27,01 (persons per sq. km) in 2022.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Harvard Forest (HFR) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

This data release presents chloride concentrations in groundwater sampled from 4,319 domestic wells across Vermont between 2011 and 2018. Ninety of these wells were sampled twice and 4,229 were sampled once. The Vermont Department of Health matched each well to geographic well information including town, county, distance to nearest road, population density and percent urban and agricultural land cover.

The world population data sourced from Facebook Data for Good is some of the most accurate population density data in the world. The data is accumulated using highly accurate technology to identify buildings from satellite imagery and can be viewed at up to 30-meter resolution. This building data is combined with publicly available census data to create the most accurate population estimates. This data is used by a wide range of nonprofit and humanitarian organizations, for example, to examine trends in urbanization and climate migration or discover the impact of a natural disaster on a region. This can help to inform aid distribution to reach communities most in need. There is both country and region-specific data available. The data also includes demographic estimates in addition to the population density information. This population data can be accessed via the Humanitarian Data Exchange website.

The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.The specific raster datasets included in this publication include:Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.Additional methodology documentation is provided with the data publication download. Metadata and Downloads.Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.

Data on aggregated radon test results in residential properties from January 1994 to December 2023 within each Vermont municipality. Radon data can inform public health outreach, educate stakeholders and the public, and encourage testing and mitigation. View this data in the Department of Health's radon risk map.Radon is a naturally occurring radioactive gas that is estimated to kill 50 Vermonters a year due to lung cancer. Radon can only be detected by testing and buildings with elevated radon levels (≥4 pCi/L (picocuries per Liter)) are found throughout the state. The average level of radon in Vermont homes is 2.4 pCi/L compared with the national average of 1.3 pCi/L. The EPA recommends that homes testing at or above 4 pCi/L be fixed, but as there is no known safe level of radon, the EPA suggests that homes testing between 2-4 pCi/L should also be fixed.This data set contains the Environmental Health Radon program’s radon in-air long term test data from 1994-2022, and the Vermont Department of Health Laboratory’s radon in-air short, medium, and long-term test data for 2020-2023. Data have been geocoded and aggregated to the town level to display the number and percent of residences tested by town and the number and percent of residences tested that exceed 4 pCi/L by town.Data SourceSource data for these maps comes from the highest radon test result ever found at a residence (many residences test more than once). Results are provided by the Radon Program long term test data (1994-2023) and the Vermont Department of Health Laboratory, short, medium, and long term test data (2020-2023). Radon results are aggregated by town based on whether the result was elevated (≥4.0 pCi/L) or not elevated (<4.0 pCi/L).Data LimitationsPrison, institutional residence, and nursing home E911 locations are not included in the aggregation of residences by town or geological area. For areas of low population density or low number of tests, data extremes carry more weight and can distort analytic results. Therefore, in the Rates of Radon Testing by Town, data for towns with fewer than 7 tested residences are not displayed; and in Elevated Radon Results, data for towns with fewer than 20 tested residences are not displayed.MethodologyRecord level radon in indoor air test results were extracted from the VDH-EH Radon database by Radon Program staff and from the LIMS system at the VDHL by laboratory staff. The Tracking analyst used SAS version 9.4 and ArcGIS Pro version 2.4.1 to process the data. Geocoded data from the Tracking program were used for the Radon Risk Maps. GIS work to populate the final maps was done collaboratively with partners from the Agency of Digital Services using ArcGIS Pro version 2.4.1.The residential data are from the VT Data – E911 Site Locations (address points) where the following were selected from the SITETYPE variable: mobile home, multi-family dwelling, other residential, single-family dwelling, residential farm, seasonal home, commercial with residence, condominium, and camp. The residential data in these maps is aggregated by town and geological area to provide the denominator for calculations.

Data on aggregated radon test results in residential properties from January 1994 to December 2023 within each geological area. View this data in the Department of Health's radon risk map.Radon is a naturally occurring radioactive gas that is estimated to kill 50 Vermonters a year due to lung cancer. Radon can only be detected by testing and buildings with elevated radon levels (≥4 pCi/L (picocuries per Liter)) are found throughout the state. The average level of radon in Vermont homes is 2.4 pCi/L compared with the national average of 1.3 pCi/L. The EPA recommends that homes testing at or above 4 pCi/L be fixed, but as there is no known safe level of radon, the EPA suggests that homes testing between 2-4 pCi/L should also be fixed. This data set contains the Environmental Health Radon program’s radon in-air long term test data from 1994-2023, and the Vermont Department of Health Laboratory’s radon in-air short, medium, and long-term test data for 2020-2023.Bedrock geology influences the amount of radon in air and water. Data is aggregated by bedrock geology type to better understand how geology affects radon in air in residences across the state. For a detailed explanation of the process used to develop the Bedrock zones map see the Read me file on DEC’s Radon page.Data SourceSource data for these maps comes from the highest radon test result ever found at a residence (many residences test more than once). Results are provided by the Radon Program long term test data (1994-2023) and the Vermont Department of Health Laboratory, short, medium, and long term test data (2020-2023). Radon results are aggregated by bedrock geology type based on whether the result was elevated (≥4.0 picocuries per liter (pCi/L)) or not elevated (<4.0 pCi/L).Data LimitationsPrison, institutional residence, and nursing home E911 locations are not included in the aggregation of residences by town or geological area. For areas of low population density or low number of tests, data extremes carry more weight and can distort analytic results. MethodologyRecord level radon in indoor air test results were extracted from the VDH-EH Radon database by Radon Program staff and from the LIMS system at the VDHL by laboratory staff. The Tracking analyst used SAS version 9.4 and ArcGIS Pro version 2.4.1 to process the data. Geocoded data from the Tracking program were used for the Radon Risk Maps. GIS work to populate the final maps was done collaboratively with partners from the Agency of Digital Services using ArcGIS Pro version 2.4.1.The residential data are from the VT Data – E911 Site Locations (address points) where the following were selected from the SITETYPE variable: mobile home, multi-family dwelling, other residential, single-family dwelling, residential farm, seasonal home, commercial with residence, condominium, and camp. The residential data in these maps is aggregated by town and geological area to provide the denominator for calculations.

The 2019 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the ""urban footprint."" There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The generalized boundaries for counties and equivalent entities are as of January 1, 2010.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Harvard Forest (HFR) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Harvard Forest (HFR) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Harvard Forest (HFR) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.

This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of Vermont. This dataset is the result of clipping the feature class 'NFHAP 2010 HCI Scores and Human Disturbance Data for the Conterminous United States linked to NHDPLUSV1.gdb' to the state boundary of Vermont. Landscape factors include land uses, population density, roads, dams, mines, and point-source pollution sites. The source datasets that were compiled and attributed to catchments were identified as being: (1) meaningful for assessing fish habitat; (2) consistent across the entire study area in the way that they were assembled; (3) representative of conditions in the past 10 years, and (4) of sufficient spatial resolution that they could be used to make valid comparisons among local catchment units. In this data set, these variables are linked to the catchments of the National Hydrography Dataset Plus Version 1 (NHDPlusV1) using the COMID identifier. They can also be linked to the reaches of the NHDPlusV1 using the COMID identifier. Catchment attributes are available for both local catchments (defined as the land area draining directly to a reach; attributes begin with "L_" prefix) and network catchments (defined by all upstream contributing catchments to the reach's outlet, including the reach's own local catchment; attributes begin with "N_" prefix). This shapefile also includes habitat condition scores created based on responsiveness of biological metrics to anthropogenic landscape disturbances throughout ecoregions. Separate scores were created by considering disturbances within local catchments, network catchments, and a cumulative score that accounted for the most limiting disturbance operating on a given biological metric in either local or network catchments. This assessment only scored reaches representing streams and rivers (see the process section for more details). Please use the following citation: Esselman, P., D.M. Infante, L. Wang, W. Taylor, W. Daniel, R. Tingley, J. Fenner, A. Cooper, D. Wieferich, D. Thornbrugh and J. Ross. (April 2011) National Fish Habitat Action Plan (NFHAP) 2010 HCI Scores and Human Disturbance Data (linked to NHDPLUSV1) for Vermont. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F7H9936Q