Geospatial data about Boulder County, Colorado Buildings. Export to CAD, GIS, PDF, CSV and access via API.

Geospatial data about Boulder County, Colorado Schools. Export to CAD, GIS, PDF, CSV and access via API.

The lakes and reservoirs in this dataset were conflated from various data sources, including NHD, Colorado Department of Natural Resources Division of Water Resources (CDSS), Boulder County, City of Boulder and individual ditch companies. The data was verified with field visits and extensive staff knowledge. The data includes links to various internal databases, as well as the Colorado Division of Water Resources database. Pre and Post 2013 Colorado Flood alignments are included. This data is appropriate for both small and large scale usage.

Geospatial data about Boulder County, Colorado Sewer Lines. Export to CAD, GIS, PDF, CSV and access via API.

ArcGIS API application that shows study site extents, sampling and sensor locations, LIDAR, glacier extents, aerial imagery extents with downloads.

Please use the links below https://bcczo.colorado.edu/gisarc/arcapi.html

For more in-depth Lidar visit our friends at OpenTopgraphy and click http://criticalzone.org/boulder/data/dataset/2921/ HERE They are large storage for all our Lidar data.

Geospatial data about Boulder County, Colorado Streets. Export to CAD, GIS, PDF, CSV and access via API.

Geospatial data about Boulder County, Colorado Addresses. Export to CAD, GIS, PDF, CSV and access via API.

The University of Colorado Boulder campus map is created, updated and published by the CU CAD/GIS Office. It consists of twenty-two separate feature classes: Property Boundaries, Building Footprints, River Lines, Lakes and Ponds, Ditches, Water Features, Dams, Road Edges, Sidewalk Edges, Structure Areas and Lines, Athletics Fields & Courts, Athletics Lines, Fence Lines, Outdoor Spaces, Parking Lots and Lines, Grounds Panels (softscape areas), Road Centerlines, Trees and Points of Interest. Each of these layers are updated as-needed by the CU CAD/GIS Office when new information is received, typically in the form of construction as-built drawings. The updates are posted to ArcGIS Online, Esri Living Atlas and Esri Community maps at least twice a year, quarterly when possible.

This 1m Digital Surface Model (DSM) is derived from first-stop Light Detection and Ranging (LiDAR) point cloud data from September 2005 for the Green Lakes Valley, near Boulder Colorado. The DSM was created from LiDAR point cloud tiles subsampled to 1-meter postings, acquired by the National Center for Airborne Laser Mapping (NCALM) project. This data was collected in collaboration between the University of Colorado, Institute of Arctic and Alpine Research (INSTAAR) and NCALM, which is funded by the National Science Foundation (NSF). The DSM has the functionality of a map layer for use in Geographic Information Systems (GIS) or remote sensing software. Total area imaged is 35 km^2. The LiDAR point cloud data was acquired with an Optech 1233 Airborne Laser Terrain Mapper (ALTM) and mounted in a twin engine Piper Chieftain (N931SA) with Inertial Measurement Unit (IMU) at a flying height of 600 m. Data from two GPS (Global Positioning System) ground stations were used for aircraft trajectory determination. The continuous DSM surface was created by mosaicing and then kriging 1 km2 LiDAR point cloud LAS-formated tiles using Golden Software's Surfer 8 Kriging algorithm. Horizontal accuracy and vertical accuracy is unknown. cm RMSE at 1 sigma. The layer is available in GEOTIF format approx. 265 MB of data. It has a UTM zone 13 projection, with a NAD83 horizonal datum and a NAVD88 vertical datum computed using NGS GEOID03 model, with FGDC-compliant metadata. A shaded relief model was also generated. A similar layer, the Digital Terrain Model (DTM), is a ground-surface elevation dataset better suited for derived layers such as slope angle, aspect, and contours. A processing report and readme file are included with this data release. The DSM is available through an unrestricted public license. The LiDAR DEMs will be of interest to land managers, scientists, and others for study of topography, ecosystems, and environmental change. NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.

This 1m Digital Surface Model (DSM) shaded relief is derived from first-stop Light Detection and Ranging (LiDAR) point cloud data from September 2005 for the Green Lakes Valley, near Boulder Colorado. The DSM was created from LiDAR point cloud tiles subsampled to 1-meter postings, acquired by the National Center for Airborne Laser Mapping (NCALM) project. This data was collected in collaboration between the University of Colorado, Institute of Arctic and Alpine Research (INSTAAR) and NCALM, which is funded by the National Science Foundation (NSF). The DSM shaded relief has the functionality of a map layer for use in Geographic Information Systems (GIS) or remote sensing software. Total area imaged is 35 km^2. The LiDAR point cloud data was acquired with an Optech 1233 Airborne Laser Terrain Mapper (ALTM) and mounted in a twin engine Piper Chieftain (N931SA) with Inertial Measurement Unit (IMU) at a flying height of 600 m. Data from two GPS (Global Positioning System) ground stations were used for aircraft trajectory determination. The continuous DSM surface was created by mosaicing and then kriging 1 km2 LiDAR point cloud LAS-formated tiles using Golden Software's Surfer 8 Kriging algorithm. Horizontal accuracy and vertical accuracy is unknown. cm RMSE at 1 sigma. The layer is available in GEOTIF format approx. 265 MB of data. It has a UTM zone 13 projection, with a NAD83 horizonal datum and a NAVD88 vertical datum computed using NGS GEOID03 model, with FGDC-compliant metadata. This shaded relief model was also generated. A similar layer, the Digital Terrain Model (DTM), is a ground-surface elevation dataset better suited for derived layers such as slope angle, aspect, and contours. A processing report and readme file are included with this data release. The DSM dataset is available through an unrestricted public license. The LiDAR DEMs will be of interest to land managers, scientists, and others for study of topography, ecosystems, and environmental change. NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.

The University of Colorado Boulder campus map is created, updated and published by the CU CAD/GIS Office. It consists of twenty feature classes that make up the campus basic geography and is published primarily for use by the campus community.

The University of Colorado Boulder campus map is created, updated and published by the CU CAD/GIS Office. It consists of twenty feature classes that make up the campus basic geography and is published primarily for use by the campus community.

The University of Colorado Boulder campus map is created, updated and published by the CU CAD/GIS Office. It consists of twenty feature classes that make up the campus basic geography and is published primarily for use by the campus community.

The University of Colorado Boulder campus map is created, updated and published by the CU CAD/GIS Office. It consists of twenty feature classes that make up the campus basic geography and is published primarily for use by the campus community.

Geospatial data about Boulder County, Colorado Streams. Export to CAD, GIS, PDF, CSV and access via API.

Geospatial data about Boulder County, Colorado Zoning Districts. Export to CAD, GIS, PDF, CSV and access via API.

The University of Colorado Boulder campus map is created, updated and published by the CU CAD/GIS Office. It consists of twenty feature classes that make up the campus basic geography and is published primarily for use by the campus community.

Geospatial data about Boulder County, Colorado Parcels. Export to CAD, GIS, PDF, CSV and access via API.

description: The U.S. Geological Survey developed this dataset as part of the Colorado Front Range Infrastructure Resources Project (FRIRP). One goal of the FRIRP was to provide information on the availability of those hydrogeologic resources that are either critical to maintaining infrastructure along the northern Front Range or that may become less available because of urban expansion in the northern Front Range. This dataset extends from the Boulder-Jefferson County line on the south, to the middle of Larimer and Weld Counties on the North. On the west, this dataset is bounded by the approximate mountain front of the Front Range of the Rocky Mountains; on the east, by an arbitrary north-south line extending through a point about 6.5 kilometers east of Greeley. This digital geospatial dataset consists of depth-to-water (unsaturated-thickness) contours that were generated from hydrogeologic data with Geographic Information System (GIS) software.; abstract: The U.S. Geological Survey developed this dataset as part of the Colorado Front Range Infrastructure Resources Project (FRIRP). One goal of the FRIRP was to provide information on the availability of those hydrogeologic resources that are either critical to maintaining infrastructure along the northern Front Range or that may become less available because of urban expansion in the northern Front Range. This dataset extends from the Boulder-Jefferson County line on the south, to the middle of Larimer and Weld Counties on the North. On the west, this dataset is bounded by the approximate mountain front of the Front Range of the Rocky Mountains; on the east, by an arbitrary north-south line extending through a point about 6.5 kilometers east of Greeley. This digital geospatial dataset consists of depth-to-water (unsaturated-thickness) contours that were generated from hydrogeologic data with Geographic Information System (GIS) software.

Maps of California's Wildland Urban Interface (WUI) generated using the Time Step Moving Window (TSMW) method outlined in the paper "Remapping California's Wildland Urban Interface: A Property-Level Time-Space Framework, 2000-2020".

Please cite the original paper:

Berg, Aleksander K, Dylan S. Connor, Peter Kedron, and Amy E. Frazier. 2024. “Remapping California’s Wildland Urban Interface: A Property-Level Time-Space Framework, 2000–2020.” Applied Geography 167 (June): 103271. https://doi.org/10.1016/j.apgeog.2024.103271.

WUI maps were generated using Zillow ZTRAX parcel level attributes joined with FEMA USA Structures building footprints and the National Land Cover Database (NLCD).

All files are geotiff rasters with WUI areas mapped at a ~30m resolution. A raster value of null indicates not WUI, raster value of 1 indicates intermix WUI, and a raster value of 2 indicates interface WUI.

Three WUI maps were generated using structures built on of before the years indicated below:

2000 - "CA_WUI_2000.tif"

2010 - "CA_WUI_2010.tif"

2020 - "CA_WUI_2020.tif"

Acknowledgments -

We thank our reviewers and editors for helping us to improve the manuscript. We gratefully acknowledge access to the Zillow Transaction and Assessment Dataset (ZTRAX) through a data use agreement between the University of Colorado Boulder, Arizona State University, and Zillow Group, Inc. More information on accessing the data can be found at http://www.zillow.com/ztrax. The results and opinions are those of the author(s) and do not reflect the position of Zillow Group. Support by Zillow Group Inc. is acknowledged. We thank Johannes Uhl and Stefan Leyk for their great work in preparing the original dataset. For feedback and comments, we also thank Billie Lee Turner II, Sharmistha Bagchi-Sen, and participants at the 2022 Global Conference on Economic Geography, the 2022 Young Economic Geographers Network meeting, and the 2023 annual meeting of the American Association of Geographers. Funding for our work has been provided by Arizona State University's Institute of Social Science Research (ISSR) Seed Grant Initiative. Additional funding was provided through the Humans, Disasters, and the Built Environment program of the National Science Foundation, Award Number 1924670 to the University of Colorado Boulder, the Institute of Behavioral Science, Earth Lab, the Cooperative Institute for Research in Environmental Sciences, the Grand Challenge Initiative and the Innovative Seed Grant program at the University of Colorado Boulder as well as the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Numbers R21 HD098717 01A1 and P2CHD066613.