Environmental Sensitivity Index (ESI) maps are an integral component in oil-spill contingency planning and assessment. They serve as a source of information in the event of an oil spill incident. ESI maps are a product of the Hazardous Materials Response Division of the Office of Response and Restoration (OR&R).ESI maps contain three types of information: shoreline habitats (classified according to their sensitivity to oiling), human-use resources, and sensitive biological resources. Most often, this information is plotted on 7.5 minute USGS quadrangles, although in Alaska, USGS topographic maps at scales of 1:63,360 and 1:250,000 are used, and in other atlases, NOAA charts have been used as the base map. Collections of these maps, grouped by state or a logical geographic area, are published as ESI atlases. Digital data have been published for most of the U.S. shoreline, including Alaska, Hawaii and Puerto Rico.

Environmental Sensitivity Index (ESI) data characterize the marine and coastal environments and wildlife based on sensitivity to spilled oil. Coastal species that are listed as threatened, endangered, or as a species of concern, by either federal or state governments, are a primary focus. A subset of the ESI data, the ESI Threatened and Endangered Species (T&E) databases focus strictly on these...

https://github.com/gruizmer/COW2NUTRIENT/tree/master/ToolPaper_DataFiles * These folders supply supporting datasets for the manuscript "COW2NUTRIENT: An environmental GIS-based decision support tool for the assessment of nutrient recovery systems in livestock facilities." * The datasets are recorder as comma-separated values (.csv) and Microsoft Excel® (.xlsx) files. Column data entries have names and units. Some data are about animal facility population and location, amount of nutrient-rich waste generated (kg/yr), amount of nutrient recovered (kg P/yr), installing, capital, and maintenance costs (USD), technologies and their ranking and frequency of being selected for each combination of normalization-aggregation methods, average chlorophyll-a concentration in water in the watershed (ug/L), and average phosphorus concentration in water in the watershed (ug/L). * The folder “Manuscript” has subfolders with datasets for creating manuscript Figures 4, 8, 9, and 10 as well as datasets for Tables 9 and 10. * The folder “Supplementary Material” holds subfolders with datasets for creating Supplementary Material Figures 1-5, 8, 9, 11, and 12. This dataset is associated with the following publication: Martin-Hernandez, E., M. Martin, and G.J. Ruiz-Mercado. A geospatial environmental and techno-economic framework for sustainable phosphorus management at livestock facilities. Resources, Conservation and Recycling. Elsevier Science BV, Amsterdam, NETHERLANDS, 175: 105843, (2021).

The Environment Map (US Edition) web map consists of vector tile layers that form a detailed basemap for the world, featuring a neutral style with content adjusted to support environment, landscape, natural resources, hydrologic and physical geography layers. The layers in this map provide unique capabilities for customization, high-resolution display and offline use in mobile devices. They are built using the same data sources used for other Esri basemaps.This basemap is available in the United States Vector Basemaps gallery and consists of 4 vector tile layers and one raster tile layer: The Environment Detail and Label (US Edition) vector tile reference layer for the world with administrative boundaries and labels; populated places with names; ocean names; topographic features; and rail, road, park, school, and hospital labels. The Environment Surface Water and Label vector tile surface water layer for the world with rivers, lakes, streams, and canals with respective labels. The Environment Watersheds vector tile layer that provides watersheds boundaries. The Environment Base multisource base layer for the world with vegetation, parks, farming areas, open space, indigenous lands, military bases, bathymetry, large scale contours, elevation values, airports, zoos, golf courses, cemeteries, hospitals, schools, urban areas, and building footprints. World Hillshade raster tile layerThe vector tile layers in this web map are built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layers referenced in this map.

File List bathymetry_30s.7z (MD5: dd855211bbcdee7d6862414da23d6da2) biogeo01_07_30s.7z (MD5: 396525db0abd9de2ede3d8fdeb15e8ee) biogeo08_17_30s.7z (MD5: 96c2417eed84e85f9896536b934c53e1) Monthly_Variables_30s.7z (MD5: 89016a8d17e8d8a1dddef0a121a83f5d)

     Additional high resolution raster files:

Sea_Ice_30s.7z (MD5: 547d355294c530f63b9b0a73dedd2f3a)

     Low resolution MARSPEC data files:

MARSPEC_2o5m.7z (MD5: 923c97d185adb0c72f158a84e2981391) MARSPEC_5m.7z (MD5: 95f7c3739c4f2889c2eff18afeffa489) MARSPEC_10m.7z (MD5: d91f3127f46f7004d116f14328bf4b71) Description Ecological niche models are widely used in terrestrial studies to address critical ecological and evolutionary questions related to past and future climate change, local adaptation and speciation, the discovery of rare endemics, and biological invasions. However the application of niche models to similar questions in marine ecosystems has lagged behind, in part due to the lack of a centralized high-resolution spatial data set representing both benthic and pelagic marine environments. Here we describe the creation of MARSPEC, a high-resolution GIS database of ocean climate layers intended for marine ecological niche modeling and other applications in marine spatial ecology. MARSPEC combines information related to topographic complexity of the seafloor with bioclimatic measures of sea surface temperature and salinity for the world ocean. We derived seven geophysical variables from a high-resolution raster grid representing depth of the seafloor (bathymetry) to characterize six facets of topographic complexity (east-west and north-south components of aspect, slope, concavity of the seafloor, and plan and profile curvature) and distance from shore. We further derived 10 bioclimatic variables describing the annual mean, range, variance and extreme values for temperature and salinity from long-term monthly climatological means obtained from remotely sensed and in situ oceanographic observations. All variables were clipped to a common land mask, interpolated to a nominal 1-km (30 arc-second) grid, and converted to an ESRI raster grid file format compatible with popular GIS programs. MARSPEC is a 10-fold improvement in spatial resolution over the next-best data set (Bio-ORACLE) and is the only high-resolution global marine data set to combine variables from the benthic and pelagic environments into a single database. Additionally, we provide the monthly climatological layers used to derive the bioclimatic variables, allowing users to calculate equivalent MARSPEC variables from anomaly data for past and future climate scenarios. A detailed description of GIS processing steps required to calculate the MARSPEC variables can be found in the metadata.

      Key words: climate change; ecological niche modeling; GIS; marine spatial ecology; ocean climate; salinity; sea surface temperature; species distribution modeling.

The present dataset provides necessary indicators of the climate change vulnerability of Bangladesh in raster form. Geospatial databases have been created in Geographic Information System (GIS) environment mainly from two types of raw data; socioeconomic data from the Bangladesh Bureau of Statistics (BBS) and biophysical maps from various government and non-government agencies. Socioeconomic data have been transformed into a raster database through the Inverse Distance Weighted (IDW) interpolation method in GIS. On the other hand, biophysical maps have been directly recreated as GIS feature classes and eventually, the biophysical raster database has been produced. 30 socioeconomic indicators have been considered, which has been obtained from the Bangladesh Bureau of Statistics. All socioeconomic data were incorporated into the GIS database to generate maps. However, the units of some variables have been adopted directly from BBS, some have been normalized based on population, and some have been adopted as percentages. 12 biophysical system indicators have also been classified based on the collected information from different sources and literature. Biophysical maps are mainly classified in relative scales according to the intensity. These geospatial datasets have been analyzed to assess the spatial vulnerability of Bangladesh to climate change and extremes. The analysis has resulted in a climate change vulnerability map of Bangladesh with recognized hotspots, significant vulnerability factors, and adaptation measures to reduce the level of vulnerability.

This dataset represents a unique compiled environmental data set for the circumpolar Arctic ocean region 45N to 90N region. It consists of 170 layers (mostly marine, some terrestrial) in ArcGIS 10 format to be used with a Geographic Information System (GIS) and which are listed below in detail. Most layers are long-term average raster GRIDs for the summer season, often by ocean depth, and represent value-added products easy to use. The sources of the data are manifold such as the World Ocean Atlas 2009 (WOA09), International Bathimetric Chart of the Arctic Ocean (IBCAO), Canadian Earth System Model 2 (CanESM2) data (the newest generation of models available) and data sources such as plankton databases and OBIS. Ocean layers were modeled and predicted into the future and zooplankton species were modeled based on future data: Calanus hyperboreus (AphiaID104467), Metridia longa (AphiaID 104632), M. pacifica (AphiaID 196784) and Thysanoessa raschii (AphiaID 110711). Some layers are derived within ArcGIS. Layers have pixel sizes between 1215.819573 meters and 25257.72929 meters for the best pooled model, and between 224881.2644 and 672240.4095 meters for future climate data. Data was then reprojected into North Pole Stereographic projection in meters (WGS84 as the geographic datum). Also, future layers are included as a selected subset of proposed future climate layers from the Canadian CanESM2 for the next 100 years (scenario runs rcp26 and rcp85). The following layer groups are available: bathymetry (depth, derived slope and aspect); proximity layers (to,glaciers,sea ice, protected areas, wetlands, shelf edge); dissolved oxygen, apparent oxygen, percent oxygen, nitrogen, phosphate, salinity, silicate (all for August and for 9 depth classes); runoff (proximity, annual and August); sea surface temperature; waterbody temperature (12 depth classes); modeled ocean boundary layers (H1, H2, H3 and Wx).This dataset is used for a M.Sc. thesis by the author, and freely available upon request. For questions and details we suggest contacting the authors. Process_Description: Please contact Moritz Schmid for the thesis and detailed explanations. Short version: We model predicted here for the first time ocean layers in the Arctic Ocean based on a unique dataset of physical oceanography. Moreover, we developed presence/random absence models that indicate where the studied zooplankton species are most likely to be present in the Arctic Ocean. Apart from that, we develop the first spatially explicit models known to science that describe the depth in which the studied zooplankton species are most likely to be at, as well as their distribution of life stages. We do not only do this for one present day scenario. We modeled five different scenarios and for future climate data. First, we model predicted ocean layers using the most up to date data from various open access sources, referred here as best-pooled model data. We decided to model this set of stratification layers after discussions and input of expert knowledge by Professor Igor Polyakov from the International Arctic Research Center at the University of Alaska Fairbanks. We predicted those stratification layers because those are the boundaries and layers that the plankton has to cross for diel vertical migration and a change in those would most likely affect the migration. I assigned 4 variables to the stratification layers. H1, H2, H3 and Wx. H1 is the lower boundary of the mixed layer depth. Above this layer a lot of atmospheric disturbance is causing mixing of the water, giving the mixed layer its name. H2, the middle of the halocline is important because in this part of the ocean a strong gradient in salinity and temperature separates water layers. H3, the isotherm is important, because beneath it flows denser and colder Atlantic water. Wx summarizes the overall width of the described water column. Ocean layers were predicted using machine learning algorithms (TreeNet, Salford Systems). Second, ocean layers were included as predictors and used to predict the presence/random absence, most likely depth and life stage layers for the zooplankton species: Calanus hyperboreus, Metridia longa, Metridia pacifica and Thysanoessa raschii, This process was repeated for future predictions based on the CanESM2 data (see in the data section). For zooplankton species the following layers were developed and for the future. C. hyperboreus: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100.For parameters: Presence/random absence, most likely depth and life stage layers M. longa: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100. For parameters: Presence/rand... Visit https://dataone.org/datasets/f63d0f6c-7d53-46ce-b755-42a368007601 for complete metadata about this dataset.

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

The Digital Environmental Geologic-GIS Map for San Antonio Missions National Historical Park and Vicinity, Texas is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (saan_environmental_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 map file (.mapx) file (saan_environmental_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (saan_environmental_geology.mxd) and individual 10.1 layer (.lyr) 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 10.1 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 GIS readme file (saan_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (saan_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 (saan_environmental_geology_metadata_faq.pdf). Please read the saan_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: 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: Texas Bureau of Economic Geology, University of Texas at Austin. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (saan_environmental_geology_metadata.txt or saan_environmental_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, 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). Purpose:

This is a link to the Department of Environmental Protection (DEP) Open Data Portal which is operated by DEP's Geospatial Data Center on an Esri platform.

NorWeST is an interagency stream temperature database and model for the western United States containing data from over 20,000 unique stream locations. Temperature observations were solicited from state, federal, tribal, private, and municipal resource organizations and processed using a custom cleaning script developed by Gwynne Chandler. Summaries of daily, weekly, and monthly means, minima, and maxima are provided for observation years. The data summaries and location information are available in user-friendly file formats that include: 1) a map (PDF) depicting the locations of in-stream thermographs (temperature sensors) for each processing unit, 2) a GIS shapefile (SHP) containing the location of these sensors for each processing unit, and 3) a tabular file (XLSX) containing observed temperature database summaries for data generally ranging from 1993 to 2015, dependent on the processing unit. Each point shapefile extent corresponds to NorWeST processing units, which generally relate to 6 digit (3rd code) hydrologic unit codes (HUCs). The tabular data can be joined to the observation point shapefile using the ID field OBSPRED_ID. The NorWeST NHDPlusV1 processing units include: Salmon, Clearwater, Spokoot, Missouri Headwaters, Snake-Bear, MidSnake, MidColumbia, Oregon Coast, South-Central Oregon, Upper Columbia-Yakima, Washington Coast, Upper Yellowstone-Bighorn, Upper Missouri-Marias, and Upper Green-North Platte. The NorWeST NHDPlusV2 processing units include: Lahontan Basin, Northern California-Coastal Klamath, Utah, Coastal California, Central California, Colorado, New Mexico, Arizona, and Black Hills.These data have many potential uses including the assessment of stream temperature regimes, development of climate scenarios, understanding habitat and climate effects on stream temperatures, describing the thermal ecology of aquatic species, and conducting climate vulnerability assessments.For more information on the NorWeST stream temperature project see: https://www.fs.usda.gov/rm/boise/AWAE/projects/NorWeST.html

This data publication originally became available via the FS Research Data Archive on 11/17/2016. On 7/27/2022 the metadata was updated to correct old URLs.

Seattle Parks and Recreation ARCGIS park feature map layer web services are hosted on Seattle Public Utilities' ARCGIS server. This web services URL provides a live read only data connection to the Seattle Parks and Recreations Environmental Learning Centers dataset.

Proof web map for GeoInquiries Advanced Environmental Science lesson on Tropical Deforestation.THE ADVANCED ENVIRONMENTAL SCIENCE AND BIOLOGY GEOINQUIRY COLLECTIONhttp://www.esri.com/geoinquiriesTo support Esri’s involvement in the White House ConnectED Initiative, GeoInquiry instructional materials using ArcGIS Online for high school biology education are now freely available.The Advanced Environmental Science and Biology GeoInquiry collection contains 15 free, web-mapping activities that correspond and extend map-based concepts in leading elementary textbooks. The activities use a standard inquiry-based instructional model, require only 15 minutes for a teacher to deliver, and are device/laptop agnostic. The activities harmonize with the Next Generation Science Standards. Activity topics include:• Population dynamics • Megacities • Down to the last drop • Dead zones (water pollution) • The Beagle’s Path • Primary productivity • Tropical Deforestation • Marine debris • El Nino (and climate) • Slowing malaria • Altered biomes • Spinning up wind power • Resource consumption and wealthTeachers, GeoMentors, and administrators can learn more at http://www.esri.com/geoinquiries

IntroductionBuilt environment attributes have been linked to cardiovascular disease (CVD) risk. Therefore, identifying built environment attributes that are associated with CVD risk is relevant for facilitating effective public health interventions.ObjectiveTo conduct a systematic review of literature to examine the influence of built environmental attributes on CVD risks.Data SourceMultiple database searches including Science direct, CINAHL, Masterfile Premier, EBSCO and manual scan of reference lists were conducted.Inclusion CriteriaStudies published in English between 2005 and April 2015 were included if they assessed one or more of the neighborhood environmental attributes in relation with any major CVD outcomes and selected risk factors among adults.Data ExtractionAuthor(s), country/city, sex, age, sample size, study design, tool used to measure neighborhood environment, exposure and outcome assessments and associations were extracted from eligible studies.ResultsEighteen studies met the inclusion criteria. Most studies used both cross-sectional design and Geographic Information System (GIS) to assess the neighborhood environmental attributes. Neighborhood environmental attributes were significantly associated with CVD risk and CVD outcomes in the expected direction. Residential density, safety from traffic, recreation facilities, street connectivity and high walkable environment were associated with physical activity. High walkable environment, fast food restaurants, supermarket/grocery stores were associated with blood pressure, body mass index, diabetes mellitus and metabolic syndrome. High density traffic, road proximity and fast food restaurants were associated with CVDs outcomes.ConclusionThis study confirms the relationship between neighborhood environment attributes and CVDs and risk factors. Prevention programs should account for neighborhood environmental attributes in the communities where people live.

Data for maps and figures in "Global Potential for Harvesting Drinking Water from Air using Solar Energy" in Nature.

Data shows polygon locations of Potential Environmental Justice Areas (PEJA) and is defined in the PEJA field. PEJA's have been identified based on data from the 2014-2018 5-year American Community Survey (ACS), conducted by the US Census Bureau. Environmental justice efforts focus on improving the environment in communities, specifically minority and low-income communities, and addressing disproportionate adverse environmental impacts that may exist in those communities. The information balloon for each census block group area displays the census block group ID, population, percent minority, percent below poverty level, county, municipality, and a link to more information on the Department of Environmental Conservation's website https://www.dec.ny.gov/public/333.html The data was collected by the US Census Bureau as part of the American Community Survey. Reported income and race/ethnicity data were analyzed by OEJ to determine the presence of Potential Environmental Justice Areas. The designated areas are then considered for additional outreach within the permitting process, for grant eligibility, and for targeted enforcement of Environmental Conservation Law violations. Utilized established methods as originally detailed in the Interim Environmental Justice Policy, US EPA Region 2, December 2000, and recommended by the Environmental Justice Advisory Group, Recommendations for the New York State Department of Environmental Conservation Environmental Justice Program, January 2, 2002. Individual thresholds for low-income populations (statewide), minority populations (rural communities), and minority populations (urban communities) were determined by using ArcGIS 10.3 (used to indicate if census block groups overlapped Census designated urban areas) and IBM SPSS Statistics 26 (to conduct a K-means clustering algorithm on ACS data for the three categories). More detail is provided under processing steps. Service updated annually.

Map Package OverviewThis map package includes all of the layers from the NTIA Permitting and Environmental Information Application. The layers included are all feature services from various Federal and State agencies. The map package was created with ArcGIS Pro 3.4.0. The map package was created to allow users easy access to all feature services including symbology. The map package will allow users to avoid downloading datasets individually and easily incorporate into their own GIS system. The map package includes three maps.1. Permitting and Environmental Information Application Layers for GIS Analysis - This map includes all of the map tabs shown in the application, except State Data which is provided in another tab. This map includes feature services that can be used for analysis with other project layers such as a route or project area. 2. Permitting and Environmental Information Application Layers – For Reference Only - This map includes layers that cannot be used for analysis since they are either imagery or tile layers.3. State Data - Reference Only - This map includes all relevant state data that is shown in the application.This map package was created on Dec.19th, 2024. It will be updated quarterly to reflect the latest changes in the application. If you have any questions regarding the map package please e-mail IFA Analytics.The NTIA Permitting and Environmental Information Application was created to help with your permitting planning and environmental review preparation efforts by providing access to multiple maps from publicly available sources, including federal review, permitting, and resource agencies. The application should be used for informational purposes only and is intended solely to assist users with preliminary identification of areas that may require permits or planning to avoid potentially significant impacts to environmental resources subject to the National Environmental Policy Act (NEPA) and other statutory requirements. Multiple maps are provided in the application which are created from public sources. This application does not have an exhaustive list of everything you need for permitting or environmental review for a project but is an initial starting point to see what might be required.DisclaimerThe NTIA Permitting and Environmental Information Application and Map Package should be used for informational purposes only and is intended solely to assist users with preliminary identification of broadband deployments that may require permits or planning to avoid potentially significant impacts to environmental resources subject to National Environmental Policy Act (NEPA) and other statutory requirements. Use of the Application presumes familiarity with training information provided for the tool. The Application is not an exhaustive or complete resource and does not and is not intended to substitute for, supersede, modify, or otherwise alter any applicable statutory or regulatory requirements, or the specific application requirements set forth in any NTIA NOFO, Terms and Conditions, or Special Award Condition. In all cases, statutory and regulatory mandates, and the requirements set forth in NTIA grant documents, shall prevail over any inconsistencies contained in these templates.The Application and Map Package relies on publicly available data available on the websites of other federal, state, local, and Tribal agencies, and in some instances, private organizations and research institutions. While NTIA continues to make amendments to its websites to comply with Section 508, NTIA cannot ensure Section 508 compliance of federal and non-federal websites or resources users may access from links on NTIA websites.

All data is presented “as is,” “as available” for informational purposes. NTIA does not warrant the accuracy, adequacy, or completeness of this information and expressly disclaims liability for any errors or omissions.

The aim is to ensure that aircraft can safely carry out their scheduled operations, and to prevent the aerodromes from becoming unusable due to the proliferation of obstacles in the surrounding area. One such possible obstacle is the vegetation growing in the zone. This study therefore focuses on measuring the height and geolocation of the obstacle in order to determine its influence on the OLS, and on determining the subsequent actions, if any, that need to be taken in regard to this vegetation element to avoid it becoming a risk to operational safety

Welcome to the KDHE Environmental Interest Finder (KEIF). Please refer to the Web App for general usage. The web app provides general information about locations across the state of Kansas that are regulated and/or monitored by at least one of our programs in the Division of Environment at KDHE. Instructions for using the site as well as frequently asked questions can be found by clicking or tapping on the Help icon in the upper right corner of the app. For further information or environmental concerns, please contact the originating program in the Division of Environment (in the location's pop-up, click or tap on the More Information On link). More information about KDHE can be found on the Kansas Department of Health and Environment website Data available for download as ArcGIS Server Feature Service via Kansas GeoPortal: https://hub.kansasgis.org/DASC.

The FGGD Digital Atlas consists of more than 100 global maps that allows to analyse food insecurity and poverty in relation to environment. It is subdivided into 6 modules as follows: Module 1 Boundaries and Topography Module 2 Population Module 3 Socio-Economic and Nutrition Indicators Module 4 Environmental Conditions Module 5 Land Use Patterns and Land Cover Module 6 Land Productivity Potential