This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

PLEASE NOTE: These data do not include data over Tasmania. Please see links relevant to that area.

GEODATA TOPO 250K Series 3 is a vector representation of the major topographic features appearing on the 1:250,000 scale NATMAPs supplied in Personal Geodatabase format and is designed for use in a range of commercial GIS software. Data is arranged within specific themes. All data is based on the GDA94 coordinate system.

GEODATA TOPO 250K Series 3 is available as a free download product in Personal Geodatabase, ArcView Shapefile or MapInfo TAB file formats. Each package includes data arranged in ten main themes - cartography, elevation, framework, habitation, hydrography, infrastructure, terrain, transport, utility and vegetation. Data is also available as GEODATA TOPO 250K Series 3 for Google Earth in kml format for use on Google Earth TM Mapping Service.

Product Specifications

Themes: Cartography, Elevation, Framework, Habitation, Hydrography, Infrastructure, Terrain, Transport, Utility and Vegetation

Coverage: National (Powerlines not available in South Australia)

Currency: Data has a currency of less than five years for any location

Coordinates: Geographical

Datum: Geocentric Datum of Australia (GDA94)

Formats: Personal Geodatabase, kml, Shapefile and MapInfo TAB

Release Date: 26 June 2006

Spatial information about the seafloor is critical for decision-making by marine resource science, management and tribal organizations. Coordinating data needs can help organizations leverage collective resources to meet shared goals. To help enable this coordination, the National Oceanic and Atmospheric Administration (NOAA) National Centers for Coastal Ocean Science (NCCOS) developed a spatial framework, process and online application to identify common data collection priorities for seafloor mapping, sampling and visual surveys off the US Caribbean territories of Puerto Rico and the US Virgin Islands. Fifteen participants from local federal, state, and academic institutions entered their priorities in an online application, using virtual coins to denote their priorities in 2.5x2.5 kilometer (nearshore) and 10x10 kilometer (offshore) grid size. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important and what data types were needed. Results were analyzed and mapped using statistical techniques to identify significant relationships between priorities, reasons for those priorities and data needs. Fifteen high priority locations were broadly identified for future mapping, sampling and visual surveys. These locations include: (1) a coastal location in northwest Puerto Rico (Punta Jacinto to Punta Agujereada), (2) a location approximately 11 km off Punta Agujereada, (3) coastal Rincon, (4) San Juan, (5) Punta Arenas (west of Vieques Island), (6) southwest Vieques, (7) Grappler Seamount, (8) southern Virgin Passage, (9) north St. Thomas, (10) east St. Thomas, (11) south St. John, (12) west offshore St. Croix, (13) west nearshore St. Croix, (14) east nearshore St. Croix, and (15) east offshore St. Croix. Participants consistently selected (1) Biota/Important Natural Area, (2) Commercial Fishing and (3) Coastal/Marine Hazards as their top reasons (i.e., justifications) for prioritizing locations, and (1) Benthic Habitat Map and (2) Sub-bottom Profiles as their top data or product needs. This ESRI shapefile summarizes the results from this spatial prioritization effort. This information will enable US Caribbean organization to more efficiently leverage resources and coordinate their mapping of high priority locations in the region.

This effort was funded by NOAA’s NCCOS and supported by CRCP. The overall goal of the project was to systematically gather and quantify suggestions for seafloor mapping, sampling and visual surveys in the US Caribbean territories of Puerto Rico and the US Virgin Islands. The results are will help organizations in the US Caribbean identify locations where their interests overlap with other organizations, to coordinate their data needs and to leverage collective resources to meet shared goals.

There were four main steps in the US Caribbean spatial prioritization process. The first step was to identify the technical advisory team, which included the 4 CRCP members: 2 from each the Puerto Rico and USVI regions. This advisory team recommended 33 organizations to participate in the prioritization. Each organization was then requested to designate a single representative, or respondent, who would have access to the web tool. The respondent would be responsible for communicating with their team about their needs and inputting their collective priorities. Step two was to develop the spatial framework and an online application. To do this, the US Caribbean was divided into 4 sub regions: nearshore and offshore for both Puerto Rico and USVI. The total inshore regions had 2,387 square grid cells approximately 2.5x2.5 km in size. The total offshore regions consisted of 438 square grid cells 10x10 km in size. Existing relevant spatial datasets (e.g., bathymetry, protected area boundaries, etc.) were compiled to help participants understand information and data gaps and to identify areas they wanted to prioritize for future data collections. These spatial datasets were housed in the online application, which was developed using Esri’s Web AppBuilder. In step three, this online application was used by 15 participants to enter their priorities in each subregion of interest. Respondents allocated virtual coins in the grid cells to denote their priorities for each region. Respondents were given access to all four regions, despite which territory they represented, but were not required to provide input into each region. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important and what data types were needed. Coin values were standardized across the nearshore and offshore zones and used to identify spatial patterns across the US Caribbean region as a whole. The number of coins were standardized because each subregion had a different number of grid cells and participants. Standardized coin values were analyzed and mapped using statistical techniques, including hierarchical cluster analysis, to identify significant relationships between priorities, reasons for those priorities and data needs. This ESRI shapefile contains the 2.5x2.5 km and 10x10 km grid cells used in this prioritization effort and associated the standardized coin values overall, as well as by organization, justification and product. For a complete description of the process and analysis please see: Kraus et al. 2020.

These are the main layers that were used in the mapping and analysis for the Santa Monica Mountains Local Coastal Plan, which was adopted by the Board of Supervisors on August 26, 2014, and certified by the California Coastal Commission on October 10, 2014. Below are some links to important documents and web mapping applications, as well as a link to the actual GIS data:

Plan Website – This has links to the actual plan, maps, and a link to our online web mapping application known as SMMLCP-NET. Click here for website. Online Web Mapping Application – This is the online web mapping application that shows all the layers associated with the plan. These are the same layers that are available for download below. Click here for the web mapping application. GIS Layers – This is a link to the GIS layers in the form of an ArcGIS Map Package, click here (LINK TO FOLLOW SOON) for ArcGIS Map Package (version 10.3). Also, included are layers in shapefile format. Those are included below.

Below is a list of the GIS Layers provided (shapefile format):

Recreation (Zipped - 5 MB - click here)

Coastal Zone Campground Trails (2012 National Park Service) Backbone Trail Class III Bike Route – Existing Class III Bike Route – Proposed

Scenic Resources (Zipped - 3 MB - click here)

Significant Ridgeline State-Designated Scenic Highway State-Designated Scenic Highway 200-foot buffer Scenic Route Scenic Route 200-foot buffer Scenic Element

Biological Resources (Zipped - 45 MB - click here)

National Hydrography Dataset – Streams H2 Habitat (High Scrutiny) H1 Habitat H1 Habitat 100-foot buffer H1 Habitat Quiet Zone H2 Habitat H3 Habitat

Hazards (Zipped - 8 MB - click here)

FEMA Flood Zone (100-year flood plain) Liquefaction Zone (Earthquake-Induced Liquefaction Potential) Landslide Area (Earthquake-Induced Landslide Potential) Fire Hazard and Responsibility Area

Zoning and Land Use (Zipped - 13 MB - click here)

Malibu LCP – LUP (1986) Malibu LCP – Zoning (1986) Land Use Policy Zoning

Other Layers (Zipped - 38 MB - click here)

Coastal Commission Appeal Jurisdiction Community Names Santa Monica Mountains (SMM) Coastal Zone Boundary Pepperdine University Long Range Development Plan (LRDP) Rural Village

Contact the L.A. County Dept. of Regional Planning's GIS Section if you have questions. Send to our email.

The Multi-temporal landslide inventory for southern Sikkim State, India, is based on two data-sources (mapped extent given in Shapefile A): Google Earth images (Shapefiles B–C) and stereoscopic Cartosat-1 satellite images (Shapefiles D–F). The landslide inventories were collected for the purpose of mapping landslide domains (regions with similar physical and environmental characteristics that specifically drive landslide style) and the data was used to give a general idea of landslides occurring in the region rather than a detailed overview. The landslide inventories are given as shapefiles with two sources of data described separately, after which a summary of all shapefiles is given.

Google Earth landslides are mapped using images from 2002 to 2019 with a mapped extent of approximately 3000 km² and was ground-truthed during a 12-day field visit from 23 February to 6 March 2019. The resultant landslide inventory contains 440 landslides with three main landslide types identified: translational slides, debris flows, and rockfalls. Translational slides include debris slides, rock slides, and unclassified translational slides. In the landslide inventory, debris flows and rockfalls are mapped as points representing their source area and translational slides are mapped as polygons representing both the source and depositional area. A complete description of the landslide types and mapping is given in Heijenk (2022, Chapter 3, section 3.4.2) The final landslide inventory (refer to how they would access it here, so a reference, or shapefile) includes the following:

• Year, the year of the first image that the landslide appears in is taken,
• Geology, the geological unit that the landslide occurs in is taken from Mottram et al. (2004),
• Area, for translational slides the area is given,
• Topographic data (elevation, aspect, slope, and curvature), which is taken from ASTER GDEM (Version 3.0, 2018, 30 m horizontal resolution, 30 m vertical resolution).

The Cartosat landslide inventory contains 44 features mapped from one pair of stereoscopic Cartosat-1 images (National Remote Sensing Centre, Cartosat-1 ID 197823411, https://www.nrsc.gov.in/, 2.5 m x 2.5 m) captured on 30 September 2011 with extents of 851 km² and 957 km². Three main landslide types have been mapped: deep-seated landslides, multi-temporal landslide areas, and rockfall areas. For deep-seated landslides, the scarp is mapped separately from the depositional area. A complete description of the methodology is given in Heijenk (2022, Chapter 3, section 3.4.3).

The following shapefiles are included in this dataset:

1. Google_Earth_mapped_extent_21Dec2021.shp: Shapefile with a polygon that denotes the mapped extent of southern Sikkim State.
2. Google_Earth_landslides_polygon_21Dec2021.shp: Shapefile with 255 polygon features, where the polygon includes the source and depositional area of translational landslides.
3. Google_Earth_landslides_point_21Dec2021.shp: Shapefile with 185 point features that denote the source area of both debris flows and rockfalls.
4. Cartosat_197823411_extents.shp: Shapefile with 2 polygon features that denote the extent of the Cartosat-1 image pair captured on 30 September 2011.
5. Cartosat_landslides_21Dec2021.shp: Shapefile with 67 polygon features that describe 44 landslide features. Some landslide features have been mapped with separate polygons for the scarp and the depositional area.
6. Cartosat_197823411clouds.shp: Shapefile with 5 polygon features that show an estimated area of the clouds that block landslide mapping in the 30 September 2011 Cartosat-1 image pair.

All shapefiles are in an WGS 84 EPSG:3857 projection.

This research was funded by the UK Natural Environment Research Council (NERC, Grant # NE/R012148/1) and the British Geological Survey (BGS, BUFI).

References:

Heijenk, R.A. (2022). Landslide Variables, Inventories, and Domains in Data-Poor Regions: A Case Study in East Sikkim, India. [PhD thesis]. King’s College London.

The Digital Geomorphic-GIS Map of Perdido Key and Santa Rosa Island (1-foot resolution 2006-2007 mapping), Florida is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (pksr_geomorphology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (pksr_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (pksr_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (guis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (guis_geomorphology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (pksr_geomorphology_metadata_faq.pdf). Please read the guis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. 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). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). 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 (pksr_geomorphology_metadata.txt or pksr_geomorphology_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 1:20,000 and United States National Map Accuracy Standards features are within (horizontally) 10.2 meters or 33.3 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, QGIS 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: https://www.nps.gov/articles/gri-geodatabase-model.htm).

This data represents a seamless bedrock geological dataset encompassing Republic of Ireland and parts of Northern Ireland. The seamless geological dataset was created from the GSI Bedrock 1:100,000 scale digital geological map series.

The dataset comprises 6 key shape files.

1) Bed100k.shp - A polygon shapefile that contains bedrock geological information on Stratigraphy, Igneous, Lithology and Diagentic codes, their unitnames and brief descriptions.

Fields: AREA: Area of the polygon in square metres. Field type: Double. PERIMETER: Perimeter of polygons in metres: Field type: Double. NEWCODE: unique identifier for every formation or member. Field type: String. SHEETNO: 100k sheets from which they originated before creating the seamless version. Field type: Double. STRATCODE: Stratigraphic code for unit, as labeled on printed maps and their legends. Field type: String. LITHCODE: Lithological code. Field type: String. DESCRIPT: Brief description of the dominant rock types. Field type: String. C,M,Y,K: cyan, magenta, yellow and black percentage. Field types: Double. UNITNAME: Name of the geological formation or member. Field Type: String.

2) Index100k.shp - An overlay polygon index of 1:100,000 scale printed map sheets.

Fields: SHEETNO: 1:100,000 printed sheet number. Field type: Double.

3) Struc100k.shp (Structural Linework) - A linework shapefile that contains structural geological linework codes and descriptions

Fields: LENGTH: Length of the feature in metres: Field type: Float. CODE: Unique identifier for structure type. Field type: String. SHEET: The 1:100,000 printed map sheet number on which the structure was originally located. Field type: Double. FOLDNAME: Name field for regionally important folds. Field type: String. FAULTNAME: Name field for regionally important faults. Field type: String. ADDITION: Additional information field for structure. Field type: String. DESCRIPT: Description of structure type. Field type: String. SLIDE: Name field for regionally important slides. Field type: String.

4) Strat100k.shp (Stratigraphic Linework) - A linework shapefile that contains stratigraphic geological line codes and descriptions.

Fields: LENGTH: Length of the feature in metres: Field type: Float. CODE: Unique identifier for stratigraphic line type. Field type: String. SHEET: The 1:100,000 printed map sheet number on which the stratigraphic line was originally located. Field type: Double. DESCRIPT: Description of stratigraphic line type. Field type: String. ADDITION: Additional information field for stratigraphic line. Field type: String. DYKELABEL: Igneous dyke code identifier. Field type: String. STRATCODE: Stratigraphic code for narrow formations or members which are represented by a line rather than a polygon in Bed100k. Field type: String. LITHCODE: Lithological code for narrow formations or members which are represented by a line rather than a polygon in Bed100k. Field type: String.

5) Sect100k.shp (Crosss section) - A linework shapefile indicating the locations of map sheet cross sections as per paper printed maps.

Fields: LENGTH: Length of the feature in metres. Field type: Double. XSECTNAME: The name of the cross section, as determined by the letters indicating the starting point, intermediate turning points and end point on the printed map sheets and as on the marginalia diagrams. Field type: String. SHEETNO: The 1:100,000 map sheet on whose marginalia the cross-section diagram is published. Field type: Double.

6) Mins100k.shp - A point shapefile contains mineral and quarry descriptions from Bedrock 1:100,000 map series. This is a subset of the MINLOCS database held by Minerals Section in the GSI.

Fields: CODE: GSI Minerals Section code for the type of deposit. Field type: String. MINTEXT: Short code indicating dominant mineral type(s). Field type: String. SHEET: 1:100,000 printed map sheet on which the deposit occurs. Field type: Long. LOCNUM: MINLOCS database unique identifier for deposit. Field type: Double. DESCRIPTION: Descriptive comment on the type of mine or quarry. Field type: String. MINLEGEND: Descriptive text, based on the MINTEXT field, lisitng the dominant mineral type(s). Field type: String.

The original printed map series and seamless dataset is based on the © Ordnance Survey of Ireland topological maps at 1/2 inch to one mile which were converted photographically to the metric 1:100,000 scale by the Geological Survey of Ireland Cartographic Unit. The topological base maps are not provided in the data set.

This dataset contains coastal features within and adjacent to the Great Barrier Reef World Heritage area. This dataset consists of two shapefiles GBR_FEATURES.shp and GBR_DRY_REEF.shp. The …Show full descriptionThis dataset contains coastal features within and adjacent to the Great Barrier Reef World Heritage area. This dataset consists of two shapefiles GBR_FEATURES.shp and GBR_DRY_REEF.shp. The GBR_FEATURES shapefile contains the following features: Queensland mainland coastline, Major and other coral reef structures (as defined by the reef shoal edge), Islands and rocks (exposed and submerged), Major coral cay features. The GBR_DRY_REEFS contains major coral reef structures (as defined by the reef shoal edge) that are tidal, drying or emergent reef areas. The reef features extend north above the GBRWHA to just within the Torres Strait region. The features have not been statistically tested with precision survey techniques. Positional accuracy varies considerably and dataset should NOT be used for navigation purposes, however, for general use the coverage can be regarded as having a nominal scale of 1:250,000. Inshore areas are significantly worse in positional accuracy than offshore areas. This dataset corresponds to the zoning within the Great Barrier Reef Marine Park effective 1st July 2004. It is derived from the Great Barrier Reef Marine Park Zoning Plan 2003. The Great Barrier Reef Marine Park is a multiple-use area. Zoning helps to manage and protect the values of the Marine Park that users enjoy. Zoning Plans define what activities occur in which locations both to protect the marine environment and to separate potentially conflicting activities. Revised zoning of the Great Barrier Reef Marine Park was introduced in July 2004 as part of the Great Barrier Reef Marine Park Authority's Representative Areas Programme. Between 1999 and 2004, the Great Barrier Reef Marine Park Authority undertook a systematic planning and consultative program to develop new zoning for the Marine Park. The primary aim of the program was to better protect the range of biodiversity in the Great Barrier Reef, by increasing the extent of no-take areas (or highly protected areas, locally known as ‘Green Zones’), ensuring they included 'representative' examples of all different habitat types - hence the name, the Representative Areas Program or RAP. Whilst increasing the protection of biodiversity, a further aim was to maximise the benefits and minimise the negative impacts of the rezoning on the existing users of the Marine Park. Both these aims were achieved by a comprehensive program of scientific input, community involvement and innovation [1]. In each zones there are a range of activities that are allowed, disallowed or require a permit. The following outlines a summary of activities that are disallowed in each zone. Please refer to [2] for a more detailed and authoritative description of all restrictions within each zone: General Use Zone: General use, some activities require a permit. Habitat Protection Zone: No trawling, some activities require permits. Conservation Park Zone: No trawling, limited crabbing and line fishing. Buffer Zone: No aquaculture, bait netting, crabbing, harvesting fishing, collecting, spearfishing, line fishing, netting and trawling. Trolling for pelagic fish is allowed. Scientific Research Zone: Research areas primarily around scientific research facilities. Same as Buffer zone but with no trolling. Marine National Park Zone (Green): 'no-take' area. The following are allowed: boating, diving, photography and limited impact research. Some other activities are allowed with permits. Preservation Zone (Pink): 'no go' area. No activities are allowed except research activities with a permit. Official maps derived from this dataset can be downloaded from the GBRMPA Zoning Maps [3] page. This dataset can now be downloaded directly from GBRMPA's Geohub. Note: This metadata record was created for the eAtlas and is not authoritative. Please contact GBRMPA for more information. GBR_FEATURES.shp: Polygon Vector Shape file (5376 features) GBR_ID: A number that is made up of a two-digit number representing the latitude band the feature is in (LAT_ID) and a three or four-digit number representing the sequential number of a particular feature complex (GROUP_ID), e.g. an island with an adjacent reef/cay/rock etc should have the same GROUP_ID (19-051) SORT_GBR_I: A whole number for sorting (19051) GBR_NAME: Great Barrier Reef MP Name (Five Trees Cay (No 1)) QLD_NAME: Queensland Government Previous Name (Five Trees Cay (No 1)) FEATURE_C: a three-digit number representing the type of feature, e,g mainland, island, cay etc (102) FEAT_NAME: Name of feature (Cay, Island, Mainland, Reef, Rock, Sand) SUB_NO: Multiple Feature Identification Number: a two-digit number that is linked to the SUB_ID of a feature. The SUB_ID is a letter that identifies multiple features of the same type in a group, e.g. multiple reefs surrounding an island are labelled a, b, c, d etc - these will be numbered 101, 102, 103, 104 etc. If the SUB_ID is "s", it refers to a single feature and will be given the number 100 (101) LABEL_ID: GBR_ID plus SUB_ID if not an S (S is a singular feature and does not need a sub-id label) (19-051a) X_COORD: Centroid Longitude (DD) in GDA94 decimal degrees (150.221849) Y_COORD: Centroid Latitude (DD) in GDA94 decimal degrees (-22.227123) CODE: GBR_ID plus SUB_NO plus FEATURE_CODE with hyphens (19-051-101-102) UNIQUE_ID: GBR_ID plus SUB_NO plus FEATURE_CODE with no hyphens. This is to be used as the unique ID for the oracle database (19051102101) GBR_DRY_REEFS.shp: This shows areas of the reef that dry or the tops of the reefs. Polygon Vector Shape file (2318 features) Errata: The following errors were determined from an analysis of this dataset undertaken by Eric Lawrey in April 2025 by comparison with Sentinel 2 composite imagery (Hammerton and Lawrey, 2024). Features with duplicate CODEs: 10-458-104-103, 12-140-100-106, 18-014-100-102, 16-028-102-102, 14-003-100-104, 20-033-100-104, 20-041-101-102, 20-227-100-104, 99-000-100-100, 23-059-100-102 Most of these duplicates correspond to what should be a multi-part polygon being split into multiple single parts with the same attributes. This can lead to the a false count in the number of reefs. This analysis found over 120 false positive reef features, 58 reefs that are actually sand banks, and 36 reefs that are actually rocky reefs rather than coral reefs. There is also at least 360 missing reefs from the mapping, predominantly in the southern GBR. It should be noted that this analysis was not comprehensive Reference: Hammerton, M., & Lawrey, E. (2024). North Australia Sentinel 2 Satellite Composite Imagery - 15th percentile true colour (NESP MaC 3.17, AIMS) (2nd Ed.) [Data set]. eAtlas. https://doi.org/10.26274/HD2Z-KM55

These are the main layers that were used in the mapping and analysis for the Santa Monica Mountains Local Coastal Plan, which was adopted by the Board of Supervisors on August 26, 2014, and certified by the California Coastal Commission on October 10, 2014. Below are some links to important documents and web mapping applications, as well as a link to the actual GIS data:

Plan Website – This has links to the actual plan, maps, and a link to our online web mapping application known as SMMLCP-NET. Click here for website. Online Web Mapping Application – This is the online web mapping application that shows all the layers associated with the plan. These are the same layers that are available for download below. Click here for the web mapping application. GIS Layers – This is a link to the GIS layers in the form of an ArcGIS Map Package, click here (LINK TO FOLLOW SOON) for ArcGIS Map Package (version 10.3). Also, included are layers in shapefile format. Those are included below.

Below is a list of the GIS Layers provided (shapefile format):

Recreation (Zipped - 5 MB - click here)

Coastal Zone Campground Trails (2012 National Park Service) Backbone Trail Class III Bike Route – Existing Class III Bike Route – Proposed

Scenic Resources (Zipped - 3 MB - click here)

Significant Ridgeline State-Designated Scenic Highway State-Designated Scenic Highway 200-foot buffer Scenic Route Scenic Route 200-foot buffer Scenic Element

Biological Resources (Zipped - 45 MB - click here)

National Hydrography Dataset – Streams H2 Habitat (High Scrutiny) H1 Habitat H1 Habitat 100-foot buffer H1 Habitat Quiet Zone H2 Habitat H3 Habitat

Hazards (Zipped - 8 MB - click here)

FEMA Flood Zone (100-year flood plain) Liquefaction Zone (Earthquake-Induced Liquefaction Potential) Landslide Area (Earthquake-Induced Landslide Potential) Fire Hazard and Responsibility Area

Zoning and Land Use (Zipped - 13 MB - click here)

Malibu LCP – LUP (1986) Malibu LCP – Zoning (1986) Land Use Policy Zoning

Other Layers (Zipped - 38 MB - click here)

Coastal Commission Appeal Jurisdiction Community Names Santa Monica Mountains (SMM) Coastal Zone Boundary Pepperdine University Long Range Development Plan (LRDP) Rural Village

Contact the L.A. County Dept. of Regional Planning's GIS Section if you have questions. Send to our email.

This dataset includes a series of modified UK administrative boundary shapefiles based on the 2011 census which are intended for use in more accurate visualisation of UK geospatial data analysis. There are two key features of these shapefiles: (1) administrative shapes have been clipped to the Ordnance Survey buildings shapefile, so that in choropleth visualisations relating to demographic data filled spaces represent populated areas of the UK rather than large undifferentiated blocks. (2) Shapefiles have been simplified to reduce loading and processing time, in the case of this repository at 100m. After testing, we have settled on a procedure to render buildings layer visually comprehensible at high zoom levels, by adding a small buffer, dissolving (so that individual overlapping shapes combine into a single more easily visualised shape) and then simplifying at 150m. It is important to emphasise that because of the use of simplification (using a Ramer–Douglas–Peucker algorithm), these shapefiles are not suitable for analysis as boundaries may not be suitably precise or accurate. For users interested in the process used to generate these files you can consult the codebase deposited on github.

Many thanks to colleagues including Alasdair Rae for recommendations on technique used here. Computations were performed using the University of Birmingham's BEAR Cloud service, which provides flexible resource for intensive computational work to the University's research community. See http://www.birmingham.ac.uk/bear for more details. Given the massive size of datasets involved (including the district buildings vector shapefile which is 1.4gb and consists of hundreds of thousands of individual shapes), this work would have been impossible without this invaluable resource. I hope that these files will be of use to colleagues who may not have access to similar large computational arrays and make the process of visualising UK boundary and census data more accurate and efficient.

Original files are under OGLv3 licenses. Derived data files, where possible are licensed for use under CC BY 4.0.

Files include the following:

Original unmodified data:

• infuse_ctry_2011.zip - original country level shapes, based on 2011 census, downloaded from https://borders.ukdataservice.ac.uk/ukborders/easy_download
• infuse_dist_lyr_2011.zip - original local authority shapes, based on 2011 census, downloaded from https://borders.ukdataservice.ac.uk/ukborders/easy_download
• TermsAndConditions.html - UK Data Service license details (OGLv3), applies to all the above
• GB_Postcodes.zip - UK postcode district shapes, prepared by Addy Pope, https://datashare.ed.ac.uk/handle/10283/2597

Derived data files:

• OS_Open_Zoomstack_district_buildings.zip - buildings layer extracted from Ordnance Survey Zoomstack package, licensed under OGLv3 and exported to gpkg format.
• *_simplified_100m.gpkg - Administrative shapes from above, simplified in R at a resolution of 100 metres.
• *_simplified_100m_buildings_overlay_simplified.gpkg - Administrative shapes from above, simplified in R at a resolution of 100 metres, and then clipped to the buildings layer.
• *_simplified_100m_buildings_overlay_simplified.gpkg - Administrative shapes from above, simplified in R at a resolution of 100 metres, and then run against the buildings layer as a difference layer. Suitable for using as an overlay as the shapes are inverse.

Users who wish to use these shapefiles in a reproducible research context may want to download individual files directly from this repository. To do so, you could use the following R code:

# load packages
require(sf) # load simplefeature data class, supercedes sp() and used for st_read
# given the size and complexity even of simplified files here, ragg is highly recommended 
# for users on macos given inefficiencies in default R graphics device
require(ragg)

# create paths as needed
if (dir.exists("data") == FALSE) {
 dir.create("data")
}

# download data files only if they aren't already present
if (file.exists("data/infuse_dist_lyr_2011.shp") == FALSE) {
 download.file("https://borders.ukdataservice.ac.uk/ukborders/easy_download/prebuilt/shape/infuse_dist_lyr_2011.zip", destfile = "data/infuse_dist_lyr_2011.zip")
 unzip("infuse_dist_lyr_2011.zip", exdir = "data")}
local_authorities <- st_read("data/infuse_dist_lyr_2011.shp")

The USGS National Hydrography Dataset (NHD) downloadable data collection from The National Map (TNM) is a comprehensive set of digital spatial data that encodes information about naturally occurring and constructed bodies of surface water (lakes, ponds, and reservoirs), paths through which water flows (canals, ditches, streams, and rivers), and related entities such as point features (springs, wells, stream gages, and dams). The information encoded about these features includes classification and other characteristics, delineation, geographic name, position and related measures, a "reach code" through which other information can be related to the NHD, and the direction of water flow. The network of reach codes delineating water and transported material flow allows users to trace movement in upstream and downstream directions. In addition to this geographic information, the dataset contains metadata that supports the exchange of future updates and improvements to the data. The NHD supports many applications, such as making maps, geocoding observations, flow modeling, data maintenance, and stewardship. For additional information on NHD, go to https://www.usgs.gov/core-science-systems/ngp/national-hydrography.

DWR was the steward for NHD and Watershed Boundary Dataset (WBD) in California. We worked with other organizations to edit and improve NHD and WBD, using the business rules for California. California's NHD improvements were sent to USGS for incorporation into the national database. The most up-to-date products are accessible from the USGS website. Please note that the California portion of the National Hydrography Dataset is appropriate for use at the 1:24,000 scale.

For additional derivative products and resources, including the major features in geopackage format, please go to this page: https://data.cnra.ca.gov/dataset/nhd-major-features Archives of previous statewide extracts of the NHD going back to 2018 may be found at https://data.cnra.ca.gov/dataset/nhd-archive.

In September 2022, USGS officially notified DWR that the NHD would become static as USGS resources will be devoted to the transition to the new 3D Hydrography Program (3DHP). 3DHP will consist of LiDAR-derived hydrography at a higher resolution than NHD. Upon completion, 3DHP data will be easier to maintain, based on a modern data model and architecture, and better meet the requirements of users that were documented in the Hydrography Requirements and Benefits Study (2016). The initial releases of 3DHP include NHD data cross-walked into the 3DHP data model. It will take several years for the 3DHP to be built out for California. Please refer to the resources on this page for more information.

The FINAL,STATIC version of the National Hydrography Dataset for California was published for download by USGS on December 27, 2023. This dataset can no longer be edited by the state stewards. The next generation of national hydrography data is the USGS 3D Hydrography Program (3DHP).

Questions about the California stewardship of these datasets may be directed to nhd_stewardship@water.ca.gov.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

These shapefiles are used to create the maps in NAM2.6.2. They are mostly derived from the input files for the groundwater model. The shape files infclude:

ag_extraction: These are points that represent the location of groundwater bores used for agricultural extraction.

boundaries: These are line shape files used for defining the location and extent of lateral boundary conditions of different stratigraphic layers of the groundwater model

coal extraction: These are polygon shape files providing the areal extent of the baseline and ACRD coal mines in the Namoi subregion that are including in the groundwater model.

grid: Polygon shape file representing the mesh of the groundwater model. It also include points that represent the midpoints of each model cell and the suset that represents the model nodes that outcrop.

obs: Shape file of observation bores, the data from which is used for constraining the groundwater model.

River: Set of shape files containing the AWRA catchments, AWRA-R nodes, network of rivers and creeks classified into important reaches and non important reaches based on the distance form the CRDP areas, extent of flood and irrigation recharge

Purpose

The purpose of this dataset is to create pretty pictures. The actual model inputs files are archived separately.

These shapefiles are used along with the software ALGOMESH to generate inputs for the models including model initial and boundary conditions.

Thease are also used to generate maps in the product 2.6.2

Dataset History

Some of the components of this dataset are source data. These include the locations of groundwater and observation bores, river and creek network.

Other components are derived:

The groundwater model mesh and model cell centres are generated in the ALGOMESH software and exported as shape file.

The coal mine extents are derived from digitized mine footprints.

Dataset Citation

Bioregional Assessment Programme (2016) Namoi groundwater model input shapefiles. Bioregional Assessment Derived Dataset. Viewed 11 December 2018, http://data.bioregionalassessments.gov.au/dataset/fb22671f-8b47-48e2-9fcd-232543fb8ad6.

Dataset Ancestors

• Derived From Murray-Darling Basin floodplain inundation 1 in 100 year extent

• Derived From Bioregional_Assessment_Programme_Catchment-scale Land Use Management (CLUM)

• Derived From NSW Office of Water - National Groundwater Information System 20141101v02

• Derived From Namoi groundwater observation bores

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From GEODATA TOPO 250K Series 3

• Derived From Hydstra Groundwater Measurement Update - NSW Office of Water, Nov2013

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.

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.

This dataset contains data derived from the GEOSAT satellite radar altimeter wave measuring program. Maps have been produced from processed data, showing attributes including mean significant wave height and the 100 year mean significant wave.

Format: shapefile.

Quality - Scope: Dataset. Absolute External Positional Accuracy: +/- one degree. Non Quantitative accuracy: Attributes are assumed to be correct.

Dataset measures wave height in metres, at 0.25m intervals:

Cover_Name, Item_Name, Description: mswaveheight, GRID-CODE, Numercial code to index the polygons mswaveheight, MSWAVE_HGT_(M), Mean significant wave height ranging 0-4.5m.

Conceptual consistency: Coverages are topologically consistent. No particular tests conducted by ERIN. Completeness omission: Complete for the Australian continent. Lineage: ERIN: Data was projected to Geographics using the WGS84 spheroid and datum to be compatible for viewing through the Australian Coastal Atlas. The data was attributed with the range of wave height in metres, at an interval of 0.25metres.

CSIRO: All CAMRIS data were stored in VAX files, MS-DOS R-base files and as a microcomputer dataset accessible under the LUPIS (Land Use Planning Information System) land allocation package. CAMRIS was established using SPANS Geographic Information System (GIS) software running under a UNIX operating system on an IBM RS 6000 platform. A summary follows of processing completed by the CSIRO: 1. r-BASE: Information imported into r-BASE from a number of different sources (ie Digitised, scanned, CD-ROM, NOAA World Ocean Atlas, Atlas of Australian Soils, NOAA GEODAS archive and The Complete Book of Australian Weather). 2. From the information held in r-BASE a BASE Table was generated incorporating specific fields. 3. SPANS environment: Works on creating a UNIVERSE with a geographic projection - Equidistant Conic (Simple Conic) and Lambert Conformal Conic, Spheroid: International Astronomical Union 1965 (Australia/Sth America); the Lower left corner and the longitude and latitude of the centre point. 4. BASE Table imported into SPANS and a BASE Map generated. 5. Categorise Maps - created from the BASE map and table by selecting out specified fields, a desired window size (ie continental or continent and oceans) and resolution level (ie the quad tree level). 6. Rasterise maps specifying key parameters such as: number of bits, resolution (quad tree level 8 lowest - 16 highest) and the window size (usually 00 or cn). 7. Gifs produced using categorised maps with a title, legend, scale and long/lat grid. 8. Supplied to ERIN with .bil; .hdr; .gif; Arc export files .e00; and text files .asc and .txt formats. 9. The reference coastline for CAMRIS was the mean high water mark (AUSLIG 1:100 000 topographic map series).

The SSURGO database contains information about soil as collected by the National Cooperative Soil Survey over the course of a century. The information can be displayed in tables or as maps and is available for most areas in the United States and the Territories, Commonwealths, and Island Nations served by the USDA-NRCS (Natural Resources Conservation Service). The information was gathered by walking over the land and observing the soil. Many soil samples were analyzed in laboratories. The maps outline areas called map units. The map units describe soils and other components that have unique properties, interpretations, and productivity. The information was collected at scales ranging from 1:12,000 to 1:63,360. More details were gathered at a scale of 1:12,000 than at a scale of 1:63,360. The mapping is intended for natural resource planning and management by landowners, townships, and counties. Some knowledge of soils data and map scale is necessary to avoid misunderstandings. The maps are linked in the database to information about the component soils and their properties for each map unit. Each map unit may contain one to three major components and some minor components. The map units are typically named for the major components. Examples of information available from the database include available water capacity, soil reaction, electrical conductivity, and frequency of flooding; yields for cropland, woodland, rangeland, and pastureland; and limitations affecting recreational development, building site development, and other engineering uses. SSURGO datasets consist of map data, tabular data, and information about how the maps and tables were created. The extent of a SSURGO dataset is a soil survey area, which may consist of a single county, multiple counties, or parts of multiple counties. SSURGO map data can be viewed in the Web Soil Survey or downloaded in ESRI® Shapefile format. The coordinate systems are Geographic. Attribute data can be downloaded in text format that can be imported into a Microsoft® Access® database. A complete SSURGO dataset consists of:

GIS data (as ESRI® Shapefiles) attribute data (dbf files - a multitude of separate tables) database template (MS Access format - this helps with understanding the structure and linkages of the various tables) metadata

Resources in this dataset:Resource Title: SSURGO Metadata - Tables and Columns Report. File Name: SSURGO_Metadata_-_Tables_and_Columns.pdfResource Description: This report contains a complete listing of all columns in each database table. Please see SSURGO Metadata - Table Column Descriptions Report for more detailed descriptions of each column.

Find the Soil Survey Geographic (SSURGO) web site at https://www.nrcs.usda.gov/wps/portal/nrcs/detail/vt/soils/?cid=nrcs142p2_010596#Datamart Title: SSURGO Metadata - Table Column Descriptions Report. File Name: SSURGO_Metadata_-_Table_Column_Descriptions.pdfResource Description: This report contains the descriptions of all columns in each database table. Please see SSURGO Metadata - Tables and Columns Report for a complete listing of all columns in each database table.

Find the Soil Survey Geographic (SSURGO) web site at https://www.nrcs.usda.gov/wps/portal/nrcs/detail/vt/soils/?cid=nrcs142p2_010596#Datamart Title: SSURGO Data Dictionary. File Name: SSURGO 2.3.2 Data Dictionary.csvResource Description: CSV version of the data dictionary

This geodatabase reflects the U.S. Geological Survey’s (USGS) ongoing commitment to its mission of understanding the nature and distribution of global mineral commodity supply chains by updating and publishing the georeferenced locations of mineral commodity production and processing facilities, mineral exploration and development sites, and mineral commodity exporting ports in Africa. The geodatabase and geospatial data layers serve to create a new geographic information product in the form of a geospatial portable document format (PDF) map. The geodatabase contains data layers from USGS, foreign governmental, and open-source sources as follows: (1) mineral production and processing facilities, (2) mineral exploration and development sites, (3) mineral occurrence sites and deposits, (4) undiscovered mineral resource tracts for Gabon and Mauritania, (5) undiscovered mineral resource tracts for potash, platinum-group elements, and copper, (6) coal occurrence areas, (7) electric power generating facilities, (8) electric power transmission lines, (9) liquefied natural gas terminals, (10) oil and gas pipelines, (11) undiscovered, technically recoverable conventional and continuous hydrocarbon resources (by USGS geologic/petroleum province), (12) cumulative production, and recoverable conventional resources (by oil- and gas-producing nation), (13) major mineral exporting maritime ports, (14) railroads, (15) major roads, (16) major cities, (17) major lakes, (18) major river systems, (19) first-level administrative division (ADM1) boundaries for all countries in Africa, and (20) international boundaries for all countries in Africa.

ps-places-metadata-v1.01

SUMMARY

This dataset comprises a pair of layers, (points and polys) which attempt to better locate "populated places" in NZ. Populated places are defined here as settled areas, either urban or rural where densitys of around 20 persons per hectare exist, and something is able to be seen from the air.

RATIONALE

The only liberally licensed placename dataset is currently LINZ geographic placenames, which has the following drawbacks: - coordinates are not place centers but left most label on 260 series map - the attributes are outdated

METHODOLOGY

This dataset necessarily involves cleaving the linz placenames set into two, those places that are poplulated, and those unpopulated. Work was carried out in four steps. First placenames were shortlisted according to the following criterion: - all places that rated at least POPL in the linz geographic places layer, ie POPL, METR or TOWN or USAT were adopted. - Then many additional points were added from a statnz meshblock density analysis.
- Finally remaining points were added from a check against linz residential polys, and zenbu poi clusters.

Spelling is broadly as per linz placenames, but there are differences for no particular reason. Instances of LINZ all upper case have been converted to sentance case. Some places not presently in the linz dataset are included in this set, usually new places, or those otherwise unnamed. They appear with no linz id, and are not authoritative, in some cases just wild guesses.

Density was derived from the 06 meshblock boundarys (level 2, geometry fixed), multipart conversion, merging in 06 usually resident MB population then using the formula pop/area*10000. An initial urban/rural threshold level of 0.6 persons per hectare was used.

Step two was to trace the approx extent of each populated place. The main purpose of this step was to determine the relative area of each place, and to create an intersection with meshblocks for population. Step 3 involved determining the political center of each place, broadly defined as the commercial center.

Tracing was carried out at 1:9000 for small places, and 1:18000 for large places using either bing or google satellite views. No attempt was made to relate to actual town 'boundarys'. For example large parks or raceways on the urban fringe were not generally included. Outlying industrial areas were included somewhat erratically depending on their connection to urban areas.

Step 3 involved determining the centers of each place. Points were overlaid over the following layers by way of a base reference:

a. original linz placenames b. OSM nz-locations points layer c. zenbu pois, latest set as of 5/4/11 d. zenbu AllSuburbsRegions dataset (a heavily hand modified) LINZ BDE extract derived dataset courtesy Zenbu. e. LINZ road-centerlines, sealed and highway f. LINZ residential areas, g. LINZ building-locations and building footprints h. Olivier and Co nz-urban-north and south

Therefore in practice, sources c and e, form the effective basis of the point coordinates in this dataset. Be aware that e, f and g are referenced to the LINZ topo data, while c and d are likely referenced to whatever roading dataset google possesses. As such minor discrepencys may occur when moving from one to the other.

Regardless of the above, this place centers dataset was created using the following criteria, in order of priority:

• attempts to represent the present (2011) subjective 'center' of each place as defined by its commercial/retail center ie. mainstreets where they exist, any kind of central retail cluster, even a single shop in very small places.
• the coordinate is almost always at the junction of two or more roads.
• most of the time the coordinate is at or near the centroid of the poi cluster
• failing any significant retail presence, the coordinate tends to be placed near the main road junction to the community.
• when the above criteria fail to yield a definitive answer, the final criteria involves the centroids of: . the urban polygons . the clusters of building footprints/locations.

To be clear the coordinates are manually produced by eye without any kind of computation. As such the points are placed approximately perhaps plus or minus 10m, but given that the roads layers are not that flash, no attempt was made to actually snap the coordinates to the road junctions themselves.

The final step involved merging in population from SNZ meshblocks (merge+sum by location) of popl polys). Be aware that due to the inconsistent way that meshblocks are defined this will result in inaccurate populations, particular small places will collect population from their surrounding area. In any case the population will generally always overestimate by including meshblocks that just nicked the place poly. Also there are a couple of dozen cases of overlapping meshblocks between two place polys and these will double count. Which i have so far made no attempt to fix.

Merged in also tla and regions from SNZ shapes, a few of the original linz atrributes, and lastly grading the size of urban areas according to SNZ 'urban areas" criteria. Ie: class codes:

1. Not used.
2. main urban area 30K+
3. secondary urban area 10k-30K
4. minor urban area 1k-10k
5. rural center 300-1K
6. village -300

Note that while this terminology is shared with SNZ the actual places differ owing to different decisions being made about where one area ends an another starts, and what constiutes a suburb or satellite. I expect some discussion around this issue. For example i have included tinwald and washdyke as part of ashburton and timaru, but not richmond or waikawa as part of nelson and picton. Im open to discussion on these.

No attempt has or will likely ever be made to locate the entire LOC and SBRB data subsets. We will just have to wait for NZFS to release what is thought to be an authoritative set.

PROJECTION

Shapefiles are all nztm. Orig data from SNZ and LINZ was all sourced in nztm, via koordinates, or SNZ. Satellite tracings were in spherical mercator/wgs84 and converted to nztm by Qgis. Zenbu POIS were also similarly converted.

ATTRIBUTES

Shapefile: Points id : integer unique to dataset name : name of popl place, string class : urban area size as above. integer tcode : SNZ tla code, integer rcode : SNZ region code, 1-16, integer area : area of poly place features, integer in square meters. pop : 2006 usually resident popluation, being the sum of meshblocks that intersect the place poly features. Integer lid : linz geog places id desc_code : linz geog places place type code

Shapefile: Polygons gid : integer unique to dataset, shared by points and polys name : name of popl place, string, where spelling conflicts occur points wins area : place poly area, m2 Integer

LICENSE

Clarification about the minorly derived nature of LINZ and google data needs to be sought. But pending these copyright complications, the actual points data is essentially an original work, released as public domain. I retain no copyright, nor any responsibility for data accuracy, either as is, or regardless of any changes that are subsequently made to it.

Peter Scott 16/6/2011

v1.01 minor spelling and grammar edits 17/6/11

The Advanced Weather Interactive Processing System (AWIPS) uses shapefiles for base maps in the system. These shapefiles contain boundaries of areas used by NWS for forecasts and warnings as well as map backgrounds.NWS BordersThe County Warning Area boundaries are the counties/zones for which each Weather Forecast Office (WFO) is responsible for issuing forecasts and warnings. The shapefile was created by aggregating public zones with the same CWA designation into a single polygon and manually adjusting the boundaries of the exceptions to the rule.The NWS county and state borders are background map used internally in NWS.Coastal Marine Zone ForecastThis map layer contains links to NWS marine weather forecasts for coastal or nearshore waters within 20nm of shore out to Day 5. It includes predictions on the likelihood of precipitation and/or reduced visibility, surface wind direction and speed, seas or combined seas, and icing. Air temperature forecasts are optional. The forecasts will also include any marine weather advisories, watches, and/or warnings. The purpose of the forecasts is to support and promote safe transportation across the coastal waters. The forecasts are issued twice per day with updates as necessary by NWS Weather Forecast Offices (WFOs) along the coast and Great Lakes.Offshore Zone ForecastsThis map layer contains links to NWS marine weather forecasts for offshore waters beyond 20 or 30nm of shore out to Day 5. The forecast provides information to mariners who travel on the oceanic waters adjacent to the U.S., its territorial coastal waters and the Caribbean Sea. The forecasts include predictions on the likelihood of precipitation and/or reduced visibility, surface wind direction and speed, seas and likelihood of icing out to Day 5 along with information about any warnings. The offshore forecasts for the Western North Atlantic and Eastern North Pacific Oceans are produced by NWS/NCEP's Ocean Prediction Center. The offshore forecasts for the Gulf of Mexico and Caribbean Sea are issued by the NWS/NCEP National Hurricane Center's Tropical Analysis and Forecast Branch (TAFB). OPC and NHC/TAFB issues the forecasts four times daily at regular intervals, with updates when necessary. The offshore forecast for the waters around Hawaii are issued by the NWS Weather Forecast Office in Honolulu, HI four times daily at regular intervals, with updates when necessary. The offshore forecasts for Alaska waters in the Bering Sea and Gulf of Alaska are issued by NWS Weather Forecast Offices in Alaska at least twice a day with updates as necessary. The WFOs in Alaska include WFO Anchorage, WFO Fairbanks, and WFO Juneau.Public Weather Zone ForecastsThis layer includes links to NWS web pages posting the latest NWS surface weather forecasts, a zone-type forecast providing the average forecast conditions across the zone, usually at the county-scale or sub-county scale. These text forecasts include predictions of weather, sky cover, maximum and minimum surface air temperatures, surface wind direction and speed, and probability of precipitation out to 7 days into the future. In addition, the forecast highlights at the top include any active weather advisories, watches, and/or warnings. These zone predictions are derived from gridded forecasts created by NWS Weather Forecast Offices throughout the U.S. The text weather forecasts are usually issued in the early morning (e.g. 4AM LT) and early evening (4PM LT). They are updated during late mornings and late night and during fast changing weather conditions.Fire Weather Zone ForecastsThis layer includes links to NWS web pages posting the latest NWS Fire Weather Planning Forecasts, a zone-type forecast providing the average fire weather conditions across the zone. According to the NWS, the forecast is "used by land management personnel primarily for input in decision-making related to pre-suppression and other planning." The forecast is valid from the time of issuance through day five and sometimes through day seven and usually has a minimum of three 12-hour time periods. The forecast will have included a discussion of weather patterns affecting the forecast zone or area, identification of any active fire weather watches/warnings and a table of predicted fire weather variables for the next two days: 1) sky/weather conditions, 2) max/min air temperatures, 3) max/min relative humidity, 4) 0-minute average wind direction/speed at 20 feet and sometimes at another height (e.g. 10,000, 15,000 ft), 5) precipitation amount, duration, and timing, 6) mixing height, 7) transport winds, 8) vent category, and 9) several fire weather indices such as Haines Index, Lightning Activity (LAL), Chance of Wetting Rainfall (CWR), Dispersion Index, Low Visibility Occurrence Risk Index (LVORI), and Max LVORI. In addition, it will usually have a forecast in plain text for days 3 to 7. Sometimes an optional outlook of expected conditions for day 6 or possibly for day 6 and 7 is expected. The forecasts are issued by NWS WFOs at least once daily during the local fire season.Metadata:CWA: https://www.weather.gov/gis/CWAmetadataCoastal Marine: https://www.weather.gov/gis/CoastalMarineMetadataOffshore: https://www.weather.gov/gis/OffshoreZoneMetadataPublic Zones: https://www.weather.gov/gis/PublicZoneMetadataFire Zones: https://www.weather.gov/gis/FireZoneMetadataCounties: https://www.weather.gov/gis/CountyMetadataStates: https://www.weather.gov/gis/StateMetadataLink to data download: https://www.weather.gov/gis/AWIPSShapefilesQuestions/Concerns about the service, please contact the DISS GIS teamTime Information:This service is not time enabled