The Digital Geologic Map of the Northern and Western Flanks of the Black Hills, Wyoming is composed of GIS data layers, two ancillary GIS tables, a Windows Help File with ancillary map text, figures and tables, GIS data layer and table FGDC metadata and ArcView 3.X legend (.AVL) files. The data were completed as a component of the Geologic Resource Evaluation (GRE) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). All GIS and ancillary tables were produced as per the NPS GIS-Geology Coverage/Shapefile Data Model (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data is available as coverage and table export (.E00) files, and as a shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 13N. That data is within the area of interest of Devils Tower National Monument.

Context

This dataset contains shapefiles outlining 558 neighborhoods in 50 major cities in New York state, notably including Albany, Buffalo, Ithaca, New York City, Rochester, and Syracuse. This adds context to your datasets by identifying the neighborhood of any locations you have, as coordinates on their own don't carry a lot of information.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. What fields does it include? What's the time period of the data and how was it collected?

Four files are included containing data about the shapes: an SHX file, a DBF file, an SHP file, and a PRJ file. Including all of them in your input data are necessary, as they all contain pieces of the data; one file alone will not have everything that you need.

Seeing how none of these files are plaintext, it can be a little difficult to get set up with them. I highly recommend using mapshaper.org to get started- this site will show you the boundaries drawn on a plane, as well as allow you to export the files in a number of different formats (e.g. GeoJSON, CSV) if you are unable to use them in the format they are provided in. Personally, I have found it easier to work with the shapefile format though.

To get started with the shapefile in R, you can use the the rgdal and rgeos packages. To see an example of these being used, be sure to check out my kernel, "Incorporating neighborhoods into your model".

Acknowledgements

These files were provided by Zillow and are available under a Creative Commons license.

Inspiration

I'll be using these in the NYC Taxi Trip Duration competition to add context to the pickup and dropoff locations of the taxi rides and hopefully greatly improve my predictions.

The statewide Texas boundary dataset is one component of the Texas Strategic Mapping Program (StratMap). The StratMap program developed seven digital base map, or ?Framework,? layers for Texas. StratMap is managed by the Texas Natural Resources Information System (TNRIS), a division of the Texas Water Development Board (TWDB). All data produced through StratMap are available in the public domain.

The StratMap boundary dataset produced files corresponding to multi-county councils of government across Texas as well as a statewide dataset. Each boundary file has five themes including state, county, city, parks, and other (i.e. federal lands, landmarks, country clubs). The data sources for each council of government coverage vary but could include digital orthophoto quads (DOQs), USGS digital raster graphics (DRGs), Texas Department of Transportation data, and local data from the council of governments or its component governments. The attribute coding scheme is designed to accommodate several basic cartographic data categories such as feature type, feature name, jurisdiction entity, data source used in feature collection, data source date and revision date(s) if applicable.

The StratMap boundary ESRI export file was exported as an ESRI shape file for editing. The county boundary along the gulfcoast was edited to reflect the 3 marine league limit instead of the 3 nautical mile limit mistakingly included in the original ESRI export file. Three new shape files were created that include the Texas gulfcoast digitized by TxDOT and aggregated by TNRIS (counties_with_Gulf_Coast, COGs_with_Gulf_Coast, and Texas_with_Gulf_Coast). Completed shape files were imported into an ESRI personal geodatabase for distribution.

This data set contains OCS block outlines in ESRI Arc/Info export and Arc/View shape file formats for the MMS Atlantic Region. OCS blocks are used to define small geographic areas within an Official Protraction Diagram (OPD) for leasing and administrative purposes. These blocks have been clipped along the Submerged Lands Act (SLA) boundary and along lines contained in the Continental Shelf Boundaries (CSB) GIS data files. Because GIS projection and topology functions can change or generalize coordinates, these GIS files are NOT an OFFICIAL record for the exact OCS block boundaries. Only the paper document or a digital image of it serve as OFFICIAL records. The data was developed within the U.S. Government; no proprietary rights may be attached to them nor may they be sold to the U.S. Government as part of any procurement of ADP products or services.This is a MD iMAP hosted service layer. Find more information at https://imap.maryland.gov.Feature Service Layer Link:https://mdgeodata.md.gov/imap/rest/services/UtilityTelecom/MD_OffshoreWindEnergyPlanning/FeatureServer/1

Crowther_Nature_Files.zip This description pertains to the original download. Details on revised (newer) versions of the datasets are listed below. When more than one version of a file exists in Figshare, the original DOI will take users to the latest version, though each version technically has its own DOI. -- Two global maps (raster files) of tree density. These maps highlight how the number of trees varies across the world. One map was generated using biome-level models of tree density, and applied at the biome scale. The other map was generated using ecoregion-level models of tree density, and applied at the ecoregion scale. For this reason, transitions between biomes or between ecoregions may be unrealistically harsh, but large-scale estimates are robust (see Crowther et al 2015 and Glick et al 2016). At the outset, this study was intended to generate reliable estimates at broad spatial scales, which inherently comes at the cost of fine-scale precision. For this reason, country-scale (or larger) estimates are generally more robust than individual pixel-level estimates. Additionally, due to data limitations, estimates for Mangroves and Tropical coniferous forest (as identified by WWF and TNC) were generated using models constructed from Topical moist broadleaf forest data and Temperate coniferous forest data, respectively. Because we used ecological analogy, the estimates for these two biomes should be considered less reliable than those of other biomes . These two maps initially appeared in Crowther et al (2015), with the biome map being featured more prominently. Explicit publication of the data is associated with Glick et al (2016). As they are produced, updated versions of these datasets, as well as alternative formats, will be made available under Additional Versions (see below).

Methods: We collected over 420,000 ground-sources estimates of tree density from around the world. We then constructed linear regression models using vegetative, climatic, topographic, and anthropogenic variables to produce forest tree density estimates for all locations globally. All modeling was done in R. Mapping was done using R and ArcGIS 10.1.

Viewing Instructions: Load the files into an appropriate geographic information system (GIS). For the original download (ArcGIS geodatabase files), load the files into ArcGIS to view or export the data to other formats. Because these datasets are large and have a unique coordinate system that is not read by many GIS, we suggest loading them into an ArcGIS dataframe whose coordinate system matches that of the data (see File Format). For GeoTiff files (see Additional Versions), load them into any compatible GIS or image management program.

Comments: The original download provides a zipped folder that contains (1) an ArcGIS File Geodatabase (.gdb) containing one raster file for each of the two global models of tree density – one based on biomes and one based on ecoregions; (2) a layer file (.lyr) for each of the global models with the symbology used for each respective model in Crowther et al (2015); and an ArcGIS Map Document (.mxd) that contains the layers and symbology for each map in the paper. The data is delivered in the Goode homolosine interrupted projected coordinate system that was used to compute biome, ecoregion, and global estimates of the number and density of trees presented in Crowther et al (2015). To obtain maps like those presented in the official publication, raster files will need to be reprojected to the Eckert III projected coordinate system. Details on subsequent revisions and alternative file formats are list below under Additional Versions.----------

Additional Versions: Crowther_Nature_Files_Revision_01.zip contains tree density predictions for small islands that are not included in the data available in the original dataset. These predictions were not taken into consideration in production of maps and figures presented in Crowther et al (2015), with the exception of the values presented in Supplemental Table 2. The file structure follows that of the original data and includes both biome- and ecoregion-level models.

Crowther_Nature_Files_Revision_01_WGS84_GeoTiff.zip contains Revision_01 of the biome-level model, but stored in WGS84 and GeoTiff format. This file was produced by reprojecting the original Goode homolosine files to WGS84 using nearest neighbor resampling in ArcMap. All areal computations presented in the manuscript were computed using the Goode homolosine projection. This means that comparable computations made with projected versions of this WGS84 data are likely to differ (substantially at greater latitudes) as a product of the resampling. Included in this .zip file are the primary .tif and its visualization support files.

References:

Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D. S., Thomas, S. M., Smith, J. R., Hintler, G., Duguid, M. C., Amatulli, G., Tuanmu, M. N., Jetz, W., Salas, C., Stam, C., Piotto, D., Tavani, R., Green, S., Bruce, G., Williams, S. J., Wiser, S. K., Huber, M. O., Hengeveld, G. M., Nabuurs, G. J., Tikhonova, E., Borchardt, P., Li, C. F., Powrie, L. W., Fischer, M., Hemp, A., Homeier, J., Cho, P., Vibrans, A. C., Umunay, P. M., Piao, S. L., Rowe, C. W., Ashton, M. S., Crane, P. R., and Bradford, M. A. 2015. Mapping tree density at a global scale. Nature, 525(7568): 201-205. DOI: http://doi.org/10.1038/nature14967Glick, H. B., Bettigole, C. B., Maynard, D. S., Covey, K. R., Smith, J. R., and Crowther, T. W. 2016. Spatially explicit models of global tree density. Scientific Data, 3(160069), doi:10.1038/sdata.2016.69.

This dataset contains the geographic data used to create maps for the San Diego County Regional Equity Indicators Report led by the Office of Equity and Racial Justice (OERJ). The full report can be found here: https://data.sandiegocounty.gov/stories/s/7its-kgpt

Demographic data from the report can be found here: https://data.sandiegocounty.gov/dataset/Equity-Report-Data-Demographics/q9ix-kfws

Filter by the Indicator column to select data for a particular indicator map.

Export notes: Dataset may not automatically open correctly in Excel due to geospatial data. To export the data for geospatial analysis, select Shapefile or GEOJSON as the file type. To view the data in Excel, export as a CSV but do not open the file. Then, open a blank Excel workbook, go to the Data tab, select “From Text/CSV,” and follow the prompts to import the CSV file into Excel. Alternatively, use the exploration options in "View Data" to hide the geographic column prior to exporting the data.

USER NOTES: 4/7/2025 - The maps and data have been removed for the Health Professional Shortage Areas indicator due to inconsistencies with the data source leading to some missing health professional shortage areas. We are working to fix this issue, including exploring possible alternative data sources.

5/21/2025 - The following changes were made to the 2023 report data (Equity Report Year = 2023). Self-Sufficiency Wage - a typo in the indicator name was fixed (changed sufficienct to sufficient) and the percent for one PUMA corrected from 56.9 to 59.9 (PUMA = San Diego County (Northwest)--Oceanside City & Camp Pendleton). Notes were made consistent for all rows where geography = ZCTA. A note was added to all rows where geography = PUMA. Voter registration - label "92054, 92051" was renamed to be in numerical order and is now "92051, 92054". Removed data from the percentile column because the categories are not true percentiles. Employment - Data was corrected to show the percent of the labor force that are employed (ages 16 and older). Previously, the data was the percent of the population 16 years and older that are in the labor force. 3- and 4-Year-Olds Enrolled in School - percents are now rounded to one decimal place. Poverty - the last two categories/percentiles changed because the 80th percentile cutoff was corrected by 0.01 and one ZCTA was reassigned to a different percentile as a result. Low Birthweight - the 33th percentile label was corrected to be written as the 33rd percentile. Life Expectancy - Corrected the category and percentile assignment for SRA CENTRAL SAN DIEGO. Parks and Community Spaces - corrected the category assignment for six SRAs.

5/21/2025 - Data was uploaded for Equity Report Year 2025. The following changes were made relative to the 2023 report year. Adverse Childhood Experiences - added geographic data for 2025 report. No calculation of bins nor corresponding percentiles due to small number of geographic areas. Low Birthweight - no calculation of bins nor corresponding percentiles due to small number of geographic areas.

Prepared by: Office of Evaluation, Performance, and Analytics and the Office of Equity and Racial Justice, County of San Diego, in collaboration with the San Diego Regional Policy & Innovation Center (https://www.sdrpic.org).

The Digital Geologic Map of Saguaro National Park and vicinity, Arizona is composed of GIS data layers, two ancillary GIS tables, a Windows Help File with ancillary map text, figures and tables, GIS data layer and table FGDC metadata and ArcView 3.X legend (.AVL) files. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). All GIS and ancillary tables were produced as per the NPS GIS-Geology Coverage/Shapefile Data Model (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data is available as coverage and table export (.E00) files, and as a shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 12N. That data is within the area of interest of Saguaro National Park.

This file presents, as of 10 June 2024, for Metropolitan France, all the overhead lines of the public electricity transmission network managed by RTE. You will find in the Export tab the different formats available, including ShapeFile. This dataset presents the sections as a broken line of identical characteristics.If multiple power lines share the same towers, they are listed in the attributes Line code n, Line name n, Line owner n. A complete line may require consolidation of multiple entries in the Overhead Lines and Underground Lines datasets if applicable as soon as its identifier appears in one of the Line Code fields. In this dataset, work identifiers refer to Transit Links (LIT - business object), while work names are the names of Links (which are a set of LITs, delimited by substations). Since a link is composed of one or more LITs, it is normal to find several objects with the same work name, while having a different identifier. The change from the old cutting (until June 2022) is the export of broken lines of identical characteristics instead of exporting only right-hand segments. There are therefore much fewer entities to handle, we go from around 256000 in the air to less than 14000 also decreasing the volume of files. Geographic accuracy has been improved and the position of the inflections coincides with the dataset of the pylons. This new division will be the only one maintained from December 2022. In addition to this dataset, for access to our mobility infrastructure data, you will find the open data map on our ArcGis Online system accessible on PC here or on the move by opening the map in ArcGIS Field Maps: INSPIRE TEN Network. This dataset is shared within the framework of Directive 2007/2/EC of the European Parliament and of the Council of 14 March 2007 establishing an Infrastructure for Spatial Information in the European Community (INSPIRE). The INSPIRE Directive applies to digital spatial data held by public authorities and requires data to be made available in accordance with harmonised technical specifications. For further information on this dataset, write to: rte-inspire-infos@rte-france.com The publication of this dataset does not exempt the user from his regulatory obligation under the anti-damage decree (DT/DICT) in the event of works or consultation of the Urban Planning Geoportal for urban planning applications (Servitudes). * * *

The Digital Geologic Map of Carlsbad Caverns National Park and Vicinity, New Mexico is composed of GIS data layers, two ancillary GIS tables, a Windows Help File with ancillary map text, figures and tables, GIS data layer and table FGDC metadata and ArcView 3.X legend (.AVL) files. The data were completed as a component of the Geologic Resources Evaluation (GRE) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). All GIS and ancillary tables were produced as per the NPS GIS-Geology Coverage/Shapefile Data Model (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data is available as coverage and table export (.E00) files, and as a shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 13N. That data is within the area of interest of Carlsbad Caverns National Park.

This file presents, as of 10 June 2024, for Metropolitan France, all the underground lines of the public electricity transmission network managed by RTE. You will find in the Export tab the different formats available, including ShapeFile. This dataset presents the sections as a broken line of identical characteristics. If multiple power lines share the same towers, they are listed in the attributes Line code n, Line name n, Line owner n. A complete line may require consolidation of multiple entries in the Overhead Lines and Underground Lines datasets if applicable as soon as its identifier appears in one of the Line Code fields. In this dataset, work identifiers refer to Transit Links (LIT - business object), while work names are the names of Links (which are a set of LITs, delimited by substations). Since a link is composed of one or more LITs, it is normal to find several objects with the same work name, while having a different identifier. The change from the old cutting (until June 2022) is the export of broken lines of identical characteristics instead of exporting only right-hand segments. There are therefore much fewer entities to manipulate, from around 355000 underground to less than 4500 also reducing the volume of files. Geographic accuracy has been improved. This new division will be the only one maintained from December 2022. In addition to this dataset, for access to our mobility infrastructure data, you will find the open data map on our ArcGis Online system accessible on PC here or on the move by opening the map in ArcGIS Field Maps: INSPIRE TEN Network. This dataset is shared within the framework of Directive 2007/2/EC of the European Parliament and of the Council of 14 March 2007 establishing an Infrastructure for Spatial Information in the European Community (INSPIRE). The INSPIRE Directive applies to digital spatial data held by public authorities and requires data to be made available in accordance with harmonised technical specifications. For further information on this dataset, write to: rte-inspire-infos@rte-france.com. The publication of this dataset does not exempt the user from his regulatory obligation under the anti-damage decree (DT/DICT) in the event of works or consultation of the Urban Planning Geoportal for urban planning applications (Servitudes).

Note: Due to the large file size the Shapefile export option does not work. To access geospatial data please select GeoJSON from the export menu This data set includes the Office of the Assessor-Recorder’s secured property tax roll spanning from July 1, 2007 to June 30, 2024. It includes all legally disclosable information, including location of property, value of property, the unique property identifier, and specific property characteristics. The data is used to accurately and fairly appraise all taxable property in the City and County of San Francisco. The Office of the Assessor-Recorder makes no representation or warranty that the information provided is accurate and/or has no errors or omissions. This dataset is updated annually after the roll is closed and certified. This typically happens by August of each year.

Exports of French administrative division at regional level (regions) from OpenStreetMap produced in the vast majority from the cadastre.

This data comes from crowdsourcing carried out by the contributors to the OpenStreetMap project and is under ODbL license which requires identical sharing and the mandatory attribution mention must be “© OpenStreetMap contributors under ODbL license” in accordance with http://osm.org/copyright

This is a semi-automatic export with lighter and topologically verified geometries (no overlap). From 2016, the geometries are original, unsimplified.

Descriptive of the contents of “regions” files

Origin

The data comes from the OpenStreetMap cartographic database. These were established from the cadastre made available by DGFiP on cadastre.gouv.fr. In addition on Mayotte where the cadastre is not available on cadastre.gouv.fr, the route of the coasts was made from the aerial images of Bing.

More info: http://prev.openstreetmap.fr/36680-communes

Format

These files are available in shapefile format, in WGS84 projection with several levels of detail (until 2015): — simplification at 5 m — simplification at 50 m — simplification at 100 m

The topology is retained during the simplification process (see: http://prev.openstreetmap.fr/blogs/cquest/limites-administratives-simplifiees)

Content

These files contain all the French regions, including the DOM and Mayotte.

For each region, the following attributes are added:

— code_insee: 2-digit INSEE code of the region (e.g. 42) — name: name of the region (e.g. Alsace) — Wikipedia: wikipedia entry (language code followed by the name of the article, e.g. en:Alsace) — Wikidata: wikidata identifier of the region — surf_km2: area area in km² on the WGS84 spheroid

History

— 01-01-2017: version based on the municipal division OSM as of 01-01-2017 including the merger of 566 communes into 178 new municipalities. — 01-01-2018: version based on OSM communal cutting at 01-01-2018 (marginal change in geometry)

Predecent versions available on: http://osm13.openstreetmap.fr/~cquest/openfla/export/

If you have any questions about these exports, you can contact exports@openstreetmap.fr

See also:

— cards in SVG format — contours of the French municipalities — contours of EPCI 2014 and Contours des EPCI 2013 — contours of the French arrondissements — contours of the French departments and SVG maps of the departments

The Digital Geologic Map of the Storm Hill Quadrangle, Wyoming is composed of GIS data layers, two ancillary GIS tables, a Windows Help File with ancillary map text, figures and tables, GIS data layer and table FGDC metadata and ArcView 3.X legend (.AVL) files. The data were completed as a component of the Geologic Resource Evaluation (GRE) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). All GIS and ancillary tables were produced as per the NPS GIS-Geology Coverage/Shapefile Data Model (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data is available as coverage and table export (.E00) files, and as a shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 13N. That data is within the area of interest of Devils Tower National Monument.

The Digital Geologic Map of the Sunset Pass Quadrangle, Utah is composed of GIS data layers, two ancillary GIS tables, a Windows Help File with ancillary map text, figures and tables, GIS data layer and table FGDC metadata and ArcView 3.X legend (.AVL) files. The data were completed as a component of the Geologic Resources Evaluation (GRE) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). All GIS and ancillary tables were produced as per the NPS GIS-Geology Coverage/Shapefile Data Model (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data is available as coverage and table export (.E00) files, and as a shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 12N. That data is within the area of interest of Golden Spike National Historic Site.

Hydrographic features for Wyoming at 1:100,000-scale, including perennial and intermittent designations and Strahler stream order attributes for streams. Does not include man-made ditches, canals and aqueducts. The data was originally produced by USGS, a Digital Line Graph (DLG) product, though this product was enhanced (edgematched, some linework and attributes corrected, stream order attribute added). A subset of this dataset is also available for distribution, including only major streams (order 4 to 7) and major lakes and reservoirs. In order to reduce the size of this subset, the line segments were dissolved to remove unncessary segments. Both datasets are available in Arc export file and shapefile format for download (see Onlink_Linkage) Statewide and tiled data: there is one export file, which when imported into ARC/INFO, will contain one coverage with both polygon (lakes, reservoirs) and line (streams) topology and two feature attribute files (.PAT and .AAT) along with three additional attribute files containing descriptive information. In shapefile format, there will be two shapefiles (polygons and lines separated), with all attribute files in Dbase format.

The Digital Geologic Map of the Happy Valley Quadrangle, Arizona is composed of GIS data layers, two ancillary GIS tables, a Windows Help File with ancillary map text, figures and tables, GIS data layer and table FGDC metadata and ArcView 3.X legend (.AVL) files. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). All GIS and ancillary tables were produced as per the NPS GIS-Geology Coverage/Shapefile Data Model (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data is available as coverage and table export (.E00) files, and as a shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 12N. That data is within the area of interest of Saguaro National Park.

The Digital Geologic Map of Bighorn Canyon National Recreation Area and Vicinity, Montana and Wyoming is composed of GIS data layers, two ancillary GIS tables, a Windows Help File with ancillary map text, figures and tables, GIS data layer and table FGDC metadata and ArcMap 9.1 layer (.LYR) files. The data were completed as a component of the Geologic Resource Evaluation (GRE) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). All GIS and ancillary tables were produced as per the NPS GRE Geology-GIS Geodatabase Data Model v. 1.4. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data is available as a 9.1 personal geodatabase (bica_geology.mdb), as coverage and table export (.E00) files, and as shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 12N. That data is within the area of interest of Bighorn Canyon National Recreation Area.

The Digital Geologic Map of the Eastern regional map of Badlands National Monument and Vicinity, South Dakota is composed of GIS data layers, two ancillary GIS tables, a Windows Help File with ancillary map text, figures and tables, GIS data layer and table FGDC metadata and ArcView 3.X legend (.AVL) files. The data were completed as a component of the Geologic Resource Evaluation (GRE) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). All GIS and ancillary tables were produced as per the NPS GIS-Geology Coverage/Shapefile Data Model (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data is available as coverage and table export (.E00) files, and as a shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 13N. That data is within the area of interest of Badlands National Park.

The Digital Geologic Map of the Storck Quadrangle, Virginia is comprised of GIS data layers, two ancillary GIS tables, a Windows Help File with ancillary map text, figures and tables, GIS data layer and table FGDC metadata, ArcMap 9.1 layer (.LYR) files, and an ArcMap 9.1 map document (.MXD) file. The data were completed as a component of the Geologic Resources Evaluation (GRE) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). All GIS and ancillary tables were produced as per the NPS GRE Geology-GIS Geodatabase Data Model v. 1.3.1 (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.htm). The GIS data is available as an 9.1 personal geodatabase (strk_geology.mdb), as coverage and table export (.E00) files, and as a shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 18N. That data is within the area of interest of Fredericksburg and Spotsylvania County Battlefields Memorial National Military Park.

The region is the top tier of local government in New Zealand. There are 16 regions of New Zealand (Part 1 of Schedule 2 of the Local Government Act 2002). Eleven are governed by an elected regional council, while five are governed by territorial authorities (the second tier of local government) who also perform the functions of a regional council and thus are known as unitary authorities. These unitary authorities are Auckland Council, Nelson City Council, Gisborne, Tasman, and Marlborough District Councils. The Chatham Islands Council also perform some of the functions of a regional council, but is not strictly a unitary authority. Unitary authorities act as regional councils for the purposes of a wide range of Acts and regulations. Regional council areas are based on water catchment areas. Regional councils are responsible for the administration of many environmental and public transport matters.Regional Councils were established in 1989 after the abolition of the 22 local government regions. The local government act 2002, requires the boundaries of regions to confirm as far as possible to one or more water catchments. When determining regional boundaries, the local Government commission gave consideration to regional communities of interest when selecting water catchments to included in a region. It also considered factors such as natural resource management, land use planning and environmental matters. Some regional boundaries are conterminous with territorial authority boundaries but there are many exceptions. An example is Taupo District, which is split between four regions, although most of its area falls within the Waikato Region. Where territorial local authorities straddle regional council boundaries, the affected area have been statistically defined in complete area units. Generally regional councils contain complete territorial authorities. The unitary authority of the Auckland Council was formed in 2010, under the Local Government (Tamaki Makarau Reorganisation) Act 2009, replacing the Auckland Regional Council and seven territorial authorities.The seaward boundary of any costal regional council is the twelve mile New Zealand territorial limit. Regional councils are defined at meshblock and area unit level.Regional Councils included in the 2013 digital pattern are:Regional Council CodeRegional Council Name01Northland Region02Auckland Region03Waikato Region04Bay of Plenty Region05Gisborne Region06Hawke's Bay Region07Taranaki Region08Manawatu-Wanganui Region09Wellington Region12West Coast Region13Canterbury Region14Otago Region15Southland Region16Tasman Region17Nelson Region18Marlborough Region99Area Outside RegionAs at 1stJuly 2007, Digital Boundary data became freely available.Deriving of Output FilesThe original vertices delineating the meshblock boundary pattern were digitised in 1991 from 1:5,000 scale urban maps and 1:50,000 scale rural maps. The magnitude of error of the original digital points would have been in the range of +/- 10 metres in urban areas and +/- 25 metres in rural areas. Where meshblock boundaries coincide with cadastral boundaries the magnitude of error will be within the range of 1–5 metres in urban areas and 5 - 20 metres in rural areas. This being the estimated magnitude of error of Landonline.The creation of high definition and generalised meshblock boundaries for the 2013 digital pattern and the dissolving of these meshblocks into other geographies/boundaries were completed within Statistics New Zealand using ESRI's ArcGIS desktop suite and the Data Interoperability extension with the following process: 1. Import data and all attribute fields into an ESRI File Geodatabase from LINZ as a shapefile2. Run geometry checks and repairs.3. Run Topology Checks on all data (Must Not Have Gaps, Must Not Overlap), detailed below.4. Generalise the meshblock layers to a 1m tolerance to create generalised dataset. 5. Clip the high definition and generalised meshblock layers to the coastline using land water codes.6. Dissolve all four meshblock datasets (clipped and unclipped, for both generalised and high definition versions) to higher geographies to create the following output data layers: Area Unit, Territorial Authorities, Regional Council, Urban Areas, Community Boards, Territorial Authority Subdivisions, Wards Constituencies and Maori Constituencies for the four datasets. 7. Complete a frequency analysis to determine that each code only has a single record.8. Re-run topology checks for overlaps and gaps.9. Export all created datasets into MapInfo and Shapefile format using the Data Interoperability extension to create 3 output formats for each file. 10. Quality Assurance and rechecking of delivery files.The High Definition version is similar to how the layer exists in Landonline with a couple of changes to fix topology errors identified in topology checking. The following quality checks and steps were applied to the meshblock pattern:Translation of ESRI Shapefiles to ESRI geodatabase datasetThe meshblock dataset was imported into the ESRI File Geodatabase format, required to run the ESRI topology checks. Topology rules were set for each of the layers. Topology ChecksA tolerance of 0.1 cm was applied to the data, which meant that the topology engine validating the data saw any vertex closer than this distance as the same location. A default topology rule of “Must Be Larger than Cluster Tolerance” is applied to all data – this would highlight where any features with a width less than 0.1cm exist. No errors were found for this rule.Three additional topology rules were applied specifically within each of the layers in the ESRI geodatabase – namely “Must Not Overlap”, “Must Not Have Gaps” and “"Area Boundary Must Be Covered By Boundary Of (Meshblock)”. These check that a layer forms a continuous coverage over a surface, that any given point on that surface is only assigned to a single category, and that the dissolved boundaries are identical to the parent meshblock boundaries.Topology Checks Results: There were no errors in either the gap or overlap checks.GeneralisingTo create the generalised Meshblock layer the “Simplify Polygon” geoprocessing tool was used in ArcGIS, with the following parameters:Simplification Algorithm: POINT_REMOVEMaximum Allowable Offset: 1 metreMinimum Area: 1 square metreHandling Topological Errors: RESOLVE_ERRORSClipping of Layers to CoastlineThe processed feature class was then clipped to the coastline. The coastline was defined as features within the supplied Land2013 with codes and descriptions as follows:11- Island – Included12- Mainland – Included21- Inland Water – Included22- Inlet – Excluded23- Oceanic –Excluded33- Other – Included.Features were clipped using the Data Interoperability extension, attribute filter tool. The attribute filter was used on both the generalised and high definition meshblock datasets creating four meshblock layers. Each meshblock dataset also contained all higher geographies and land-water data as attributes. Note: Meshblock 0017001 which is classified as island, was excluded from the clipped meshblock layers, as most of this meshblock is oceanic. Dissolve meshblocks to higher geographiesStatistics New Zealand then dissolved the ESRI meshblock feature classes to the higher geographies, for both the full and clipped dataset, generalised and high definition datasets. To dissolve the higher geographies, a model was built using the dissolver, aggregator and sorter tools, with each output set to include geography code and names within the Data Interoperability extension. Export to MapInfo Format and ShapfilesThe data was exported to MapInfo and Shapefile format using ESRI's Data Interoperability extension Translation tool. Quality Assurance and rechecking of delivery filesThe feature counts of all files were checked to ensure all layers had the correct number of features. This included checking that all multipart features had translated correctly in the new file.