This layer contains 30 minute driving times from each SAMHSA treatment center in Tennessee. This map depicts the locations of SAMHSA Treatment Programs in Tennessee as of 09/18/2019. The map also contains 60 and 30 minute drive time analysis polygons and 30 minute walking analysis polygons.Data was downloaded from https://dpt2.samhsa.gov/treatment/ and geocoded in ArcGIS Online. Locations have not been verified. Drive and walking time polygons were generated in ArcGIS Online.

DATA LINKED FROM WA Dept. of Health Downloadable Data Sets (current 10/21/2020)This data set is used as a reference layer for geographic information systems to show the approximate locations of public water supply wellhead protection areas in Washington state.Polygons depict time of travel estimates for active group a public drinking water supplies. Source location data were obtained from the Washington State Department of Health, Office of Drinking Water.Original layer herePolygons depict time of travel estimates for active public drinking water supplies. Source location data were obtained from the Washington State Department of Health, Office of Drinking Water.Metadata: Affected Area/"Assigned"/1000ftMetadata: 6 monthMetadata: 1 yearMetadata: 5 yearMetadata: 10 year

Polygons depict time of travel estimates for active public drinking water supplies. Source location data were obtained from the Washington State Department of Health, Office of Drinking Water.

30-60-90-minute drive-time polygons towards WA State Park entrance points. Feature layer generated from running the Create Drive Times solution.Analysis run on February 10, 2023. Settings used:1. Source layer: Park Entrances2. Measure: Driving time, 30- 60- 90-minutes, not using traffic, travel direction Towards Facility3. Areas from different points: Overlap

Tabular Summaries - Communities at Risk As part of Montana DNRC’s Montana Wildfire Risk Assessment (MWRA), wildfire risk to homes, commercial buildings, and other structures was assessed across the state. The purpose of this assessment is to identify the counties and communities whose structures are most threatened by wildfire—both on average and in total. The risk-to-structures methods used for this assessment are identical to the methods used for structures within the overall MWRA project. See earlier section 3.4.1 of the report (page 20) for details. This portion of the report addresses only the tabular summaries. The summary methods used in this section were customized to the MWRA results from similar methods previously developed for the Pacific Northwest Risk Assessment (PNRA) and for the national Wildfire Risk to Communities (WRC) project.  Mean Risk to Structures We calculated the Mean Risk to Structures as the product of Mean Conditional Risk to Structures and Mean Burn Probability (multiplied by 1000 to remove decimal places). This is the primary variable by which the summary polygons are ranked. Like the components used to calculate it, Mean Risk to Structures is not a cumulative measure for a summary polygon, so it does not necessarily increase as the number or importance of structures increases. It represents the average of the structures in the polygon regardless of the total number or importance of structures. Total Structure Risk We calculated Total Structure Risk as the product of Mean Risk to Structures and Total Structure Importance. This is the secondary variable by which the summary polygons are ranked. Unlike the previous measures, the total importance of structures (their number and mean importance) strongly influences Total Structure Risk. The risk-to-structures results were summarized for two primary sets of summary polygons: MT Counties MT Communities Expanded Area Each set of summary polygons captures nearly all structures in Montana, without overlap. In the MT Counties set, a summary polygon is an individual county (e.g. Ravalli County). In the MT Communities (core plus zone combined) set called MT Communities Expanded Area, a summary polygon is the community core plus the zone surrounding the core (as defined below). Expanded Areas include populated areas outside of official community boundaries that are closer to the selected community than to any other community. Long definition: Populated areas not within the boundaries of a community were associated with the community to which they were closest, as measured by travel time. Travel time is influenced by road networks, associated travel speeds, and physical barriers such as water. Populated areas greater than 45 minutes travel time from any community are not included within the Expanded Area for any community. For this assessment, a community core was defined as a Populated Place Area (PPA) as identified by the U.S. Census Bureau. PPAs include incorporated cities and towns as well as Census Designated Places (CDPs). A CDP is an unincorporated concentration of population—a statistical counterpart to incorporated cities and towns. There are 364 PPAs across Montana. Of those, 127 (35 percent) are incorporated cities or towns, and 235 (65 percent) are CDPs. Two PPAs—Butte-Silver Bow and Anaconda-Deer Lodge—are unique in that they represent the balance of a county that is not otherwise incorporated; they are much larger in size than most PPAs. In the PPA dataset, the CDPs represent the location of highest concentration of population for a community; they do not include the less-densely populated areas surrounding the PPA. We refer to the U.S. Census PPA delineation as the community “core.” Approximately 66 percent of Montana’s total structure importance can be found within these PPA core areas (Figure A.1 of the Montana Wildfire Risk Assessment report). To include the populated area and structures surrounding the PPAs, Ager and others (2019) used a travel-time analysis to delineate the land areas closest by drive-time to each PPA core, up to a maximum of 45 minutes travel time. Approximately 33 percent of Montana’s total structure importance can be found within 45 minutes travel time of the cores. Only 1 percent of the total structure importance is not within 45-minutes travel time of any community core.

The dashboard was creating using Business Analyst Infographics. Read more about it here: https://www.esri.com/en-us/arcgis/products/data/overview?rmedium=www_esri_com_EtoF&rsource=/en-us/arcgis/products/esri-demographics/overview Data Source: U.S. Census Bureau, Census 2020 Summary File 1, 2021 American Community Survey(ACS), and ESRI 2022 Demographics and Tapestry Segmentation. For more information on Esri Demographics see HERE and for Tapestry see HERE.Geographies: The council district boundaries used in this dashboard are those that were effective as of May 6, 2023.Much of the science for determining the data for an irregular polygon is explained here:https://doc.arcgis.com/en/community-analyst/help/calculation-estimates-for-user-created-areas.htmCalculation estimates for user-created areasBusiness Analyst employs a GeoEnrichment service which uses the concept of a study area to define the location of the point or area that you want to enrich with additional information. If one or more points is input as a study area, the service will create a one-mile ring buffer around the points or points to collect and append enrichment data. You can optionally change the ring buffer size or create drive-time service areas around a point.The GeoEnrichment service uses a sophisticated geographic retrieval methodology to aggregate data for rings and other polygons. A geographic retrieval methodology determines how data is gathered and summarized or aggregated for input features. For standard geographic units, such as states, provinces, counties, or postal codes, the link between a designated area and its attribute data is a simple one-to-one relationship. For example, if an input study trade area contains a selection of ZIP Codes, the data retrieval is a simple process of gathering the data for those areas.Data Allocation MethodThe Data Allocation method allocates block group data to custom areas by examining where the population is located within the block group and determines how much of the population of a block group overlaps a custom area. This method is used in the United States, and similarly in Canada. The population data reported for census blocks, a more granular level of geography than block groups, is used to determine where the population is distributed within a block group. If the geographic center of a block falls within the custom area, the entire population for the block is used to weight the block group data. The geographic distribution of the population at the census block level determines the proportion of census block group data that is allocated to user specified areas as shown in the example.Note:Depending on the data, households, housing units or businesses at the block group level are used as weights. Employing block centriods is superior because it accounts for the possibility that the population may not be evenly distributed geographically throughout a block group.