This data set consists of the Arizona state boundary. The data are created to serve as base information for use in GIS systems for a variety of planning and analysis purposes. These data do not represent a legal record

Tree locations were extracted from LiDAR collected by Woolpert as part of the National Geospatial-Intelligence Agency (NGA) Homeland Security Infrastructure Program. The data were collected in 2014 and made public in 2017 by the USGS. Data covers portions of the greater metropolitan Phoenix area. Details of methods for deriving the layers are limited to the Woolpert report provided by the USGS.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. State Legislative Districts (SLDs) are the areas from which members are elected to state legislatures. The SLDs embody the upper (senate - SLDU) and lower (house - SLDL) chambers of the state legislature. Nebraska has a unicameral legislature, and the District of Columbia has a single council, both of which the Census Bureau treats as upper-chamber legislative areas for the purpose of data presentation. A unique three-character census code, identified by state participants, is assigned to each SLD within a state. States that had SLDU updates between the previous and current session include Georgia, Minnesota, Montana, North Carolina, North Dakota, Ohio, Washington, and Wisconsin. In Connecticut, Illinois, Louisiana, New Hampshire, Wisconsin, and Puerto Rico, the Redistricting Data Program (RDP) participant did not define the SLDUs to cover the entirety of the state or state equivalent area. In the areas with no SLDUs defined, the code ""ZZZ"" has been assigned, which is treated as a single SLDU for purposes of data presentation. There are no SLDU TIGER/Line shapefiles for the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands). The state legislative district boundaries reflect information provided to the Census Bureau by the states by May 31, 2024. Note: Michigan is required by court order to redraw their state senate districts. However, these new SLDUs were not drawn by May 31, 2024, and will not be used until the next SLDU elections in 2026.

The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.Download the National Hydrography Dataset file geodatabase v2.2.1.

Wastewater-based epidemiology, which is the science of studying community sewage for public health information, is one method of getting information about community health and narcotics consumption. In an effort to prevent and reduce opioid misuse, the City of Tempe is working with scientists from Arizona State University's Biodesign Institute to study the city's wastewater. This feature layer view shows the testing results from wastewater collection areas which are comprised of merged public sewage drainage basins that flow to a shared testing location for the opioid wastewater study. The collection area testing data are provided by scientists from Arizona State University's Biodesign Institute. The wastewater is tested for the presence of prescription opioid parent drug compounds such as Fentanyl, Oxycodone, Codeine, the illegally made opioid parent drug compound Heroin and opioid metabolite drug compounds including Norfentanyl, 6-Acetylmorphine and Noroxycodone. Additional Information Source: Arizona State University's Biodesign Institute provided Excel document. Contact: Kimberly Sotelo Contact E-Mail: Kimberly_Sotelo@tempe.gov Data Source Type: Table Preparation Method: Excel Spreadsheet entered into ArcGIS Survey 123 form which populates ArcGIS Online feature layer table. Publish Frequency: Weekly, as data becomes available Publish Method: The collection area testing data are provided by scientists from Arizona State University's Biodesign Institute and published to an ArcGIS Online feature layer table. Data Dictionary

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.

Index Grids dataset current as of 2002. This statewide data set consists of polygons representing the 1:250,000 USGS Quad boundaries spanning Arizona. The Lat/Long grid has a cell size of 2 degree Longitude x 1 degree Latitude and covers an area equal to 128 USGS 7.5 minute quadrangles..

Cities, Towns and Villages dataset current as of 2008. Inc_Cities.

Digital Elevation Model (DEM) dataset current as of 1999. Hillshade 10M - created by processing the U.S. Geological Survey National Elevation Dataset..

Cities, Towns and Villages dataset current as of unknown. City Township as CAD data.

This dataset consists of the county boundaries in Arizona. The data are created to serve as base information for use in GIS systems for a variety of planning and analysis purposes. Use of data for Engineering work is prohibited.

Forests and Forest Cover dataset current as of unknown. Natural Vegetation (AZGFD) - This data set consists of Arizona's natural vegetation..

Layer can be downloaded at: https://asu.maps.arcgis.com/home/item.html?id=0f3f2464210544868499f4f45af889ed#overviewThe U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released four National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, and 2011. 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 2016. The NLCD 2016 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 2016 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2016: a streamlined process for assembling and preprocessing 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 2016 production. The performance of the developed strategies and methods were tested in twenty World Reference System-2 path/row throughout the conterminous U.S. An overall agreement ranging from 71% to 97% between land cover classification and reference data was achieved for all tested area and all years. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2016 operational mapping. Questions about the NLCD 2016 land cover product can be directed to the NLCD 2016 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.*This land cover layer was extracted from the United States land cover layer and reprojected to UTM12.

Topographic Digital Raster Graphics dataset current as of 1996. DRG - 1:250,000 Topo - Quads (7.5 Minute).

Parcels and Land Ownership dataset current as of 2008. Generalized Land Ownership information..

The raw data contains information from multiple sources. Most of the data was originally downloaded from the Hawaii GIS portal (https://planning.hawaii.gov/gis/) and is freely available. Hyperspectral data to create the forest cover map was obtained from the Global Airborne Observatory (https://gdcs.asu.edu/programs/global-airborne-observatory ). A more in-depth description of the data can be found in the associated paper.

Parcels and Land Ownership dataset current as of unknown.

These INEGI maps were scanned for use in GIS programs for the Proyecto Arqueologico La Mixtequilla.

When city leaders in Tempe, Arizona took a more data-driven and transparent approach to management, the ethos spread across departments. As a wastewater testing partnership with Arizona State University (ASU) moved past the pilot stage, data showing levels of illegal drug use was made visible to the public to show the local impact of the opioid crisis. Now researchers have tuned this same wastewater testing system to detect bio markers in the novel coronavirus (SARS-CoV2), displaying the level of COVID-19 genome copies per liter of wastewater in the city._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

Master's Thesis. In 1837 the Ioway Indians drew a map to bring to treaty talks with the United States government. The 1837 Ioway Map project uses Geographic Information Systems (GIS) to help extract cultural, archaeological, and historical information from this rare document. Project goals include: documenting Ioway cartographic conventions; georeferencing the Ioway map to a modern base map; extracting spatial, historical, ecological and archaeological information from the georeferenced map; and designing a variety of digital (CD, web site) and non-digital (museum exhibit) presentation formats to broadly disseminate the project results. Centered on what is now the state of Iowa, the 1837 map shows 51 rivers, nine lakes, 23 villages, and over two dozen important Ioway Indian trails. Map features are unlabeled, but historic records indicate that it was designed around two major rivers, the Mississippi and the Missouri. GIS tools were helpful in evaluating the probable identifications of a number of the other hydrographic features. The Ioway encoded information about village size and population in their symbology, information that was systematically documented using pan, zoom, measurement, and geostatistical tools, with the results stored in attribute tables.