Explore, visualise and interact with youth-centered data. Includes data on poverty, education, employment, and demographics.

Dataset is an overview of food access indicators for low-income and other census tracts using different measures of supermarket accessibility. This dataset provides food access data for populations within census tracts; and offers census-tract-level data on food access that can be used for community planning or research purposes.Data from USDA Economic Research Service (ERS) Food Access Research Atlas, 2019. Last updated 4/27/2021.See also USDA map service at https://gisportal.ers.usda.gov/server/rest/services/FARA/FARA_2019/MapServer.

This dataset contains Arizona county boundaries for use in joining EPHT Data Explorer data to county-level geography using the "GeogID" column.Update Frequency: N/A

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 mirrors the feeder level smart meter data published by Northern Power grid (NPG) as mandated by Ofgem. The data here is aggregated to feeder level, with half hourly time granularity. There are some restrictions on data publication - one of the main ones being that only aggregations containing 5 or more smart meters are published. The intention is to update this on a monthly basis. Note licensing here is not CC as with other DNOs, but Northern Powergrid Open Data Licence v1.0 - https://northernpowergrid.opendatasoft.com/p/opendatalicence/ Attribution statement: Supported by Northern Powergrid Open Data

This explorer provides sample premium information for individual ACA-compliant health insurance plans available to Iowans for 2026 based on age, rating area and metal level. These are premiums for individuals, not families. Please note that not every plan ID is available in every county. On or after November 1, 2025, please go to www.healthcare.gov to determine if your plan is available in the county you reside in.

CalFresh Cases in August 2022 in Los Angeles County, count per Census Tract. The CalFresh Program (formerly known as Food Stamps) helps low-income households increase their food-buying power to meet their household’s nutritional needs. In this dataset, a "Case" could refer to an individual or a household - any recipient of CalFresh benefits. In Los Angeles County, eligibility depends on income as compared to federal poverty level and other variables. As an imperfect indicator of CalFresh "gap" - where people who are eligible have not taken advantage of the benefit - this dataset divides CalFresh cases by number of households below different income levels. Layer also has information about "public assistance" from ACS table S1702.For more information about this dataset, please contact egis@isd.lacounty.govFor more information about CalFresh, please visit https://dpss.lacounty.gov/en/food/calfresh.html

A geospatial interface will be developed using ArcIMS software. The interface will provide a means of accessing information stored in the SOFIA database and the SOFIA data exchange web site through a geospatial query. The spatial data will be served using the ArcSDE software, which provides a mechanism for storing spatial data in a relational database. A spatial database will be developed from existing data sets, including national USGS data sets, the Florida Geographic Digital Library, and other available data sets. Additional data sets will be developed from the published data sets available from PBS and other projects.

The South Florida restoration effort requires multidisciplinary information relating to present and historical conditions for use in responsible decision-making. The South Florida Information Access (SOFIA) database is the cornerstone of information management for the South Florida place-based science program. Currently, the SOFIA web site and database have a minimal geospatial interface which relies on the Geo-Data Explorer (GeoDE) system developed by the USGS Energy Resources Program in Reston. A geospatial interface using currently available commercial software (ArcIMS) is needed to develop a more easily maintained and user-friendly interface. Developing an interface that is directly connected to the SOFIA website and database will provide a more stable long term solution to providing a geospatial interface.

This application compares changes between aggregated 2011 National Land Cover Database land cover categories with similarly aggregated land cover categories from The Clark Labs 2050 Conterminous US Land Cover Prediction. It also provides a few summary statistics about possible changes in developed, forest and agricultural land cover. Look for the soon to be released Clark Labs American Land Change Explorer application, which provides exhaustive analysis and summaries of potential transitions from each of the NLCD categories to each of the projected 2050 categories.The Clark Labs 2050 Conterminous US Land Cover Prediction© 2016 Clark LabsIntroductionThe Clark Labs’ conterminous US land cover prediction for 2050 was produced as part of the development of the Land Change Explorer – a web application to illustrate the potential of predictive land change modeling and to introduce potential users to the Land Change Modeler – a cloud-based software service for land change modeling to be offered in the ArcGIS Marketplace.ProcedureThe prediction is based on an empirical modeling of the relationship between land cover change from 2001 to 2011 and a series of explanatory variables. The land cover data were at a 30 meter resolution from the National Land Cover Database (NLCD). The explanatory variables(1) were:ElevationSlopeProximity to primary roadsProximity to secondary roadsProximity to local roadsProximity to high intensity developmentProximity to open waterProximity to cropland (used only for transitions to cropland)Protected areasCounty subdivisions or counties/incorporated places (depending on the state)(2)The modeling procedure used is a newly developed algorithm suitable for distributed computing in a cloud computing environment(3). Briefly, the procedure is based of kernel density estimation of the normalized likelihood of change associated with varying levels of each independent variable. These estimates are then aggregated by means of a locally-weighted average where the weights are based on the degree of conviction each variable has about the outcome at that specific pixel. Testing has shown it to be comparable in skill to a multi-layer perceptron neural network with the added advantages of rapid calculation and capability of being distributed across multiple computer nodes.Because the drivers of change can vary over space, modeling was done separately for each state. All transitions that met or exceed 2 km2 in area (at the state level) were modeled independently. Within a single state, as many as 128 individual transitions might occur. In total, over the 48 conterminous states, 3330 transitions were modeled. The modeling process initially establishes the potential to transition. This potential is expressed as a continuous value from 0 to 1 at each pixel for each transition. The procedure then uses the Markovian assumption that the rate of transition experienced in the historical period (2001-2011 in this case) will continue into the future. A competitive greedy selection process then allocates the projected change(4).ValidationIn the training process for each transition, 50% of historical instances of change and 50% of an equal-sized sample of pixels eligible to change, but which did not (e.g., persistence), were reserved for model validation. The median accuracy over all 3330 transitions was 80% with 79% of change validation pixels being correctly predicted and 83% of persistence pixels being correctly predicted. Thus the models, on average, are quite balanced in their ability to predict change and persistence.The accuracy associated with more specific transitions varied. A key objective was to be able to monitor and project anthropogenic changes and thus the explanatory variables chosen were focused on such drivers. Consequently, the median accuracy of natural to developed transitions (such as deciduous forest to low intensity development) was 92%. Again, accuracy was evenly balanced (93% for change / 91% for persistence).Accuracy for transitions between developed categories was lower at 77% (80% change and 75% persistence). A large part of this is because of the inconsistent manner in which roads are classified in the NLCD system. Roads are classified as one of the developed categories (high, medium, low and open development) based on the amount of impervious surface detected within pixels. Alignment of image pixels can cause this to vary resulting in roads that frequently switch classes between the years mapped.Natural transitions, such as forest to shrub, had the lowest overall accuracy at 74%. This was expected because many drivers cannot be predicted with the variables used. An example would be forest fires caused by lightning. This is also reflected in the fact that accuracy for predicting change was only 71% while that for predicting persistence was 78%.Finally, in states with significant cropland development, natural to cropland transitions were modeled with a 79% overall median accuracy. Accuracies for change and persistence were 78% and 81% respectively.DisclaimerNote that there are many highly plausible future outcomes and the specific scenario presented is only one of these (albeit judged to be the most plausible). Also note that each state is modeled separately (on the assumption that drivers of change many differ between states). As a consequence, there may be some mismatches at the boundaries between states. Generally, these are only evident for states that have large quantities of natural to natural transitions (e.g., with forest plantation crop cycles or frequent fire) where the accuracy is lower. Also note that the protected areas layer does not include all protected areas. Some local conservation land may be missing. Finally, note that the modeling is based on the assumption that rate of change experienced within the historical period (2001-2011) will persist into the future.1 Elevation data were from the National Elevation Database while slope was derived from those data. All roads data were acquired from the Census Bureau TIGER line files for 2014. Earlier road data would have been preferred, but errors in earlier TIGER line files were deemed to be unacceptable. Country subdivisions, counties and incorporated places were also acquired from the Census Bureau. Protected areas came from the Protected Areas Database of the USGS National Gap Analysis Program. All proximity layers were derived by Clark Labs.2 In some states, planning jurisdiction is controlled by county subdivisions (such as in New England), while in others, planning is governed by a combination of counties and incorporated places (such as in many of the western states).3 Eastman, J.R., Crema, S.C., and Rush, H.R., (forthcoming) A Weighted Normalized Likelihood Procedure for Empirical Land Change Modeling.4 Greedy selection assumes that the specific pixels that will change are those that are ranked the highest. Conflicts are resolved by assigning them to the transition with the highest marginal transition potential.

These data depict geographic features on the surface of the earth. They were created primarily as a visual aid for urban and county planning. Standards-based geospatial metadata did not accompany the source files for the collection. There is however a standards-based metadata record for the collection that was provided by Aerials Express. That collection-level metadata record indicates a time period of content of 20050510. Additionally, flight reports indicate dates in late May. This is inconsistent with the metadata table available on EarthExplorer.The source data for this service are available for download from USGS EarthExplorer. Individual image tiles can be downloaded using the Idaho Aerial Imagery Explorer.These data can be bulk downloaded from a web accessible folder.Source Metadata From USGS EarthExplorer:Beginning Date: 2009/07/23Ending Date: 2009/07/23EPSG: 26911Map Projection Description: NAD83 / UTM zone 11NState/Province/Country: ID/WADatum: NAD83Dataset: 200907_spokane_wa_0x3000_utm_clrProjection Zone: 11Sensor: UNKNOWNSensor Type: ColorNumber of Bands: 3Vendor: AERIALS_EXPRESSResolution: 0.3Units of Resolution: METERAgency: USGSUsers should be aware that temporal changes may have occurred since these data were collected and that some parts of these data may no longer represent actual surface conditions. Users should not use these data for critical applications without a full awareness of the limitations of these data as described in the lineage or elsewhere.