This is the study area associated with the project: “Status and Trends of Deciduous Communities in the Bighorn Mountains”. The aim of the study is to assess the current trends of deciduous communities in the Bighorn National Forest in north-central Wyoming. The data here represents phase I of the project, completed in FY2017. The USGS created a synthesis map of coniferous and deciduous communities in the Bighorn Mountains of Wyoming using a species distribution modeling approach developed in the Wyoming Landscape Conservation Initiative (WLCI) (Assal et al. 2015). The modeling framework utilized a number of topographic covariates and temporal remote sensing data from the early, mid and late growing season to capitalize on phenological differences in vegetation types. We used the program RandomForest in the R statistical program to generate probability of occurrence models for deciduous and coniferous vegetation. The binary maps were combined into a synthesis map using the procedure from Assal et al. 2015. In Phase II of this project (to be completed in FY2018 and 2019), the USGS will conduct a preliminary assessment on the baseline condition of riparian deciduous communities. This will be a proof-of-concept study where the USGS will apply a framework used in prior research in upland aspen and sagebrush communities to detect trends in riparian vegetation condition from the mid-1980s to present. Literature Cited Assal et al. 2015: https://doi.org/10.1080/2150704X.2015.1072289

This case study document provides information on how Google Maps is using our open datasets and articulates citizen benefits. This case study document provides information on how Google Maps is using our open datasets and articulates citizen benefits.

The development and generation of the datasets that are published through this data release, were based on the results and findings of the report: Kohn, M.S. and Patton, T.T., 2018, Flood-Inundation Maps for the South Platte River at Fort Morgan, Colorado, 2018: U.S. Geological Survey Scientific Investigations Report 2018-5114, 14 p., https://doi.org/10.3133/sir20185114. The geospatial dataset contain final versions of the raster and vector geospatial data and related metadata. The geospatial data include inundation extents, corresponding inundation depths, and the study area boundaries. Digital flood-inundation maps for a 4.5-mile reach of the South Platte River at Fort Morgan, Colorado from Morgan County Road 16 to Morgan County 20.5, were created by the U.S. Geological Survey (USGS) in cooperation with the Colorado Water Conservation Board. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science web site (https://water.usgs.gov/osw/flood_inundation/), depict estimates of the areal extent and depth of flooding corresponding to select water levels (stages) at USGS streamgage 06759500, South Platte River at Fort Morgan. Current conditions for estimating near-real-time areas of inundation using USGS streamgage information are available through the National Water Information System web interface or the National Weather Service (NWS) Advanced Hydrologic Prediction Service (http:/water.weather.gov/ahps/). Water-profiles were computed for the stream reach by means of a one-dimensional, step-backwater model. The September 15, 2013 and May 20, 2017 floods were used to calibrate the model, and the June 15, 2015 and May 29, 2017 floods were used to independently validate the model. Nine pressure transducers were deployed to record the stage at nine different locations along the reach and to document the floods of May 20 and 29, 2017 at the South Platte River at Fort Morgan streamgage. The calibrated hydraulic model was then used to determine 16 water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from 12 ft (3.66 m) or below bankfull to 27 ft (8.23 m), which is 1 ft (0.3 m) greater than the highest recorded water level (25.73 ft [7.84 m] on September 15, 2013) at the South Platte River at Fort Morgan streamgage during its period of record and the 2013 flood exceeds the major flood stage of 21.5 ft (6.55 m) by more than 4 ft (1.2 m) as defined by the National Weather Service. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging) to delineate the area flooded at stages ranging from 12-ft to 27-ft. The availability of these inundation maps, along with internet information regarding the current stage from the USGS streamgage 06759500, South Platte River at Fort Morgan, Colorado, and forecast river stages from the NWS Advanced Hydrologic Prediction Service, provides emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.

This is the current Medical Service Study Area. California Medical Service Study Areas are created by the California Department of Health Care Access and Information (HCAI).Check the Data Dictionary for field descriptions.Search for the Medical Service Study Area data on the CHHS Open Data Portal.Checkout the California Healthcare Atlas for more Medical Service Study Area information.This is an update to the MSSA geometries and demographics to reflect the new 2020 Census tract data. The Medical Service Study Area (MSSA) polygon layer represents the best fit mapping of all new 2020 California census tract boundaries to the original 2010 census tract boundaries used in the construction of the original 2010 MSSA file. Each of the state's new 9,129 census tracts was assigned to one of the previously established medical service study areas (excluding tracts with no land area), as identified in this data layer. The MSSA Census tract data is aggregated by HCAI, to create this MSSA data layer. This represents the final re-mapping of 2020 Census tracts to the original 2010 MSSA geometries. The 2010 MSSA were based on U.S. Census 2010 data and public meetings held throughout California.Source of update: American Community Survey 5-year 2006-2010 data for poverty. For source tables refer to InfoUSA update procedural documentation. The 2010 MSSA Detail layer was developed to update fields affected by population change. The American Community Survey 5-year 2006-2010 population data pertaining to total, in households, race, ethnicity, age, and poverty was used in the update. The 2010 MSSA Census Tract Detail map layer was developed to support geographic information systems (GIS) applications, representing 2010 census tract geography that is the foundation of 2010 medical service study area (MSSA) boundaries. ***This version is the finalized MSSA reconfiguration boundaries based on the US Census Bureau 2010 Census. In 1976 Garamendi Rural Health Services Act, required the development of a geographic framework for determining which parts of the state were rural and which were urban, and for determining which parts of counties and cities had adequate health care resources and which were "medically underserved". Thus, sub-city and sub-county geographic units called "medical service study areas [MSSAs]" were developed, using combinations of census-defined geographic units, established following General Rules promulgated by a statutory commission. After each subsequent census the MSSAs were revised. In the scheduled revisions that followed the 1990 census, community meetings of stakeholders (including county officials, and representatives of hospitals and community health centers) were held in larger metropolitan areas. The meetings were designed to develop consensus as how to draw the sub-city units so as to best display health care disparities. The importance of involving stakeholders was heightened in 1992 when the United States Department of Health and Human Services' Health and Resources Administration entered a formal agreement to recognize the state-determined MSSAs as "rational service areas" for federal recognition of "health professional shortage areas" and "medically underserved areas". After the 2000 census, two innovations transformed the process, and set the stage for GIS to emerge as a major factor in health care resource planning in California. First, the Office of Statewide Health Planning and Development [OSHPD], which organizes the community stakeholder meetings and provides the staff to administer the MSSAs, entered into an Enterprise GIS contract. Second, OSHPD authorized at least one community meeting to be held in each of the 58 counties, a significant number of which were wholly rural or frontier counties. For populous Los Angeles County, 11 community meetings were held. As a result, health resource data in California are collected and organized by 541 geographic units. The boundaries of these units were established by community healthcare experts, with the objective of maximizing their usefulness for needs assessment purposes. The most dramatic consequence was introducing a data simultaneously displayed in a GIS format. A two-person team, incorporating healthcare policy and GIS expertise, conducted the series of meetings, and supervised the development of the 2000-census configuration of the MSSAs.MSSA Configuration Guidelines (General Rules):- Each MSSA is composed of one or more complete census tracts.- As a general rule, MSSAs are deemed to be "rational service areas [RSAs]" for purposes of designating health professional shortage areas [HPSAs], medically underserved areas [MUAs] or medically underserved populations [MUPs].- MSSAs will not cross county lines.- To the extent practicable, all census-defined places within the MSSA are within 30 minutes travel time to the largest population center within the MSSA, except in those circumstances where meeting this criterion would require splitting a census tract.- To the extent practicable, areas that, standing alone, would meet both the definition of an MSSA and a Rural MSSA, should not be a part of an Urban MSSA.- Any Urban MSSA whose population exceeds 200,000 shall be divided into two or more Urban MSSA Subdivisions.- Urban MSSA Subdivisions should be within a population range of 75,000 to 125,000, but may not be smaller than five square miles in area. If removing any census tract on the perimeter of the Urban MSSA Subdivision would cause the area to fall below five square miles in area, then the population of the Urban MSSA may exceed 125,000. - To the extent practicable, Urban MSSA Subdivisions should reflect recognized community and neighborhood boundaries and take into account such demographic information as income level and ethnicity. Rural Definitions: A rural MSSA is an MSSA adopted by the Commission, which has a population density of less than 250 persons per square mile, and which has no census defined place within the area with a population in excess of 50,000. Only the population that is located within the MSSA is counted in determining the population of the census defined place. A frontier MSSA is a rural MSSA adopted by the Commission which has a population density of less than 11 persons per square mile. Any MSSA which is not a rural or frontier MSSA is an urban MSSA. Last updated December 6th 2024.

Mineral Land Classification studies are produced by the State Geologist as specified by the Surface Mining and Reclamation Act (SMARA, PRC 2710 et seq.) of 1975. To address mineral resource conservation, SMARA mandated a two-phase process called classification-designation. Classification is carried out by the State Geologist and designation is a function of the State Mining and Geology Board. The classification studies contained here evaluate the mineral resources and present this information in the form of Mineral Resource Zones. The objective of the classification-designation process is to ensure, through appropriate local lead agency policies and procedures, that mineral materials will be available when needed and do not become inaccessible as a result of inadequate information during the land-use decision-making process.

Maps and climate diagrams for study sites.

Recommendations for the suitable contents of the geospatial datasets presenting the distribution of languages including the benefits of each, and our solutions (selected in the case study) concerning the Uralic languages.

The list of study sites, meteorological stations and locations of interest that are shown on the Bonanza Creek Long Term Ecological Research site (BNZ LTER) internet map server (IMS, available at http://www.lter.uaf.edu/ims_intro.cfm) is generated from the LTER study sites database. The information is converted into a shapefile and posted to the IMS. Some study sites shown on the main LTER website will not appear on the IMS because they do not have location coordinates. In all cases the most up-to-date information will be found on the (study sites website ).

The spatial information represented on the IMS is available to the public according to the restrictions outlined in the LTER data policy. The dataset represented here consists of the map layers shown on the IMS. The information consists of shapefiles in Environmental Systems Research Institute (ESRI) format. Users of this dataset should be aware that the contents are dynamic. Portions of the information shown on the IMS are derived from the Bonanza Creek LTER databank and are constantly being updated.

The Fisheries Research Web Map provides a summary of PIFSC fisheries research in the Mariana Archipelago. Included are data from the the Resource Assessment Investigation of the Mariana Archipelago and the Fisheries Research and Monitoring Division's Life History Program.To access metadata for this project and the associated datasets, please visit the following url: InPort Metadata Catalog Item #24436.

Students will recognise differences between large-scale and small-scale maps.Other New Zealand GeoInquiry instructional material freely available at https://arcg.is/1GPDXe

This dataset describes the boundary of the study area used to analyze regeneration and change in status of native ohia forests in the wet habitat on the eastern side of the island of Hawaii. This area includes forests that were heavily impacted by landscape-level canopy dieback in the 1970s as well as forests that were not affected with tree canopy death or defoliation.

The BOREAS HYD-09 team collected data on precipitation and streamflow over portions of the NSA and SSA. This data set contains Cartesian maps of rain accumulation for 1-hour and daily periods during the summer of 1994 over the SSA only (not the full view of the radar)

The Digital Geologic-GIS Map of parts of Great Sand Dunes National Park and Preserve (Sangre de Cristo Mountains and part of the Dunes), Colorado is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (gsam_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro 3.X map file (.mapx) file (gsam_geology.mapx) and individual Pro 3.X layer (.lyrx) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (grsa_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (grsa_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (gsam_geology_metadata_faq.pdf). Please read the grsa_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (gsam_geology_metadata.txt or gsam_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS Pro, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Open the Data Resource: https://cicgis.org/portal/apps/storymaps/stories/b519e88ccc8c4c4c8d4c870f64e210ed Forest conservation and tree planting are central strategies to achieve the goals laid out in the 2014 Chesapeake Bay Watershed Agreement and are reinforced in many parts of the Maryland legal code. To monitor forest and tree canopy cover status and progress toward its commitments, the Maryland General Assembly enacted legislation (House Bill 991) in 2021 requiring a Technical Study of Changes in Maryland’s Forest Cover and Tree Canopy. The Maryland Forest Technical Study Story Map presents the results of this study, which improves Maryland’s statewide inventory of forest and tree canopy cover, assesses near and long-term change and assesses the effectiveness of forest and tree programs operating in the state. Notably, this study makes use of a newly released, innovative, very high-resolution (1-m) land use and land cover dataset for the Chesapeake Bay watershed, used for the first time to monitor individual trees within and outside forests across Maryland. This is complemented by moderate-resolution satellite imagery, ground observations and other research to generate insights on the status of tree canopy cover in the state.

The NFHL FIRMs and FIS are the official regulatory products for the NFIP.

This dataset contains the interviews conducted at four universities. The objective of the research was to understand how Internet of Things applications could support strategic decision-making processes at universities. Therefore, interviews were conducted to map a decision-making process at each university (i.e. the design or adjustment of the campus strategy). The outcomes of the first set of interviews were used to conduct process and information analysis. The process analysis was validated in a second round of interviews. The information analysis is based on the process analysis and connects process activities to information needs (which can be delivered by the Internet of Things).

CONTRIBUTORS

This package contains a project specific geodatabase and map (.mxd) for Watershed study projects. For directions on using this file, see the GIS Standards Technical Memorandum on the Standards Page.

Regression model with task success as the dependent variable (see S3 Table for odds ratios).

LC approach: set of ecosystem service maps based on land cover; EV approach: set of ecosystem service maps based on environmental variables. JRC approach: set of data driven ecosystem service maps. IVM approach: set of ecosystem service maps of intermediate complexity.Overview of the ecosystem service datasets analysed in this study.

This map was created for the US National Science Foundation Land-Atmosphere-Ice Interactions (LAII) Flux Study and the Arctic Transitions in the Land-Atmosphere System (ATLAS) Study (OPP-9318530 and OPP-9415554). The map covers all of northern Alaska, from the Brooks Range divide to the coast. It is a raster (tif) map, with 50 m pixels, and 9 land cover categories. It is based on an unsupervised classification of a Landsat Multispectral Scanner (MSS) composite created by the National Mapping Division, U.S. Geological Survey EROS data center, Anchorage, Alaska. Geobotanical maps and earlier Landsat-derived maps of the region were used to interpret the spectral classes. References Muller, S. V., A. E. Racoviteanu, and D. A. Walker. 1999. Landsat MSS-derived land-cover map of northern Alaska: Extrapolation methods and a comparison with photo-interpreted and AVHRR-derived maps. International Journal of Remote Sensing 20:2921-2946.