AndreaBozzo/ceres-open-data-index huggingface dataset:
Sacramento Parking Map Data Download (GDB/KMZ)

This map is intended to assist staff and visitors to the California Energy Commission headquarters, 715 P Street, 3rd Floor, Sacramento, CA 95814 with nearby parking and transportation information. This information was collected from various sources and updated to the best of our knowledge. If you notice any inaccuracies, please email us at gis@energy.ca.gov and we will update this information. For more information on conditions of use, please visit https://www.energy.ca.gov/conditions-of-use.
Elk Home Range - Potter-Redwood Valley - 2023-2024 [ds3191]

The project lead for the collection of this data was Carrington Hilson. Elk (9 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2023-2024. The Potter-Redwood Valley herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed at 6.5 hour intervals in the dataset. To improve the quality of the data set, all points with DOP values greater than 5 and those points visually assessed as a bad fix by the analyst were removed. The methodology used for this migration analysis allowed for the mapping of the herd's home range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 8 elk, including 15 annual home range sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours and a fixed motion variance of 1000. Home range is visualized as the 50th percentile contour (high use) and the 99th percentile contour of the year-round utilization distribution. Home range designations for this herd may expand with a larger sample.
Case Overview Department of Child Support Services

This data table provides the basic information for California child support cases, such as number of cases per county, the number of cases where the child is receiving public assistance, number of cases where the child formerly received public assistance, but no longer does, and the number of cases where the child has never received public assistance. The data is sorted by counties and broken into Current Public Assistance, Former Public Assistance and Never Assisted case types. [DCSSOpenData@dcss.ca.gov] for any questions.
Elk Home Range - Lone Pine - 2023-2024 [ds3192]

The project lead for the collection of this data was Carrington Hilson. Elk (2 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2023-2024. The Lone Pine herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed at 6.5 hour intervals in the dataset. To improve the quality of the data set, all points with DOP values greater than 5 and those points visually assessed as a bad fix by the analyst were removed. The methodology used for this migration analysis allowed for the mapping of the herd's home range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 2 elk, including 2 annual home range sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours and a fixed motion variance of 1000. Home range is visualized as the 50th percentile contour (high use) and the 99th percentile contour of the year-round utilization distribution. Home range designations for this herd may expand with a larger sample.
SB 535 Disadvantaged Communities 2022 (Tribal update 2024)

This map shows the 2022 disadvantaged communities designated by CalEPA for the purpose of SB 535. These areas represent: * Highest scoring 25% of census tracts from [CalEnviroScreen 4.0], along with census tracts scoring in the top 5% of the Pollution Burden indicator but without an overall CalEnviroScreen score due to due to unavailable or unreliable Population Characteristics indicator data and score. * All census tracts currently identified as disadvantaged but not in the highest scoring 25% census tracts in version 4.0 (i.e., the highest scoring 25% of census tracts in CalEnviroScreen 3.0 along with the census tracts with the highest 5% Pollution Burden scores, but without an overall CalEnviroScreen score). [See the Disadvantaged Communities 2017 map here]. * Federally recognized tribal areas as identified by the Census in the 2021 American Indian Areas Related National Geodatabase. A Tribe may establish that a particular area of land is under its control, for purposes of this designation, by requesting a consultation with the CalEPA Deputy Secretary for Environmental Justice, Tribal Affairs and Border Relations at [TribalAffairs@calepa.ca.gov] Please see CalEPA's Draft Designation of Disadvantaged Communities Pursuant to Senate Bill 535 document as well as CalEPA's [California Climate Investments to Benefit Disadvantaged Communities] page.
Vegetation - Lower Feather River Update - 2018 [ds2891]

The Feather River was mapped using 2018 National Agricultural Inventory Program (NAIP) imagery, and using a classification developed by the California Native Plant Society (CNPS) based in the National Vegetation Classification System (NVCS). A report describing mapping methodology for a similar project can be found here: [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=184992]. For more information about California's classification, see [https://vegetation.cnps.org/].Changes in a remap effort can occur due to several factors, some due to physical change and some due to non-physical change. Examples of factors that represent physical change are: stand maturation or decadence, fire, erosion, deposition, anthropogenic disturbances such as scraping/clearing, urban development, agricultural development, and restoration. Non-physical changes include: improvement in imagery quality, incorrect original mapping, imagery shifts, improvement of older linework, and change in level mapped (classification). Imagery quality continues to improve with time, which allows the photointerpreter to better determine which species are present and in what amounts to determine its best classification. A photointerpreter could also disagree with the previous mapper’s decisions and may change the classification in the remapping effort. A more subtle map change, which does not affect the acreages significantly, is when the photointerpreter improves old linework to make it better fit the classification type visually. Also, when the NAIP imagery is ortho-rectified, in some years it does not perfectly align with the year it is being compared to and linework needs to be shifted/redrawn. Lastly, a photointerpreter may have remapped an Alliance at the Group level due to lack of confidence at the Alliance level, and vice versa the interpreter may have mapped an Alliance that was previously mapped at Group level due to better confidence at that level. All of these factors should be taken into consideration when using and comparing these data to previous efforts.
Vegetation - Sequoia and Kings Canyon National Parks Vegetation Mapping Project [ds984]

High resolution vegetation polygons mapped by the National Park Service. The Vegetation Map of Sequoia and Kings Canyon National Parks was produced over an eight year period (2000-2007) to describe and map the vascular plant associations of the two southern Sierra Nevada national parks. Over 80,000 individual vegetation polygons are mapped to 172 vegetation classes. Overall spatial accuracy of the resulting map products is equivalent to 1:24000 National Map Accuracy Standards. Vegetation delineations are based on photo interpretation of 1:15,840 scale color infrared photography acquired by Pacific Western Technologies (PWT) during two missions conducted in July of 2000 and 2001. Photo interpretation and automation, including rectification to the 1:24000 Digital Photo Orthoquads (DOQQs) was performed by Aerial Information Systems (AIS) under subcontract to Environmental Systems Research Institute (ESRI), who provided project management and final cartographic products. Development of the National Vegetation Classification Standard (NVCS) compliant mapping classification and accuracy assessment of the final map products, along with all associated field sampling, was performed by NPS ecologists at Sequoia and Kings Canyon National Parks. Overall map accuracy is 80% at the association level and 86% at the alliance level (based on 2409 field assessments). This version also contains CALVEG crosswalks used by the California U.S. Forest Service. The Vegetation Mapping Inventory (VMI) is a cooperative effort by the Inventory and Monitoring Division (IMD) of the National Park Service (NPS) and the Biological Resource Division of the U.S. The minimum mapping unit (MMU) is 1/2 hectare.
Black Bear Habitat Model for NSNF Connectivity - CDFW [ds1008]

The Maxent modeling algorithm was used to build the species distribution model at 270 m spatial resolution using species occurrence points and environmental layers as predictors (Phillips et al. 2006). Species occurrence points were primarily obtained from CNDDB (California Natural Diversity Database) and other CDFW sources, GBIF (Global Biodiversity Information Facility), PRBO (Point Blue Conservation Science) and Arctos museum databases. Vegetation, distance to water, elevation, and bioclimatic variables (Franklin et al. 2013) were used as predictor variables. The models were run at 270 m spatial resolution with five replications using cross-validation as a method of sample evaluation. Cross-validation involved the partitioning of the sample data into n subsets, fitting the models to n-1subsets, and testing the model on the one subset not used in fitting the model. Initial model runs showed that our models converged around 2,000 iterations and for this reason we ran all models with 2,500 maximum iterations. Maxent was implemented in R using the ‘dismo''package (Hijmans et al. 2011). Model evaluation was carried out using the ‘PresenceAbsence''package in R (Freeman & Moisen 2008). We used AUC as a metric to evaluate model performance. The package also computes threshold values using several accuracy metrics to translate predicted probability maps into binary suitable and unsuitable habitats. We selected the MeanProb, a threshold set based on the mean predicted probability of species occurrences. The output from Maxent are grid datasets in a multiband ‘tif''format with one band for each replication. We averaged the five replicated maps and created a mean grid for each species. The grid was then symbolized to represent low (threshold-50), medium (50-75) and high (75-100) habitat suitability, with pixel values that are below the threshold excluded. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].
NLCD 2019 Land Cover California Subset

The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
NLCD 2013 Land Cover California Subset

The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
NLCD 2011 Land Cover California Subset

The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
NLCD 2008 Land Cover California Subset

The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
NLCD 2006 Land Cover California Subset

The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.