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

This paper provides an abstract analysis of parallel processing strategies for spatial and spatio-temporal data. It isolates aspects such as data locality and computational locality as well as redundancy and locally sequential access as central elements of parallel algorithm design for spatial data. Furthermore, the paper gives some examples from simple and advanced GIS and spatial data analysis highlighting both that big data systems have been around long before the current hype of big data and that they follow some design principles which are inevitable for spatial data including distributed data structures and messaging, which are, however, incompatible with the popular MapReduce paradigm. Throughout this discussion, the need for a replacement or extension of the MapReduce paradigm for spatial data is derived. This paradigm should be able to deal with the imperfect data locality inherent to spatial data hindering full independence of non-trivial computational tasks. We conclude that more research is needed and that spatial big data systems should pick up more concepts like graphs, shortest paths, raster data, events, and streams at the same time instead of solving exactly the set of spatially separable problems such as line simplifications or range queries in manydifferent ways.

Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and outdoor recreation access across the nation. This data release presents results from statistical summaries of the PAD-US 3.0 protection status (by GAP Status Code) and public access status for various land unit boundaries (Protected Areas Database of the United States 3.0 Vector Analysis and Summary Statistics). Summary statistics are also available to explore and download (Comma-separated Table [CSV], Microsoft Excel Workbook (.xlsx), Portable Document Format [.pdf] Report) from the PAD-US Lands and Inland Water Statistics Dashboard ( https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-statistics ). The vector GIS analysis file, source data used to summarize statistics for areas of interest to stakeholders (National, State, Department of the Interior Region, Congressional District, County, EcoRegions I-IV, Urban Areas, Landscape Conservation Cooperative), and complete Summary Statistics Tabular Data (CSV) are included in this data release. Raster GIS analysis files are also available for combination with other raster data (Protected Areas Database of the United States (PAD-US) 3.0 Raster Analysis). The PAD-US 3.0 Combined Fee, Designation, Easement feature class in the full inventory, with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class (Protected Areas Database of the United States (PAD-US) 3.0, https://doi.org/10.5066/P9Q9LQ4B), was modified to prioritize and remove overlapping management designations, limiting overestimation in protection status or public access statistics and to support user needs for vector and raster analysis data. Analysis files in this data release were clipped to the Census State boundary file to define the extent and fill in areas (largely private land) outside the PAD-US, providing a common denominator for statistical summaries.

In this course, you will explore the concepts, principles, and practices of acquiring, storing, analyzing, displaying, and using geospatial data. Additionally, you will investigate the science behind geographic information systems and the techniques and methods GIS scientists and professionals use to answer questions with a spatial component. In the lab section, you will become proficient with the ArcGIS Pro software package. This course will prepare you to take more advanced geospatial science courses. You will be asked to work through a series of modules that present information relating to a specific topic. You will also complete a series of lab exercises, assignments, and less guided challenges. Please see the sequencing document for our suggestions as to the order in which to work through the material. To aid in working through the lecture modules, we have provided PDF versions of the lectures with the slide notes included. This course makes use of the ArcGIS Pro software package from the Environmental Systems Research Institute (ESRI), and directions for installing the software have also been provided. If you are not a West Virginia University student, you can still complete the labs, but you will need to obtain access to the software on your own.

The reference spatial database for 2019 contains 5142 plots. We use it to calculate a land use map from satellite images. It is organized according to a nested 3-level nomenclature. This is an update of the 2018 database. The sources and techniques used to build the database by land use groups are described below: For agricultural areas, we use a land use database based on farmers' declarations (for EU subsidies). This is the "Registre Parcellaire Graphique" (RPG) published in France by the French Institute for Geographical and Forestry Informations (IGN). The description of this data is available here: http://professionnels.ign.fr/doc/DC_DL_RPG-2-0.pdf. These vector data localize the crops. The release times imply that we use the RPG for last year (2018). It is therefore necessary to verify the good coherence of the data with the image at very high spatial resolution (VHSR) Pleiades. The RPG provides little information on arboriculture. For these classes we called on colleagues specialized in mango, lychee and citrus crops who are familiar with their area and can locate plots in the VHSR image. The plots of the "greenhouse or shade cultivation" class are derived from the "industrial building" layer of the IGN's "BD Topo" product. A random selection of 20% of the polygons in the layer height field allows to keep a diversity of greenhouse types. Each polygon was verified by photo-interpretation of the Pleiades image. If the greenhouse or shade was not visible in the image, the polygon was removed. The distinction between mowed and grazed grasslands was completed through collaboration with colleagues from the SELMET joint research unit (Emmanuel Tillard, Expédit Rivière, Colas Gabriel Tovmassian and Jeanne Averna). For natural areas , there is no regularly updated mapping, but the main classes can be recognized from the GIS layers of government departments that manage these areas (ONF and DEAL). Two specific classes have been added (identified by photo-interpretation): a class of shadows due to the island's steep relief (areas not visible because of the cast shade) and a class of vegetation located on steep slopes facing the morning sun called "rampart moor". The polygons for the distinction of savannahs have been improved thanks to the knowledge of Xavier Amelot (CNRS), Béatrice Moppert and Quentin Rivière (University of La Réunion). For wet land areas , the "marsh" and "water" classes were obtained by photo-interpretation of the 2019 Pleiades image. These classes are easily recognizable on this type of image. For urban areas we randomly selected polygons from the IGN BD Topo product. For the housing type building, 4 building height classes have previously been created (depending on the height of the layer field) in order to preserve a good diversity of the types of buildings present on the island. A random selection of polygons from each class was then made. The "built" layer was completed by a random selection of industrial buildings from the "industrial built" layer of the IGN's BD TOPO product. This selection was made in the "nature" field of the layer (i‧e. the following types: silo, industrial and livestock). The "photovoltaic panel" class was obtained by photo-interpretation of the polygons on 2019 Pleiades image. La base de données spatiale de référence pour 2019, est constituée de 5142 polygones. Nous l'utilisons pour calculer une carte d'occupation du sol à partir d'images satellites. Elle est organisée selon une nomenclature emboitée à 3 niveaux. Il s'agit d'une mise à jour de la base de données pour 2018. Voici une brève description des sources et techniques utilisées pour la constituer en fonction des groupes d’occupation du sol : Pour les espaces agricoles , nous disposons d’une base de données d’occupation du sol basée sur les déclarations que font des agriculteurs pour demander les subventions de l’Union Européenne. Il s’agit du Registre Parcellaire Graphique (RPG) diffusé en France par l’Institut français de l’information géographique et forestière (IGN). La description de cette donnée est disponible ici : http://professionnels.ign.fr/doc/DC_DL_RPG-2-0.pdf. Ces données vecteur sont précises et peuvent servir de modèle pour localiser les cultures. Les délais de diffusion impliquent que nous utilisons le RPG de l’année N -1. Il est donc nécessaire de vérifier la bonne cohérence des données par photo-interprétation de l’image THRS. Le RPG fournit peu d’informations sur l’arboriculture. Pour ces classes nous avons fait appel aux collègues techniciens spécialisés dans les cultures de mangues, litchis et agrumes qui connaissent bien leur secteur et peuvent localiser des parcelles sur l’image THRS. Les parcelles de la classe « culture sous serre ou ombrage » sont issues de la couche « bâti industriel » de la BD Topo de l’IGN. Une sélection aléatoire de 20% des polygones dans le champ hauteur de la couche de l’IGN permet de conserver une diversité des types de serre. Chacun des polygones...

This data release contains the analytical results and the evaluated source data files of a geospatial analysis for identifying areas in Alaska that may have potential for sediment-hosted Pb-Zn (lead-zinc) deposits. The spatial analysis is based on queries of statewide source datasets Alaska Geochemical Database (AGDB3), Alaska Resource Data File (ARDF), and Alaska Geologic Map (SIM3340) within areas defined by 12-digit HUCs (subwatersheds) from the National Watershed Boundary dataset. The packages of files available for download are: 1. The results in geodatabase format are in SedPbZn_Results_gdb.zip. The analytical results for sediment-hosted Pb-Zn deposits are in a polygon feature class which contains the points scored for each source data layer query, the accumulative score, and a designation for high, medium, or low potential and high, medium, or low certainty for sediment-hosted Pb-Zn deposits for each HUC. The data is described by FGDC metadata. An mxd file, layer file, and cartographic feature classes are provided for display of the results in ArcMap. Files sedPbZn_scoring_tables.pdf (list of the scoring parameters for the analysis) and sedPbZn_Results_gdb_README.txt (description of the files in this download package) are included. 2. The results in shapefile format are in SedPbZn_Results_shape.zip. The analytical results for sediment-hosted Pb-Zn deposits are in a polygon feature class which contains the points scored for each source data layer query, the accumulative score, and designation for high, medium, or low potential and high, medium, or low certainty for sediment-hosted Pb-Zn deposits for each HUC. The results are also provided as a CSV file. The data is described by FGDC metadata. Files sedPbZn_scoring_tables.pdf (list of the scoring parameters for the analysis) and sedPbZn_Results_shape_README.txt (description of the files in this download package) are included. 3. The source data in geodatabase format are in SedPbZn_SourceData_gdb.zip. Data layers include AGDB3, ARDF, lithology from SIM3340, and HUC subwatersheds, with FGDC metadata. An mxd file and cartographic feature classes are provided for display of the source data in ArcMap. Also included are two python scripts 1) to score the ARDF records based on the presence of certain keywords, and 2) to evaluate the ARDF, AGDB3, and lithology layers for the potential for sediment-hosted Pb-Zn deposits within subwatershed polygons. Users may modify the scripts to design their own analyses. Files sedPbZn_scoring_table.pdf (list of the scoring parameters for the analysis) and sedPbZn_sourcedata_gdb_README.txt (description of the files in this download package) are included. 4. The source data in shapefile and CSV format are in SedPbZn_SourceData_shape.zip. Data layers include ARDF and lithology from SIM3340, and HUC subwatersheds, with FGDC metadata. The ARDF keyword tables available in the geodatabase package are presented here as CSV files. All data files are described with the FGDC metadata. Files sedPb_Zn_scoring_table.pdf (list of the scoring parameters for the analysis) and sedPbZn_sourcedata_shapefile_README.txt (description of the files in this download package) are included. 5. Appendices 2, 3 and 4, which are cited by the larger work OFR2020-1147. Files are presented in XLSX and CSV formats.

For a description of the DEM and the steps for its compilation see or download the accompanying pdf document .For the DEM and Hillshade data, download the zip file.Files and information are also available here: https://www.academia.edu/23922627/A_Digital_Elevation_Model_for_Cyprus_based_on_the_ALOS_2_W3D30_Digital_Surface_ModelAll data for the production of this DEM are © Japan Aerospace Exploration Agency (JAXA).Data used for the production of the 1:5000 coastline used to clip the DEM are © Department of Lands and Surveys Cyprus (DLS).The dataset is available to use with no charge and is provided under the same conditions set by JAXA, as follows:- When the user provides or publishes the products and services to a third party using this dataset, it is necessary to display that the original data is provided by JAXA.- You are kindly requested to show the copyright (© JAXA) and the source of data, when you publish the fruits using this dataset.- JAXA does not guarantee the quality and reliability of this dataset and JAXA assume no responsibility whatsoever for any direct or indirect damage and loss caused by use of this dataset. Also, JAXA will not be responsible for any damages of users due to changing, deleting or terminating the provision of this dataset.

Compressed file SedPbZn_Results_gdb.zip contains the analytical results of a geospatial analysis for identifying areas in Alaska that may have potential for sediment-hosted Pb-Zn (lead-zinc) deposits. The spatial analysis is based on queries of statewide source datasets Alaska Geochemical Database (AGDB3), Alaska Resource Data File (ARDF), and Alaska Geologic Map (SIM3340) within areas defined by 12-digit HUCs (subwatersheds) from the National Watershed Boundary dataset. An ArcMap mxd file, layer file, and geodatabase with cartographic layers is provided for users to view the analytical results in ArcMap. Additional files include FGDC metadata, a pdf showing the query parameters for the analysis, and a README file. Compressed file SedPbZn_Results_shape.zip contains a shapefile version of the geospatial analysis. Additional files include the analytical results in CSV format, FGDC metadata, a pdf showing the query parameters for the analysis, and a README file.

CrimeMapTutorial is a step-by-step tutorial for learning crime mapping using ArcView GIS or MapInfo Professional GIS. It was designed to give users a thorough introduction to most of the knowledge and skills needed to produce daily maps and spatial data queries that uniformed officers and detectives find valuable for crime prevention and enforcement. The tutorials can be used either for self-learning or in a laboratory setting. The geographic information system (GIS) and police data were supplied by the Rochester, New York, Police Department. For each mapping software package, there are three PDF tutorial workbooks and one WinZip archive containing sample data and maps. Workbook 1 was designed for GIS users who want to learn how to use a crime-mapping GIS and how to generate maps and data queries. Workbook 2 was created to assist data preparers in processing police data for use in a GIS. This includes address-matching of police incidents to place them on pin maps and aggregating crime counts by areas (like car beats) to produce area or choropleth maps. Workbook 3 was designed for map makers who want to learn how to construct useful crime maps, given police data that have already been address-matched and preprocessed by data preparers. It is estimated that the three tutorials take approximately six hours to complete in total, including exercises.

This dataset is a compilation of available oil and gas pipeline data and is maintained by BSEE. Pipelines are used to transport and monitor oil and/or gas from wells within the outer continental shelf (OCS) to resource collection locations. Currently, pipelines managed by BSEE are found in Gulf of Mexico and southern California waters.

© MarineCadastre.gov This layer is a component of BOEMRE Layers.

This Map Service contains many of the primary data types created by both the Bureau of Ocean Energy Management (BOEM) and the Bureau of Safety and Environmental Enforcement (BSEE) within the Department of Interior (DOI) for the purpose of managing offshore federal real estate leases for oil, gas, minerals, renewable energy, sand and gravel. These data layers are being made available as REST mapping services for the purpose of web viewing and map overlay viewing in GIS systems. Due to re-projection issues which occur when converting multiple UTM zone data to a single national or regional projected space, and line type changes that occur when converting from UTM to geographic projections, these data layers should not be used for official or legal purposes. Only the original data found within BOEM/BSEE’s official internal database, federal register notices or official paper or pdf map products may be considered as the official information or mapping products used by BOEM or BSEE. A variety of data layers are represented within this REST service are described further below. These and other cadastre information the BOEM and BSEE produces are generated in accordance with 30 Code of Federal Regulations (CFR) 256.8 to support Federal land ownership and mineral resource management.

For more information – Contact: Branch Chief, Mapping and Boundary Branch, BOEM, 381 Elden Street, Herndon, VA 20170. Telephone (703) 787-1312; Email: mapping.boundary.branch@boem.gov

The REST services for National Level Data can be found here: http://gis.boemre.gov/arcgis/rest/services/BOEM_BSEE/MMC_Layers/MapServer

REST services for regional level data can be found by clicking on the region of interest from the following URL: http://gis.boemre.gov/arcgis/rest/services/BOEM_BSEE

Individual Regional Data or in depth metadata for download can be obtained in ESRI Shape file format by clicking on the region of interest from the following URL: http://www.boem.gov/Oil-and-Gas-Energy-Program/Mapping-and-Data/Index.aspx

Currently the following layers are available from this REST location:

OCS Drilling Platforms -Locations of structures at and beneath the water surface used for the purpose of exploration and resource extraction. Only platforms in federal Outer Continental Shelf (OCS) waters are included. A database of platforms and rigs is maintained by BSEE.

OCS Oil and Natural Gas Wells -Existing wells drilled for exploration or extraction of oil and/or gas products. Additional information includes the lease number, well name, spud date, the well class, surface area/block number, and statistics on well status summary. Only wells found in federal Outer Continental Shelf (OCS) waters are included. Wells information is updated daily. Additional files are available on well completions and well tests. A database of wells is maintained by BSEE.

OCS Oil & Gas Pipelines -This dataset is a compilation of available oil and gas pipeline data and is maintained by BSEE. Pipelines are used to transport and monitor oil and/or gas from wells within the outer continental shelf (OCS) to resource collection locations. Currently, pipelines managed by BSEE are found in Gulf of Mexico and southern California waters.

Unofficial State Lateral Boundaries - The approximate location of the boundary between two states seaward of the coastline and terminating at the Submerged Lands Act Boundary. Because most State boundary locations have not been officially described beyond the coast, are disputed between states or in some cases the coastal land boundary description is not available, these lines serve as an approximation that was used to determine a starting point for creation of BOEM’s OCS Administrative Boundaries. GIS files are not available for this layer due to its unofficial status.

BOEM OCS Administrative Boundaries - Outer Continental Shelf (OCS) Administrative Boundaries Extending from the Submerged Lands Act Boundary seaward to the Limit of the United States OCS (The U.S. 200 nautical mile Limit, or other marine boundary)For additional details please see the January 3, 2006 Federal Register Notice.

BOEM Limit of OCSLA ‘8(g)’ zone - The Outer Continental Shelf Lands Act '8(g) Zone' lies between the Submerged Lands Act (SLA) boundary line and a line projected 3 nautical miles seaward of the SLA boundary line. Within this zone, oil and gas revenues are shared with the coastal state(s). The official version of the ‘8(g)’ Boundaries can only be found on the BOEM Official Protraction Diagrams (OPDs) or Supplemental Official Protraction described below.

Submerged Lands Act Boundary - The SLA boundary defines the seaward limit of a state's submerged lands and the landward boundary of federally managed OCS lands. The official version of the SLA Boundaries can only be found on the BOEM Official Protraction Diagrams (OPDs) or Supplemental Official Protraction Diagrams described below.

Atlantic Wildlife Survey Tracklines(2005-2012) - These data depict tracklines of wildlife surveys conducted in the Mid-Atlantic region since 2005. The tracklines are comprised of aerial and shipboard surveys. These data are intended to be used as a working compendium to inform the diverse number of groups that conduct surveys in the Mid-Atlantic region.The tracklines as depicted in this dataset have been derived from source tracklines and transects. The tracklines have been simplified (modified from their original form) due to the large size of the Mid-Atlantic region and the limited ability to map all areas simultaneously.The tracklines are to be used as a general reference and should not be considered definitive or authoritative. This data can be downloaded from http://www.boem.gov/uploadedFiles/BOEM/Renewable_Energy_Program/Mapping_and_Data/ATL_WILDLIFE_SURVEYS.zip

BOEM OCS Protraction Diagrams & Leasing Maps - This data set contains a national scale spatial footprint of the outer boundaries of the Bureau of Ocean Energy Management’s (BOEM’s) Official Protraction Diagrams (OPDs) and Leasing Maps (LMs). It is updated as needed. OPDs and LMs are mapping products produced and used by the BOEM to delimit areas available for potential offshore mineral leases, determine the State/Federal offshore boundaries, and determine the limits of revenue sharing and other boundaries to be considered for leasing offshore waters. This dataset shows only the outline of the maps that are available from BOEM.Only the most recently published paper or pdf versions of the OPDs or LMs should be used for official or legal purposes. The pdf maps can be found by going to the following link and selecting the appropriate region of interest. http://www.boem.gov/Oil-and-Gas-Energy-Program/Mapping-and-Data/Index.aspx Both OPDs and LMs are further subdivided into individual Outer Continental Shelf(OCS) blocks which are available as a separate layer. Some OCS blocks that also contain other boundary information are known as Supplemental Official Block Diagrams (SOBDs.) Further information on the historic development of OPD's can be found in OCS Report MMS 99-0006: Boundary Development on the Outer Continental Shelf: http://www.boemre.gov/itd/pubs/1999/99-0006.PDF Also see the metadata for each of the individual GIS data layers available for download. The Official Protraction Diagrams (OPDs) and Supplemental Official Block Diagrams (SOBDs), serve as the legal definition for BOEM offshore boundary coordinates and area descriptions.

BOEM OCS Lease Blocks - Outer Continental Shelf (OCS) lease blocks serve as the legal definition for BOEM offshore boundary coordinates used to define small geographic areas within an Official Protraction Diagram (OPD) for leasing and administrative purposes. OCS blocks relate back to individual Official Protraction Diagrams and are not uniquely numbered. Only the most recently published paper or pdf

Carbon County Cadastral Data ResourcesA snapshot of property and parcel data for June 2022.Department of Revenue Orion SQL property record database provided as both an SQL database and as tables in a file geodatabase.File Geodatabase and Shapefile options for parcel polygon GIS data.Visit the Montana State Library Cadastral MSDI page for more information on cadastral data and Orion property database : MSDI Cadastral (mt.gov)The Montana Cadastral Framework shows the taxable parcels and tax-exempt parcels for most of Montana. The parcels contain selected information such as owner names, property and owner addresses, assessed value, agricultural use, and tax district information that were copied from the Montana Department of Revenue's ORION tax appraisal database. The data are maintained by the MT Department of Revenue, except for Ravalli, Silver Bow, Missoula, Flathead and Yellowstone counties that are maintained by the individual counties. The Revenue and county data are integrated by Montana State Library staff. Each parcel contains an attribute called ParcelID (geocode) that is the parcel identifier. View a pdf map of the counties that were updated this month here: https://ftpgeoinfo.msl.mt.gov/Data/Spatial/MSDI/Cadastral/Parcels/Statewide/MonthlyCadastralUpdateMap.pdf The parcel boundaries were aligned to fit with the Bureau of Land Management Geographic Coordinate Database (GCDB) of public land survey coordinates. Parcels whose legal descriptions consisted of aliquot parts of the public land survey system were created from the GCDB coordinates by selecting and, when necessary, subdividing public land survey entities. Other parcels were digitized from paper maps and the data from each map were transformed to fit with the appropriate GCDB boundaries.

Parcels and property data maintained and provided by Lee County Property Appraiser are converted to points. Property attribute data joined to parcel GIS layer by Lee County Government GIS. This dataset is generally used in spatial analysis.Process description: Parcel polygons, condominium points and property data provided by the Lee County Property Appraiser are processed by Lee County's GIS Department using the following steps:Join property data to parcel polygons Join property data to condo pointsConvert parcel polygons to points using ESRI's ArcGIS tool "Feature to Point" and designate the "Source" field "P".Load Condominium points into this layer and designate the "Source" field "C". Add X/Y coordinates in Florida State Plane West, NAD 83, feet using the "Add X/Y" tool.Projected coordinate system name: NAD_1983_StatePlane_Florida_West_FIPS_0902_FeetGeographic coordinate system name: GCS_North_American_1983

 Name
 Type
 Length
 Description


 STRAP
 String
 25
 17-digit Property ID (Section, Township, Range, Area, Block, Lot)


 BLOCK
 String
 10
 5-digit portion of STRAP (positions 9-13)


 LOT
 String
 8
 Last 4-digits of STRAP


 FOLIOID
 Double
 8
 Unique Property ID


 MAINTDATE
 Date
 8
 Date LeePA staff updated record


 MAINTWHO
 String
 20
 LeePA staff who updated record


 UPDATED
 Date
 8
 Data compilation date


 HIDE_STRAP
 String
 1
 Confidential parcel ownership


 TRSPARCEL
 String
 17
 Parcel ID sorted by Township, Range & Section


 DORCODE
 String
 2
 Department of Revenue. See https://leepa.org/Docs/Codes/DOR_Code_List.pdf


 CONDOTYPE
 String
 1
 Type of condominium: C (commercial) or R (residential)


 UNITOFMEAS
 String
 2
 Type of Unit of Measure (ex: AC=acre, LT=lot, FF=frontage in feet)


 NUMUNITS
 Double
 8
 Number of Land Units (units defined in UNITOFMEAS)


 FRONTAGE
 Integer
 4
 Road Frontage in Feet


 DEPTH
 Integer
 4
 Property Depth in Feet


 GISACRES
 Double
 8
 Total Computed Acres from GIS


 TAXINGDIST
 String
 3
 Taxing District of Property


 TAXDISTDES
 String
 60
 Taxing District Description


 FIREDIST
 String
 3
 Fire District of Property


 FIREDISTDE
 String
 60
 Fire District Description


 ZONING
 String
 10
 Zoning of Property


 ZONINGAREA
 String
 3
 Governing Area for Zoning


 LANDUSECOD
 SmallInteger
 2
 Land Use Code


 LANDUSEDES
 String
 60
 Land Use Description


 LANDISON
 String
 5
 BAY,CANAL,CREEK,GULF,LAKE,RIVER & GOLF


 SITEADDR
 String
 55
 Lee County Addressing/E911


 SITENUMBER
 String
 10
 Property Location - Street Number


 SITESTREET
 String
 40
 Street Name


 SITEUNIT
 String
 5
 Unit Number


 SITECITY
 String
 20
 City


 SITEZIP
 String
 5
 Zip Code


 JUST
 Double
 8
 Market Value


 ASSESSED
 Double
 8
 Building Value + Land Value


 TAXABLE
 Double
 8
 Taxable Value


 LAND
 Double
 8
 Land Value


 BUILDING
 Double
 8
 Building Value


 LXFV
 Double
 8
 Land Extra Feature Value


 BXFV
 Double
 8
 Building Extra Feature value


 NEWBUILT
 Double
 8
 New Construction Value


 AGAMOUNT
 Double
 8
 Agriculture Exemption Value


 DISAMOUNT
 Double
 8
 Disability Exemption Value


 HISTAMOUNT
 Double
 8
 Historical Exemption Value


 HSTDAMOUNT
 Double
 8
 Homestead Exemption Value


 SNRAMOUNT
 Double
 8
 Senior Exemption Value


 WHLYAMOUNT
 Double
 8
 Wholly Exemption Value


 WIDAMOUNT
 Double
 8
 Widow Exemption Value


 WIDRAMOUNT
 Double
 8
 Widower Exemption Value


 BLDGCOUNT
 SmallInteger
 2
 Total Number of Buildings on Parcel


 MINBUILTY
 SmallInteger
 2
 Oldest Building Built


 MAXBUILTY
 SmallInteger
 2
 Newest Building Built


 TOTALAREA
 Double
 8
 Total Building Area


 HEATEDAREA
 Double
 8
 Total Heated Area


 MAXSTORIES
 Double
 8
 Tallest Building on Parcel


 BEDROOMS
 Integer
 4
 Total Number of Bedrooms


 BATHROOMS
 Double
 8
 Total Number of Bathrooms / Not For Comm


 GARAGE
 String
 1
 Garage on Property 'Y'


 CARPORT
 String
 1
 Carport on Property 'Y'


 POOL
 String
 1
 Pool on Property 'Y'


 BOATDOCK
 String
 1
 Boat Dock on Property 'Y'


 SEAWALL
 String
 1
 Sea Wall on Property 'Y'


 NBLDGCOUNT
 SmallInteger
 2
 Total Number of New Buildings on ParcelTotal Number of New Buildings on Parcel


 NMINBUILTY
 SmallInteger
 2
 Oldest New Building Built


 NMAXBUILTY
 SmallInteger
 2
 Newest New Building Built


 NTOTALAREA
 Double
 8
 Total New Building Area


 NHEATEDARE
 Double
 8
 Total New Heated Area


 NMAXSTORIE
 Double
 8
 Tallest New Building on Parcel


 NBEDROOMS
 Integer
 4
 Total Number of New Bedrooms


 NBATHROOMS
 Double
 8
 Total Number of New Bathrooms/Not For Comm


 NGARAGE
 String
 1
 New Garage on Property 'Y'


 NCARPORT
 String
 1
 New Carport on Property 'Y'


 NPOOL
 String
 1
 New Pool on Property 'Y'


 NBOATDOCK
 String
 1
 New Boat Dock on Property 'Y'


 NSEAWALL
 String
 1
 New Sea Wall on Property 'Y'


 O_NAME
 String
 30
 Owner Name


 O_OTHERS
 String
 120
 Other Owners


 O_CAREOF
 String
 30
 In Care Of Line


 O_ADDR1
 String
 30
 Owner Mailing Address Line 1


 O_ADDR2
 String
 30
 Owner Mailing Address Line 2


 O_CITY
 String
 30
 Owner Mailing City


 O_STATE
 String
 2
 Owner Mailing State


 O_ZIP
 String
 9
 Owner Mailing Zip


 O_COUNTRY
 String
 30
 Owner Mailing Country


 S_1DATE
 Date
 8
 Most Current Sale Date > $100.00


 S_1AMOUNT
 Double
 8
 Sale Amount


 S_1VI
 String
 1
 Sale Vacant or Improved


 S_1TC
 String
 2
 Sale Transaction Code


 S_1TOC
 String
 2
 Sale Transaction Override Code


 S_1OR_NUM
 String
 13
 Original Record (Lee County Clerk)


 S_2DATE
 Date
 8
 Previous Sale Date > $100.00


 S_2AMOUNT
 Double
 8
 Sale Amount


 S_2VI
 String
 1
 Sale Vacant or Improved


 S_2TC
 String
 2
 Sale Transaction Code


 S_2TOC
 String
 2
 Sale Transaction Override Code


 S_2OR_NUM
 String
 13
 Original Record (Lee County Clerk)


 S_3DATE
 Date
 8
 Next Previous Sale Date > $100.00


 S_3AMOUNT
 Double
 8
 Sale Amount


 S_3VI
 String
 1
 Sale Vacant or Improved


 S_3TC
 String
 2
 Sale Transaction Code


 S_3TOC
 String
 2
 Sale Transaction Override Code


 S_3OR_NUM
 String
 13
 Original Record (Lee County Clerk)


 S_4DATE
 Date
 8
 Next Previous Sale Date > $100.00


 S_4AMOUNT
 Double
 8
 Sale Amount


 S_4VI
 String
 1
 Sale Vacant or Improved


 S_4TC
 String
 2
 Sale Transaction Code


 S_4TOC
 String
 2
 Sale Transaction Override Code


 S_4OR_NUM
 String
 13

Park County Cadastral Data ResourcesA snapshot of property and parcel data for June 2022.Department of Revenue Orion SQL property record database provided as both an SQL database and as tables in a file geodatabase.File Geodatabase and Shapefile options for parcel polygon GIS data.Visit the Montana State Library Cadastral MSDI page for more information on cadastral data and Orion property database : MSDI Cadastral (mt.gov)The Montana Cadastral Framework shows the taxable parcels and tax-exempt parcels for most of Montana. The parcels contain selected information such as owner names, property and owner addresses, assessed value, agricultural use, and tax district information that were copied from the Montana Department of Revenue's ORION tax appraisal database. The data are maintained by the MT Department of Revenue, except for Ravalli, Silver Bow, Missoula, Flathead and Yellowstone counties that are maintained by the individual counties. The Revenue and county data are integrated by Montana State Library staff. Each parcel contains an attribute called ParcelID (geocode) that is the parcel identifier. View a pdf map of the counties that were updated this month here: https://ftpgeoinfo.msl.mt.gov/Data/Spatial/MSDI/Cadastral/Parcels/Statewide/MonthlyCadastralUpdateMap.pdf The parcel boundaries were aligned to fit with the Bureau of Land Management Geographic Coordinate Database (GCDB) of public land survey coordinates. Parcels whose legal descriptions consisted of aliquot parts of the public land survey system were created from the GCDB coordinates by selecting and, when necessary, subdividing public land survey entities. Other parcels were digitized from paper maps and the data from each map were transformed to fit with the appropriate GCDB boundaries.

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.

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GIS In Utility Industry Market Size 2025-2029

The gis in utility industry market size is forecast to increase by USD 3.55 billion, at a CAGR of 19.8% between 2024 and 2029.

The utility industry's growing adoption of Geographic Information Systems (GIS) is driven by the increasing need for efficient and effective infrastructure management. GIS solutions enable utility companies to visualize, analyze, and manage their assets and networks more effectively, leading to improved operational efficiency and customer service. A notable trend in this market is the expanding application of GIS for water management, as utilities seek to optimize water distribution and reduce non-revenue water losses. However, the utility GIS market faces challenges from open-source GIS software, which can offer cost-effective alternatives to proprietary solutions. These open-source options may limit the functionality and support available to users, necessitating careful consideration when choosing a GIS solution. To capitalize on market opportunities and navigate these challenges, utility companies must assess their specific needs and evaluate the trade-offs between cost, functionality, and support when selecting a GIS provider. Effective strategic planning and operational execution will be crucial for success in this dynamic market.

What will be the Size of the GIS In Utility Industry Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe Global Utilities Industry Market for Geographic Information Systems (GIS) continues to evolve, driven by the increasing demand for advanced data management and analysis solutions. GIS services play a crucial role in utility infrastructure management, enabling asset management, data integration, project management, demand forecasting, data modeling, data analytics, grid modernization, data security, field data capture, outage management, and spatial analysis. These applications are not static but rather continuously unfolding, with new patterns emerging in areas such as energy efficiency, smart grid technologies, renewable energy integration, network optimization, and transmission lines. Spatial statistics, data privacy, geospatial databases, and remote sensing are integral components of this evolving landscape, ensuring the effective management of utility infrastructure. Moreover, the adoption of mobile GIS, infrastructure planning, customer service, asset lifecycle management, metering systems, regulatory compliance, GIS data management, route planning, environmental impact assessment, mapping software, GIS consulting, GIS training, smart metering, workforce management, location intelligence, aerial imagery, construction management, data visualization, operations and maintenance, GIS implementation, and IoT sensors is transforming the industry. The integration of these technologies and services facilitates efficient utility infrastructure management, enhancing network performance, improving customer service, and ensuring regulatory compliance. The ongoing evolution of the utilities industry market for GIS reflects the dynamic nature of the sector, with continuous innovation and adaptation to meet the changing needs of utility providers and consumers.

How is this GIS In Utility Industry Industry segmented?

The gis in utility industry industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ProductSoftwareDataServicesDeploymentOn-premisesCloudGeographyNorth AmericaUSCanadaEuropeFranceGermanyRussiaMiddle East and AfricaUAEAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW).

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.In the utility industry, Geographic Information Systems (GIS) play a pivotal role in optimizing operations and managing infrastructure. Utilities, including electricity, gas, water, and telecommunications providers, utilize GIS software for asset management, infrastructure planning, network performance monitoring, and informed decision-making. The GIS software segment in the utility industry encompasses various solutions, starting with fundamental GIS software that manages and analyzes geographical data. Additionally, utility companies leverage specialized software for field data collection, energy efficiency, smart grid technologies, distribution grid design, renewable energy integration, network optimization, transmission lines, spatial statistics, data privacy, geospatial databases, GIS services, project management, demand forecasting, data modeling, data analytics, grid modernization, data security, field data capture, outage ma

This dataset contains the results of Land Use/Land Cover changes (LULC) analysis within the Bagno Chlebowo peatland (52°43'54''N, 16°44'7''E). Data was created as a result of analysis of archival and contemporary cartographic materials (Ur-messtischblatt maps, sheet 1713 from 1832; Messtischblatt from 1892, Polajewo sheet) and the BDOT10k geospatial database (from 2020).The data was created in GIS software and may be displayed, validated, and edited in an open software, e.g., QGIS.The dataset contains the following LULC classes: wetland, forest, open water, open area, and streams.The shx, dbf, cpg, prj, and qmd are supported by the open QGIS software and are auxiliary files for the correct operation of the shp file. The final results of the spatial analysis are displayed in LULC1823-2020.pdf.

Enterprise GIS (Data and applications)

Enterprise is roughly defined as: A geographic information system that is integrated through an entire organization so that a large number of users can manage, share, and use spatial data and related information to address a variety of needs, including data creation, modification, visualization, analysis, and dissemination.

Disclaimer The accuracy is not to be taken / used as data produced by a Registered Professional Land Surveyor for the State of Texas. For this level of detail, supervision and certification of the produced data by a Registered Land Surveyor for the State of Texas would be required. "This product is for informational purposes and may not have been prepared for or be suitable for legal, engineering, or surveying purposes. It does not represent an on-the-ground survey and represents only the approximate relative location of property boundaries."

(Texas Government Code § 2051.102)

https://gis.dallascityhall.com/documents/COD_DataDisclaimer.pdf

The Metropolitan Planning Organization (MPO) Roadways feature layer shows MPO affiliation as derived from event mapping Feature 124, characteristic MPOAREA from the FDOT Roadway Characteristics Inventory data. MPOs are federally mandated transportation planning organizations (TPO) comprised of representatives from local governments and transportation authorities. The MPO's role is to develop and maintain the required transportation plans for a metropolitan area boundary to ensure that federal funds support local priorities. Code 00-None is coded only for counties having partial coverage by an MPO. For counties without an MPO, no code is required. For more information on MPOs, see the MPO Program Management Handbook: http://www.fdot.gov/planning/Policy/metrosupport/Resources/FDOT%20MPO%20Handbook.pdf This dataset is maintained by the Transportation Data & Analytics office (TDA). The source spatial data for this hosted feature layer was created on: 08/16/2025.For more details please review the FDOT RCI Handbook Download Data: Enter Guest as Username to download the source shapefile from here: https://ftp.fdot.gov/file/d/FTP/FDOT/co/planning/transtat/gis/shapefiles/mpoarea.zip

This data was collected as part of a project through the Smith College Botanic Garden summer internship. The project was called "Invasive Species Eradication on Slopes of Paradise Pond" and was collected in areas outside of the A,B,C, and D areas of the Mill River Invasive Species Eradication Project. Areas included the Japanese Tea Hut, Rhododendron Garden, and downslope from the President's Residence.

This data was collected in the summer of 2011 by Botanic Garden Summer Intern Ollie Schwartz ‘13. This was collected in areas including the Japanese Tea Hut, Rhododendron Garden, and downslope from the President's Residence by Paradise Pond at Smith College and includes only the Japanese Barberry plants found that summer.

View complete metadata here: http://www.science.smith.edu/departments/sal/maps/metadata/invasives/2011/Barberry_2011.pdf

Visit our website for more information: www.smith.edu/gis

NorWeST is an interagency stream temperature database and model for the western United States containing data from over 20,000 unique stream locations. Temperature observations were solicited from state, federal, tribal, private, and municipal resource organizations and processed using a custom cleaning script developed by Gwynne Chandler. Summaries of daily, weekly, and monthly means, minima, and maxima are provided for observation years. The data summaries and location information are available in user-friendly file formats that include: 1) a map (PDF) depicting the locations of in-stream thermographs (temperature sensors) for each processing unit, 2) a GIS shapefile (SHP) containing the location of these sensors for each processing unit, and 3) a tabular file (XLSX) containing observed temperature database summaries for data generally ranging from 1993 to 2015, dependent on the processing unit. Each point shapefile extent corresponds to NorWeST processing units, which generally relate to 6 digit (3rd code) hydrologic unit codes (HUCs). The tabular data can be joined to the observation point shapefile using the ID field OBSPRED_ID. The NorWeST NHDPlusV1 processing units include: Salmon, Clearwater, Spokoot, Missouri Headwaters, Snake-Bear, MidSnake, MidColumbia, Oregon Coast, South-Central Oregon, Upper Columbia-Yakima, Washington Coast, Upper Yellowstone-Bighorn, Upper Missouri-Marias, and Upper Green-North Platte. The NorWeST NHDPlusV2 processing units include: Lahontan Basin, Northern California-Coastal Klamath, Utah, Coastal California, Central California, Colorado, New Mexico, Arizona, and Black Hills.These data have many potential uses including the assessment of stream temperature regimes, development of climate scenarios, understanding habitat and climate effects on stream temperatures, describing the thermal ecology of aquatic species, and conducting climate vulnerability assessments.For more information on the NorWeST stream temperature project see: https://www.fs.usda.gov/rm/boise/AWAE/projects/NorWeST.html

This data publication originally became available via the FS Research Data Archive on 11/17/2016. On 7/27/2022 the metadata was updated to correct old URLs.