Princeton_Dinky

This layer is maintained by the GIS Department. Data is derived from a combination of the 2019 TRO-7, imagery from the 2012 PTC (Positive Train Control) Survey, 2017-2019 NAIK survey and the 2007 LiDAR survey.

General Highway Map, Princeton Township, Mille Lacs County Minnesota. Prepared by the Minnesota Department of Transportation Office of Transportation System Management in cooperation with U.S. Department of Transportation Federal Highway Administration.

This layer is sourced from gis.co.collin.tx.us.

This layer is sourced from gis.co.collin.tx.us.

For visualizing renewable energy Candidate Project Areas (CPAs) using GIS software. A growing number of pledges are being made by major corporations, municipalities, states, and national governments to reach net-zero emissions by 2050 or sooner. This dataset provides granular guidance on what getting to net-zero really requires and on actions needed to translate these pledges into tangible progress.

This data set contains the GIS data for solar, land-based wind, and offshore wind Candidate Project Areas (CPAs) under base and constrained land use assumptions (BLUA, CLUA). Each record in this dataset represents a “Candidate Project Area” with attributes such as nameplate capacity, annual generation, model region, distance to transmission, etc. The Lawrence Berkeley National Lab MAPRE tools (https://mapre.lbl.gov/gis-tools/) were used to create this dataset, along with input assumptions adapted from Wu et al 2020 (Grace C Wu et al 2020 Environ. Res. Lett. 15 074044). A full description of the processes used to generate this dataset can be found in Annex D of the main NZA report. The main report and report annexes can be found at https://netzeroamerica.princeton.edu/.

What's new in this version:

1. Reverted to earlier version of CPA dataset, prior to removal of densely populated areas, and prior to removal of existing and planned facilities. CPAs now include areas with population density up to 100 person/km2, and they have an attribute indicating the population density. Users can apply their own population density filters and thresholds.
2. Added attributes indicating the following, in separate columns for each CPA: Human Modification Index (HMI), prime farmland, land cover type, presence of existing facility, presence of planned facility

Data sources:

Population density: Rose, Amy N., McKee, Jacob J., Sims, Kelly M., Bright, Edward A, Reith, Andrew E., and Urban, Marie L. “LandScan 2019.” Oak Ridge National Laboratory, 2020. https://landscan.ornl.gov/landscan-datasets

HMI: Theobald, David et al. “Detailed Temporal Mapping of Global Human Modification from 1990 to 2017.” Dryad, 2020. https://doi.org/10.5061/dryad.n5tb2rbs1.

Prime farmland: “USA Soils Farmland Class.” USDA NRCS, Esri, October 1, 2019. https://landscape11.arcgis.com/arcgis/rest/services/USA_Soils_Farmland_Class/ImageServer.

Land cover: NLCD 2016. https://www.mrlc.gov/data?f%5B0%5D=category%3Aland%20cover&f%5B1%5D=region%3Aconus

Homer, Collin G., Dewitz, Jon A., Jin, Suming, Xian, George, Costello, C., Danielson, Patrick, Gass, L., et al. “Conterminous United States Land Cover Change Patterns 2001–2016 from the 2016 National Land Cover Database: ISPRS Journal of Photogrammetry and Remote Sensing, v. 162, p. 184–199, At.” ISPRS Journal of Photogrammetry and Remote Sensing, v. 162, p. 184–199, April 2020. https://doi.org/10.1016/j.isprsjprs.2020.02.019.

Existing solar arrays: Carr, N.B., Fancher, T.S., Freeman, A.T., and Battles Manley, H.M. “Surface Area of Solar Arrays in the Conterminous United States: U.S. Geological Survey Data Release,” 2016. http://dx.doi.org/10.5066/F79S1P57.

Existing wind turbines: Hoen, B.D., Diffendorfer, J.E., Rand, J.T., Kramer, L.A., Garrity, C.P., and Hunt, H.E. “United States Wind Turbine Database (Ver. 3.3, January 14, 2021).” U.S. Geological Survey, American Clean Power Association, and Lawrence Berkeley National Laboratory, 2018. https://doi.org/10.5066/F7TX3DN0.

Planned wind and solar facilities: “EIA (Last) (2019). Preliminary Monthly Electric Generator Inventory (Based on Form EIA-860M as a Supplement to Form EIA-860).” U.S. Energy Information Administration (EIA), n.d. https://www.eia.gov/electricity/data/eia860m/.

Geospatial data about Bureau County, Illinois Corporation Boundaries. Export to CAD, GIS, PDF, CSV and access via API.

Princeton DataLink Dynamic Map

Arts based development as a means of economic and community development is a relatively recent phenomenon. Through arts based development strategies, creative placemaking attempts to foster economic and social community development through methodology grounded in community based research. Creative placemaking intentionally integrates the arts within a community and advocates for the cross pollination of business and development ideas between community members and actors, while placing emphasis on the uniqueness of a location and cultural values. (Coutler et al.)

This layer is a component of BaseLayers.

The site suitability criteria included in the techno-economic land use screens are listed below. As this list is an update to previous cycles, tribal lands, prime farmland, and flood zones are not included as they are not technically infeasible for development. The techno-economic site suitability exclusion thresholds are presented in Table 1. Distances indicate the minimum distance from each feature for commercial scale solar development.Attributes:Steeply sloped areas: change in vertical elevation compared to horizontal distancePopulation density: the number of people living in a 1 km2 areaUrban areas: defined by the U.S. Census.8Water bodies: defined by the U.S. National Atlas Water Feature Areas, available from Argonne National Lab Energy Zone Mapping Tool9Railways: a comprehensive database of North America's railway system from the Federal Railroad Administration (FRA), available from Argonne National Lab Energy Zone Mapping ToolMajor highways: available from ESRI Living Atlas10Airports: The Airports dataset including other aviation facilities as of July 13, 2018 is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics' (BTS's) National Transportation Atlas Database (NTAD). The Airports database is a geographic point database of aircraft landing facilities in the United States and U.S. Territories. Attribute data is provided on the physical and operational characteristics of the landing facility, current usage including enplanements and aircraft operations, congestion levels and usage categories. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product. Available from Argonne National Lab Energy Zone Mapping ToolActive mines: Active Mines and Mineral Processing Plants in the United States in 200311Military Lands: Land owned by the federal government that is part of a US military base, camp, post, station, yard, center or installation.Table 1 Solar Steeply sloped areas >10o Population density >100/km2 Capacity factor <20% Urban areas <500 m Water bodies <250 m Railways <30 m Major highways <125 m Airports <1000 m Active mines <1000 m Military Lands <1000m For more information about the processes and sources used to develop the screening criteria see sources 1-7 in the footnotes. Data updates occur as needed, corresponding to typical 3-year CPUC IRP planning cycles.Footnotes:[1] Lopez, A. et. al. “U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis,” 2012. https://www.nrel.gov/docs/fy12osti/51946.pdf[2] https://greeningthegrid.org/Renewable-Energy-Zones-Toolkit/topics/social-environmental-and-other-impacts#ReadingListAndCaseStudies[3] Multi-Criteria Analysis for Renewable Energy (MapRE), University of California Santa Barbara. https://mapre.es.ucsb.edu/[4] Larson, E. et. al. “Net-Zero America: Potential Pathways, Infrastructure, and Impacts, Interim Report.” Princeton University, 2020. https://environmenthalfcentury.princeton.edu/sites/g/files/toruqf331/files/2020-12/Princeton_NZA_Interim_Report_15_Dec_2020_FINAL.pdf.[5] Wu, G. et. al. “Low-Impact Land Use Pathways to Deep Decarbonization of Electricity.” Environmental Research Letters 15, no. 7 (July 10, 2020). https://doi.org/10.1088/1748-9326/ab87d1.[6] RETI Coordinating Committee, RETI Stakeholder Steering Committee. “Renewable Energy Transmission Initiative Phase 1B Final Report.” California Energy Commission, January 2009.[7] Pletka, Ryan, and Joshua Finn. “Western Renewable Energy Zones, Phase 1: QRA Identification Technical Report.” Black & Veatch and National Renewable Energy Laboratory, 2009. https://www.nrel.gov/docs/fy10osti/46877.pdf.[8] https://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2019&layergroup=Urban+Areas[9] https://ezmt.anl.gov/[10] https://www.arcgis.com/home/item.html?id=fc870766a3994111bce4a083413988e4[11] https://mrdata.usgs.gov/mineplant/CreditsTitle: Techno-economic screening criteria for utility-scale solar photovoltaic energy installations for Integrated Resource PlanningPurpose for creation: These exclusion criteria are for use in electric system planning, capacity expansion modeling, and integrated resource planning.Keywords: solar, photovoltaic, resource potential, techno-economic, PV, IRPExtent: western states of the contiguous U.S.Use LimitationsThe geospatial data created by the use of these techno-economic screens inform high-level estimates of technical renewable resource potential for electric system planning and should not be used, on their own, to guide siting of generation projects nor assess project-level impacts. Confidentiality: PublicContactEmily Leslie Emily@MontaraMtEnergy.comSam Schreiber sam.schreiber@ethree.com Jared Ferguson Jared.Ferguson@cpuc.ca.gov Oluwafemi Sawyerr femi@ethree.com

The site suitability criteria included in the techno-economic land use screens are listed below. As this list is an update to previous cycles, tribal lands, prime farmland, and flood zones are not included as they are not technically infeasible for development. The techno-economic site suitability exclusion thresholds are presented in table 1. Distances indicate the minimum distance from each feature for commercial scale wind developmentAttributes: Steeply sloped areas: change in vertical elevation compared to horizontal distancePopulation density: the number of people living in a 1 km2 area Urban areas: defined by the U.S. Census. Water bodies: defined by the U.S. National Atlas Water Feature Areas, available from Argonne National Lab Energy Zone Mapping Tool Railways: a comprehensive database of North America's railway system from the Federal Railroad Administration (FRA), available from Argonne National Lab Energy Zone Mapping Tool Major highways: available from ESRI Living Atlas Airports: The Airports dataset including other aviation facilities as of July 13, 2018 is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics's (BTS's) National Transportation Atlas Database (NTAD). The Airports database is a geographic point database of aircraft landing facilities in the United States and U.S. Territories. Attribute data is provided on the physical and operational characteristics of the landing facility, current usage including enplanements and aircraft operations, congestion levels and usage categories. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product. Available from Argonne National Lab Energy Zone Mapping Tool Active mines: Active Mines and Mineral Processing Plants in the United States in 2003Military Lands: Land owned by the federal government that is part of a US military base, camp, post, station, yard, center, or installation. Table 1 Wind Steeply sloped areas >10o Population density >100/km2 Capacity factor <20% Urban areas <1000 m Water bodies <250 m Railways <250 m Major highways <125 m Airports <5000 m Active mines <1000 m Military Lands <3000m For more information about the processes and sources used to develop the screening criteria see sources 1-7 in the footnotes. Data updates occur as needed, corresponding to typical 3-year CPUC IRP planning cyclesFootnotes:[1] Lopez, A. et. al. “U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis,” 2012. https://www.nrel.gov/docs/fy12osti/51946.pdf[2] https://greeningthegrid.org/Renewable-Energy-Zones-Toolkit/topics/social-environmental-and-other-impacts#ReadingListAndCaseStudies[3] Multi-Criteria Analysis for Renewable Energy (MapRE), University of California Santa Barbara. https://mapre.es.ucsb.edu/[4] Larson, E. et. al. “Net-Zero America: Potential Pathways, Infrastructure, and Impacts, Interim Report.” Princeton University, 2020. https://environmenthalfcentury.princeton.edu/sites/g/files/toruqf331/files/2020-12/Princeton_NZA_Interim_Report_15_Dec_2020_FINAL.pdf.[5] Wu, G. et. al. “Low-Impact Land Use Pathways to Deep Decarbonization of Electricity.” Environmental Research Letters 15, no. 7 (July 10, 2020). https://doi.org/10.1088/1748-9326/ab87d1.[6] RETI Coordinating Committee, RETI Stakeholder Steering Committee. “Renewable Energy Transmission Initiative Phase 1B Final Report.” California Energy Commission, January 2009.[7] Pletka, Ryan, and Joshua Finn. “Western Renewable Energy Zones, Phase 1: QRA Identification Technical Report.” Black & Veatch and National Renewable Energy Laboratory, 2009. https://www.nrel.gov/docs/fy10osti/46877.pdf.[8]https://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2019&layergroup=Urban+Areas[9]https://ezmt.anl.gov/[10]https://www.arcgis.com/home/item.html?id=fc870766a3994111bce4a083413988e4[11]https://mrdata.usgs.gov/mineplant/Credits Title: Techno-economic screening criteria for utility-scale wind energy installations for Integrated Resource Planning Purpose for creation: These site suitability criteria are for use in electric system planning, capacity expansion modeling, and integrated resource planning. Keywords: wind energy, resource potential, techno-economic, IRP Extent: western states of the contiguous U.S. Use Limitations The geospatial data created by the use of these techno-economic screens inform high-level estimates of technical renewable resource potential for electric system planning and should not be used, on their own, to guide siting of generation projects nor assess project-level impacts.Confidentiality: Public ContactEmily Leslie Emily@MontaraMtEnergy.comSam Schreiber sam.schreiber@ethree.com Jared Ferguson Jared.Ferguson@cpuc.ca.govOluwafemi Sawyerr femi@ethree.com

Geospatial data about Collin County, Texas Major Crime (2016). Export to CAD, GIS, PDF, CSV and access via API.

U.S. Geographic Names Information System Populated Places represents an automated inventory of the proper names and locations of physical and cultural geographic features located throughout the United States and its Territories.

Geospatial data about Miami-Dade County, Florida Environmentally Endangered Lands (EEL). Export to CAD, GIS, PDF, CSV and access via API.

Geospatial data about Miami-Dade County, Florida Natural Forest Communities (NFC). Export to CAD, GIS, PDF, CSV and access via API.

Map of the Solomon Islands

U.S. ZIP Code Areas represents five-digit ZIP Code areas used by the U.S. Postal Service to deliver mail more effectively. The first digit of a five-digit ZIP Code divides the country into 10 large groups of states numbered from 0 in the Northeast to 9 in the far West. Within these areas, each state is divided into an average of 10 smaller geographical areas, identified by the 2nd and 3rd digits. These digits, in conjunction with the first digit, represent a sectional center facility or a mail processing facility area. The 4th and 5th digits identify a post office, station, branch or local delivery area.Read More

This is a Sanborn map of Red Bank, Monmouth County, New Jersey, and vicinity shown at a scale of 1:600.

Weekly Eviction Data 2020

  Weekly Eviction Data 2020 
  Geography Level: Census (Only for Boston, Cincinnati, Cleveland, Houston, Jacksonville, Kansas City, Milwaukee, St Louis), Zip Code (Only for Austin, Pittsburgh, Richmond)Item Vintage: 2020
  Update Frequency: WeeklyAgency: Princeton Eviction LabAvailable File Type: Excel with PDF Report 

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