Zoning map of the City of Boulder, CO

The Boulder Valley Comprehensive Plan (BVCP) land use map defines the desired future land use pattern for the Boulder Valley regarding location, type, and intensity of development.

This digital map shows the geographic extent of rock stratigraphic units (formations) as compiled by Colton in 1976 under the Front Range Urban Corridor Geology Program. Colton used his own geologic mapping and previously published geologic maps to compile one map having a single classification of geologic units. The resulting published color paper map (USGS Map I-855-G, Colton, 1978) was intended for land-use planning and to depict the regional geology. In 1997-1999, another USGS project designed to address urban growth issues was undertaken. This project, the USGS Front Range Infrastructure Resources Project, undertook to digitize Colton's map at 1:100,000 scale, making it useable in Geographical Information Systems (GIS). That product is described here. In general, the digitized map depicts in its western part Precambrian igneous and metamorphic rocks, Pennsylvanian and younger sedimentary rock units, major faults, and brecciated zones along an eastern strip (5-20 km wide) of ...

Infrastructure, such as roads, airports, water and energy transmission and distribution facilities, sewage treatment plants, and many other facilities, is vital to the sustainability and vitality of any populated area. Rehabilitation of existing and development of new infrastructure requires three natural resources: natural aggregate (stone, sand, and gravel), water, and energy http://rockyweb.cr.usgs.gov/frontrange/overview.htm.

The principal goals of the U.S. Geological Survey (USGS) Front Range Infrastructure Resources Project (FRIRP) were to develop information, define tools, and demonstrate ways to: (1) implement a multidisciplinary evaluation of the distribution and quality of a region's infrastructure resources, (2) identify issues that may affect availability of resources, and (3) work with cooperators to provide decision makers with tools to evaluate alternatives to enhance decision-making. Geographic integration of data (geospatial databases) can provide an interactive tool to facilitate decision-making by stakeholders http://rockyweb.cr.usgs.gov/frontrange/overview.htm.

This digital map shows bedding attitude data displayed over the geographic extent of rock stratigraphic units (formations) as compiled by Colton in 1976 (U.S.Geological Survey Map I-855-G) under the Front Range Urban Corridor Geology Program. Colton used his own mapping and published geologic maps having varied map unit schemes to compile one map with a uniform classification of geologic units. The resulting published color paper map was intended for planning for use of land in the Front Range Urban Corridor. In 1997-1999, under the USGS Front Range Infrastructure Resources Project, Colton's map was digitized to provide data at 1:100,000 scale to address urban growth issues(see cross-reference). In general, the west part of the map shows a variety of Precambrian igneous and metamorphic rocks, major faults and brecciated zones along an eastern strip (5-20 km wide) of the Front Range. The eastern and central part of the map (Colorado Piedmont) depicts a mantle of Quaternary unconsolidated deposits and interspersed Cretaceous or Tertiary-Cretaceous sedimentary rock outcrops. The Quaternary mantle is comprised of eolian deposits (quartz sand and silt), alluvium (gravel, sand, and silt of variable composition), colluvium, and few landslides. At the mountain front, north-trending, dipping Paleozoic and Mesozoic sandstone and shale formations (and sparse limestone) form hogbacks, intervening valleys, and in range-front folds, anticlines, and fault blocks. Localized dikes and sills of Tertiary rhyodacite and basalt intrude rocks near the range front, mostly in the Boulder area.

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.

The dataset was generated to describe historical land-use and land-cover (LULC)for the northern Colorado urban Front Range (which includes the cities of Boulder, Fort Collins, Greeley, and Denver) for an area covering approximately 1,023,660 hectares. The Front Range urban landscape is diverse and interspersed with highly productive agriculture as well as natural land cover types including evergreen forest in the Rocky Mountain foothills and Great Plains grassland. To understand the dynamics of urban growth, raster maps were created at a 1-meter resolution for each of four time steps, nominally 1937, 1957, 1977, and 1997. In total, 8 to 38 LULC classes were identified using manual interpretation techniques, aerial photographs, historical maps, and other available information. The maps provide high resolution spatial data for understanding the historical progression of urbanization and will allow further analysis of the effects of urban growth on social and ecological systems.

This 1m Digital Terrain Model (DTM) 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 DTM shaded relief 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 DTM 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 DTM 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 Surface Model (DSM), is a first-stop elevation layer. A processing report and readme file are included with this data release. The DTM 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.

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.

We collected open and publicly available data resources from the web from administrative, county- or state-level institutions in the United States and integrated and harmonized cadastral parcel data, tax assessment data, and building footprint data for 33 counties, where building footprint data and building construction year information was available. The result of this effort is a unique dataset which we call the Multi-Temporal Building Footprint Dataset for 33 U.S. Counties (MTBF-33). MTBF-33 contains over 6.2 million building footprints including their construction year, and is available in ESRI Shapefile format (Spatial reference system: SR-ORG:7480), organized per county. We compared the MTBF-33 dataset quantitatively to other building footprint data sources, achieving an overall F-1 score of 0.93. Moreover, we compared the MTBF-33 dataset qualitatively to urban extents from historical maps and find high levels of agreement. The MTBF-33 dataset can be used to support historical building stock assessments, to derive retrospective depictions of built-up areas from 1900 to 2015, at fine spatial and temporal grain and can be used for data validation purposes, or to train statistical learning approaches aiming to extract historical information on human settlements from remote sensing data, historical maps, or similar data sources.

Data sources: Boulder County (Colorado) Open Data Catalog / Florida Geographic Data Library / Hillsborough County, Florida / City of Tampa / Manatee County, Florida / Sarasota County, Florida / City of Evansville, Vanderburgh County, Indiana / Baltimore County Government, Maryland / Bureau of Geographic Information (MassGIS), Commonwealth of Massachusetts, Executive Office of Technology and Security Services / City of Boston / MetroGIS, Minnesota Geospatial Commons, Minnesota Geospatial Information Office, Anoka County, Carver County, Dakota County, Hennepin County, Ramsey County, and Washington County, Minnesota / Monmouth County, New Jersey / City of New York / Mecklenburg County, North Carolina. Data scraping was performed in 2016.

This vector shapefile is a polygon shapefile outlining the extent of the "NWT" project area, for the Niwot Ridge Long Term Ecological Research (LTER) project. The shapefile also covers the Green Lakes Valley portion of the Boulder Creek Critical Zone Observatory (CZO). Other datasets available in this series includes orthorectified aerial photograph mosaics (for 1953, 1972, 1985, approximately 1990, 1999, 2000, 2002, 2004, 2006 and 2008), digital elevation models (DEM's), and accessory map layers. Together, the DEM's and imagery will be of interest to students, research scientists, and others for observation and analysis of natural features and ecosystems. 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.