Dataset from National Heritage Board. For more information, visit https://data.gov.sg/datasets/d_31e16b12809e66673e90d8b04fdee1b2/view

Explore the historical Whois records related to geojson.info (Domain). Get insights into ownership history and changes over time.

USGS developed The National Map Gazetteer as the Federal and national standard (ANSI INCITS 446-2008) for geographic nomenclature based on the Geographic Names Information System (GNIS). The National Map Gazetteer contains information about physical and cultural geographic features, geographic areas, and locational entities that are generally recognizable and locatable by name (have achieved some landmark status) and are of interest to any level of government or to the public for any purpose that would lead to the representation of the feature in printed or electronic maps and/or geographic information systems. The dataset includes features of all types in the United States, its associated areas, and Antarctica, current and historical, but not including roads and highways. The dataset holds the federally recognized name of each feature and defines the feature location by state, county, USGS topographic map, and geographic coordinates. Other attributes include names or spellings other than the official name, feature classification, and historical and descriptive information. The dataset assigns a unique, permanent feature identifier, the Feature ID, as a standard Federal key for accessing, integrating, or reconciling feature data from multiple data sets. This dataset is a flat model, establishing no relationships between features, such as hierarchical, spatial, jurisdictional, organizational, administrative, or in any other manner. As an integral part of The National Map, the Gazetteer collects data from a broad program of partnerships with federal, state, and local government agencies and other authorized contributors. The Gazetteer provides data to all levels of government and to the public, as well as to numerous applications through a web query site, web map, feature and XML services, file download services, and customized files upon request. The National Map download client allows free downloads of public domain geographic names data by state in a pipe-delimited text format. For additional information on the GNIS, go to https://www.usgs.gov/tools/geographic-names-information-system-gnis. See https://apps.nationalmap.gov/help/ for assistance with The National Map viewer, download client, services, or metadata. Data Refreshed March, 2025

Viabundus.eu is a freely accessible online street map of late medieval and early modern northern Europe (1350-1650). Originally conceived as the digitisation of Friedrich Bruns and Hugo Weczerka's Hansische Handelsstraßen (1962) atlas of land roads in the Hanseatic area, the Viabundus map moves beyond that. It includes among others: a database with information about settlements, towns, tolls, staple markets and other information relevant for the pre-modern traveller; a route calculator; a calendar of fairs; and additional land routes as well as water ways.

Viabundus is a work in progress. Version 1.3, released on 17 March 2024, contains a rough digitisation of the land routes from Hansische Handelsstraßen, as well as a thoroughly researched road network for the current-day Netherlands, Denmark, the German states of Lower Saxony, Schleswig-Holstein, Thuringia, Saxony-Anhalt, Brandenburg, Mecklenburg-Vorpommern, Hesse and North Rhine-Westfalia, and parts of Poland (Pomerania, Royal Prussia, Greater Poland). The inclusion of other regions is currently being planned. Additions to the dataset will be released as new versions in the future.

The project's homepage viabundus.eu contains a web map application to explore the data. To allow for more advanced spatial and historical analyses, the underlying dataset is available for download under the CC-BY-SA license.

The dataset is designed as a network model and therefore consists of two main elements: 1) a relational database of nodes, i.e. geographical places, with historical information about settlements, towns, tolls, staple markets, fairs, bridges, ferries, harbours and shipping locks; 2) a database with edges, i.e. the geospatial representations of the land and water routes that connected these nodes. The entire database is available in CSV format (with geospatial geometry as WKT); the edges and the outlines of towns in the 16th century are also separately available as geojson and GML files. For more information about the structure of the dataset, theoretical considerations and sources, please consult the enclosed documentation file.

Geojson data containing all objects with lat-longs and associated descriptive metadata.

Last updated on 06/17/2022

Overview

The national fire history perimeter data layer of conglomerated Agency Authoratative perimeters was developed in support of the WFDSS application and wildfire decision support for the 2021 fire season. The layer encompasses the final fire perimeter datasets of the USDA Forest Service, US Department of Interior Bureau of Land Management, Bureau of Indian Affairs, Fish and Wildlife Service, and National Park Service, the Alaska Interagency Fire Center, CalFire, and WFIGS History. Perimeters are included thru the 2021 fire season. Requirements for fire perimeter inclusion, such as minimum acreage requirements, are set by the contributing agencies.

WFIGS, NPS and CALFIRE data now include Prescribed Burns.

Several data sources were used in the development of this layer:

• Alaska fire history
• USDA FS Regional Fire History Data
• BLM Fire Planning and Fuels
• National Park Service - Includes Prescribed Burns
• Fish and Wildlife Service
• Bureau of Indian Affairs
• CalFire FRAS - Includes Prescribed Burns
• WFIGS - BLM & BIA and other S&L

Fire perimeter data are often collected at the local level, and fire management agencies have differing guidelines for submitting fire perimeter data. Often data are collected by agencies only once annually. If you do not see your fire perimeters in this layer, they were not present in the sources used to create the layer at the time the data were submitted. A companion service for perimeters entered into the WFDSS application is also available, if a perimeter is found in the WFDSS service that is missing in this Agency Authoratative service or a perimeter is missing in both services, please contact the appropriate agency Fire GIS Contact listed in the table below.

Geojson data containing all objects with lat-longs and associated descriptive metadata.

Explore the historical Whois records related to json.fit (Domain). Get insights into ownership history and changes over time.

The right to hold a market on England was historically granted by royal charter. Such a privilege provides an alternative to population size as a proxy for importance. This dataset contains the latitude and longitude I have determined for the market place in 698 historic English market towns which are recorded in a list published in "Owen's New Book of Fairs" (London: 1813), https://www.google.co.nz/books/edition/Owen_s_New_Book_of_Fairs_A_new_edition_e/lrdVAAAAcAAJ?hl=en&gbpv=1&dq=adwalton&pg=PR3&printsec=frontcover

Inclusion of a town in this list does not imply that the market was still being held as of 1813.

A full description of the method for determining the location, as well as the data in both GeoJSON and CSV formats can be found in the associated Github repository: https://github.com/philipallfrey/english-market-towns-1813

This file contains three datasets:

accidents.geojson: car related accident records in Kiel

districts.geojson: district data in Kiel

roads.geojson: road data in Kiel

Sources: It is believed that these datasets are the result from webscraping. The original sources are unknown.

Disclaimer: I do not own the data.

Here you can find monthly anonymised trip data from Bergen City Bike. Are you doing anything cool with this data? Let us know on post@bergenbysykkel.no.

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

Bleeper is a licensed dockless bike-share scheme within the Dublin region. This page includes an API developed according to the General Bikeshare Feed Specification (GBFS) (e.g.) information about vehicles, stations, pricing, etc. The current location of the vehicles is updated every five minutes. In addition, this page includes historical files of bike location data. Disclaimer - Please note that some of the historical files are empty due to historical data issues.

This dataset includes polygons with a minimum of 40 acres of woodlands per square mile as depicted in William H. Brewer�s 1873 map of woodland density and covers the conterminous United States. Each polygon has been labeled with the density category (1-5) depicted on the original map. This dataset was created by georeferencing a scanned version of the source map and by heads-up digitizing each woodland density polygon.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

Open Geospatial Consortium Web Feature Service (WFS). Supported WFS versions include 1.0.0, 1.1.0, and 2.0.0. Supported output formats include CSV, GeoJSON, GeoJSON-P, GML, KML, and Shapefile (Zipped).

This data archive contains digital elevation models (DEMs) and orthoimages generated from scanned historical aerial photographs from the North American Glacier Aerial Photography program available from the NSF Arctic Data Center (ADC, arcticdata.io).

The scanned images were preprocessed using the Historical Image Pre-Processing v0.1 software. Photogrammetric processing was performed with the Historical Structure from Motion v0.1 software.

All DEM and orthoimage products are provided in the UTM Zone 10N (EPSG:32610) projected coordinate system. Elevation values are in meters above the WGS84 ellipsoid.

See manuscript and supplement for processing details and further dataset description.

This release contains data products for two study sites in Washington state, USA:

Mount Baker 1970-09-09 1970-09-29 1974-08-10 1977-09-27 1979-10-06 1987-08-21 1990-09-05 1991-09-09 1992-09-15 1992-09-18

South Cascade 1967-09-21 1970-09-29 1974-08-10 1977-10-03 1979-08-20 1979-10-06 1984-08-14 1986-09-05 1987-08-21 1990-09-05 1991-09-09 1992-07-28 1992-09-15 1992-09-18 1992-10-06 1994-09-06 1996-09-10 1997-09-23

The 00_thumbnails.jpg provides a quicklook overview at both sites.

The DEM and ortho file names are structured as follows: hsfm_NAGAP_[site-name]_[date]_[type].tif

For example: hsfm_NAGAP_south-cascade_19670921_ortho.tif

Where: [site-name] = Either mount-baker or south-cascade [date] = Image acquisition date in YYYYMMDD format [type] = File type

For each DEM and ortho pair, we provide the following: _1m_dem.tif = Digital elevation model posted at 1 m resolution _ortho.tif = Orthoimage mosaic posted at the median image ground sample distance, rounded up to the nearest second decimal place. _metadata.tar.gz = Metadata tarball containing: _ortho_footprints.geojson = Orthoimage mosaic footprint polygons provided in GeoJSON format (EPSG:4326) _dem_footprints.geojson = DEM footprint polygons provided in GeoJSON format (EPSG:4326) _cameras.csv = Image file names, positions, and orientations

The dataset used in this project integrates multiple sources to analyze terrain characteristics and detect lost Amazonian settlements. It consists of the following key components:

1. LiDAR Elevation Data Source: NASA GEDI, OpenTopography Format: .las, .tif (GeoTIFF for raster elevation models) Purpose: Identifies terrain anomalies, elevation shifts, and artificial landforms indicative of past human settlements.

2. Multispectral Satellite Imagery Source: Sentinel-2 (ESA), Landsat-8 (USGS/NASA) Format: .tif, .jp2 (Multiband raster images) Purpose: Detects vegetation disruptions, soil composition changes, and historical land modifications.

3. Hydrology & Environmental Data Source: HydroSHEDS (World Wildlife Fund), EarthData (NASA) Format: .tif (Hydrological raster models), .shp (Shapefiles for river networks) Purpose: Confirms settlement sustainability by analyzing proximity to historical water sources.

4. Historical Maps & Indigenous Land Records Source: Library of Congress, National Geographic Archives Format: .geojson, .shp, scanned historical maps Purpose: Validates AI-predicted locations using 18th-century expedition routes and Indigenous accounts.

Over 3400 Geotagged historical photographs taken in the area of the City of Port Adelaide Enfield. The dates range from the 1840's to 1990. This data can be downloaded as points from downloaded …Show full descriptionOver 3400 Geotagged historical photographs taken in the area of the City of Port Adelaide Enfield. The dates range from the 1840's to 1990. This data can be downloaded as points from downloaded GeoJSON files (these contain Title, Description and Year taken and reference Flickr for the actual photos). The geoJSON files are named by subject matter. eg. "People - Clergy" All photos reside on Flickr along with their Title, Description, Year taken, Tags (Keywords) and Geotags. https://www.flickr.com/photos/paelocalhistory/sets All are arranged in albums under the Port Adelaide Enfield Local History Photos Flickr page. Approximately 800 of these images were sourced from the State Library of SA and were geotagged and attributed with metadata by the Council. Flickr page: https://www.flickr.com/photos/paelocalhistory/sets These images can also be browsed over a map in this application: https://mapping.portenf.sa.gov.au/history

You can check the fields description in the documentation: current Full database: https://docs.dataforseo.com/v3/databases/google/full/?bash; Historical Full database: https://docs.dataforseo.com/v3/databases/google/history/full/?bash.

Full Google Database is a combination of the Advanced Google SERP Database and Google Keyword Database.

Google SERP Database offers millions of SERPs collected in 67 regions with most of Google’s advanced SERP features, including featured snippets, knowledge graphs, people also ask sections, top stories, and more.

Google Keyword Database encompasses billions of search terms enriched with related Google Ads data: search volume trends, CPC, competition, and more.

This database is available in JSON format only.

You don’t have to download fresh data dumps in JSON – we can deliver data straight to your storage or database. We send terrabytes of data to dozens of customers every month using Amazon S3, Google Cloud Storage, Microsoft Azure Blob, Eleasticsearch, and Google Big Query. Let us know if you’d like to get your data to any other storage or database.

Geojson data containing all objects with lat-longs and associated descriptive metadata.