Anyone who has taught GIS using Census Data knows it is an invaluable data set for showing students how to take data stored in a table and join it to boundary data to transform this data into something that can be visualised and analysed spatially. Joins are a core GIS skill and need to be learnt, as not every data set is going to come neatly packaged as a shapefile or feature layer with all the data you need stored within. I don't know how many times I taught students to download data as a table from Nomis, load it into a GIS and then join that table data to the appropriate boundary data so they could produce choropleth maps to do some visual analysis, but it was a lot! Once students had gotten the hang of joins using census data they'd often ask why this data doesn't exist as a prepackaged feature layer with all the data they wanted within it. Well good news, now a lot off it is and it's accessible through the Living Atlas! Don't get me wrong I fully understand the importance of teaching students how to perform joins but once you have this understanding if you can access data that already contains all the information you need then you should be taking advantage of it to save you time. So in this exercise I am going to show you how to load English and Welsh Census Data from the 2021 Census into the ArcGIS Map Viewer from the Living Atlas and produce some choropleth maps to use to perform visual analysis without having to perform a single join.

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.

GIS layers symbolizing various data in the WSPMS database This featureclass has a text field with values Very Poor, Poor, Fair, Good and Very Good indicating the lowest category of PSC or RCN, Rutting and IRI.A brief user guide is located at: https://data.wsdot.wa.gov/geospatial/DOT_WSPMS/WSPMSFeatureClassFieldDescription.docx.

The project lead for the collection of this data was Carrington Hilson. Elk (3 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2017-2021. The Red Schoolhouse 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 between 1-6 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual pronghorn is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. 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 3 elk, including 7 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. Large water bodies were clipped from the final output. 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.

Private wells in this layer come from the Department of Environmental Conservation's Water Supply Data Composite. Managed by the Water Supply Division's Well Driller and Well Location Program, the database contains private well information submitted by Vermont licensed well drillers. Licensed well drillers have been required to submit well completion reports (well logs) to the state since 1966. Well tags have been required since 1986. NOTE: the data contained here is only as accurate as what was submitted - many wells were completed, but not reported, many reports have missing information, were recorded inaccurately or poorly located geographically. Help us improve our database. Click the appropriate link within the feature's attributes to report a missing/inaccurate well report.Data is updated daily.For the Lithology Reports associated with Private Wells, download the Lithology Reports here: Private Wells - Lithology Reports

This dataset reproduces and preserves "NYPD B Summons - Year to Date" as updated May 4, 2021 on the NYC Open Data portal. However, neither these data, nor the original dataset are reliable and should not be used by researchers. Specifically, mapping the data using the GIS coordinates will reveal no correlation between the NYPD command owning a given summons, and the geographic location of that issuance. For example, on 2/4/2021, the 69th Precinct issued 21 summonses according to these data. But According to the GIS coordinates, non were actually issued within the 68th precinct itself. Rather, their coordinates array them across the entire city. A ticket sent to NYC Open Data elicited a response of 5/24/21 that there is some problem in the "address validation system". I requested that the inaccuracies be noted in the original dataset until fixed, but have not yet received a response.

Description of original dataset:

Moving violation summonses issued by the NYPD.
This dataset documents the issuance of moving violations (B summonses) by the NYPD. The data is collected from members of service upon completion of the New York State form. Each record represents a summons (vioaltion). The data can be used for analyzing traffic enforcement activity by the NYPD.

Boundaries of various types of public agencies with responsibilities that include in part or primarily flood control, system maintenance, and improvement. In California, there are a variety of political entites that are granted self-taxation powers under various California codes in order to perform the basic goal of flood management within an area. This dataset compiles many of the various datasets together to provide the information in one location. It also includes districts that are no longer active political/management entities for archival or historical purposes. The primary type of flood agency in California are known as reclamation districts, and so represent the majority of the records in this database. The quality of the boundary accuracy is highly variable, due to a variety of reasons, including the fact that the original legal boundaries are frequently tied to Swamp Land Survey boundaries that themselves are poorly located by modern mapping standards. This set of boundary delineations represents the latest in a series of nearly 20 significant revisions primarily by DWR Delta Levees Program between 2000-2017 to a dataset first produced by Office of Emergency Services during the 1997 floods. The accuracy and completeness of the data are therefore higher in the Delta than elsewhere. The Division of Flood Management then stored the boundaries in their levee geodatabase that feeds the web mapping application known as FERIX. To produce this final dataset, in 2018 the Division of Engineering Geodetic Branch merged the data used by FERIX, along with other datasets used by the Delta Levees Program, and normalized the attribute table.

The project lead for the collection of this data was Carrington Hilson. Elk (3 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2017-2021. The Davison 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 between 1-6 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual pronghorn is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. 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 3 elk, including 9 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. Large water bodies were clipped from the final output. 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.

The project lead for the collection of this data was Carrington Hilson. Elk (4 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2017-2021. The Gilbert 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 between 1-6 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual pronghorn is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. 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 3 elk, including 5 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. Large water bodies were clipped from the final output. 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.

The project lead for the collection of this data was Carrington Hilson. Elk (15 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2018-2021. The Mad River 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 between 1-6 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual pronghorn is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. 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 11 elk, including 23 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. Large water bodies were clipped from the final output. 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.

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.

Drainage class (natural)" refers to the frequency and duration of wet periods under conditions similar to those under which the soil formed. Alterations of the water regime by human activities, either through drainage or irrigation, are not a consideration unless they have significantly changed the morphology of the soil. Seven classes of natural soil drainage are recognized-excessively drained, somewhat excessively drained, well drained, moderately well drained, somewhat poorly drained, poorly drained, and very poorly drained. These classes are defined in the "Soil Survey Manual.The State of Connecticut defines inland wetlands based on soils. The Connecticut Inland Wetlands and Watercourses Act defines wetland soils to include any of the soil types designated as poorly drained, very poorly drained, alluvial, or floodplain by the National Cooperative Soil Survey, as may be periodically amended, of the Natural Resources Conservation Service of the United States Department of Agriculture.Map units dominated by Connecticut inland wetland soils may have inclusions of non-wetland soils, and non-wetland map units may have inclusions of Connecticut inland wetland soils. On site investigation is necessary to determine the presence or absence of wetland soils in a particular area.This data set is a digital soil survey and generally is the mostdetailed level of soil geographic data developed by the NationalCooperative Soil Survey. The information was prepared by digitizingmaps, by compiling information onto a planimetric correct baseand digitizing, or by revising digitized maps using remotelysensed and other information.This data set consists of georeferenced digital map data andcomputerized attribute data. The map data are in a soil survey areaextent format and include a detailed, field verified inventoryof soils and miscellaneous areas that normally occur in a repeatablepattern on the landscape and that can be cartographically shown atthe scale mapped. The soil map units are linked to attributes in theNational Soil Information System relational database, which givesthe proportionate extent of the component soils and their properties.

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

In May of 2001, the California Department of Fish and Game (CDFG) conducted an aerial photographic survey of the California coast and the offshore Channel Islands to obtain a minimum estimate of the population of harbor seals (Phoca vitulina richardsi) living in California. The developed photographs were examined to count the number of harbor seals present and determine the location of each haul-out site by comparison to photos taken in previous surveys. This survey was successful in obtaining nearly complete coverage of all known haul-out areas. The 1995 survey was the last complete coverage. The total county for 2001 is 12,312 harbor seals. This result is the lowest ever recorded by CDFG for a combined count of the mainland and all offshore islands. The California Department of Fish and Game (CDFG) conducted two surveys in 2002 in an attempt to provide better coverage and to lessen the chances of weather related problems. A total of 16 days were scheduled for aerial surveys from May 19 to July 19, 2002. Total count for Survey 1 was 10,541 harbor seals, while that for Survey 2 was 8,374 harbor seals. Camera problems that produced un-readable film and poor weather conditions prevented a complete assessment by either survey. The lack of complete coverage by either Survey 1 or 2 limited the total number of harbor seals counted. This is especially true in areas where past surveys revealed high concentrations of seals such as the northern Channel Islands and Point Reyes - Sonoma County coast. The California Department of Fish and Game (CDFG) conducted two aerial surveys in 2003 in an attempt to provide better coverage and to lessen the chances of weather related problems. A total of 20 days were scheduled for aerial surveys from May 25 to July 20, 2003. Complete coverage was achieved in both surveys. This was the first time CDFG conducted these surveys using a digital imaging camera. Total count for Survey 1 was 17,415, while that for Survey 2 was 17,778 harbor seals.

RTB Maps is a cloud-based electronic Atlas. We used ArGIS 10 for Desktop with Spatial Analysis Extension, ArcGIS 10 for Server on-premise, ArcGIS API for Javascript, IIS web services based on .NET, and ArcGIS Online combining data on the cloud with data and applications on our local server to develop an Atlas that brings together many of the map themes related to development of roots, tubers and banana crops. The Atlas is structured to allow our participating scientists to understand the distribution of the crops and observe the spatial distribution of many of the obstacles to production of these crops. The Atlas also includes an application to allow our partners to evaluate the importance of different factors when setting priorities for research and development. The application uses weighted overlay analysis within a multi-criteria decision analysis framework to rate the importance of factors when establishing geographic priorities for research and development.Datasets of crop distribution maps, agroecology maps, biotic and abiotic constraints to crop production, poverty maps and other demographic indicators are used as a key inputs to multi-objective criteria analysis.Further metadata/references can be found here: http://gisweb.ciat.cgiar.org/RTBmaps/DataAvailability_RTBMaps.htmlDISCLAIMER, ACKNOWLEDGMENTS AND PERMISSIONS:This service is provided by Roots, Tubers and Bananas CGIAR Research Program as a public service. Use of this service to retrieve information constitutes your awareness and agreement to the following conditions of use.This online resource displays GIS data and query tools subject to continuous updates and adjustments. The GIS data has been taken from various, mostly public, sources and is supplied in good faith.RTBMaps GIS Data Disclaimer• The data used to show the Base Maps is supplied by ESRI.• The data used to show the photos over the map is supplied by Flickr.• The data used to show the videos over the map is supplied by Youtube.• The population map is supplied to us by CIESIN, Columbia University and CIAT.• The Accessibility map is provided by Global Environment Monitoring Unit - Joint Research Centre of the European Commission. Accessibility maps are made for a specific purpose and they cannot be used as a generic dataset to represent "the accessibility" for a given study area.• Harvested area and yield for banana, cassava, potato, sweet potato and yam for the year 200, is provided by EarthSat (University of Minnesota’s Institute on the Environment-Global Landscapes initiative and McGill University’s Land Use and the Global Environment lab). Dataset from Monfreda C., Ramankutty N., and Foley J.A. 2008.• Agroecology dataset: global edapho-climatic zones for cassava based on mean growing season, temperature, number of dry season months, daily temperature range and seasonality. Dataset from CIAT (Carter et al. 1992)• Demography indicators: Total and Rural Population from Center for International Earth Science Information Network (CIESIN) and CIAT 2004.• The FGGD prevalence of stunting map is a global raster datalayer with a resolution of 5 arc-minutes. The percentage of stunted children under five years old is reported according to the lowest available sub-national administrative units: all pixels within the unit boundaries will have the same value. Data have been compiled by FAO from different sources: Demographic and Health Surveys (DHS), UNICEF MICS, WHO Global Database on Child Growth and Malnutrition, and national surveys. Data provided by FAO – GIS Unit 2007.• Poverty dataset: Global poverty headcount and absolute number of poor. Number of people living on less than $1.25 or $2.00 per day. Dataset from IFPRI and CIATTHE RTBMAPS GROUP MAKES NO WARRANTIES OR GUARANTEES, EITHER EXPRESSED OR IMPLIED AS TO THE COMPLETENESS, ACCURACY, OR CORRECTNESS OF THE DATA PORTRAYED IN THIS PRODUCT NOR ACCEPTS ANY LIABILITY, ARISING FROM ANY INCORRECT, INCOMPLETE OR MISLEADING INFORMATION CONTAINED THEREIN. ALL INFORMATION, DATA AND DATABASES ARE PROVIDED "AS IS" WITH NO WARRANTY, EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO, FITNESS FOR A PARTICULAR PURPOSE. By accessing this website and/or data contained within the databases, you hereby release the RTB group and CGCenters, its employees, agents, contractors, sponsors and suppliers from any and all responsibility and liability associated with its use. In no event shall the RTB Group or its officers or employees be liable for any damages arising in any way out of the use of the website, or use of the information contained in the databases herein including, but not limited to the RTBMaps online Atlas product.APPLICATION DEVELOPMENT:• Desktop and web development - Ernesto Giron E. (GeoSpatial Consultant) e.giron.e@gmail.com• GIS Analyst - Elizabeth Barona. (Independent Consultant) barona.elizabeth@gmail.comCollaborators:Glenn Hyman, Bernardo Creamer, Jesus David Hoyos, Diana Carolina Giraldo Soroush Parsa, Jagath Shanthalal, Herlin Rodolfo Espinosa, Carlos Navarro, Jorge Cardona and Beatriz Vanessa Herrera at CIAT, Tunrayo Alabi and Joseph Rusike from IITA, Guy Hareau, Reinhard Simon, Henry Juarez, Ulrich Kleinwechter, Greg Forbes, Adam Sparks from CIP, and David Brown and Charles Staver from Bioversity International.Please note these services may be unavailable at times due to maintenance work.Please feel free to contact us with any questions or problems you may be having with RTBMaps.

NOTICE TO PROVISIONAL 2023 LAND USE DATA USERS: Please note that on December 6, 2024 the Department of Water Resources (DWR) published the Provisional 2023 Statewide Crop Mapping dataset. The link for the shapefile format of the data mistakenly linked to the wrong dataset. The link was updated with the appropriate data on January 27, 2025 and a notice was posted on the shapefile download site. If you downloaded the Provisional 2023 Statewide Crop Mapping dataset in shapefile format between December 6, 2024 and January 27, we encourage you to redownload the data. The Map Service and Geodatabase formats were correct as posted on December 06, 2024. Thank you for your interest in DWR land use datasets. The California Department of Water Resources (DWR) has been collecting land use data throughout the state and using it to develop agricultural water use estimates for statewide and regional planning purposes, including water use projections, water use efficiency evaluations, groundwater model developments, climate change mitigation and adaptations, and water transfers. These data are essential for regional analysis and decision making, which has become increasingly important as DWR and other state agencies seek to address resource management issues, regulatory compliances, environmental impacts, ecosystem services, urban and economic development, and other issues. Increased availability of digital satellite imagery, aerial photography, and new analytical tools make remote sensing-based land use surveys possible at a field scale that is comparable to that of DWR’s historical on the ground field surveys. Current technologies allow accurate large-scale crop and land use identifications to be performed at desired time increments and make possible more frequent and comprehensive statewide land use information. Responding to this need, DWR sought expertise and support for identifying crop types and other land uses and quantifying crop acreages statewide using remotely sensed imagery and associated analytical techniques. Currently, Statewide Crop Maps are available for the Water Years 2014, 2016, 2018- 2022 and PROVISIONALLY for 2023. Historic County Land Use Surveys spanning 1986 - 2015 may also be accessed using the CADWR Land Use Data Viewer: https://gis.water.ca.gov/app/CADWRLandUseViewer. For Regional Land Use Surveys follow: https://data.cnra.ca.gov/dataset/region-land-use-surveys. For County Land Use Surveys follow: https://data.cnra.ca.gov/dataset/county-land-use-surveys. For a collection of ArcGIS Web Applications that provide information on the DWR Land Use Program and our data products in various formats, visit the DWR Land Use Gallery: https://storymaps.arcgis.com/collections/dd14ceff7d754e85ab9c7ec84fb8790a.

This dataset contains data from the Sign Faces and Sign Assemblies layers. Signs are categorized by condition (Good, Fair, Poor) and Speed (Speed Limit and Speed Related). These datasets are Pathway data layers that were collected in the Summer of 2023 via LiDAR inventory. Data is updated on a two year cycle. For questions on the data please contact Ed Graves edgraves@utah.gov. To download either of these data layers, please visit UDOT's Open Data Site.

The project leads for the collection of this data were Josh Bush and Tom Batter. Elk (8 adult females, 11 adult males) from the Bear Creek Ranch – Antelope Valley herd were captured and equipped with Lotek GPS collars (LifeCycle 800 GlobalStar, Lotek Wireless, Newmarket, Ontario, Canada), transmitting data from 2017-2022. The study area was within the Bear Valley and Cache Creek Elk Management Units, west of Route 20 south to Wilber Springs, where certain individuals appear to cross this highway. Route 20 is likely a barrier to movement to the east as this herd does not overlap with the Cortina Ridge herd on the other side of this highway. The Bear Creek Ranch – Antelope Valleyherd contains short distance, elevation-based movements likely due to seasonal habitat conditions, but this herd does not migrate between traditional summer and winter seasonal ranges. Instead, the herd displays a residential pattern, slowly moving up or down elevational gradients. 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 13-hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual elk is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this analysis allowed for the mapping of the herd’s annual range based on a small sample. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 17 elk in total, including 37 year-long sequences, location, date, time, and average location error as inputs in Migration Mapper to assess annual range. Annual range BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Population-level annual range designations for this herd may expand with a larger sample, filling in some of the gaps between high-use annual range polygons in the map. Annual range is visualized as the 50th percentile contour (high use) and the 99th percentile contour of the year-round utilization distribution.

The project lead for the collection of this data was Erin Zulliger. Elk (5 adult females) were captured and equipped with GPS collars (Litetrack/Pinpoint Iridium collars, Lotek Wireless Inc., Newmarket, Ontario, Canada or Vectronic Aerospace) transmitting data from 2019-2023. The Dixie 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 1-6 hour intervals in the dataset. To improve the quality of the data set, the GPS data locations fixed in 2D space and 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 annual range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 5 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. Home range is visualized as the 50th percentile contour (high use) and the 99th percentile contour of the year-round utilization distribution. Annual home range designations for this herd may expand with a larger sample.

The project lead for the collection of this data was Carrington Hilson. Elk (4 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2017-2021. The Rowdy 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 between 1-6 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual pronghorn is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. 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 4 elk, including 7 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. Large water bodies were clipped from the final output. Home range is visualized as the 50thpercentile contour (high use) and the 99thpercentile contour of the year-round utilization distribution. Home range designations for this herd may expand with a larger sample.