This is an exercise on the use of Postal Code Conversion Files (PCCF) with GIS. (Note: Data associated with this exercise is available on the DLI FTP site under folder 1873-299.)

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The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. In order to begin the data conversion process, a hardcopy version of the base was needed. The designated base was the USGS digital orthophoto quarter quads (DOQQ) series. The 41 DOQQ files for Isle Royale were standard black and white USGS products made from 1992 1:40,000 scale NAPP photography. Creation of the DOQQ base plots required having the images plotted onto clear mylar at the same scale as the aerial photographs, approximately 1:15,840. To minimize the number of map sheets, two DOQQ images were plotted together so that each plot represented one half of a USGS topographic quadrangle map. A total of 22 mylar base plots were generated using a Hewlett Packard 755CM plotter

The whooping crane (Grus americana) is a bird species in North America currently protected under federal endangered species legislation in the United States and Canada. The only self-sustaining and wild population of whooping cranes nests in and around Wood Buffalo National Park near the provincial border of Northwest Territories and Alberta, Canada. Cranes from this population migrate through the Great Plains of North America and winter along the Gulf Coast of Texas at Aransas National Wildlife Refuge and surrounding lands. These data support efforts to delineate a migration corridor for this population that can be used for conservation planning activities, including targeting conservation, mitigation, and recovery actions and assessing threats.

Abstract

This dataset was derived by the Bioregional Assessment Programme. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

This dataset converts the original Digital Atlas of Australian Soils (GUID: 9e7d2f5b-ff51-4f0f-898a-a55be8837828) shapefile into the Australian soil classification, as per data from Conversion of the Atlas of Australian Soils to the Australian Soil Classification (GUID: 295707d5-2774-4ca5-a539-6c0426bbd662). A Layer file is also supplied using the RGB colour reference table, also found in the Conversion of the Atlas of Australian Soils to the Australian Soil Classification dataset.

Purpose

Provides a spatial and cartographic representation of the Digital Atlas of Australian Soils shapefile into the new Australian soil classification.

Dataset History

From the Conversion of the Atlas of Australian Soils to the Australian Soil Classification dataset (GUID: 295707d5-2774-4ca5-a539-6c0426bbd662) the file asclut.txt was converted to .csv format and field headings added (MAP_UNIT, SOIL_CODE, SOIL_SYMBOL, SOIL).

This csv file (asclut.csv) was joined to the Digital Atlas of Australian Soils (GUID: 9e7d2f5b-ff51-4f0f-898a-a55be8837828), soilAtlas2M shapefile on the common 'MAP_UNIT' field. The resulting join was saved as 'soilAtlas2M_ASC_Conversion.shp'

The symbology of this shapefile was updated by matching the RGB values provided in the 'asc_colours.xls' spreadsheet from the Conversion of the Atlas of Australian Soils to the Australian Soil Classification dataset (GUID: 295707d5-2774-4ca5-a539-6c0426bbd662) to the 'SOIL' field. A Layer File was created 'soilAtlas2M_ASC_Conversion.lyr'

Dataset Citation

Bioregional Assessment Programme (2015) Spatial Data Conversion of the Atlas of Australian Soils to the Australian Soil Classification v01. Bioregional Assessment Derived Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/6f804e8b-2de9-4c88-adfa-918ec327c32f.

Dataset Ancestors

• Derived From Digital Atlas of Australian Soils

• Derived From Conversion of the Atlas of Australian Soils to the Australian Soil Classification

The zoning boundaries map layer is an integral part of the planning data in the City of Worcester Geographic Information System. This data is used by many City Departments in case review, code enforcement, and long range planning. Planning data layers are accessed by personnel in most City departments for basic applications such as viewing, querying, and map output production. More advanced user applications may focus on planning analysis, spatial analysis, presentation output, and review of proposed development. The zoning boundaries data layer is governed by ordinance and is only changed accordingly. The zoning data layer was digitized by L. Robert Kimball & Associates, Inc. as part of a data conversion project in 1995. Further updates have been made by the City of Worcester since that time to reflect ordinance changes.Informing Worcester is the City of Worcester's open data portal where interested parties can obtain public information at no cost.

The Geological Atlas of the Western Canada Sedimentary Basin was designed primarily as a reference volume documenting the subsurface geology of the Western Canada Sedimentary Basin. This GIS dataset is one of a collection of shapefiles representing part of Chapter 31 of the Atlas, Petroleum Generation and Migration in the Western Canada Sedimentary Basin, Figure 21, Heavy Oil Migration System. Shapefiles were produced from archived digital files created by the Alberta Geological Survey in the mid-1990s, and edited in 2005-06 to correct, attribute and consolidate the data into single files by feature type and by figure.

The MA Department of Environmental Protection (MassDEP) Recycling, Composting and Waste Conversion Operations point dataset contains the locations of site assignment exempt solid waste recycling, composting and waste conversion operations as defined in 310 CMR 16.04 and 310 CMR 16.05.Compost Handling Facilities subject to 310 CMR 19.00 can be found in the Solid Waste Handling Facilities service.This layer does not include farm compost sites registered with the Mass Department of Agricultural Resources (MDAR).View full metadata.Feature service also available.

Status: COMPLETED 2010. The data was converted from the most recent (2010) versions of the adopted plans, which can be found at https://cms3.tucsonaz.gov/planning/plans/Supplemental Information: In March 2010, Pima Association of Governments (PAG), in cooperation with the City of Tucson (City), initiated the Planned Land Use Data Conversion Project. This 9-month effort involved evaluating mapped land use designations and selected spatially explicit policies for nearly 50 of the City's adopted neighborhood, area, and subregional plans and converting the information into a Geographic Information System (GIS) format. Further documentation for this file can be obtained from the City of Tucson Planning and Development Services Department or Pima Association of Governments Technical Services. A brief summary report was provided, as requested, to the City of Tucson which highlights some of the key issues found during the conversion process (e.g., lack of mapping and terminology consistency among plans). The feature class "Plan_boundaries" represents the boundaries of the adopted plans. The feature class "Plan_mapped_land_use" represents the land use designations as they are mapped in the adopted plans. Some information was gathered that is implicit based on the land use designation or zones (see field descriptions below). Since this information is not explicitly stated in the plans, it should only be viewed by City staff for general planning purposes. The feature class "Plan_selected_policies" represents the spatially explicit policies that were fairly straightforward to map. Since these policies are not represented in adopted maps, this feature class should only be viewed by City staff for general planning purposes only.2010 - created by Jamison Brown, working as an independent contractor for Pima Association of Governments, created this file in 2010 by digitizing boundaries as depicted (i.e. for the mapped land use) or described in the plans (i.e. for the narrative policies). In most cases, this involved tracing based on parcel (paregion) or street center line (stnetall) feature classes. Snapping was used to provide line coincidence. For some map conversions, freehand sketches were drawn to mimick the freehand sketches in the adopted plan.Field descriptions: Field descriptions for the "Plan_boundaries" feature class: Plan_Name: Plan name Plan_Type: Plan type (e.g., Neighborhood Plan) Plan_Num: Plan number ADOPT_DATE: Date of Plan adoption IMPORTANT: A disclaimer about the data as it is unofficial. URL: Uniform Resource Locator Field descriptions for the "Plan_mapped_land_use" feature class: Plan_Name: Plan name Plan_Type: Plan type (e.g., Neighborhood Plan) Plan_Num: Plan number LU_DES: Land use designation (e.g., Low density residential) LISTED_ALLOWABLE_ZONES: Allowable zones as listed in the Plan LISTED_RAC_MIN: Minimum residences per acre (if applicable), as listed in the Plan LISTED_RAC_TARGET: Target residences per acre (if applicable), as listed in the Plan LISTED_RAC_MAX: Maximum residences per acre (if applicable), as listed in the Plan LISTED_FAR_MIN: Minimum Floor Area Ratio (if applicable), as listed in the Plan LISTED_FAR_TARGET: Target Floor Area Ratio (if applicable), as listed in the Plan LISTED_FAR_MAX: Maximum Floor Area Ratio (if applicable), as listed in the Plan BUILDING_HEIGHT_MAX Building height maximum (ft.) if determined by Plan policy IMPORTANT: A disclaimer about the data as it is unofficial. URL: Uniform Resource Locator IMPLIED_ALLOWABLE_ZONES: Implied (not listed in the Plan) allowable zones IMPLIED_RAC_MIN: Implied (not listed in the Plan) minimum residences per acre (if applicable) IMPLIED_RAC_TARGET: Implied (not listed in the Plan) target residences per acre (if applicable) IMPLIED_RAC_MAX: Implied (not listed in the Plan) maximum residences per acre (if applicable) IMPLIED_FAR_MIN: Implied (not listed in the Plan) minimum Floor Area Ratio (if applicable) IMPLIED_FAR_TARGET: Implied (not listed in the Plan) target Floor Area Ratio (if applicable) IMPLIED_FAR_MAX: Implied (not listed in the Plan) maximum Floor Area Ratio (if applicable) IMPLIED_LU_CATEGORY: Implied (not listed in the Plan) general land use category. General categories used include residential, office, commercial, industrial, and other.PurposeLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Dataset ClassificationLevel 0 - OpenKnown UsesLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Known ErrorsLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Data ContactLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Update FrequencyLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.

This data set is part of a collection of real estate data concerning current and historic U.S. Army Corps of Engineers Civil Works projects whose real property interests are managed by the U.S. Army Corps of Engineers. This data set covers projects within the United States, including Alaska and Hawaii. Source data used to produce it includes paper charts and maps, legacy data from the Real Estate Management Information System (REMIS) and legal descriptions. Methods used include coordinate geometry entry (COGO) from legal descriptions, coincident features, scanned drawings, aerial imagery and other parcel data. Details pertaining to the sources methods and accuracy of specific features can be found in the feature level metadata associated with the individual features.

VDatum is designed to vertically transform geospatial data among a variety of tidal, orthometric and ellipsoidal vertical datums - allowing users to convert their data from different horizontal/vertical references into a common system and enabling the fusion of diverse geospatial data in desired reference levels.This particular layer allows you to convert from NAVD 88 to MHHW.Units: metersThese data are a derived product of the NOAA VDatum tool and they extend the tool's Mean Higher High Water (MHHW) tidal datum conversion inland beyond its original extent.VDatum was designed to vertically transform geospatial data among a variety of tidal, orthometric and ellipsoidal vertical datums - allowing users to convert their data from different horizontal/vertical references into a common system and enabling the fusion of diverse geospatial data in desired reference levels (https://vdatum.noaa.gov/). However, VDatum's conversion extent does not completely cover tidally-influenced areas along the coast. For more information on why VDatum does not provide tidal datums inland, see https://vdatum.noaa.gov/docs/faqs.html.Because of the extent limitation and since most inundation mapping activities use a tidal datum as the reference zero (i.e., 1 meter of sea level rise on top of Mean Higher High Water), the NOAA Office for Coastal Management created this dataset for the purpose of extending the MHHW tidal datum beyond the areas covered by VDatum. The data do not replace VDatum, nor do they supersede the valid datum transformations VDatum provides. However, the data are based on VDatum's underlying transformation data and do provide an approximation of MHHW where VDatum does not provide one. In addition, the data are in a GIS-friendly format and represent MHHW in NAVD88, which is the vertical datum by which most topographic data are referenced.Data are in the UTM NAD83 projection. Horizontal resolution varies by VDatum region, but is either 50m or 100m. Data are vertically referenced to NAVD88 meters.More information about the NOAA VDatum transformation and associated tools can be found here.

Introduction

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

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

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

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

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

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

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

Data processing

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

Version

Version 2022.1.

Acknowledgements

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

Citation

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

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

Contacts

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

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

Institution: Kunming Institute of Botany, Chinese Academy of Sciences

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

Copyright

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

The Geological Atlas of the Western Canada Sedimentary Basin was designed primarily as a reference volume documenting the subsurface geology of the Western Canada Sedimentary Basin. This GIS dataset is one of a collection of shapefiles representing part of Chapter 31 of the Atlas, Petroleum Generation and Migration in the Western Canada Sedimentary Basin, Figure 21, Heavy Oil Migration System. Shapefiles were produced from archived digital files created by the Alberta Geological Survey in the mid-1990s, and edited in 2005-06 to correct, attribute and consolidate the data into single files by feature type and by figure.

[Metadata] Description: Land Use Land Cover of main Hawaiian Islands as of 1976

This dataset includes high quality (800 Dots Per Inch - DPI), 24 bit color images of Minnesota's original Public Land Survey (PLS) plats created during the first government land survey of the state from 1848 to 1907. Currently housed at the Office of the Secretary of State, these plats were created by the U.S. Surveyor General's Office. This collection of more than 3,600 maps also includes later General Land Office (GLO) and the Bureau of Land Management (BLM) maps - up to the year 2001.

Minnesota's survey plat maps serve as the fundamental legal records for real estate in the state; all property titles and descriptions stem from them. They also serve as an essential resource for surveyors and as an analytical tool for the state's physical geography prior to European settlement. Finally, they serve as a testimony to years and years of hard work by the surveying community, often under challenging conditions.

In recent years the deteriorating physical condition of the older maps and the needs of technologically more sophisticated researchers, who require access to the maps, have made handling the original paper records increasingly less practical. To meet this challenge, the Office of the Secretary of State, the State Archives of the Minnesota Historical Society, the Minnesota Department of Transportation, MnGeo (formerly the Land Management Information Center - LMIC) and the Minnesota Association of County Surveyors collaborated in a digitization project which produced images of the maps in standard TIFF, JPEG and PDF formats - nearly 1.5 terabytes worth of data. Funding was provided by the Minnesota Department of Transportation.

This dataset represents a unique compiled environmental data set for the circumpolar Arctic ocean region 45N to 90N region. It consists of 170 layers (mostly marine, some terrestrial) in ArcGIS 10 format to be used with a Geographic Information System (GIS) and which are listed below in detail. Most layers are long-term average raster GRIDs for the summer season, often by ocean depth, and represent value-added products easy to use. The sources of the data are manifold such as the World Ocean Atlas 2009 (WOA09), International Bathimetric Chart of the Arctic Ocean (IBCAO), Canadian Earth System Model 2 (CanESM2) data (the newest generation of models available) and data sources such as plankton databases and OBIS. Ocean layers were modeled and predicted into the future and zooplankton species were modeled based on future data: Calanus hyperboreus (AphiaID104467), Metridia longa (AphiaID 104632), M. pacifica (AphiaID 196784) and Thysanoessa raschii (AphiaID 110711). Some layers are derived within ArcGIS. Layers have pixel sizes between 1215.819573 meters and 25257.72929 meters for the best pooled model, and between 224881.2644 and 672240.4095 meters for future climate data. Data was then reprojected into North Pole Stereographic projection in meters (WGS84 as the geographic datum). Also, future layers are included as a selected subset of proposed future climate layers from the Canadian CanESM2 for the next 100 years (scenario runs rcp26 and rcp85). The following layer groups are available: bathymetry (depth, derived slope and aspect); proximity layers (to,glaciers,sea ice, protected areas, wetlands, shelf edge); dissolved oxygen, apparent oxygen, percent oxygen, nitrogen, phosphate, salinity, silicate (all for August and for 9 depth classes); runoff (proximity, annual and August); sea surface temperature; waterbody temperature (12 depth classes); modeled ocean boundary layers (H1, H2, H3 and Wx).This dataset is used for a M.Sc. thesis by the author, and freely available upon request. For questions and details we suggest contacting the authors. Process_Description: Please contact Moritz Schmid for the thesis and detailed explanations. Short version: We model predicted here for the first time ocean layers in the Arctic Ocean based on a unique dataset of physical oceanography. Moreover, we developed presence/random absence models that indicate where the studied zooplankton species are most likely to be present in the Arctic Ocean. Apart from that, we develop the first spatially explicit models known to science that describe the depth in which the studied zooplankton species are most likely to be at, as well as their distribution of life stages. We do not only do this for one present day scenario. We modeled five different scenarios and for future climate data. First, we model predicted ocean layers using the most up to date data from various open access sources, referred here as best-pooled model data. We decided to model this set of stratification layers after discussions and input of expert knowledge by Professor Igor Polyakov from the International Arctic Research Center at the University of Alaska Fairbanks. We predicted those stratification layers because those are the boundaries and layers that the plankton has to cross for diel vertical migration and a change in those would most likely affect the migration. I assigned 4 variables to the stratification layers. H1, H2, H3 and Wx. H1 is the lower boundary of the mixed layer depth. Above this layer a lot of atmospheric disturbance is causing mixing of the water, giving the mixed layer its name. H2, the middle of the halocline is important because in this part of the ocean a strong gradient in salinity and temperature separates water layers. H3, the isotherm is important, because beneath it flows denser and colder Atlantic water. Wx summarizes the overall width of the described water column. Ocean layers were predicted using machine learning algorithms (TreeNet, Salford Systems). Second, ocean layers were included as predictors and used to predict the presence/random absence, most likely depth and life stage layers for the zooplankton species: Calanus hyperboreus, Metridia longa, Metridia pacifica and Thysanoessa raschii, This process was repeated for future predictions based on the CanESM2 data (see in the data section). For zooplankton species the following layers were developed and for the future. C. hyperboreus: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100.For parameters: Presence/random absence, most likely depth and life stage layers M. longa: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100. For parameters: Presence/rand... Visit https://dataone.org/datasets/f63d0f6c-7d53-46ce-b755-42a368007601 for complete metadata about this dataset.

This dataset may be a mix of two years and is updated as the data is released for each county. For example, one county may have data from 2014 while a neighboring county may have had a more recent release of 2016 data. For specific years, please check the service that specifies the year, i.e. California Important Farmland: 2016.Established in 1982, Government Code Section 65570 mandates FMMP to biennially report on the conversion of farmland and grazing land, and to provide maps and data to local government and the public.The Farmland Mapping and Monitoring Program (FMMP) provides data to decision makers for use in planning for the present and future use of California's agricultural land resources. The data is a current inventory of agricultural resources. This data is for general planning purposes and has a minimum mapping unit of ten acres.

This layer package contains GIS data in Esri file geodatabase format. This data is also available for download as a zip archive in shapefile format.Digital parcel files for the Town of Amherst, MA as of December 31, 2013. The Town converted its existing analog tax maps to digital format in 1998. At the time of conversion, tax maps consisted of 108 27"x39" mylar sheets at 1"=100', originally created in 1957 from controlled and rectified photography taken by Air Survey Inc. (VA) in 1956. The tax maps were scanned, and digital line files were created with text annotation. The new line files were then overlayed onto digital color orthophotos produced in 1999, and updated, by first matching road right-of-ways, then adjusting all parcel boundaries. This data set is a spatial view that is created through a one-to-many join between TOA_Parcels_Poly and TOA_CAMA_TABLE. The join is through Map & Lot, which creates stacked parcel polygons in cases where there are multiple block numbers (accounts) for one parcel; this occurs primarily with condominium complexes, as well as with properties with agricultural preservation restrictions. This data set is refreshed on a nightly basis & reflects current information from the Town of Amherst Assessor's Vision Appraisal Database.