Open the Data Resource: https://gis.chesapeakebay.net/cross-git/overview/ This story map summarizes the data assembled and the scoring criteria recommended by the subject matter experts involved in the Chesapeake Bay Program's Cross-GIT Mapping Project. It also presents the composite results of the analyses. Access the Cross-GIT HUC-12 Conservation Composite: https://gis.chesapeakebay.net/ags/rest/services/InterGIT/HUC12_Cons_Composite/MapServer Access the Cross-GIT HUC-12 Restoration Composite: https://gis.chesapeakebay.net/ags/rest/services/InterGIT/HUC12_Rest_Composite/MapServer

Geologic map data in shapefile format that includes faults, unit contacts, unit polygons, attitudes of strata and faults, and surficial geothermal features. 5 cross-sections in Adobe Illustrator format. Comprehensive catalogue of drill-hole data in spreadsheet, shapefile, and Geosoft database formats. Includes XYZ locations of well heads, year drilled, type of well, operator, total depths, well path data (deviations), lithology logs, and temperature data. 3D model constructed with EarthVision using geologic map data, cross-sections, drill-hole data, and geophysics.

Donation sent to the University of Idaho Library Government Documents Librarian a CD containing General Land Office maps on it. A readme file on the CD contains this information:"I obtained the attached GLO maps from Mitch Price at River Design Group who obtained them from another source. These maps apparently do not have a date, I assume it was stripped off when they were rectified. These maps show the Great Northern Rail line, it arrived in Bonners Ferry in 1892. The Spokane International Railroad (Union Pacific purchased this line) built a bridge across the Kootenai R. in 1906." "I am a bit puzzled on the map dates, the Kootenai River Master Plan indicated these maps are 1862-65 but they also show the Great Northern Rail line but not the Spokane International Railroad which seems to place them somewhere between 1892 - 1906 unless perhaps they were revised at a later date."Gary Barton USGS Tacoma, WA 253-552-1613 officegbarton@usgs.gov

To access parcel information:Enter an address or zoom in by using the +/- tools or your mouse scroll wheel. Parcels will draw when zoomed in.Click on a parcel to display a popup with information about that parcel.Click the "Basemap" button to display background aerial imagery.From the "Layers" button you can turn map features on and off.Complete Help (PDF)Parcel Legend:Full Map LegendAbout this ViewerThis viewer displays land property boundaries from assessor parcel maps across Massachusetts. Each parcel is linked to selected descriptive information from assessor databases. Data for all 351 cities and towns are the standardized "Level 3" tax parcels served by MassGIS. More details ...Read about and download parcel dataUpdatesV 1.1: Added 'Layers' tab. (2018)V 1.2: Reformatted popup to use HTML table for columns and made address larger. (Jan 2019)V 1.3: Added 'Download Parcel Data by City/Town' option to list of layers. This box is checked off by default but when activated a user can identify anywhere and download data for that entire city/town, except Boston. (March 14, 2019)V 1.4: Data for Boston is included in the "Level 3" standardized parcels layer. (August 10, 2020)V 1.4 MassGIS, EOTSS 2021

To access parcel information:Enter an address or zoom in by using the +/- tools or your mouse scroll wheel. Parcels will draw when zoomed in.Click on a parcel to display a popup with information about that parcel.Click the "Basemap" button to display background aerial imagery.From the "Layers" button you can turn map features on and off. Check on 'Download Parcel Data by City/Town' and click in the map for links to download all parcel data for that municipality.Complete Help (PDF)Parcel Legend:Full Map LegendAbout this ViewerThe map displays land property boundaries from assessor parcel maps across Massachusetts. Parcel information is from local assessor databases. More...Read about and download parcel dataAlso available: an accessible, non-map-based Property Information FinderDISCLAIMER: Assessor’s parcel mapping is a representation of property boundaries, not an authoritative source. The authoritative record of property boundaries is recorded at the registries of deeds. A legally authoritative map of property boundaries can only be produced by a professional land surveyor.V 1.4 MassGIS, EOTSS 2021

Map Index Sheets from Block and Lot Grid of Property Assessment and based on aerial photography, showing 1983 datum with solid line and NAD 27 with 5 second grid tics and italicized grid coordinate markers and outlines of map sheet boundaries. Each grid square is 3500 x 4500 feet. Each Index Sheet contains 16 lot/block sheets, labeled from left to right, top to bottom (4 across, 4 down): A, B, C, D, E, F, G, H, J, K, L, M, N, P, R, S. The first (4) numeric characters in a parcelID indicate the Index sheet in which the parcel can be found, the alpha character identifies the block in which most (or all) of the property lies.

These data were compiled to demonstrate new predictive mapping approaches and provide comprehensive gridded 30-meter resolution soil property maps for the Colorado River Basin above Hoover Dam. Random forest models related environmental raster layers representing soil forming factors with field samples to render predictive maps that interpolate between sample locations. Maps represented soil pH, texture fractions (sand, silt clay, fine sand, very fine sand), rock, electrical conductivity (ec), gypsum, CaCO3, sodium adsorption ratio (sar), available water capacity (awc), bulk density (dbovendry), erodibility (kwfact), and organic matter (om) at 7 depths (0, 5, 15, 30, 60, 100, and 200 cm) as well as depth to restrictive layer (resdept) and surface rock size and cover. Accuracy and error estimated using a 10-fold cross validation indicated a range of model performances with coefficient of variation (R2) for models ranging from 0.20 to 0.76 with mean of 0.52 and a standard deviation of 0.12. Models of pH, om and ec had the best accuracy (R2 > 0.6). Most texture fractions, CaCO3, and SAR models had R2 values from 0.5-0.6. Models of kwfact, dbovendry, resdept, rock models, gypsum and awc had R2 values from 0.4-0.5 excepting near surface models which tended to perform better. Very fine sands and 200 cm estimates for other models generally performed poorly (R2 from 0.2-0.4), and sample size for the 200 cm models was too low for reliable model building. More than 90% of the soils data used was sampled since 2000, but some older samples are included. Uncertainty estimates were also developed by creating relative prediction intervals, which allow end users to evaluate uncertainty easily.

These data were compiled to demonstrate new predictive mapping approaches and provide comprehensive gridded 30-meter resolution soil property maps for the Colorado River Basin above Hoover Dam. Random forest models related environmental raster layers representing soil forming factors with field samples to render predictive maps that interpolate between sample locations. Maps represented soil pH, texture fractions (sand, silt clay, fine sand, very fine sand), rock, electrical conductivity (ec), gypsum, CaCO3, sodium adsorption ratio (sar), available water capacity (awc), bulk density (dbovendry), erodibility (kwfact), and organic matter (om) at 7 depths (0, 5, 15, 30, 60, 100, and 200 cm) as well as depth to restrictive layer (resdept) and surface rock size and cover. Accuracy and error estimated using a 10-fold cross validation indicated a range of model performances with coefficient of variation (R2) for models ranging from 0.20 to 0.76 with mean of 0.52 and a standard deviation of 0.12. Models of pH, om and ec had the best accuracy (R2 > 0.6). Most texture fractions, CaCO3, and SAR models had R2 values from 0.5-0.6. Models of kwfact, dbovendry, resdept, rock models, gypsum and awc had R2 values from 0.4-0.5 excepting near surface models which tended to perform better. Very fine sands and 200 cm estimates for other models generally performed poorly (R2 from 0.2-0.4), and sample size for the 200 cm models was too low for reliable model building. More than 90% of the soils data used was sampled since 2000, but some older samples are included. Uncertainty estimates were also developed by creating relative prediction intervals, which allow end users to evaluate uncertainty easily.

These parcel boundaries represent legal descriptions of property ownership, as recorded in various public documents in the local jurisdiction. The boundaries are intended for cartographic use and spatial analysis only, and not for use as legal descriptions or property surveys. Tax parcel boundaries have not been edge-matched across municipal boundaries.

The Dane County Parcel Database was derived from a variety of source maps including U.S. General Land Office survey plats, deed descriptions, subdivision plats, certified survey maps and right-of-way plats. All new parcels are entered into the database using coordinate geometry (COGO). The map provides a representation of the geometry and topology of tax parcels. The attributes are derived from the Dane County Treasurers database. It is not intended to be used for the legal determination of land ownership or to be in any way a substitute for the land ownership and interest descriptions contained in individual deeds.

These data were compiled to demonstrate new predictive mapping approaches and provide comprehensive gridded 30-meter resolution soil property maps for the Colorado River Basin above Hoover Dam. Random forest models related environmental raster layers representing soil forming factors with field samples to render predictive maps that interpolate between sample locations. Maps represented soil pH, texture fractions (sand, silt clay, fine sand, very fine sand), rock, electrical conductivity (ec), gypsum, CaCO3, sodium adsorption ratio (sar), available water capacity (awc), bulk density (dbovendry), erodibility (kwfact), and organic matter (om) at 7 depths (0, 5, 15, 30, 60, 100, and 200 cm) as well as depth to restrictive layer (resdept) and surface rock size and cover. Accuracy and error estimated using a 10-fold cross validation indicated a range of model performances with coefficient of variation (R2) for models ranging from 0.20 to 0.76 with mean of 0.52 and a standard deviation of 0.12. Models of pH, om and ec had the best accuracy (R2 > 0.6). Most texture fractions, CaCO3, and SAR models had R2 values from 0.5-0.6. Models of kwfact, dbovendry, resdept, rock models, gypsum and awc had R2 values from 0.4-0.5 excepting near surface models which tended to perform better. Very fine sands and 200 cm estimates for other models generally performed poorly (R2 from 0.2-0.4), and sample size for the 200 cm models was too low for reliable model building. More than 90% of the soils data used was sampled since 2000, but some older samples are included. Uncertainty estimates were also developed by creating relative prediction intervals, which allow end users to evaluate uncertainty easily.

Yearly effective energy and mass transfer (EEMT) (MJ m−2 yr−1) was calculated for the Catalina Mountains by summing the 12 monthly values. Effective energy and mass flux varies seasonally, especially in the desert southwestern United States where contemporary climate includes a bimodal precipitation distribution that concentrates in winter (rain or snow depending on elevation) and summer monsoon periods. This seasonality of EEMT flux into the upper soil surface can be estimated by calculating EEMT on a monthly basis as constrained by solar radiation (Rs), temperature (T), precipitation (PPT), and the vapor pressure deficit (VPD): EEMT = f(Rs,T,PPT,VPD). Here we used a multiple linear regression model to calculate the monthly EEMT that accounts for VPD, PPT, and locally modified T across the terrain surface. These EEMT calculations were made using data from the PRISM Climate Group at Oregon State University (www.prismclimate.org). Climate data are provided at an 800-m spatial resolution for input precipitation and minimum and maximum temperature normals and at a 4000-m spatial resolution for dew-point temperature (Daly et al., 2002). The PRISM climate data, however, do not account for localized variation in EEMT that results from smaller spatial scale changes in slope and aspect as occurs within catchments. To address this issue, these data were then combined with 10-m digital elevation maps to compute the effects of local slope and aspect on incoming solar radiation and hence locally modified temperature (Yang et al., 2007). Monthly average dew-point temperatures were computed using 10 yr of monthly data (2000–2009) and converted to vapor pressure. Precipitation, temperature, and dew-point data were resampled on a 10-m grid using spline interpolation. Monthly solar radiation data (direct and diffuse) were computed using ArcGIS Solar Analyst extension (ESRI, Redlands, CA) and 10-m elevation data (USGS National Elevation Dataset [NED] 1/3 Arc-Second downloaded from the National Map Seamless Server at seamless.usgs.gov). Locally modified temperature was used to compute the saturated vapor pressure, and the local VPD was estimated as the difference between the saturated and actual vapor pressures. The regression model was derived using the ISOHYS climate data set comprised of approximately 30-yr average monthly means for more than 300 weather stations spanning all latitudes and longitudes (IAEA).

Normally, any FIRM that has associated flood profiles has cross sections. The S_XS table contains information about cross section lines. These lines usually represent the locations of channel surveys performed for input into the hydraulic model used to calculate flood elevations. Sometimes cross sections are interpolated between surveyed cross sections using high accuracy elevation data. Depending on the zone designation (Zone AE, Zone A, etc.), these locations may be shown on Flood Profiles in the FIS report and can be used to cross reference the Flood Profiles to the planimetric depiction of the flood hazards. This information is used in the Floodway Data Tables in the FIS report, as well as on the FIRM panels.Flood hazard and supporting data are developed using specifications for horizontal control consistent with 1:12,000–scale mapping. If you plan to display maps from the National Flood Hazard Layer with other map data for official purposes, ensure that the other information meets FEMA’s standards for map accuracy.The minimum horizontal positional accuracy for base map hydrographic and transportation features used with the NFHL is the NSSDA radial accuracy of 38 feet. USGS imagery and map services that meet this standard can be found by visiting the Knowledge Sharing Site (KSS) for Base Map Standards (420). Other base map standards can be found athttps://riskmapportal.msc.fema.gov/kss/MapChanges/default.aspx. You will need a username and password to access this information.The NFHL data are from FEMA’s Flood Insurance Rate Map (FIRM) databases. New data are added continually. The NFHL also contains map changes to FIRM data made by Letters of Map Revision (LOMRs).The NFHL is stored in North American Datum of 1983, Geodetic Reference System 80 coordinate system, though many of the NFHL GIS web services support the Web Mercator Sphere projection commonly used in web mapping applications.

The 'Stems' data are from an individual tree segmentation (Swetnam and Falk 2014) derived from the 2010 snow-off lidar and biomass-carbon allometric equations. The purpose of the dataset is to evaluate the distribution of aboveground carbon across an elevation gradient in temperature and precipitation.

The '10m Topo points' data are derived from a bare earth digital elevation model (DEM) generated from the 2010 snow-off lidar flight, these include the topographic metrics and the biomass-carbon for each pixel derived from the sum of STEMS. The purpose of the dataset is to evaluate the distribution of aboveground carbon across an elevation gradient in temperature and precipitation.

A total of three catchments in Boulder Creek were analyzed: Como Creek, Gordon Gulch, and Betasso Preserve.

Significance Statement: Forest carbon reservoirs in complex terrain along an elevation-climate gradient spanning an 11 Celsius range in mean annual temperature (MAT) and a 50 cm yr-1 range in mean annual precipitation (MAP) did not exhibit the expected response of increasing in size with greater MAP and idealized MAT. Within catchments, the distribution of mean and peak carbon storage doubled in size for valleys versus ridges. These results suggest spatial variations in carbon storage relate more to topographically mediated water availability, as well as aspect (energy-balance) and topographic curvature (a proxy for soil depth and depth to ground water), than elevation-climate gradients. Consequently, lateral redistribution of precipitation across topographic position may either moderate or exacerbate regional climatic controls over ecosystem productivity and tree-level responses during drought.

The Unpublished Digital Geologic-GIS Map of the Whiskeytown Quadrangle, California is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (whsk_geology.gdb), a 10.1 ArcMap (.mxd) map document (whsk_geology.mxd), individual 10.1 layer (.lyr) files for each GIS data layer, an ancillary map information document (whis_geology_gis_readme.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.txt) and FAQ (.pdf) formats, and a GIS readme file (whis_geology_gis_readme.pdf). Please read the whis_geology_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O'Meara (stephanie.omeara@colostate.edu; see contact information below). Presently, a GRI Google Earth KMZ/KML product doesn't exist for this map. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (whsk_geology_metadata.txt or whsk_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in ArcGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone 10N. The data is within the area of interest of Whiskeytown National Recreation Area.

This map shows the free and open data status of county public geospatial (GIS) data across Minnesota. The accompanying data set can be used to make similar maps using GIS software.

Counties shown in this dataset as having free and open public geospatial data (with or without a policy) are: Aitkin, Anoka, Becker, Beltrami, Benton, Big Stone, Carlton, Carver, Cass, Chippewa, Chisago, Clay, Clearwater, Cook, Crow Wing, Dakota, Douglas, Grant, Hennepin, Hubbard, Isanti, Itasca, Kittson, Koochiching, Lac qui Parle, Lake, Lyon, Marshall, McLeod, Meeker, Mille Lacs, Morrison, Mower, Norman, Olmsted, Otter Tail, Pipestone, Polk, Pope, Ramsey, Renville, Rice, Scott, Sherburne, Stearns, Steele, Stevens, St. Louis, Traverse, Waseca, Washington, Wilkin, Winona, Wright and Yellow Medicine.

To see if a county's data is distributed via the Minnesota Geospatial Commons, check the Commons organizations page: https://gisdata.mn.gov/organization

To see if a county distributes data via its website, check the link(s) on the Minnesota County GIS Contacts webpage: https://www.mngeo.state.mn.us/county_contacts.html

This dataset is a series of digital map-posters accompanying the AdaptNRM Guide: Helping Biodiversity Adapt: supporting climate adaptation planning using a community-level modelling approach.

These represent supporting materials and information about the community-level biodiversity models applied to climate change. Map posters are organised by four biological groups (vascular plants, mammals, reptiles and amphibians), two climate change scenario (1990-2050 MIROC5 and CanESM2 for RCP8.5), and five measures of change in biodiversity.

The map-posters present the nationally consistent data at locally relevant resolutions in eight parts – representing broad groupings of NRM regions based on the cluster boundaries used for climate adaptation planning (http://www.environment.gov.au/climate-change/adaptation) and also Nationally.

Map-posters are provided in PNG image format at moderate resolution (300dpi) to suit A0 printing. The posters were designed to meet A0 print size and digital viewing resolution of map detail. An additional set in PDF image format has been created for ease of download for initial exploration and printing on A3 paper. Some text elements and map features may be fuzzy at this resolution.

Each map-poster contains four dataset images coloured using standard legends encompassing the potential range of the measure, even if that range is not represented in the dataset itself or across the map extent.

Most map series are provided in two parts: part 1 shows the two climate scenarios for vascular plants and mammals and part 2 shows reptiles and amphibians. Eight cluster maps for each series have a different colour theme and map extent. A national series is also provided. Annotation briefly outlines the topics presented in the Guide so that each poster stands alone for quick reference.

An additional 77 National maps presenting the probability distributions of each of 77 vegetation types – NVIS 4.1 major vegetation subgroups (NVIS subgroups) - are currently in preparation.

Example citations:

Williams KJ, Raisbeck-Brown N, Prober S, Harwood T (2015) Generalised projected distribution of vegetation types – NVIS 4.1 major vegetation subgroups (1990 and 2050), A0 map-poster 8.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

Williams KJ, Raisbeck-Brown N, Harwood T, Prober S (2015) Revegetation benefit (cleared natural areas) for vascular plants and mammals (1990-2050), A0 map-poster 9.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

This dataset has been delivered incrementally. Please check that you are accessing the latest version of the dataset. Lineage: The map posters show case the scientific data. The data layers have been developed at approximately 250m resolution (9 second) across the Australian continent to incorporate the interaction between climate and topography, and are best viewed using a geographic information system (GIS). Each data layers is 1Gb, and inaccessible to non-GIS users. The map posters provide easy access to the scientific data, enabling the outputs to be viewed at high resolution with geographical context information provided.

Maps were generated using layout and drawing tools in ArcGIS 10.2.2

A check list of map posters and datasets is provided with the collection.

Map Series: 7.(1-77) National probability distribution of vegetation type – NVIS 4.1 major vegetation subgroup pre-1750 #0x

8.1 Generalised projected distribution of vegetation types (NVIS subgroups) (1990 and 2050)

9.1 Revegetation benefit (cleared natural areas) for plants and mammals (1990-2050)

9.2 Revegetation benefit (cleared natural areas) for reptiles and amphibians (1990-2050)

10.1 Need for assisted dispersal for vascular plants and mammals (1990-2050)

10.2 Need for assisted dispersal for reptiles and amphibians (1990-2050)

11.1 Refugial potential for vascular plants and mammals (1990-2050)

11.1 Refugial potential for reptiles and amphibians (1990-2050)

12.1 Climate-driven future revegetation benefit for vascular plants and mammals (1990-2050)

12.2 Climate-driven future revegetation benefit for vascular reptiles and amphibians (1990-2050)

The Canadian County Parcel Data Public View is a set of geospatial features representing the surface ownership of property in fee simple for property tax purposes as required by 68 O.S. § 2821 and other related data used to produce the parcels such as subdivision boundaries and subdivision lots. The data is created from source documentation filed with the Canadian County Clerk's Office including deeds, easements, and plats. Other data sources such as filed Certified Corner Records filed with the State of Oklahoma or highway plans produced by the Department of Transportation may be used to adjust parcel boundaries. Single legal descriptions may be split up into two or more parcels if the description crosses the boundaries of multiple taxing jurisdictions or crosses quarter section boundaries. Accuracy of parcel data can vary considerably due to a combination of factors. Most parcels and subdivision legal descriptions reference a quarter section or quarter section corner. The accuracy of the quarter section corners is discussed with Canadian County's Public Land Survey System Data. Accuracy is further enhanced or degraded by the quality of the legal description used to create the feature. Generally, legal descriptions created from surveys will have higher accuracy the newer they were created due to improvements in the field of surveying. However, it can be difficult to determine the age of a legal description as descriptions are generally reused on subsequent deeds after the description was first created. Legal descriptions can occasionally contain updated bearings and distances and may denote the updates. The Assessor's Office uses the latest available legal description for creating parcels. Legal descriptions may lack specificity such as the use of "North" instead of a measured bearing or have missing parameters such as missing bearings for curved boundaries. In these cases, parcel data accuracy can be degraded. Further, if a legal description contains a specific landmark or boundary, sometimes called a "bound", the boundary is drawn to that point or landmark regardless of whether the bearing and/or distance accurately arrive at that point. For instance, if a legal description reads "...to the south line of the southeast quarter", the boundary is drawn to the south line of the quarter section even if the bearing and distance are short of or extend beyond that point. Because parcel data must be created for the entire county regardless of the accuracy of the descriptions used to create those parcels, parcels may need to be "stretched" or "squeezed" to make them fit together. When possible, the Assessor's Office relies on the most accurate legal descriptions to set the boundaries and then fits older boundaries to them. Due to the large number of variables, parcel data accuracy cannot be guaranteed nor can the level of accuracy be described for the entire dataset. While Canadian County makes every reasonable effort to make sure parcel data is accurate, this data cannot be used in place of a survey performed by an Oklahoma Licensed Professional Land Surveyor.ParcelDataExternal - Polygons representing surface fee simple title. This parcel data formatted and prepared for public use. Some fields may be blank to comply with 22 O.S. § 60.14 & 68 O.S. § 2899.1Attributes:Account (account): The unique identifier for parcel data generated by the appraisal software used by the Assessor's Office"A" Number (a_number): An integer assigned in approximate chronological order to represent each parcel divided per quarter sectionParcel ID (parcel_id): Number used to identify parcels geographically, see Parcel Data Export Appendix A for an in-depth explanation. This identifier is not unique for all parcelsParcel Size (parcel_size): Size of the parcels, must be used in conjunction with following units fieldParcel Size Units (parcel_size_units): Units for the size of the parcel. Can be "Acres" or "Lots" for parcels within subdivisions that are valued per lotOwner's Name (owners_name): Name of the surface owner of the property in fee simple on recordMailing Information (mail_info): Extra space for the owners name if needed or trustee namesMailing Information 2 (mail_info2): Forwarded mail or "In care of" mailing informationMailing Address (mail_address): Mailing address for the owner or forwarding mailing addressMailing City (mail_city): Mailing or postal cityMailing State (mail_state): Mailing state abbreviated to standard United States Postal Service codesMailing ZIP Code (mail_zip): Mailing ZIP code as determined by the United States Postal ServiceTax Area Code (tax_area): Integer numeric code representing an area in which all the taxing jurisdictions are the same. See Parcel Data Appendix B for a more detailed description of each tax areaTax Area Description (tax_area_desc): Character string code representing the tax area. See Parcel Data Appendix B for a more detailed description of each tax areaProperty Class (prop_class): The Assessor's Office classification of each parcel by rural (no city taxes) or urban (subject to city taxes) and exempt, residential, commercial, or agriculture. This classification system is for property appraisal purposes and does not reflect zoning classifications in use by municipalities. See Parcel Data Appendix B for a more detailed description of each property classificationLegal Description (legal): A highly abbreviated version of the legal description for each parcel. This legal description may not match the most recent legal description for any given property due to administrative divisions as described above, or changes made to the property by way of recorded instruments dividing smaller parcels from the original description. This description may NOT be used in place of a true legal descriptionSubdivision Code (subdiv_code): A numeric code representing a recorded subdivision plat which contains the parcel. This value will be "0" for any parcel not part of a recorded subdivision plat.Subdivision Name (subdiv_name): The name of the recorded subdivision plat abbreviated as needed to adapt to appraisal software field limitationsSubdivision Block Number (subdiv_block): Numeric field representing the block number of a parcel. This value will be "0" if the parcel is not in a recorded subdivision plat or if the plat did not contain block numbersSubdivision Lot Number (subdiv_lot): Numeric field representing the lot number of a parcel. This value will be "0" if the parcel is not in a recorded subdivision platTownship Number (township): Numeric field representing the Public Land Survey System tier or township the parcel is located in. All townships or tiers in Canadian County are north of the base line of the Indian Meridian.Range Number (range): Numeric field representing the Public Land Survey System range the parcel is located in. All Ranges in Canadian County are west of the Indian MeridianSection Number (section): Numeric field representing the Public Land Survey System section number the parcel is located inQuarter Section Code (quarter_sec): Numeric field with a code representing the quarter section a majority of the parcel is located in, 1 = Northeast Quarter, 2 = Northwest Quarter, 3 = Southwest Quarter, 4 = Southeast QuarterSitus Address (situs): Address of the property itself if it is knownSitus City (situs_city): Name of the city the parcel is actually located in (regardless of the postal city) or "Unincorporated" if the parcel is outside any incorporated city limitsSitus ZIP Code (situs_zip): ZIP Code as determined by the United States Postal Service for the property itself if it is knownLand Value (land_val): Appraised value of the land encompassed by the parcel as determined by the Assessor's OfficeImprovement Value (impr_val): Appraised value of the improvements (house, commercial building, etc.) on the property as determined by the Assessor's OfficeManufactured Home Value (mh_val): Appraised value of any manufactured homes on the property and owned by the same owner of the land as determined by the Assessor's OfficeTotal Value (total_val): Total appraised value for the property as determined by the Assessor's OfficeTotal Capped Value (cap_val): The capped value as required by Article X, Section 8B of the Oklahoma ConstitutionTotal Assessed Value (total_assess): The capped value multiplied by the assessment ratio of Canadian County, which is 12% of the capped valueHomestead Exempt Amount (hs_ex_amount): The amount exempt from the assessed value if a homestead exemption is in placeOther Exempt Value (other_ex_amount): The amount exempt from the assessed value if other exemptions are in placeTaxable Value (taxable_val): The amount taxes are calculated on which is the total assessed value minus all exemptionsSubdivisions - Polygons representing a plat or subdivision filed with the County Clerk of Canadian County. Subdivision boundaries may be revised by vacations of the plat or subdivision or by replatting a portion or all of a subdivision. Therefore, subdivision boundaries may not match the boundaries as shown on the originally filed plat.Attributes:Subdivision Name (subdivision_name): The name of the plat or subdivisionSubdivision Number (subdivision_number): An ID for each subdivision created as a portion of the parcel ID discussed in Parcel Data Export Appendix APlat Book Number (book): The book number for the recorded documentPlat Book Page Number (page): The page number for the recorded documentRecorded Acres (acres): The number of acres within the subdivision if knownRecorded Date (recorded_date): The date the document creating the subdivision was recordedDocument URL (clerk_url): URL to download a copy of the document recorded by the Canadian County Clerk's OfficeBlocks - Polygons derived from subdivision lots representing the blocks

arcgis-pro arcgis-pro-map-file digital-data digital-geologic-gis-map digital-geologic-map faults-guanflt file-geodatabase geodatabase geologic-attitude-observation-localities-guanatd geologic-contacts-guanglga geologic-cross-section-lines-guansec geologic-gis-map geologic-map geologic-measurement-localities-guangml geologic-point-features-guangpf geologic-resource-evaluation geologic-resources-division geologic-resources-inventory geologic-sample-localities-guangsl geologic-unit-descriptions geologic-unit-information-table geologic-units geologic-units-guanglg geology geology-help-map-document geopackage gis-data gpkg grd gre gri guardian-angels-quadrangle hazard-feature-lines-guanhzl linear-geologic-units-guangln linear-joints-guanjln map-symbology-guansym map-table national-park-service north-america nps ogc open-geospatial-consortium qgis source-geologic-maps source-map-information-table source-maps structure-contour-lines top-of-the-navajo-sandstone-jn-guancn3 top-of-the-springdale-sandstone-member-of-the-kayenta-formation-jks-guancn5 unit-table united-states us usa utah zion zion-national-park

This data is part of the series of maps that covers the whole of Australia at a scale of 1:250 000 (1cm on a map represents 2.5km on the ground) and comprises 513 maps. This is the largest scale at which published topographic maps cover the entire continent. Data is downloadable in various distribution formats.