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.
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.
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 data set contains vector polygons representing the boundaries of all hardcopy cartographic products and digital data extents produced as part of the Environmental Sensitivity Index (ESI) for Southern California. This data set comprises a portion of the ESI data for Southern California. 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.Please note that this data was selected from a larger dataset for use in the San Diego Ocean Planning Partnership, a collaborative pilot project between the California State Lands Commission and the Port of San Diego. For more information about the Partnership, please visit: https://www.sdoceanplanning.org/When within the San Diego Ocean Planning Partnership web mapping application, clicking on a polygon will present a link to an online version of the map. To add the data itself to the application, please use the add data widget and the following web service URL: https://idpgis.ncep.noaa.gov/arcgis/rest/services/NOS_ESI/ESI_SouthernCalifornia_Data/MapServer
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.
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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
The Environment Map (World 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. This basemap consists of 4 vector tile layers and one raster tile layer: The Environment Detail and Label 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.Learn more about this basemap in Environment Map for All.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 layer item referenced in this map.
The NMEDB is a publicly available application that consolidates New Mexico's environmental and public health data in an interactive map of the state. The NMEDB allows agencies, the public, private industry, and conservation practitioners to obtain a comprehensive view of what's happening on New Mexico's landscapes to enable data-driven decisions and minimize negative impacts to human health, plants, animals, land, air, and water.
NOAA Environmental Response Mapping ToolThis online mapping tool integrates static and real-time data, including Environmental Sensitivity Index maps, ship locations, weather, and ocean currents, into an easy-to-use format. The tool helps emergency responders and environmental resource managers better understand incidents that may adversely impact the environment, including oil spills, chemical spills, and vessel groundings. The tool covers the entire U.S. coastline, providing local response, infrastructure, and environmental data that allow users to find resources at risk, evaluate response plans, perform Natural Resource Damage Assessments, and track restoration activities, all while maintaining data security.
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🇦🇺 호주
https://research.csiro.au/dap/licences/csiro-data-licence/https://research.csiro.au/dap/licences/csiro-data-licence/
****UPDATED**** This collection contains a 9-second gridded dataset (ESRI binary float format in GDA94) showing the generalised projected future (2050-centred) potential pre-clearing vegetation patterns of 77 Major Vegetation Sub-groups (MVS classes) derived from the maximum of their respective predicted probabilities for each grid cell (V_85MIR50_MXC - MaxClass). Two additional datasets show the maximum probability in each gird cell that was used to assign that class (V_85MIR50_MXP - MaxProb), and the number of classes with non-zero probabilities with potential to represent their type in each grid cell (V_85MIR50_NMC - NumClasses). The predicted probabilities for each class were derived based on their distribution patterns and correlation with baseline ecological environments (c.1990 climates, substrate and landform). The pre-clearing vegetation patterns and classification derive from version 4.1 of “Australia - Estimated Pre1750 Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product)” developed by the Australian Government Department of the Environment and collaborating State agencies. A kernel regression was used with c.155,000 locations of training classes for the 77 MVS classes attributed with 17 GDM-scaled environmental predictors for Vascular Plants representing baseline ecological environments. These details are provided with the data package “Potential vegetation redistribution: Australia - 9second gridded projection to 2050, pre-clearing extents of 77 Major Vegetation Sub-groups using kernel regression with GDM-scaled environments for Vascular Plants (GDM: VAS_v5_r11; CMIP5: MIROC5 RCP 8.5)”. The GDM-scaled environmental predictors are available with the “VAS_v5_r11” data package. This dataset projects the generalised potential pre-clearing vegetation patterns based on 2050-centred (30 year average) future climates derived from the MIROC5 global climate model for the emission scenario defined by a representative concentration pathway of 8.5.
The accuracy of projections is limited by the quality of the vegetation mapping used to train the models and the accuracy of environmental variables delimiting substrate boundaries and disturbance regimes. Uncertainty or errors in the underlying vegetation map and environmental data will be reproduced by the models. Furthermore, variables describing the relationship between extreme climatic events and ecological disturbance regimes, that have significant structural influences on vegetation, are not directly included in these models.
The data are provided as 9-second (approximately 250m), ESRI binary float grid format in GDA94. This dataset series and its use is described in the AdaptNRM Guide “Helping biodiversity adapt to climate change: a community-level modelling approach”, available online at: www.adaptnrm.org Lineage: Predictive models of vegetation classes were derived using the two-step process originally developed for individual species distribution modelling with GDM (described in Elith et al. 2006). The first step uses a Generalised Dissimilarity Model (GDM) of vascular plants (VAS_V5_R11) to derive a set of scaled environmental variables for current (e.g. 1990 baseline) and future climates (e.g. 2050). The second step applies this data in a kernel regression to predict each vegetation class using training data derived from the pre-clearing mapping of 77 Major Vegetation Sub-groups. The training data comprised c.155,000 locations defined by randomly sampling within each vegetation class, proportional to their observed areal extent. These locations were then attributed with the baseline values of the GDM-scaled environmental variables. Separate kernel regressions were then run for the baseline and future climate scenarios using the baseline training data. In this way, the future distribution of each vegetation class was projected based on its affinity with present-day ecological environments.
At any location (grid cell), the kernel regression considers the surrounding relative density of training sites of the target vegetation class as a proportion of other types and generates a predicted probability for that class for the focal grid cell. A probability surface for the predicted proportions, varying from 0 to 1, is generated for each of the 77 mapped Major Vegetation Sub-groups. This method is infrequently used in ecology because of the need first to scale and reduce the dimensionality of the predictor variables (Lowe 1995). The GDM step reduces dimensionality (by choosing the variables to use) and scales the predictor variables using similarity-decay functions which equate to the multivariate distances expected by kernel regression. The kernel regression thus incorporates interactions by modelling ecological distances and vegetation class densities within a truly multivariate predictor space, with no assumption of additivity.
Kernel regression aims to optimise model performance in terms of the accuracy of predictions at any single location according to the area predicted for each class. The predicted proportions of common vegetation types are typically greater than for rarer vegetation types. Therefore, cell by cell, the class with the maximum probability selected to represent spatially varying vegetation class mosaics on a single map (essentially one dimension) will often be the common type, at the expense of locally rare and nationally rare types. Therefore, the best way to view the results, and to inform planning, is the individual probability surfaces. These properly reveal where the rarer vegetation types have a likelihood of persistence. Higher probabilities associated with other vegetation types at the same location can be viewed as a measure of the extent to which those other vegetation types may compete. However the outcome, at least in the medium term, may be more driven by the extant occurrence of ecosystems and their ability to persist under marginal conditions.
Generalised maps assembled from individual projected vegetation class probabilities indicate which of the baseline vegetation classes may be most suited to the environment of a particular location in the future. However, the suitability of that vegetation class to the future environment may still be relatively low and a number of other vegetation classes may be almost equally suited. A more conservative view can be obtained from maps of the projected probabilities for individual vegetation classes (see related materials for the individual probability datasets).
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ClimateForecasts is a database that provides environmental data for 15,504 weather station locations and 49 environmental variables, including 38 bioclimatic variables, 8 soil variables and 3 topographic variables. Data were extracted from the same 30 arc-seconds global grid layers that were prepared when making the TreeGOER (Tree Globally Observed Environmental Ranges) database that is available from https://doi.org/10.5281/zenodo.7922927. Details on the preparations of these layers are provided by Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology 29: 6303–6318. https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914. A similar extraction process was used for the CitiesGOER database that is also available from Zenodo via https://zenodo.org/doi/10.5281/zenodo.8175429.
ClimateForecasts (as the CitiesGOER) was designed to be used together with TreeGOER and possibly also with the GlobalUsefulNativeTrees database (Kindt et al. 2023) to allow users to filter suitable tree species based on environmental conditions of the planting site. One example of combining data from these different sets in the R statistical environment is available from this Rpub: https://rpubs.com/Roeland-KINDT/1114902.
The identities including the geographical coordinates of weather stations were sourced from Meteostat, specifically by downloading (17-FEB-2024) the ‘lite dump’ data set with information for active weather stations only. Two weather stations where the country could not be determined from the ISO 3166-1 code of ‘XA’ were removed. If weather stations had the same name, but occurred in different ISO 3166-2 regions, this region code was added to the name of the weather station between square brackets. Afterwards duplicates (weather stations of the same name and region) were manually removed.
Bioclimatic variables for future climates correspond to the median values from 24 Global Climate Models (GCMs) for Shared Socio-Economic Pathway (SSP) 1-2.6 for the 2050s (2041-2060), from 21 GCMs for SSP 3-7.0 for the 2050s and from 13 GCMs for SSP 5-8.5 for the 2090s. Similar methods were used to calculate these median values as in the case studies for the TreeGOER manuscript (calculations were partially done via the BiodiversityR::ensemble.envirem.run function and with downscaled bioclimatic and monthly climate 2.5 arc-minutes future grid layers available from WorldClim 2.1).
Maps were added in version 2024.03 where locations of weather stations were shown on a map of the Climatic Moisture Index (CMI). These maps were created by a similar process as in the TreeGOER Global Zones Atlas from the environmental raster layers used to create the TreeGOER via the terra package (Hijmans et al. 2022, version 1.7-46) in the R 4.2.1 environment. Added country boundaries were obtained from Natural Earth as Admin 0 – countries vector layers (version 5.1.1). Also added after obtaining them from Natural Earth were Admin 0 – Breakaway, Disputed areas (version 5.1.0, coloured yellow in the atlas) and Roads (version 5.0.0, coloured red in the atlas). For countries where the GlobalUsefulNativeTrees database included subnational levels, boundaries were added and depicted as dot-dash lines. These subnational levels correspond to level 3 boundaries in the World Geographical Scheme for Recording Plant Distributions. These were obtained from https://github.com/tdwg/wgsrpd. Check Brummit 2001 for details such as the maps shown at the end of this document.
Maps for version 2024.07 modified the dimensions of the sheets to those used in version 2024.06 of the TreeGOER Global Zones Atlas. Another modification was the inclusion of Natural Earth boundaries for Lakes (version 5.0.0, coloured darkblue in the atlas).
Version 2024.10 includes a new data set that documents the location of the city locations in Holdridge Life Zones. Information is given for historical (1901-1920), contemporary (1979-2013) and future (2061-2080; separately for RCP 4.5 and RCP 8.5) that are available for download from DRYAD and were created for the following article: Elsen et al. 2022. Accelerated shifts in terrestrial life zones under rapid climate change. Global Change Biology, 28, 918–935. https://doi.org/10.1111/gcb.15962. Version 2024.10 further includes Holdridge Life Zones for the climates available from the previously included climates, calculating biotemperatures and life zones with similar methods as used by Holdridge (1947; 1967) and Elsen et al. (2022) (for future climates, median values were determined first for monthly maximum and minimum temperatures across GCMs ). The distributions of the 48,129 species documented in TreeGOER across the Holdridge Life Zones are given in this Zenodo archive: https://zenodo.org/records/14020914.
Version 2024.11 includes a new data set that documents the location of the weather stations in Köppen-Geiger climate zones. Information is given for historical (1901-1930, 1931-1960, 1961-1990) and future (2041-2070 and 2071-2099) climates, with for the future climates seven scenarios each (SSP 1-1.9, SSP 1-2.6, SSP 2-4.5, SSP 3-7.0, SSP 4-3.4, SSP 4-6.0 and SSP 5-8.5). This data set was created from raster layers available via: Beck, H.E., McVicar, T.R., Vergopolan, N. et al. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Sci Data 10, 724 (2023). https://doi.org/10.1038/s41597-023-02549-6.
Version 2025.03 includes extra columns for the baseline, 2050s and 2090s datasets that partially correspond to climate zones used in the GlobalUsefulNativeTrees database. One of these zones are the Whittaker biome types, available as a polygon from the plotbiomes package (see also here). Whittaker biome types were extracted with similar R scripts as described by Kindt 2025 (these were also used to calculate environmental ranges of TreeGOER species, as archived here).
Version 2025.03 further includes information for the baseline climate on the steady state water table depth, obtained from a 30 arc-seconds raster layer calculated by the GLOBGM v1.0 model (Verkaik et al. 2024).
When using ClimateForecasts in your work, cite this depository and the following:
Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086
Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography, 41(2), 291–307. https://doi.org/10.1111/ecog.02880
Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., & Rossiter, D. (2021). SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. SOIL, 7(1), 217–240. https://doi.org/10.5194/soil-7-217-2021
Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914.
Meteostat (2024) Weather stations: Lite dump with active weather stations. https://github.com/meteostat/weather-stations (accessed 17-FEB-2024)
When using information from the Holdridge Life Zones, also cite:
Elsen, P. R., Saxon, E. C., Simmons, B. A., Ward, M., Williams, B. A., Grantham, H. S., Kark, S., Levin, N., Perez-Hammerle, K.-V., Reside, A. E., & Watson, J. E. M. (2022). Accelerated shifts in terrestrial life zones under rapid climate change. Global Change Biology, 28, 918–935. https://doi.org/10.1111/gcb.15962
When using information from Köppen-Geiger climate zones, also cite:
Beck, H.E., McVicar, T.R., Vergopolan, N., Berg, A., Lutsko, N.J., Dufour, A., Zeng, Z., Jiang, X., van Dijk, A.I. and Miralles, D.G. 2023. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Sci Data 10, 724. https://doi.org/10.1038/s41597-023-02549-6
When using information on the Whittaker biome types, also cite:
Ricklefs, R. E., Relyea, R. (2018). Ecology: The Economy of Nature. United States: W.H. Freeman.
Whittaker, R. H. (1970). Communities and ecosystems.
Valentin Ștefan, & Sam Levin. (2018). plotbiomes: R package for plotting Whittaker biomes with ggplot2 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.7145245
When using information on the steady state water table depth, also cite:
Verkaik, J., Sutanudjaja, E. H., Oude Essink, G. H., Lin, H. X., & Bierkens, M. F. (2024). GLOBGM v1. 0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model. Geoscientific Model Development, 17(1), 275-300. https://gmd.copernicus.org/articles/17/275/2024/
The development of ClimateForecasts and its partial integration in version 2024.03 of the GlobalUsefulNativeTrees database was supported by the Darwin Initiative to project DAREX001 of Developing a Global Biodiversity Standard certification for tree-planting and restoration, by Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia to the Provision of Adequate Tree Seed Portfolio project in Ethiopia, by the Green Climate Fund through the IUCN-led Transforming the Eastern Province of Rwanda through Adaptation project and through the Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso, by the Bezos Earth Fund to the Quality Tree Seed for Africa in Kenya and Rwanda project and by the German International Climate Initiative (IKI) to the regional tree seed programme on The Right Tree for the Right Place for the Right Purpose in Africa.
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Environmental maps associated with https://zenodo.org/deposit/7233945 .
Maps are provide in two formats (PLY and PCD).
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Environmental mapping dataset
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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
TOXMAP® is a Geographic Information System (GIS) that uses maps of the United States and Canada to help users visually explore data primarily from the EPA's Toxics Release Inventory (TRI) and Superfund Program, as well as some non-EPA datasets. TOXMAP helps users create nationwide, regional, or local area maps showing where TRI chemicals are released on-site into the air, water, and ground. It also provides facility and release details, color-codes release amounts for a single year or year range, and aggregates release data over multiple years. Maps also show locations of Superfund National Priorities List (NPL) sites, listing all chemical contaminants present at these sites. Two versions of TOXMAP are available: the classic version of TOXMAP released in 2004, and a new version of TOXMAP based on Adobe® Flash/Flex technology. The new version provides an improved map appearance and interactive capabilities and additional datasets such as EPA coal plant emissions data and U.S. commercial nuclear power plants.
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This dataset is built from the Overture 2023-07-26-alpha.0 version of open map data by the Overture Maps Foundation. This dataset compiles building footprints and their attributes for individual cities for convenient and lightweight spatial analytics.Credits: Overture Maps FoundationLicense: https://opendatacommons.org/licenses/odbl/
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
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Areas which the Secretary of State considers to be urban (with a population greater than or equal to 100,000 people) where, under the Environmental Noise Directive (Round 3), Defra is required to undertake Strategic Noise Mapping.
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The files found here are regularly-updated
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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The Web Map Service (WMS) is defined by the Open Geospatial Consortium (OGC) in order to dynamically produce maps from geographic information. This standard defines a "map" as a representation of geographic information in the form of a digital image file. The maps produced by WMS are normally generated in an image format such as PNG or JPEG and can be invoked by any web protocol or software trained for the visualization of this type of services. The standard defines three operations: GetCapabilities, GetFeature and DescribeFeatureType.
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Display the location of the environmental inspection and testing institution permitted by the Ministry of the Environment.
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.