The classification of land according to what activities take place on it or how it is being used; for example, agricultural, industrial, residential, rural, or commercial. Land use information and analysis is a fundamental tool in the planning process.

DVRPC’s 2020 land use file is based on digital orthophotography created from aerial surveillance completed in the spring of 2020. This dataset supports many of DVRPC's planning analysis goals.

Every five years, since 1990, the Delaware Valley Regional Planning Commission (DVRPC) has produced a GIS Land Use layer for its 9-county region.

lu20cat: Land use main category two-digit code.

lu20catn: Land use main category name.

lu20cat

lu20catn

1 - Residential

3 - Industrial

4 - Transportation

5 - Utility

6 - Commercial

7 - Institutional

8 - Military

9 - Recreation

10 - Agriculture

11 - Mining

12 - Wooded

13 - Water

14 - Undeveloped

lu20sub: Land use subcategory five-digit code. (refer to this data dictionary for code description)

lu20subn: Land use subcategory name.

lu20dev: Development status.

mixeduse: Mixed-Use status (Y/N). Features belonging to one of the Mixed-Use subcategories (Industrial: Mixed-Use, Multifamily Residential: Mixed-Use, or Commercial: Mixed-Use).

acres: Area of feature, in US acres.

geoid: 10-digit geographic identifier. In all DVRPC counties other than Philadelphia, a GEOID is assigned by municipality. In Philadelphia, it is assigned by County Planning Area (CPA).

state_name, co_name, mun_name: State name, county name, municipal/CPA name. In Philadelphia, County Planning Area (CPA) names are used in place of municipal names.

The classification of land according to what activities take place on it or how it is being used; for example, agricultural, industrial, residential, rural, or commercial. Land use information and analysis is a fundamental tool in the planning process.

DVRPC’s 2023 land use file is based on digital orthophotography created from aerial surveillance completed in the spring of 2023. This dataset supports many of DVRPC's planning analysis goals.

Every five years, since 1990, the Delaware Valley Regional Planning Commission (DVRPC) has produced a GIS Land Use layer for its 9-county region.

lu23cat: Land use main category two-digit code.

lu23catn: Land use main category name.

lu23cat

lu23catn

• 1 - Residential

• 3 - Industrial

• 4 - Transportation

• 5 - Utility

• 6 - Commercial

• 7 - Institutional

• 8 - Military

• 9 - Recreation

• 10 - Agriculture

• 11 - Mining

• 12 - Wooded

• 13 - Water

• 14 - Undeveloped

lu23sub: Land use subcategory five-digit code. (refer to this data dictionary for code description)

lu23subn: Land use subcategory name.

lu23dev: Development status.

mixeduse: Mixed-Use status (Y/N). Features belonging to one of the Mixed-Use subcategories (Industrial: Mixed-Use, Multifamily Residential: Mixed-Use, or Commercial: Mixed-Use).

acres: Area of feature, in US acres.

geoid: 10-digit geographic identifier. In all DVRPC counties other than Philadelphia, a GEOID is assigned by municipality. In Philadelphia, it is assigned by County Planning Area (CPA).

state_name, co_name, mun_name: State name, county name, municipal/CPA name. In Philadelphia, County Planning Area (CPA) names are used in place of municipal names.

Database and source codes used to investigate the impact of permafrost thaw on industrial legacies in the Arctic:

• The file Industrial_Sites.zip includes geospatial data (point shape file) on the location of industrial sites above 55°N. The data synergizes data from OpenStreetMap (OSM) and the Nordregio Atlas of population, society and economy in the Arctic from 2019.

• The file Contaminated_Sites_Program_Alaska.zip includes geospatial data extracted from the database of the Contaminated Sites Program in Alaska. The data set contains location, associated industrial sector, first date of registration, and associated chemical substances.

• The file Contaminated_Sites_Canada.zip includes geospatial data extracted from the database of the Federal Contaminated Sites Inventory (FCSI) of Canada. The data set contains besides location and the local permafrost probability, additional information on contamination type and the treatment status.

• The file Contaminated_Sites_Russia.zip contains geodata about industrial contamination events in Russia. The dataset was created based on a Google search using keywords in Russian ("загрязнение" - pollution; "разлив нефтепродуктов" - oil spill; "техногенная авария" - industrial accident or disaster; "Арктика" - Arctic; "мерзлота" - permafrost) and a search of several online media (including local and federal news portals). The database includes the locations of contamination events, the date, and information about the type of event, as well as the link to the media source. In some cases, the contaminated area and volume of the spill are also provided.

• The file Point_Process_Modeling.zip contains geospatial data of industrial sites and contaminated sites in Alaska and Canada and an R script used to fit two point process models to the data. The results deliver intensity maps of contaminated sites in the Arctic permafrost region.

• The file PanArctic_Simulations.zip includes the source code of the CryoGrid permafrost model (Julia Language v. 0.6.4) and a start script with all parameters and forcing data (JSON) required to run the model for industrial sites located in the Arctic permafrost region.

• The file Analysis_Industrial_Contaminated_Sites_Arctic.zip contains data and scripts to analyze permafrost degradation at industrial sites and contaminated sites in the Arctic following the simulations performed with PanArctic_Simulations.zip and the site locations contained in Industrial_Sites.zip and the intensity map derived by Point_Process_Modelling.zip.

• The file Geospatial_DataCollection.zip contains the complete database used to analyze and visualize the occurences of industrial contaminations in the Arctic.

• The file Visualization_Geospatial_DataCollection.zip contains a collection of additional python scripts used to plot data and analysis results contained in Geospatial_DataCollection.zip.

Please note that all paths pointing to the datasets used in the scripts have to be changed accordingly.

description: INDUSTRIAL_PARKS_INDOT_IN is a point shapefile that shows industrial park locations in Indiana. It is derived from data provided by personnel of the Indiana Department of Transportation (INDOT). This data set is a composite of data obtained from different sources. For some facilities, the source of locational data is unknown. For other facilities, address matching was used to obtain locations. And in some places, the centriods of zip-code areas and (or) counties are used. Consequently, points may be located several miles from the actual facility. Therefore, this data set is intended for general use only, and is not intended to replace an on-the-ground survey.; abstract: INDUSTRIAL_PARKS_INDOT_IN is a point shapefile that shows industrial park locations in Indiana. It is derived from data provided by personnel of the Indiana Department of Transportation (INDOT). This data set is a composite of data obtained from different sources. For some facilities, the source of locational data is unknown. For other facilities, address matching was used to obtain locations. And in some places, the centriods of zip-code areas and (or) counties are used. Consequently, points may be located several miles from the actual facility. Therefore, this data set is intended for general use only, and is not intended to replace an on-the-ground survey.

Abstract: Brownfields are defined by the Florida Department of Environmental Protection (FDEP) as abandoned, idled, or underused industrial and commercial facilities where expansion or redevelopment is complicated by real or perceived environmental contamination. The primary goals of Florida's Brownfields Redevelopment Act (Ch. 97-277, Laws of Florida, codified at ss. 376.77-.85, F.S.) are to reduce health and environmental hazards on existing commercial and industrial sites that are abandoned or underused due to these hazards and create financial and regulatory incentives to encourage redevelopment and voluntary cleanup of contaminated properties. A 'brownfield area' means a contiguous area of one or more brownfield sites, some of which may not be contaminated, that has been designated as such by a local government resolution. Such areas may include all or portions of community redevelopment areas, enterprise zones, empowerment zones, other such designated economically deprived communities and areas, and Environmental Protection Agency (EPA) designated brownfield pilot projects. This layer provides a polygon representation of the boundaries of these designated Brownfield Areas in Volusia County. The data presented here is an extract of the the FDEP statewide Brownfield Areas shapefile and contains only areas within Volusia County.Purpose: Brownfields data is provided to assist in the determination of areas with potential for economic revitalization.Accuracy: This layer provides a polygon representation of the boundaries of designated Brownfield Areas within Florida where a resolution has been passed by a local municipality. These polygons were created using various methodologies for creating digital spatial data depending upon the type of information provided by the municipality within the resolution and loosely dependent upon the cadastral (parcel) boundaries provided by the County Property Appraisers at the time when the spatial boundaries were developed. Since the data quality of the cadastral layer varies from county to county, the data quality of the brownfield area polygons will also vary. These methodologies include the following list: Coordinate Geometry (COGO) created from legal descriptions of the area specified in the resolution. Many of the brownfields include a large area with hundreds of property parcels. These boundaries were compared with the most recent cadastral boundaries available from the County Property Appraiser and spatially adjusted where necessary to clearly include the property parcels marked for redevelopment, the roadway right of way areas (where applicable), city boundaries, political boundaries, and other economic incentive zones. Shapefiles, Coverages and CADD files of the area were provided by the local municipality or planning and development agencies in some cases. These shapefiles were used without modification to the geometry to match cadastral boundaries. Coverages and CADD files were converted to a format compatible with the process software. Paper Maps showing the scaled boundaries of the area in relation to property parcels, geographic landmarks, and land surveys are sometimes included in the resolution. These maps are compared to available GIS boundaries in order to locate the specific property parcels referenced. The available cadastral boundaries are used to represent the area where only paper maps or parcel folio numbers are provided.All inquiries concerning this data should be directed to the Florida Department of Environmental Protection (FDEP) OTIS/GIS Section. GIS.Librarian@dep.state.fl.usOnline linkage to the Original Metadata

Special Service Areas (SSA) boundaries in Chicago. The Special Service Area program is a mechanism used to fund expanded services and programs through a localized property tax levy within contiguous industrial, commercial and residential areas. The enhanced services and programs are in addition to services and programs currently provided through the city. SSA-funded projects could include, but are not limited to, security services, area marketing and advertising assistance, promotional activities such as parades and festivals, or any variety of small scale capital improvements that could be supported through a modest property tax levy.

This is a GIS file set of the Gede ruins. The data was generated from laser scans, photogrammetric techniques and GPS data. The data maps the site of the Gede ruins in Kilifi County in Kenya. All data is in either the unprojected Geographic (GCS WGS84) or the projected Universal Transverse Mercator 37 South (UTM37S WGS84) coordinate system.The data is packaged as an ESRI Map Package (.mpk). If you are not an ESRI user and wish to unpack the package please rename the file extension to .zip and use a programme, such as 7-Zip, to unpack the package. The package contains shapefiles and images which are compatible with most GIS software. The ruins of Gede (also Gedi), a traditional Arab-African Swahili town, are located just off Kenya’s coastline, some 90km north of Mombasa. Gede was a small town built entirely from stones and rocks, and most of the original foundations are still visible today. Remaining structures at the site include coral stone buildings, mosques, houses and a palace. The town was abandoned in the early 17th century, and Gede’s buildings date back to the 15th century, although it is believed that the site could have been inhabited as early as the 11th or 12th century. The Zamani Project spatially documented the Gede ruins in 2010. In addition to the three principal structures of the Great Mosque, the Small Mosque and the Palace, remains of other structures in the immediate vicinity were also documented.The Zamani Project seeks to increase awareness and knowledge of tangible cultural heritage in Africa and internationally by creating metrically accurate digital representations of historical sites. Digital spatial data of cultural heritage sites can be used for research and education, for restoration and conservation and as a record for future generations. The Zamani Project operates as a non-profit organisation within the University of Cape Town.This text has been adapted from the UNESCO website (https://whc.unesco.org/en/tentativelists/5501/).The Zamani Project received funding from the Andrew W Mellon Foundation at the time of the project.

The Residential Schools Locations Dataset in shapefile format contains the locations (latitude and longitude) of Residential Schools and student hostels operated by the federal government in Canada. All the residential schools and hostels that are listed in the Indian Residential School Settlement Agreement are included in this data set, as well as several Industrial schools and residential schools that were not part of the IRRSA. This version of the dataset doesn’t include the five schools under the Newfoundland and Labrador Residential Schools Settlement Agreement. The original school location data was created by the Truth and Reconciliation Commission, and was provided to the researcher (Rosa Orlandini) by the National Centre for Truth and Reconciliation in April 2017. The data set was created by Rosa Orlandini, and builds upon and enhances the previous work of the Truth and Reconcilation Commission, Morgan Hite (creator of the Atlas of Indian Residential Schools in Canada that was produced for the Tk'emlups First Nation and Justice for Day Scholar's Initiative, and Stephanie Pyne (project lead for the Residential Schools Interactive Map). Each individual school location in this dataset is attributed either to RSIM, Morgan Hite, NCTR or Rosa Orlandini. Many schools/hostels had several locations throughout the history of the institution. If the school/hostel moved from its’ original location to another property, then the school is considered to have two unique locations in this data set,the original location and the new location. For example, Lejac Indian Residential School had two locations while it was operating, Stuart Lake and Fraser Lake. If a new school building was constructed on the same property as the original school building, it isn't considered to be a new location, as is the case of Girouard Indian Residential School. When the precise location is known, the coordinates of the main building are provided, and when the precise location of the building isn’t known, an approximate location is provided. For each residential school institution location, the following information is provided: official names, alternative name, dates of operation, religious affiliation, latitude and longitude coordinates, community location, Indigenous community name, contributor (of the location coordinates), school/institution photo (when available), location point precision, type of school (hostel or residential school) and list of references used to determine the location of the main buildings or sites. The geographic coordinate system for this dataset is WGS 1984. The data in shapefile format [IRS_locations.zip] can be viewed and mapped in a Geographic Information System software. Detailed metadata in xml format is available as part of the data in shapefile format. In addition, the field name descriptions (IRS_locfields.csv) and the detailed locations descriptions (IRS_locdescription.csv) should be used alongside the data in shapefile format.

This dataset features a map of building types for Germany on a 10m grid based on Sentinel-1A/B and Sentinel-2A/B time series. A random forest classification was used to map the predominant type of buildings within a pixel. We distinguish single-family residential buildings, multi-family residential buildings, commercial and industrial buildings and lightweight structures. Building types were predicted for all pixels where building density > 25 %. Please refer to the publication for details.

Temporal extent

Sentinel-2 time series data are from 2018. Sentinel-1 time series data are from 2017.

Data format

The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). Metadata are located within the Tiff, partly in the FORCE domain. There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Building type values are categorical, according to the following scheme:

0 - No building

1 - Commercial and industrial buildings

2 - Single-family residential buildings

3 - Lightweight structures

4 - Multi-family residential buildings

Further information

For further information, please see the publication or contact Franz Schug (franz.schug@geo.hu-berlin.de).
A web-visualization of this dataset is available here.

Publication

Schug, F., Frantz, D., van der Linden, S., & Hostert, P. (2021). Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. PLOS ONE. DOI: 10.1371/journal.pone.0249044

Acknowledgements

The dataset was generated by FORCE v. 3.1 (paper, code), which is freely available software under the terms of the GNU General Public License v. >= 3. Sentinel imagery were obtained from the European Space Agency and the European Commission.

Funding
This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

OUTDATED. See the current data at https://data.cityofchicago.org/d/kjav-iyuj -- Special Service Areas (SSA) boundaries in Chicago. The Special Service Area program is a mechanism used to fund expanded services and programs through a localized property tax levy within contiguous industrial, commercial and residential areas. The enhanced services and programs are in addition to services and programs currently provided through the city. SSA-funded projects could include, but are not limited to, security services, area marketing and advertising assistance, promotional activities such as parades and festivals, or any variety of small scale capital improvements that could be supported through a modest property tax levy. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ).

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

Access National Hydrography ProductsThe National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Reach addresses establish the locations of these entities relative to one another within the NHD surface water drainage network, much like addresses on streets. Once linked to the NHD by their reach addresses, the upstream/downstream relationships of these water-related entities--and any associated information about them--can be analyzed using software tools ranging from spreadsheets to geographic information systems (GIS). GIS can also be used to combine NHD-based network analysis with other data layers, such as soils, land use and population, to help understand and display their respective effects upon one another. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all.Statements of attribute accuracy are based on accuracy statements made for U.S. Geological Survey Digital Line Graph (DLG) data, which is estimated to be 98.5 percent. One or more of the following methods were used to test attribute accuracy: manual comparison of the source with hardcopy plots; symbolized display of the DLG on an interactive computer graphic system; selected attributes that could not be visually verified on plots or on screen were interactively queried and verified on screen. In addition, software validated feature types and characteristics against a master set of types and characteristics, checked that combinations of types and characteristics were valid, and that types and characteristics were valid for the delineation of the feature. Feature types, characteristics, and other attributes conform to the Standards for National Hydrography Dataset (USGS, 1999) as of the date they were loaded into the database. All names were validated against a current extract from the Geographic Names Information System (GNIS). The entry and identifier for the names match those in the GNIS. The association of each name to reaches has been interactively checked, however, operator error could in some cases apply a name to a wrong reach.Points, nodes, lines, and areas conform to topological rules. Lines intersect only at nodes, and all nodes anchor the ends of lines. Lines do not overshoot or undershoot other lines where they are supposed to meet. There are no duplicate lines. Lines bound areas and lines identify the areas to the left and right of the lines. Gaps and overlaps among areas do not exist. All areas close.The completeness of the data reflects the content of the sources, which most often are the published USGS topographic quadrangle and/or the USDA Forest Service Primary Base Series (PBS) map. The USGS topographic quadrangle is usually supplemented by Digital Orthophoto Quadrangles (DOQs). Features found on the ground may have been eliminated or generalized on the source map because of scale and legibility constraints. In general, streams longer than one mile (approximately 1.6 kilometers) were collected. Most streams that flow from a lake were collected regardless of their length. Only definite channels were collected so not all swamp/marsh features have stream/rivers delineated through them. Lake/ponds having an area greater than 6 acres were collected. Note, however, that these general rules were applied unevenly among maps during compilation. Reach codes are defined on all features of type stream/river, canal/ditch, artificial path, coastline, and connector. Waterbody reach codes are defined on all lake/pond and most reservoir features. Names were applied from the GNIS database. Detailed capture conditions are provided for every feature type in the Standards for National Hydrography Dataset available online through https://prd-wret.s3-us-west-2.amazonaws.com/assets/palladium/production/atoms/files/NHD%201999%20Draft%20Standards%20-%20Capture%20conditions.PDF.Statements of horizontal positional accuracy are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For horizontal accuracy, this standard is met if at least 90 percent of points tested are within 0.02 inch (at map scale) of the true position. Additional offsets to positions may have been introduced where feature density is high to improve the legibility of map symbols. In addition, the digitizing of maps is estimated to contain a horizontal positional error of less than or equal to 0.003 inch standard error (at map scale) in the two component directions relative to the source maps. Visual comparison between the map graphic (including digital scans of the graphic) and plots or digital displays of points, lines, and areas, is used as control to assess the positional accuracy of digital data. Digital map elements along the adjoining edges of data sets are aligned if they are within a 0.02 inch tolerance (at map scale). Features with like dimensionality (for example, features that all are delineated with lines), with or without like characteristics, that are within the tolerance are aligned by moving the features equally to a common point. Features outside the tolerance are not moved; instead, a feature of type connector is added to join the features.Statements of vertical positional accuracy for elevation of water surfaces are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For vertical accuracy, this standard is met if at least 90 percent of well-defined points tested are within one-half contour interval of the correct value. Elevations of water surface printed on the published map meet this standard; the contour intervals of the maps vary. These elevations were transcribed into the digital data; the accuracy of this transcription was checked by visual comparison between the data and the map.

# Dutch Distribution Centres 2021 Geodata

The Geodataset includes 26.951 logistics buildings in the Netherlands, built before November 2021, as well as the business estates they are in, and the outline of the East-Southeast freight corridor of the Netherlands - the busiest logistics area of the country. The used definition of the logistics sector includes trade, import, export, wholesale, transportation, and warehousing activities. To avoid irrelevant buildings such as small, dedicated office buildings and electrical installations, only logistics buildings larger than 500 m² were considered in the data collection. Additionally, 4.533 large buildings with retail centres on industrial sites were included, where consumers ‘pick their own orders’, such as construction materials stores. As well as 782 buildings with logistics co-activity, for example a hospital with a logistics entity for medical materials. Manufacturing and recycling facilities, which often include logistics activities, are not included unless a separate logistics company is registered in the building. Several of the analyses by the author are exclusively focused on XL (> 20.000 m²) and XXL DCs (>40.000 m²), used by traders and logistics service providers. Depending on the analysis, therefore, subsets of certain functions and size classes of DCs are used.

The dataset contains a Geopackage with equivalents in ESRI shapefile format. The buildings layer with attributes is also provided as a Geojson file and CSV data table (without geometry). Besides this README file, a method describing the compilation of the data is provided (Pdf).

An earlier version of this dataset, covering only the area within the East-Southeast corridor, was released in 2021 (DOI:10.4121/14791686). The new dataset substitutes the former.

An interactive zoomable map with timeline slider is available on: http://mertennefs.eu/landscapes-of-trade/

Some interactive metrics, showing the extent and potential of the dataset, are shown in this RShiny app: https://mertennefs.shinyapps.io/distributioncentres_geodata_app/

## Collaboration

The data were gathered and compiled by Merten Nefs, in collaboration with Thomas Bonte and Carlijn Ligterink (Vrije Universiteit Amsterdam), Vera Loefs and Ana Luisa Moura (Deltametropolis Association); with the help and data provision by Frank van Oort (Erasmus School of Economics) and Hans van Amsterdam (Netherlands Environmental Assessment Agency, PBL). The data have been used for research, media articles and academic work such as the PhD research project Landscapes of Trade, by Merten Nefs, at TUDelft and Erasmus School of Economics. More information at www.landscapesoftrade.nl

## Disclaimer

The buildings were selected by and joined with employment data of Lisa 2020, and therefore reasons back in time from this point and assumes the same function as well for 2021. Discrepancies between earlier data (Lisa 2017) and 2020 can be viewed and filtered out via the 'Current' attribute below. Another important aspect is the overlap of logistics buildings over time: a small portion of these has replaced former buildings on the same site that have been demolished. Such 'brownfield' developments amount to ca. 10% of the dataset - as established by a test sample - whereas ca. 90% are 'greenfield' developments. More information on the data collection and compilation method is found in the accompanying pdf file.

VITAL SIGNS INDICATOR Greenfield Development (LU5)

FULL MEASURE NAME The acres of construction on previously undeveloped land

LAST UPDATED November 2019

DESCRIPTION Greenfield development refers to construction on previously undeveloped land and the corresponding expansion of our region’s developed footprint, which includes the extent of urban and built-up lands. The footprint is defined as land occupied by structures, with a building density of at least 1 unit to 1.5 acres.

DATA SOURCE Department of Conservation: Farmland Mapping and Monitoring Program GIS Data Tables/Layers (1990-2016) https://www.conservation.ca.gov/dlrp/fmmp

U.S. Census Bureau: Decennial Census Population by Census Block Group (2000-2010) http://factfinder.census.gov

U.S. Census Bureau: American Community Survey (5-year) Population by Census Block Group (2000-2017) http://factfinder.census.gov

METHODOLOGY NOTES (across all datasets for this indicator) For regional and local data, FMMP maps the extent of “urban and built-up” lands, which generally reflect the developed urban footprint of the region. The footprint is defined as land occupied by structures with building density of at least 1 unit to 1.5 acres. Uses include residential, industrial, commercial, construction, institutional, public administration, railroad and other transportation yards, cemeteries, airports, golf courses, sanitary landfills, sewage treatment, water control structures, and other developed purposes.

To determine the amount of greenfield development (in acres) occurring in a given two-year period, the differences in urban footprint are computed on a county-level. FMMP makes slight refinements to urban boundaries over time, so changes in urban footprint +/- 100 acres are not regionally significant. The GIS shapefile represents the 2016 urban footprint and thus does not show previously urbanized land outside of the footprint (i.e. Hamilton Air Force Base).

For metro comparisons, a different methodology had to be used to avoid the geospatial limitations associated with FMMP. U.S. Census population by census block group was gathered for each metro area for 2000, 2010, and 2017. Population data for years 2000 and 2010 come from the Decennial Census while data for 2018 comes from the 2017 5-year American Community Survey. The block group was considered urbanized if its average/gross density was greater than 1 housing unit per acre (a slightly higher threshold than FMMP uses for its definition). Because a block group cannot be flagged as partially urbanized, and non-residential uses are not fully captured, the urban footprint of the region calculated with this methodology is smaller than in FMMP. The metro data should be primarily used for looking at comparative growth rate in greenfield development rather than the acreage totals themselves.

The Nacatoch Sand in northeastern and Arkansas, is sources of groundwater for agricultural, domestic, industrial, and public use in Clay and Green Counties. Water-level altitudes measured in 12 wells completed in the Nacatoch Sand and were used to create a potentiometric-surface map. Potentiometric surfaces show that groundwater in the Nacatoch aquifer flows southeast toward the Mississippi River in northeastern Arkansas.

Special Service Areas (SSA) boundaries in Chicago. To view or use these shapefiles, compression software and special GIS software, such as ESRI ArcGIS, is required. The Special Service Area program is a mechanism used to fund expanded services and programs through a localized property tax levy within contiguous industrial, commercial and residential areas. The enhanced services and programs are in addition to services and programs currently provided through the city. SSA-funded projects could include, but are not limited to, security services, area marketing and advertising assistance, promotional activities such as parades and festivals, or any variety of small scale capital improvements that could be supported through a modest property tax levy

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

description: The Floodplain Mapping/Redelineation study deliverables depict and quantify the flood risks for the study area. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual- chance flood event, and areas of minimal flood risk. The Floodplain Mapping/Redelineation flood risk boundaries are derived from the engineering information Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). Michael Baker Jr. Alexandria office received 2 paper site-plans for the Dansville/Sparta NY Industrial Park from BME Associates, Doug B. Eldred, P.E. as a better source of contours compared to the original topographic study used in the current effective maps. The site-plans were scanned into a TIF, geo-referenced and fit to the Sparta base-map data. A Michael Baker engineer used the contours on the site-plans to adjust the floodplain boundary to match the contours in the Industrial Park. An individual shape file was created just for the Industrial Park area.; abstract: The Floodplain Mapping/Redelineation study deliverables depict and quantify the flood risks for the study area. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual- chance flood event, and areas of minimal flood risk. The Floodplain Mapping/Redelineation flood risk boundaries are derived from the engineering information Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). Michael Baker Jr. Alexandria office received 2 paper site-plans for the Dansville/Sparta NY Industrial Park from BME Associates, Doug B. Eldred, P.E. as a better source of contours compared to the original topographic study used in the current effective maps. The site-plans were scanned into a TIF, geo-referenced and fit to the Sparta base-map data. A Michael Baker engineer used the contours on the site-plans to adjust the floodplain boundary to match the contours in the Industrial Park. An individual shape file was created just for the Industrial Park area.

Summary

Geojson files used to visualize geospatial layers relevant to identifying and assessing trucking fleet decarbonization opportunities with the MIT Climate & Sustainability Consortium's Geospatial Trucking Industry Decarbonization Explorer (Geo-TIDE) tool.

Relevant Links

Link to the online version of the tool (requires creation of a free user account).

Link to GitHub repo with source code to produce this dataset and deploy the Geo-TIDE tool locally.

Funding

This dataset was produced with support from the MIT Climate & Sustainability Consortium.

Original Data Sources

These geojson files draw from and synthesize a number of different datasets and tools. The original data sources and tools are described below:

Filename(s) Description of Original Data Source(s) Link(s) to Download Original Data License and Attribution for Original Data Source(s)

faf5_freight_flows/*.geojson

trucking_energy_demand.geojson

highway_assignment_links_*.geojson

infrastructure_pooling_thought_experiment/*.geojson

Regional and highway-level freight flow data obtained from the Freight Analysis Framework Version 5. Shapefiles for FAF5 region boundaries and highway links are obtained from the National Transportation Atlas Database. Emissions attributes are evaluated by incorporating data from the 2002 Vehicle Inventory and Use Survey and the GREET lifecycle emissions tool maintained by Argonne National Lab.

Shapefile for FAF5 Regions

Shapefile for FAF5 Highway Network Links

FAF5 2022 Origin-Destination Freight Flow database

FAF5 2022 Highway Assignment Results

Attribution for Shapefiles: United States Department of Transportation Bureau of Transportation Statistics National Transportation Atlas Database (NTAD). Available at: https://geodata.bts.gov/search?collection=Dataset.

License for Shapefiles: This NTAD dataset is a work of the United States government as defined in 17 U.S.C. § 101 and as such are not protected by any U.S. copyrights. This work is available for unrestricted public use.

Attribution for Origin-Destination Freight Flow database: National Transportation Research Center in the Oak Ridge National Laboratory with funding from the Bureau of Transportation Statistics and the Federal Highway Administration. Freight Analysis Framework Version 5: Origin-Destination Data. Available from: https://faf.ornl.gov/faf5/Default.aspx. Obtained on Aug 5, 2024. In the public domain.

Attribution for the 2022 Vehicle Inventory and Use Survey Data: United States Department of Transportation Bureau of Transportation Statistics. Vehicle Inventory and Use Survey (VIUS) 2002 [supporting datasets]. 2024. https://doi.org/10.21949/1506070

Attribution for the GREET tool (original publication): Argonne National Laboratory Energy Systems Division Center for Transportation Research. GREET Life-cycle Model. 2014. Available from this link.

Attribution for the GREET tool (2022 updates): Wang, Michael, et al. Summary of Expansions and Updates in GREET® 2022. United States. https://doi.org/10.2172/1891644

grid_emission_intensity/*.geojson

Emission intensity data is obtained from the eGRID database maintained by the United States Environmental Protection Agency.

eGRID subregion boundaries are obtained as a shapefile from the eGRID Mapping Files database.

eGRID database

Shapefile with eGRID subregion boundaries

Attribution for eGRID data: United States Environmental Protection Agency: eGRID with 2022 data. Available from https://www.epa.gov/egrid/download-data. In the public domain.

Attribution for shapefile: United States Environmental Protection Agency: eGRID Mapping Files. Available from https://www.epa.gov/egrid/egrid-mapping-files. In the public domain.

US_elec.geojson

US_hy.geojson

US_lng.geojson

US_cng.geojson

US_lpg.geojson

Locations of direct current fast chargers and refueling stations for alternative fuels along U.S. highways. Obtained directly from the Station Data for Alternative Fuel Corridors in the Alternative Fuels Data Center maintained by the United States Department of Energy Office of Energy Efficiency and Renewable Energy.

US_elec.geojson

US_hy.geojson

US_lng.geojson

US_cng.geojson

US_lpg.geojson

Attribution: U.S. Department of Energy, Energy Efficiency and Renewable Energy. Alternative Fueling Station Corridors. 2024. Available from: https://afdc.energy.gov/corridors. In the public domain.

These data and software code ("Data") are provided by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC ("Alliance"), for the U.S. Department of Energy ("DOE"), and may be used for any purpose whatsoever.

daily_grid_emission_profiles/*.geojson

Hourly emission intensity data obtained from ElectricityMaps.

Original data can be downloaded as csv files from the ElectricityMaps United States of America database

Shapefile with region boundaries used by ElectricityMaps

License: Open Database License (ODbL). Details here: https://www.electricitymaps.com/data-portal

Attribution for csv files: Electricity Maps (2024). United States of America 2022-23 Hourly Carbon Intensity Data (Version January 17, 2024). Electricity Maps Data Portal. https://www.electricitymaps.com/data-portal.

Attribution for shapefile with region boundaries: ElectricityMaps contributors (2024). electricitymaps-contrib (Version v1.155.0) [Computer software]. https://github.com/electricitymaps/electricitymaps-contrib.

gen_cap_2022_state_merged.geojson

trucking_energy_demand.geojson

Grid electricity generation and net summer power capacity data is obtained from the state-level electricity database maintained by the United States Energy Information Administration.

U.S. state boundaries obtained from this United States Department of the Interior U.S. Geological Survey ScienceBase-Catalog.

Annual electricity generation by state

Net summer capacity by state

Shapefile with U.S. state boundaries

Attribution for electricity generation and capacity data: U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/electricity/data/state/. In the public domain.

electricity_rates_by_state_merged.geojson

Commercial electricity prices are obtained from the Electricity database maintained by the United States Energy Information Administration.

Electricity rate by state

Attribution: U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/electricity/data.php. In the public domain.

demand_charges_merged.geojson

demand_charges_by_state.geojson

Maximum historical demand charges for each state and zip code are derived from a dataset compiled by the National Renewable Energy Laboratory in this this Data Catalog.

Historical demand charge dataset

The original dataset is compiled by the National Renewable Energy Laboratory (NREL), the U.S. Department of Energy (DOE), and the Alliance for Sustainable Energy, LLC ('Alliance').

Attribution: McLaren, Joyce, Pieter Gagnon, Daniel Zimny-Schmitt, Michael DeMinco, and Eric Wilson. 2017. 'Maximum demand charge rates for commercial and industrial electricity tariffs in the United States.' NREL Data Catalog. Golden, CO: National Renewable Energy Laboratory. Last updated: July 24, 2024. DOI: 10.7799/1392982.

eastcoast.geojson

midwest.geojson

la_i710.geojson

h2la.geojson

bayarea.geojson

saltlake.geojson

northeast.geojson

Highway corridors and regions targeted for heavy duty vehicle infrastructure projects are derived from a public announcement on February 15, 2023 by the United States Department of Energy.

The shapefile with Bay area boundaries is obtained from this Berkeley Library dataset.

The shapefile with Utah county boundaries is obtained from this dataset from the Utah Geospatial Resource Center.

Shapefile for Bay Area country boundaries

Shapefile for counties in Utah

Attribution for public announcement: United States Department of Energy. Biden-Harris Administration Announces Funding for Zero-Emission Medium- and Heavy-Duty Vehicle Corridors, Expansion of EV Charging in Underserved Communities (2023). Available from https://www.energy.gov/articles/biden-harris-administration-announces-funding-zero-emission-medium-and-heavy-duty-vehicle.

Attribution for Bay area boundaries: San Francisco (Calif.). Department Of Telecommunications and Information Services. Bay Area Counties. 2006. In the public domain.

Attribution for Utah boundaries: Utah Geospatial Resource Center & Lieutenant Governor's Office. Utah County Boundaries (2023). Available from https://gis.utah.gov/products/sgid/boundaries/county/.

License for Utah boundaries: Creative Commons 4.0 International License.

incentives_and_regulations/*.geojson

State-level incentives and regulations targeting heavy duty vehicles are collected from the State Laws and Incentives database maintained by the United States Department of Energy's Alternative Fuels Data Center.

Data was collected manually from the State Laws and Incentives database.

Attribution: U.S. Department of Energy, Energy Efficiency and Renewable Energy, Alternative Fuels Data Center. State Laws and Incentives. Accessed on Aug 5, 2024 from: https://afdc.energy.gov/laws/state. In the public domain.

These data and software code ("Data") are provided by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC ("Alliance"), for the U.S. Department of Energy ("DOE"), and may be used for any purpose whatsoever.

costs_and_emissions/*.geojson

diesel_price_by_state.geojson

trucking_energy_demand.geojson

Lifecycle costs and emissions of electric and diesel trucking are evaluated by adapting the model developed by Moreno Sader et al., and calibrated to the Run on Less dataset for the Tesla Semi collected from the 2023 PepsiCo Semi pilot by the North American Council for Freight Efficiency.

In

This data set contains small-scale base GIS data layers compiled by the National Park Service Servicewide Inventory and Monitoring Program and Water Resources Division for use in a Baseline Water Quality Data Inventory and Analysis Report that was prepared for the park. The report presents the results of surface water quality data retrievals for the park from six of the United States Environmental Protection Agency's (EPA) national databases: (1) Storage and Retrieval (STORET) water quality database management system; (2) River Reach File (RF3) Hydrography; (3) Industrial Facilities Discharges; (4) Drinking Water Supplies; (5) Water Gages; and (6) Water Impoundments. The small-scale GIS data layers were used to prepare the maps included in the report that depict the locations of water quality monitoring stations, industrial discharges, drinking intakes, water gages, and water impoundments. The data layers included in the maps (and this dataset) vary depending on availability, but generally include roads, hydrography, political boundaries, USGS 7.5' minute quadrangle outlines, hydrologic units, trails, and others as appropriate. The scales of each layer vary depending on data source but are generally 1:100,000.