Grid-based building morphological parameters with global coverage at 30-arc second spatial resolution are currently available in GeoTIFF format. Provided datasets contains three-building morphological parameters (the mean building height Have, plan area density PAD and frontal area density FAD) and two-aerodynamic parameters (aerodynamic roughness length z0 and zero-place displacement d) and sky-view factor (svf).The building morphological datasets were estimated from the global databases such as population, nighttime light, impervious surface area and gross domestic products. Two aerodynamic parameters and sky-view factors are calculated using the empirical equations discussed by Kanda et al. (2013) and Kanda et al. (2005), respectively.1. Raster files: (parameter name)_2013.tifFormat: GeoTIFFProjection: WGS 1984 World Mercator projectionSpatial resolution: 30-arc secondData list: Have_2013.tif, PAD_2013.tif, FAD_2013.tif, d_2013.tif, z0_2013.tif, svf_2013.tif2. Building Original DataFormat: Microsoft Excel WorkbookOriginal_building_data.xlsx contains observed building morphological parameters calculated from three- and two-dimensional building databases, and global databases (impervious surface area ISA and population density adjusted by nighttime light PopdenVIIRS) at each grid code.Validation_analysis.xlsx contains building morphological parameters calculated from three-dimensional building database (observed) and parameters estimated from global databases (predicted) at one-km spatial resolution in Berlin, Singapore and Osaka.Additional_validation_UScities.xlsx contains building morphological parameters at one-km resolution by NUDAPT database (observed) and estimated from global databases (predicted) for 42 US cities. We used this data in the Supplementary Discussion. Megacities_statistic.xlsx contains GDPcity, the maximum, minimum, mean value and standard deviation of each predicted building morphological parameters at 37 megacities. 3. Source CodeProgramming language 1: Python site package in ArcGIS v10.3.1Calculate_parameters.py contains code for calculating observed building morphological parameters from grid-based two- and three-dimensional building database input. We recommend using this script after using the Split By Attributing Tools to convert a fishnet building footprint map into multiple grids.Modifying_population_by_nightlight.py contains code for adjusted population density by nighttime light at each grid.Programming language 2: Python v2.7Converting_grids.py contains code for converting grid-based population density adjusted by nighttime light into a global map. This source code is used after running Modifying_population_by_nightlight.py.

📋 Dataset Description

This dataset provides an in-depth analysis of the Urban Heat Island (UHI) effect, which refers to urban areas experiencing higher temperatures compared to surrounding rural areas. The data aims to help researchers, urban planners, and environmental scientists understand how urbanization affects local climates, energy consumption, air quality, and public health. By combining temperature, land cover, population density, and environmental factors, this dataset offers insights into how cities around the world are impacted by climate change.

⚡ Usage

• Urban Planning: Helping cities design more sustainable and climate-resilient infrastructures.
• Climate Change Research: Studying the effects of urbanization on global temperature patterns.
• Public Health: Analyzing the relationship between urban heat and heat-related health issues.
• Energy Management: Optimizing energy consumption strategies for cities.
• Environmental Policy: Supporting data-driven policy decisions for healthier, greener cities.

The global Human Modification map (HM) provides a cumulative measure of human modification of terrestrial lands across the globe at a 1-km resolution. It is a continuous 0-1 metric that reflects the proportion of a landscape modified based on modeling the physical extents of 13 anthropogenic stressors and their estimated impacts using spatially-explicit global datasets with a median year of 2016. The HM map was developed by scientists at The Nature Conservancy and Conservation Science Partners. For questions or feedback on this map please contact Christina Kennedy (ckennedy@tnc.org) or James Oakleaf. We welcome the opportunity to hear from and to collaborate with researchers on projects that integrate the HM map into their work.For a description of the methods and to cite the use of HM map, see:Kennedy CM, Oakleaf JR, Theobald DM, Baruch‐Mordo S, Kiesecker J. Managing the middle: A shift in conservation priorities based on the global human modification gradient. Glob Change Biol. 2019;25:811–826. https://doi.org/10.1111/gcb.14549Interactive data viewer available at: http://s3.amazonaws.com/DevByDesign-Web/Apps/gHM/index.html

For lands used to produce crops, CEC developed a suitability model to simultaneously evaluate several factors that impact an area’s relative implication for croplands. In the CEC land use screens, implication is defined as a possible significance or a likely consequence of an action. For example, planning for energy infrastructure development in areas with more factors that support high-value croplands has implications for opportunities to preserve agricultural land. The variables used in the CEC Cropland Index Model contain information on soil quality (CA Revised Storie Index, Electrical Conductivity, and Sodium Adsorption Ratio), farmland designations (Prime Farmland, Unique Farmland and Farmland of Statewide Importance), and current existence of crops (as indicated by the California Statewide Crop Mapping). The CEC Cropland Index Model does not include statewide information for grazing lands or rangelands, and it is only applied to solar technology. Each input data layer is transformed onto a common scale and weighted according to each dataset’s relative importance. The result is a summation of the input data layers into a single-gridded map. This final model output provides a numerically weighted index of importance for croplands at a given location. The classified version of the model output, given in this dataset, partitions the CEC Cropland Index Model at the mean into areas of high and low implication. The high implication area is used as an exclusion in the CEC Land Use Screens for solar technology. These regions have a relatively higher implication for cropland than the lower implication region. The table below provides data sources that the CEC Cropland Index Model relies on. For a complete description of the model and its use in the 2023 CEC Land-Use Screens, please refer to the Land Use Screens Staff Report in the CEC Energy Planning Library. Dataset Name Source Usage Gridded Soil Survey Geographic (gSSURGO) Database Soil Survey Staff. 2020. "The Gridded Soil Survey Geographic (gSSURGO) Database for California." United States Department of Agriculture, Natural Resources Conservation Service. https://gdg.sc.egov.usda.gov/ Provides CA Revised Storie Index, Electrical Conductivity, and Sodium Adsorption Ratio for the CEC Cropland Index Model for the Core and SB 100 Terrestrial Climate Resilience Screens for solar resource potential California Important Farmland "2018 California Important Farmland.” Farmland Mapping and Monitoring Program." California Department of Conservation. https://www.conservation.ca.gov/dlrp/fmmp Prime Farmland, Unique Farmland, and Farmland of Statewide Importance is used in the CEC Cropland Index Model for the Core and SB 100 Terrestrial Climate Resilience Screens for solar resource potential California Statewide Crop Mapping (2019) "2019 California Statewide Crop Mapping." California Department of Water Resources. https://data.cnra.ca.gov/dataset/statewide-crop-mapping The footprint is used as part of the mask for the CEC Cropland Index Model’s domain of analysis for the Core and SB 100 Terrestrial Climate Resilience Screens for solar resource potential

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

The land overlays are developed from amalgamated units from the land use capability (LUC) assessment of the New Zealand Land Resource Inventory (NZLRI) Gisborne East Coast Region, Second Edition, June 1999. In the NZLRI the maximum erosion severity has been assessed for each LUC unit and this is based on the geology, soil type, steepness, climate and vegetation cover. The land overlays comprise the following LUC units: Land Overlay 1 Classes I-IV and VIe1, 2, 3, 5, 7 and 8 inclusive, Land Overlay 2 Balance of Class VI, Land Overlay 3 (including 3A) Classes VII & VIII (see C7.1.5 for detailed units).

Erosion is the natural process of soil and rock wearing away and being moved through the landscape. Erosion includes sheet, wind, creep, slump, flow, rill, earthflow, gully, tunnel gully and stream erosion. In areas undisturbed by human activity, the rate of erosion is determined by geology and weather. Land uses, particularly those that reduce vegetation cover or disturb the soil can lead to a much faster rate of erosion. This is especially heightened in naturally unstable areas where even relatively minor landuse activities can have major impacts. Erosion that has been increased by human action is called induced or accelerated erosion and despite the voluntary erosion control efforts of many landowners and kaitiaki, this remains the major landuse issue in the district that also impacts on the district’s waterways and coastal seabed and key assets such as roading.

Land-use and land-cover change matrix for the years 2010 to 2030 (103 km2).

This data layer contains geothermal resource areas and their technical potential used in long-term electric system modeling for Integrated Resource Planning and SB 100. Geothermal resource areas are delineated by Known Geothermal Resource Areas (KGRAs) (Geothermal Map of California, 2002), other geothermal fields (CalGEM Field Admin Boundaries, 2020), and Bureau of Land Management (BLM) Geothermal Leasing Areas (California BLM State Office GIS Department, 2010). The fields that are considered in our assessment have enough information known about the geothermal reservoir that an electric generation potential was estimated by USGS (Williams et al. 2008) or estimated by a BLM Environmental Impact Statement (El Centro Field Office, 2007). For the USGS identified geothermal systems, any point that lies within 2 km of a field is summed to represent the total mean electrical generation potential from the entire field.

Geothermal field boundaries are constructed for identified geothermal systems that lie outside of an established geothermal field. A circular footprint is assumed with a radius determined by the area needed to support the mean resource potential estimate, assuming a 10 MW/km² power density.

Several geothermal fields have power plants that are currently generating electricity from the geothermal source. The total production for each geothermal field is estimated by the CA Energy Commission’s Quarterly Fuel and Energy Report that tracks all power plants greater than 1 MW. The nameplate capacity of all generators in operation as of 2021 were used to inform how much of the geothermal fields are currently in use. This source yields inconsistent results for the power plants in the Geysers. Instead, an estimate from the net energy generation from those power plants is used. Using these estimates, the net undeveloped geothermal resource potential can be calculated.

Finally, we apply the protected area layer for geothermal to screen out those geothermal fields that lie entirely within a protected area. The protected area layer is compiled from public and private lands that have special designations prohibiting or not aligning with energy development.

This layer is featured in the CEC 2023 Land-Use Screens for Electric System Planning data viewer.

For more information about this layer and its use in electric system planning, please refer to the Land Use Screens Staff Report in the CEC Energy Planning Library.

Change Log:

Version 1.1 (January 18, 2024)

• ProtectedArea_Exclusion field was updated to correct for the changes to the Protected Area Layer. A Development Focus Area on Bureau of Land Management (BLM) land that overlays the Coso Hot Springs allows its resource potential to be considered in the statewide estimate.

Data Dictionary:

Total_MWe_Mean: The estimated resource potential from each geothermal field. All geothermal fields, except for Truckhaven, was given an estimate by Williams et al. 2008. If more than one point resource intersects (within 2km of) the field, the sum of the individual geothermal systems was used to estimate the magnitude of the resource coming from the entire geothermal field. Estimates are given in MW.

Total_QFER_NameplateCapacity: The total nameplate capacities of all generators in operation as of 2021 that intersects (within 2 km of) a geothermal field. The resource potential already in use for the Geysers is determined by Lovekin et al. 2004. Estimates are given in MW.

ProtectedArea_Exclusion: Binary value representing whether a field is excluded by the land-use screen or not. Fields that are excluded have a value of 1; those that aren’t have a value of 0.

NetUndevelopedRP: The net undeveloped resource potential for each geothermal field. This field is determined by subtracting the total resource potential in use (Total_QFER_NameplateCapacity) from the total estimated resource potential (Total_MWe_Mean). Estimates are given in MW.

Acres_GeothermalField: This is the geodesic acreage of each geothermal field. Values are reported in International Acres using a NAD 1983 California (Teale) Albers (Meters) projection.

References:

1. Geothermal Map of California, S-11. California Department of Conservation, 2002. https://www.conservation.ca.gov/calgem/geothermal/maps/Pages/index.aspx
2. CalGEM Field Admin Boundaries, 2020. https://gis.conservation.ca.gov/server/rest/services/CalGEM/Admin_Bounds/MapServer
   
3. California BLM State Office GIS Department, California BLM Verified and Potential Geothermal Leases in California, 2010. https://databasin.org/datasets/5ec77a1438ab4402bf09ef9bfd7f04d9/
   
4. Williams, Colin F., Reed, Marshall J., Mariner, Robert H., DeAngelo, Jacob, Galanis, S. Peter, Jr. 2008. "Assessment of moderate- and high-temperature geothermal resources of the United States: U.S. Geological Survey Fact Sheet 2008-3082." 4 p. https://certmapper.cr.usgs.gov/server/rest/services/geothermal/westus_favoribility_systems/MapServer/0
5. El Centro Field Office, Bureau of Land Management (2007). Final Environmental Impact Statement for the Truckhaven Geothermal Leasing Area (Publication Index Number: BLM/CA/ES-2007-017+3200). United States Department of the Interior Bureau of Land Management.
6. Lovekin, James W., Subir K. Sanyal, Christopher W. Klein. 2004. “New Geothermal Site Identification and Qualification.” Richmond, California: