Coordinate System: Parcels will be loaded into Utah’s statewide coordinate system, Universal Transverse Mercator coordinates (NAD83, Zone 12 North). However, boundaries stored in other industry standard coordinate systems will be accepted if they are both defined within the data file(s) and documented in the metadata (see below).Descriptive Attributes:Database Field/Column Definitions: The table below indicates the field names and definitions for attributes requested for each Tax Parcel Polygon record.FIELD NAME FIELD TYPE LENGTH DESCRIPTION EXAMPLE SHAPE (expected) Geometry n/a The boundary of an individual parcel or merged parcels that corresponds with a single county tax notice ex. polygon boundary in UTM NAD83 Zone 12 N or other industry standard coordinates including state plane systemsCOUNTY_NAME Text 20 - County name including spaces ex. BOX ELDERCOUNTY_ID (expected) Text 2 - County ID Number ex. Beaver = 1, Box Elder = 2, Cache = 3,..., Weber = 29ASSESSOR_SRC (expected) Text 100 - Website URL, will be to County Assessor in most all cases ex. webercounty.org/assessorBOUNDARY_SRC (expected) Text 100 - Website URL, will be to County Recorder in most all cases ex. webercounty.org/recorderDISCLAIMER (added by State) Text 50 - Disclaimer URL ex. gis.utah.gov...CURRENT_ASOF (expected) Date - Parcels current as of date ex. 01/01/2016PARCEL_ID (expected) Text 50 - County designated Unique ID number for individual parcels ex. 15034520070000PARCEL_ADD (expected, where available) Text 100 - Parcel’s street address location. Usually the address at recordation ex. 810 S 900 E #304 (example for a condo)TAXEXEMPT_TYPE (expected) Text 100 - Primary category of granted tax exemption ex. None, Religious, Government, Agriculture, Conservation Easement, Other Open Space, OtherTAX_DISTRICT (expected, where applicable) Text 10 - The coding the county uses to identify a unique combination of property tax levying entities ex. 17ATOTAL_MKT_VALUE (expected) Decimal - Total market value of parcel's land, structures, and other improvements as determined by the Assessor for the most current tax year ex. 332000LAND _MKT_VALUE (expected) Decimal - The market value of the parcel's land as determined by the Assessor for the most current tax year ex. 80600PARCEL_ACRES (expected) Decimal - Parcel size in acres ex. 20.360PROP_CLASS (expected) Text 100 - Residential, Commercial, Industrial, Mixed, Agricultural, Vacant, Open Space, Other ex. ResidentialPRIMARY_RES (expected) Text 1 - Is the property a primary residence(s): Y'(es), 'N'(o), or 'U'(nknown) ex. YHOUSING_CNT (expected, where applicable) Text 10 - Number of housing units, can be single number or range like '5-10' ex. 1SUBDIV_NAME (optional) Text 100 - Subdivision name if applicable ex. Highland Manor SubdivisionBLDG_SQFT (expected, where applicable) Integer - Square footage of primary bldg(s) ex. 2816BLDG_SQFT_INFO (expected, where applicable) Text 100 - Note for how building square footage is counted by the County ex. Only finished above and below grade areas are counted.FLOORS_CNT (expected, where applicable) Decimal - Number of floors as reported in county records ex. 2FLOORS_INFO (expected, where applicable) Text 100 - Note for how floors are counted by the County ex. Only above grade floors are countedBUILT_YR (expected, where applicable) Short - Estimated year of initial construction of primary buildings ex. 1968EFFBUILT_YR (optional, where applicable) Short - The 'effective' year built' of primary buildings that factors in updates after construction ex. 1980CONST_MATERIAL (optional, where applicable) Text 100 - Construction Material Types, Values for this field are expected to vary greatly by county ex. Wood Frame, Brick, etc Contact: Sean Fernandez, Cadastral Manager (email: sfernandez@utah.gov; office phone: 801-209-9359)