This Guide is designed to assist you with adding and viewing data on a map within the Department of Climate Change, Energy, the Environment and Water's Find Environmental Data (FED) geospatial data catalogue.This Guide assumes that you are familiar with locating data within FED. For further assistance see the Finding Data Guide.

This Guide is designed to assist you with adding and viewing data on a map within the Department of Climate Change, Energy, the Environment and Water's Find Environmental Data (FED) geospatial data catalogue. This Guide assumes that you are familiar with locating data within FED. For further assistance see the Finding Data Guide.

Geospatial Environmental Mapping System (GEMS) provides geospatial layers and access to dynamic mapping and environmental monitoring data for LM sites. Analytical chemistry data, groundwater depths and elevations, well logs, well construction data, georeferenced boundaries, sampling locations and photo's are available via GEMS.

Abstract

MEJ aims to create easy-to-use, publicly-available maps that paint a holistic picture of intersecting environmental, social, and health impacts experienced by communities across the US.

With guidance from the residents of impacted communities, MEJ combines environmental, public health, and demographic data into an indicator of vulnerability for communities in every state. MEJ’s goal is to fill an existing data gap for individual states without environmental justice mapping tools, and to provide a valuable tool for advocates, scholars, students, lawyers, and policy makers.

Methodology

The negative effects of pollution depend on a combination of vulnerability and exposure. People living in poverty, for example, are more likely to develop asthma or die due to air pollution. The method MEJ uses, following the method developed for CalEnviroScreen, reflects this in the two overall components of a census tract’s final “Cumulative EJ Impact”: population characteristics and pollution burden. The CalEnviroScreen methodology was developed through an intensive, multi-year effort to develop a science-backed, peer-reviewed tool to assess environmental justice in a holistic way, and has since been replicated by several other states.

CalEnviroScreen Methodology:

• Population characteristics are a combination of socioeconomic data (often referred to as the social determinants of health) and health data that together reflect a populations' vulnerability to pollutants. Pollution burden is a combination of direct exposure to a pollutant and environmental effects, which are adverse environmental conditions caused by pollutants, such as toxic waste sites or wastewater releases. Together, population characteristics and pollution burden help describe the disproportionate impact that environmental pollution has on different communities.

• Every indicator is ranked as a percentile from 0 to 100 and averaged with the others of the same component to form an overall score for that component. Each component score is then percentile ranked to create a component percentile. The Sensitive Populations component score, for example, is the average of a census tract’s Asthma, Low Birthweight Infants, and Heart Disease indicator percentiles, and the Sensitive Populations component percentile is the percentile rank of the Sensitive Populations score.

• The Population Characteristics score is the average of the Sensitive Populations component score and the Socioeconomic Factors component score. The Population Characteristics percentile is the percentile rank of the Population Characteristics score.

• The Pollution Burden score is the average of the Pollution Exposure component score and one half of the Environmental Effects component score (Environmental Effects may have a smaller effect on health outcomes than the indicators included the Exposures component so are weighted half as much as Exposures). The Pollution Burden percentile is the percentile rank of the Pollution Burden score.

• The Populaton Characteristics and Pollution Burden scores are then multiplied to find the final Cumulative EJ Impact score for a census tract, and then this final score is percentile-ranked to find a census tract's final Cumulative EJ Impact percentile.

• Census tracts with no population aren't given a Population Characteristics score.

• Census tracts with an indicator score of zero are assigned a percentile rank of zero. Percentile rank is then only calculated for those census tracts with a score above zero.

• Census tracts that are missing data for more than two indicators don't receive a final Cumulative EJ Impact ranking.

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This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information If you have questions about the underlying data stored here, please contact the Environmental Protection Agency. If you have questions or recommendations related to this metadata entry and extracted data, please contact the CAFE Data Management team at: climatecafe@bu.edu "EJScreen is EPA's environmental justice mapping and screening tool that provides EPA with a nationally consistent dataset and approach for combining environmental and socioeconomic indicators. Advanced users can download the full EJScreen datasets as raw data files. The available data includes the EJScreen as compared to the state or nation. They are available for download at the block group or tract level resolution in geodatabase (.gdb) or comma separated values (.csv) formats. There is a data dictionary spreadsheet (.xlsx) that provides a description of each of the column names. Additionally, the data is published as a feature service for users that wish to view the data within GIS software. These data downloads are recommended for users with GIS and statistical expertise, and it is essential that anyone using the raw data understand the caveats and limitations described in the EJScreen Technical Documentation." [Quote from https://www.epa.gov/ejscreen/download-ejscreen-data]

The Ecosystem Mapping Layer was created by the Taranaki Regional Council to support the identification and analysis of potential ecosystems and associated threat categories within the region. The dataset combines multiple data sources to provide accurate spatial information essential for conservation planning and ecosystem management. This layer aids in the understanding of regional ecosystems and the threats they face, contributing to informed decision-making in environmental monitoring and resource management.Title: Ecosystem Mapping LayerDate created: 05/10/2020Last updated: 12/02/2024Layers:Potential Ecosystems: Feature layer representing the distribution of potential ecosystems in the region.Potential Ecosystem Threat Categories: Feature layer identifying the threat levels faced by different ecosystems.Purpose: To provide accurate spatial data on potential ecosystems and their associated threats for environmental conservation and resource management in the Taranaki Region.Language: EnglishFormat: Vector (Polygon)Type: Feature LayerSpatial Coverage: Taranaki Region, New ZealandProjection: NZGD2000 / New Zealand Transverse Mercator 2000Source: Derived from multiple environmental data sources and updated with aerial photography for accuracy.Version Control: v1.0

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 2002 Set:

This data set contains vector lines representing the shoreline and coastal habitats of Connecticut classified according to the Environmental Sensitivity Index (ESI) classification system. ESI data characterize the marine and coastal environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats, sensitive biological resources, and human-use resources.The ESI data were collected, mapped, and digitized to provide environmental data for oil spill planning and response. The Clean Water Act with amendments by the Oil Pollution Act of 1990 requires response plans for immediate and effective protection of sensitive resources.

This data set contains vector polygons representing the shoreline and coastal habitats of Connecticut classified according to the Environmental Sensitivity Index (ESI) classification system. ESI data characterize the marine and coastal environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats, sensitive biological resources, and human-use resources. The ESI data were collected, mapped, and digitized to provide environmental data for oil spill planning and response. The Clean Water Act with amendments by the Oil Pollution Act of 1990 requires response plans for immediate and effective protection of sensitive resources.

EPA Environmental Justice Screening and Mapping ToolThis environmental justice screening and mapping tool provides demographic and environmental data layers to identify communities that are disproportionately affected by environmental hazards. When combined, these two data types create environmental justice indexes that give a clearer picture of communities adversely affected. This tool is intended to be used at the screening level as a user-defined interface to identify areas that are at potential risk. In most cases, data resolution is at the census block group level.

Map containing Alaska Department of Environmental Conservation (ADEC) Contaminated Sites, Groundwater Plume, PFAS, Water Quality Standards (ADEC), Drinking Water Protection Areas, and Solid Waste Sites/Boundaries data, Alaska Resource Data File (USGS), Surface Well Location (AOGCC), Formerly Used Defense Sites - FUDS and Interim Risk Management - IRM (USACE). Alaska Department of Natural Resources (ADNR) Land Ownership, Potential Hazardous Sites and Landfill data, as well as other land use.

Environmental Justice Screening and Mapping Tool (EJScreen) is EPA's environmental justice mapping and screening tool that provides EPA with a nationally consistent dataset and approach for combining environmental and socioeconomic indicators.

This study focuses on the use of citizen science and GIS tools for collecting and analyzing data on Rose Swanson Mountain in British Columbia, Canada. While several organizations collect data on wildlife habitats, trail mapping, and fire documentation on the mountain, there are few studies conducted on the area and citizen science is not being addressed. The study aims to aggregate various data sources and involve citizens in the data collection process using ArcGIS Dashboard and ArcGIS Survey 123. These GIS tools allow for the integration and analysis of different kinds of data, as well as the creation of interactive maps and surveys that can facilitate citizen engagement and data collection. The data used in the dashboard was sourced from BC Data Catalogue, Explore the Map, and iNaturalist. Results show effective citizen participation, with 1073 wildlife observations and 3043 plant observations. The dashboard provides a user-friendly interface for citizens to tailor their map extent and layers, access surveys, and obtain information on each attribute included in the pop-up by clicking. Analysis on classification of fuel types, ecological communities, endangered wildlife species presence and critical habitat, and scope of human activities can be conducted based on the distribution of data. The dashboard can provide direction for researchers to develop research or contribute to other projects in progress, as well as advocate for natural resource managers to use citizen science data. The study demonstrates the potential for GIS and citizen science to contribute to meaningful discoveries and advancements in areas.

This map shows known or potential environmental resources within the I-80 PEL Study Area. Resources shown in this map include streams, wetlands, conservation lands, threatened and endangered species, wildlife management areas, floodplains, cultural resources, cemeteries, regulated materials, woodlands, prairies, and unique landforms.

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Environmental mapping dataset

Environmental Data from the paper 'Combining Disparate Data Sources for Improved Poverty Prediction and Mapping' (Pokhriyal and Jacques, 2017, www.pnas.org/cgi/doi/10.1073/pnas.1700319114).For data sources, see Table S1 in the supplementray information provided with the paper.LEGEND

  LC11
  Post-flooding or irrigated croplands (or aquatic)


  LC14
  Rainfed croplands


  LC20
  Mosaic cropland (50-70%) / vegetation (grassland/shrubland/forest) (20-50%)


  LC30
  Mosaic vegetation (grassland/shrubland/forest) (50-70%) / cropland (20-50%) 


  LC40
  Closed to open (>15%) broadleaved evergreen or semi-deciduous forest (>5m)


  LC50
  Closed (>40%) broadleaved deciduous forest (>5m)


  LC60
  Open (15-40%) broadleaved deciduous forest/woodland (>5m)


  LC70
  Closed (>40%) needleleaved evergreen forest (>5m)


  LC90
  Open (15-40%) needleleaved deciduous or evergreen forest (>5m)


  LC100
  Closed to open (>15%) mixed broadleaved and needleleaved forest (>5m)


  LC110
  Mosaic forest or shrubland (50-70%) / grassland (20-50%)


  LC120
  Mosaic grassland (50-70%) / forest or shrubland (20-50%) 


  LC130
  Closed to open (>15%) (broadleaved or needleleaved, evergreen or deciduous) shrubland (15%) herbaceous vegetation (grassland, savannas or lichens/mosses)


  LC150
  Sparse (15%) broadleaved forest regularly flooded (semi-permanently or temporarily) - Fresh or brackish water


  LC170
  Closed (>40%) broadleaved forest or shrubland permanently flooded - Saline or brackish water


  LC180
  Closed to open (>15%) grassland or woody vegetation on regularly flooded or waterlogged soil - Fresh, brackish or saline water


  LC190
  Artificial surfaces and associated areas (Urban areas >50%)


  LC200
  Bare areas


  LC210
  Water bodies


  LC220
  Permanent snow and ice


  LC230
  No data (burnt areas, clouds,…)






  Bio_10
  Mean Temperature of Warmest Quarter


  Bio_11
  Mean Temperature of Coldest Quarter


  Bio_12
  Annual Precipitation


  Bio_13
  Precipitation of Wettest Month


  Bio_14
  Precipitation of Driest Month


  Bio_15
  Precipitation Seasonality (Coefficient of Variation)


  Bio_16
  Precipitation of Wettest Quarter


  Bio_17
  Precipitation of Driest Quarter


  Bio_18
  Precipitation of Warmest Quarter


  Bio_19
  Precipitation of Coldest Quarter


  Bio_1
  Annual Mean Temperature


  Bio_2
  Mean Diurnal Range (Mean of monthly (max temp - min temp))


  Bio_3
  Isothermality (BIO2/BIO7) (* 100)


  Bio_5
  Max Temperature of Warmest Month


  Bio_6
  Min Temperature of Coldest Month


  Bio_7
  Temperature Annual Range (BIO5-BIO6)


  Bio_8
  Mean Temperature of Wettest Quarter


  Bio_9
  Mean Temperature of Driest Quarter

This record is for Approval for Access product AfA439. A habitat map derived from airborne data, specifically CASI (Compact Airborne Spectrographic Imager) and LIDAR (Light Detection and Ranging) data.

The habitat map is a polygon shapefile showing site relevant habitat classes. Geographical coverage is incomplete because of limits in data available. It includes those areas where the Environment Agency, Natural England and the Regional Coastal Monitoring Programme have carried out sufficient aerial and ground surveys in England.

The habitat map is derived from CASI multispectral data, LIDAR elevation data and other GIS products. The classification uses ground data from sites collected near to the time of CASI capture. We use ground data to identify the characteristics of the different habitats in the CASI and LIDAR data. These characteristics are then used to classify the remaining areas into one of the different habitats.

Habitat maps generated by Geomatics are often derived using multiple data sources (e.g. CASI, LIDAR and OS-base mapping data), which may or may not have been captured coincidentally. In instances where datasets are not coincidentally captured there may be some errors brought about by seasonal, developmental or anthropological change in the habitat.

The collection of ground data used in the classification has some limitations. It has not been collected at the same time as CASI or LIDAR capture; it is normally within a couple of months of CASI capture. Some variations between the CASI data and situation on site at the time of ground data collection are possible. A good spatial coverage of ground data around the site is recommended, although not always practically achievable. For a class to be mapped on site there must have been samples collected for it on site. If the class is not seen on site or samples are not collected for a class, it cannot be mapped.

No quantitative accuracy assessment has been carried out on the habitat map, although the classification was trained using ground data and the final habitat map has been critically evaluated using Aerial Photography captured simultaneously with the CASI data by the processors and independently by habitat specialists. Please note that this content contains Ordnance Survey data © Crown copyright and database right [2014] and you must ensure that a similar attribution statement is contained in any sub-licences of the Information that you grant, together with a requirement that any further sub-licences do the same.

This dataset is a series of digital map-posters accompanying the AdaptNRM Guide: Implications of Climate Change for Biodiversity: a community-level modelling approach.

These represent supporting materials and information about the community-level biodiversity models applied to climate change. Map posters are organised by four biological groups (vascular plants, mammals, reptiles and amphibians), two climate change scenario (1990-2050 MIROC5 and CanESM2 for RCP8.5), and six measures of change in biodiversity.

The map-posters present the nationally consistent data at locally relevant resolutions in eight parts – representing broad groupings of NRM regions based on the cluster boundaries for the NRM fund (http://www.climatechange.gov.au/reducing-carbon/land-sector-measures/nrm-fund/stream-2) and also Nationally.

Map-posters are provided in PNG image format at moderate resolution (300dpi) to suit A0 printing as well as in PDF format to suit initial printing. The posters were designed to meet A0 print size and digital viewing resolution. An additional set in PDF image format has been created for ease of download for initial exploration and printing on A3 paper. Some text elements and map features may be fuzzy at this lower resolution.

Each map-poster contains four dataset images coloured using standard legends encompassing the potential range in ecological similarity (from 0 to 1), even if that range is not represented in the dataset itself or across the map extent.

Each map series is provided in two parts: part 1 shows the two climate scenarios for vascular plants and mammals and part 2 shows reptiles and amphibians. Eight cluster maps for each series have a different colour theme and map extent. A national series is also provided. Annotation briefly outlines the topics presented in the Guide so that each poster stands alone for quick reference.

Example citation: Williams KJ, Raisbeck-Brown N, Harwood T, Prober S (2014) Novel ecological environments for vascular plants and mammals (1990-2050), A0 map-poster 3.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

This dataset has been delivered incrementally. All maps are now available, some that were previously available may have been updated. Please check that you are accessing the latest version of the dataset. Lineage: The map posters show case the scientific data. The data layers have been developed at approximately 250m resolution (9 second) across the Australian continent to incorporate the interaction between climate and topography, and are best viewed using a geographic information system (GIS). Each data layers is 1Gb, and inaccessible to non-GIS users. The map posters provide easy access to the scientific data, enabling the outputs to be viewed at high resolution with geographical context information provided.

Maps were generated using layout and drawing tools in ArcGIS 10.2.2

A check list of map posters and datasets is provided with the collection.

Map Series: 1.1 Potential degree of ecological change for vascular plants and mammals (1990-2050) 1.2 Potential degree of ecological change for reptiles and amphibians (1990-2050) 2.1 Disappearing ecological environments for vascular plants and mammals (1990-2050) 2.2 Disappearing ecological environments for reptiles and amphibians (1990-2050) 3.1 Novel ecological environments for vascular plants and mammals (1990-2050) 3.2 Novel ecological environments for reptiles and amphibians (1990-2050) 4.1 Change in effective area of similar ecological environments (intact) for vascular plants and mammals (1990-2050) 4.2 Change in effective area of similar ecological environments (intact) for reptiles and amphibians (1990-2050) 5.1 Change in effective area of similar ecological environments (cleared) for vascular plants and mammals (1990-2050) 5.2 Change in effective area of similar ecological environments (cleared) for reptiles and amphibians (1990-2050) 6.1 Composite ecological change for vascular plants and mammals (1990-2050) 6.2 Composite ecological change for reptiles and amphibians (1990-2050)

HazMatMapper is an online and interactive geographic visualization tool designed to facilitate exploration of transnational flows of hazardous waste in North America (http://geography.wisc.edu/hazardouswaste/map/). While conventional narratives suggest that wealthier countries such as Canada and the United States (US) export waste to poorer countries like Mexico, little is known about how waste trading may affect specific sites within any of the three countries. To move beyond anecdotal discussions and national aggregates, we assembled a novel geographic dataset describing transnational hazardous waste shipments from 2007 to 2012 through two Freedom of Information Act requests for documents held by the US Environmental Protection Agency. While not yet detailing all of the transnational hazardous waste trade in North America, HazMatMapper supports multiscale and site-specific visual exploration of US imports of hazardous waste from Canada and Mexico. It thus enables academic researchers, waste regulators, and the general public to generate hypotheses on regional clustering, transnational corporate structuring, and environmental justice concerns, as well as to understand the limitations of existing regulatory data collection itself. Here, we discuss the dataset and design process behind HazMatMapper and demonstrate its utility for understanding the transnational hazardous waste trade.

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: