Part of the American Dream is owning your own home. This map shows the percentage of occupied housing that has a mortgage or loan in the U.S., by state, county, tract and block group. The data shown is from the U.S. Census Bureau's SF1 and TIGER data sets for 2010. See the map package for this data. The map is multi-scale – it switches from state, to county, to tract, to block group data as the map zooms in. The polygon data was shorelined and selected rivers and lakes were also erased from the boundaries to give a more familiar look at specific scales. At each scale, a simple popup shares a few key statistics in a paragraph, and displays a chart of housing by type of ownership.The thematic classifications are centered around the U.S. average for housing with mortgages (about 40%). The center classification is characterized as “Average.” Its break points are based on one-half standard deviation around the mean. Breakpoints for the “Low” and “High” classes are also determined from one-half standard deviation (9.7%). “Very Low” and “Very High” classes capture the remaining values.The thematic colors use colors chosen to emphasize the “high” end of the values. Darker colors are used to represent high values, while lighter colors represent low values. The “Average” class color is neutral. As you zoom into the map, a stroke is added to the polygon boundaries to delineate the county, tract and block group boundaries without allowing them to dominate the map (as is the case with black, white or other strong colors for boundaries).The light gray canvas basemap was selected for this web map to draw attention to the thematic content.

View on Map View ArcGIS Service BTM Standard deviation – this mosaic dataset is part of a series of seafloor terrain datasets aimed at providing a consistent baseline to assist users in consistently characterizing Aotearoa New Zealand seafloor habitats. This series has been developed using the tools provided within the Benthic Terrain Model (BTM [v3.0]) across different multibeam echo-sounder datasets. The series includes derived outputs from 50 MBES survey sets conducted between 1999 and 2020 from throughout the New Zealand marine environment (where available) covering an area of approximately 52,000 km2. Consistency and compatibility of the benthic terrain datasets have been achieved by utilising a common projected coordinate system (WGS84 Web Mercator), resolution (10 m), and by using a standard classification dictionary (also utilised by previous BTM studies in NZ). However, we advise caution when comparing the classification between different survey areas.Derived BTM outputs include the Bathymetric Position Index (BPI); Surface Derivative; Rugosity; Depth Statistics; Terrain Classification. A standardised digital surface model, and derived hillshade and aspect datasets have also been made available. The index of the original MBES survey surface models used in this analysis can be accessed from https://data.linz.govt.nz/layer/95574-nz-bathymetric-surface-model-index/The full report and description of available output datasets are available at: https://www.doc.govt.nz/globalassets/documents/science-and-technical/drds367entire.pdf

This web map is used by Esri's Green Infrastructure Initiative to power the "Filter Your Intact Landscape Cores" app and the "Prioritize Your Intact Landscape Cores" app. It displays modeled Intact Habitat Cores, or minimally disturbed natural areas. This map represents modeled Intact Habitat Cores, or minimally disturbed natural areas at least 100 acres in size and greater than 200 meters wide. Esri created these data following a methodology outlined by the Green Infrastructure Center Inc. These data were generated using 2011 National Land Cover Data. Cores were derived from all “natural” land cover classes and excluded all “developed” and “agricultural” classes including crop, hay and pasture lands. The resulting cores were tested for size and width requirements (at least 100 acres in size and greater than 200 meters wide) and then converted into unique polygons. This process resulted in the generation of over 550,000 cores.

Cores were then overlaid with a diverse assortment of physiographic, biologic and hydrographic layers to populate each core with attributes (53 in total) related to the landscape characteristics found within. These data were also compiled to compute a “core quality index”, or score related to the perceived ecological value of each core, to provide users with additional insight related to the importance of each core when compared to all others. See this map image layer for a version that includes popups and ability to query the data.

The source data used to derive this attribution is as follows:

Number of endemic species (Mammals, Fish, Reptiles, Amphibians, Trees) (Jenkins, Clinton N., et. al, (April 21, 2015) US protected lands mismatch biodiversity priorities, PNAS vol.112, no. 16)

Priority Index areas: Endemic species, small home range size and low protection status. (Jenkins, Clinton N., et. al, (April 21, 2015) US protected lands mismatch biodiversity priorities, PNAS vol.112, no. 16)

Unique ecological systems (based upon work by Aycrig, Jocelyn L, et. al. (2013) Representation of Ecological Systems within the Protected Areas Network of the Continental United States. PLos One 8(1):e54689). New data constructed by Esri staff, using TNC Ecological Regions as summary areas.

Ecologically relevant landforms (Theobald DM, Harrison-Atlas D, Monahan WB, Albano CM (2015) Ecologically-Relevant Maps of Landforms and Physiographic Diversity for Climate Adaptation Planning. PLoS ONE 10(12): e0143619. doi:10.1371/journal.pone.0143619

Local Landforms (produced 3/2016) by Deniz Basaran and Charlie Frye, Esri, 30 m* resolution. "Improved Hammond’s Landform Classification and Method for Global 250-m Elevation Data" by Karagulle, Deniz; Frye, Charlie; Sayre, Roger; Breyer, Sean; Aniello, Peter; Vaughan, Randy; Wright, Dawn, has been successfully submitted online and is presently being given consideration for publication in Transactions in GIS. *We scaled the neighborhood windows from the 250-meter method described in the paper, and then applied that to 30-meter data in the U.S.

National Elevation Dataset, USGS, 30 m resolution

NWI – National Wetlands Inventory “ Classification of Wetlands and Deepwater Habitats of the United States”. U.S. Department of the Interior, Fish and Wildlife Service, Washington, DC. FWS/OBS-79/31 , U.S. Fish and Wildlife Service, Division of Habitat and Resource Conservation (prepared 10/2015)

NLCD 2011 – National LandCover Database 2011 (downloaded 1/2016) Homer, C.G., et. al. 2015,Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing, v. 81, no. 5, p. 345-354

NHDPlusV2 Received from Charlie Frye, Esri 3/2016. Produced by the EPA with support from the USGS.

gSSURGO –Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. Web Soil Survey. Accessed 3/2016, 30 m resolution

GAP Level 3 Ecological System Boundaries (downloaded 4/ 2016) NOAA CCAP Coastal Change Analysis Program Regional Land Cover and Change–downloaded by state (3/2016) from: C-CAP FTP Tool, see Description of this 30 m resolution, 2010 edition of data

NHD USGS National Hydrography Dataset

TNC Terrestrial Ecoregions (downloaded 3/2016)

2015 LCC Network Areas

Evaluation:

The creation of a national core quality index is a very ambitious objective, given the extreme variability in ecosystem conditions across the United States. The additional attributes were intended to provide flexibility in accommodating regional or local environmental differences across the U.S.

Scripts for constructing local cores and scoring them using the Green Infrastructure Center’s methodology are available on Esri's Green Infrastructure web site.

Two general approaches were used in the developing core quality index values. The first (default) follows the guidance of the Green Infrastructure Center’s scoring approach developed for the southeastern US where size of the core is the primary determinant of quality. The second; Bio-Weights puts more emphasis on bio-diversity and uniqueness ecosystem type and de-emphasizes slightly the importance of core size. This is to compensate for the very large intact core habitat areas in the west and southwest which also have comparatively low biodiversity values.

Scoring values:

Default Weights

0.4, # Acres0.1, # THICKNESS0.05, # TOPOGRAPHIC DIVERSITY (Standard Deviation)0.1, # Biodiversity Priority Index (SPECIES RICHNESS in GIC original version)0.05, # PERCENTAGE WETLAND COVER0.03, # Ecological Land Unit – Shannon-Weaver Index (SOIL VARIETY in GIC original version)0.02, # COMPACTNESS RATIO (AREA RELATIVE TO THE AREA OF A CIRCLE WITH THE SAME PERIMETER LENGTH)0.1, # STREAM DENSITY (LINEAR FEET/ACRE)0.05, # Ecological System Redundancy (RARE/THREATENED/ENDANGERED SPECIES ABUNDANCE (Number of occurrences) in GIC original version) 0.1, # Endemic Species Max (RARE/THREATENED/ENDANGERED SPECIES DIVERSITY (Number of unique species in a core) in GIC original version)

Bio-Weights

0.2, # Acres0.1, # THICKNESS0.05, # TOPOGRAPHIC DIVERSITY (Standard Deviation)0.25, # Biodiversity Priority Index (SPECIES RICHNESS in GIC original version)0.05, # PERCENTAGE WETLAND COVER0.03, # Ecological Land Unit – Shannon-Weaver Index (SOIL VARIETY in GIC original version)0.02, # COMPACTNESS RATIO (AREA RELATIVE TO THE AREA OF A CIRCLE WITH THE SAME PERIMETER LENGTH)0.1, # STREAM DENSITY (LINEAR FEET/ACRE)0.1, # Ecological System Redundancy (RARE/THREATENED/ENDANGERED SPECIES ABUNDANCE (Number of occurrences) in GIC original version) 0.1, # Endemic Species Max (RARE/THREATENED/ENDANGERED SPECIES DIVERSITY (Number of unique species in a core) in GIC original version)

Aim

To understand the representativeness and accuracy of expert range maps, and explore alternate methods for accurately mapping species distributions.

Location

Global

Time period

Contemporary

Major taxa studied

Terrestrial vertebrates, and Odonata

Methods

We analyzed the biases in 50,768 animal IUCN, GARD and BirdLife species maps, assessed the links between these maps and existing political and various non-ecological boundaries to assess their accuracy for certain types of analysis. We cross-referenced each species map with data from GBIF to assess if maps captured the whole range of a species, and what percentage of occurrence points fall within the species’ assessed ranges. In addition, we use a number of alternate methods to map diversity patterns and compare these to high resolution models of distribution patterns.

Results

On average 20-30% of species’ non-coastal range boundaries overlapped with administrative national boundaries. In total, 60% of areas with the highest spatial turnover in species (high densities of species range boundaries marking high levels of shift in the community of species present) occurred at political boundaries, especially commonly in Southeast Asia. Different biases existed for different taxa, with gridded analysis in reptiles, river-basins in Odonata (except the Americas) and county-boundaries for Amphibians in the US. On average, up to half (25-46%) species recorded range points fall outside their mapped distributions. Filtered Minimum-convex polygons performed better than expert range maps in reproducing modeled diversity patterns.

Main conclusions

Expert range maps showed high bias at administrative borders in all taxa, but this was highest at the transition from tropical to subtropical regions. Methods used were inconsistent across space, time and taxa, and ranges mapped did not match species distribution data. Alternate approaches can better reconstruct patterns of distribution than expert maps, and data driven approaches are needed to provide reliable alternatives to better understand species distributions.

Methods Materials and methods

We use a combination of approaches to explore the relationship between species range maps and geopolitical boundaries and a subset of geographic features. In some cases we used the density of species range boundaries to explore the relationship between these and various features (i.e. administrative boundaries, river basin boundaries etc.). Additionally, species richness and spatial turnover are used to explore changes in richness over short geographic distances. Analyses were conducted in R statistical software unless noted otherwise. All code scripts are available at https://github.com/qiaohj/iucn_fix. Workflows are shown in Figure S1a-c with associated scripts listed.

Species ranges and boundary density maps

ERMs (Expert range maps) were downloaded from the IUCN RedList website for mammals (5,709 species), odonates (2,239 species) and amphibians (6,684 species; https://www.iucnredlist.org/resources/grid/spatial-data). Shapefile maps for birds were downloaded from BirdLife (10,423 species, http://datazone.birdlife.org/species/requestdis), and for reptiles from the Global Assessment of Reptile Distributions (GARD) (10,064 species; Roll et al., 2017). Each species’ polygon boundaries were converted to a polylines to show the boundary of each species range (Figure S1a-II; codes are lines 7 – 18 in line2raster_xxxx.r ; xxxx varies based on the taxa). The associated shapefile was then split to produce independent polyline files for each species within each taxon (see Figure S1a-I, codes are lines 29 to 83 in the same file above.).

To generate species boundary density maps, species range boundaries were rasterized at 1km spatial resolution with an equal area projection (Eckert-IV), and stacked to form a single raster for each taxon (at the level of amphibians, odonates, etc.). This represented the number of species in each group and their overlapping range boundaries (Figure S1b-II, codes are in line2raster_all.r). Each cell value indicated the number of species whose distribution boundaries overlapped with each cell, enabling us to overlay this rasterized information with other features (i.e. administrative boundaries) so that the overlaps between them can be calculated in R. These species boundary density maps underlie most subsequent analyses. R code and caveats are given in the supplements, links are provided in text and Figure S1.

Geographic boundaries

Spatial exploration of species range boundaries in ArcGIS suggested that numerous geographic datasets (i.e. political and in few cases geographic features such as river basins) were used to delineate the species ranges for different regions and taxa (this is sometimes part of the methodology in developing ERMs as detailed by Ficetola et al., 2014). Thus in addition to analyzing the administrative bias and the percentage of occurrence records within each species’ ERM for all taxa, additional analyses were conducted when other biases were evident in any given taxa or region (detailed later in methods on a case-by-case basis).

For all taxa, we assessed the percentage of overlap between species range boundaries and national and provincial boundaries by digitizing each to 1km (equivalent to buffering thie polyline by 500m), both with and without coastal boundaries. An international map was used because international (Western) assessors use them, and does not necessarily denote agreed country boundaries (https://gadm.org/). The different buffers (500m, 1000m, 2500m, 5000m) were added to these administrative boundaries in ArcMap to account for potential, insignificant deviations from political boundaries (Figure S1b). An R script for the same function is provided in “country_line_buffer.r”.

To establish where multiple species shared range boundaries we reclassified the species range boundary density rasters for each taxa into richness classes using the ArcMap quartile function (Figure S1). From these ten classes the percentage of the top-two, and top-three quartiles of range densities within different buffers (500m, 1000m, 2500m, 5000m) was calculated per country to determine what percentage of highest range boundary density approximately followed administrative borders. This was done because people drawing ERMs may use detailed administrative maps or generalize near political borders, or may use political shapefiles that deviate slightly. It is consequently useful to include varying distances from administrative features to assess how range boundary densities vary in relation to administrative boundaries. Analyses of relationships between individual species range boundaries and administrative boundaries (coastal, non-coastal) were made in R and scripts provided (quantile_country_buffer_overlap.r).

Spatial turnover and administrative boundaries

Heatmaps of species richness were generated by summing entire sets of compiled species ranges for each taxon in polygonal form (Figure 1; Figure S1b-I). To assess abrupt diversity changes, standard deviations for 10km blocks were calculated using the block statistics function in ArcMap. Abrupt changes in diversity were signified by high standard deviations based on the cell statistics function in ArcGIS, which represented rapid changes in the number of species present. Maps were then classified into ten categories using the quartile function. Given the high variation in maximum diversity and taxonomic representation, only the top two –three richness categories were retained per taxon. This was then extracted using 1km buffers of national administrative boundaries to assess percentages of administrative boundaries overlapping turnover hotspots by assessing what proportion of political boundaries were covered by these turnover hotspots.

Taxon-specific analyses

Data exploration and mapping exposed taxon and regional-specific biases requiring additional analysis. Where other biases and irregularities were clear from visual inspection of the range boundary density maps for each taxa, the possible causes of biases were assessed by comparing range boundary density maps to high-resolution imagery and administrative maps via the ArcGIS server (AGOL). Standardized overlay of the taxon boundary sets with administrative or geophysical features from the image-server revealed three types of bias which were either spatially or taxonomically limited between: 1) amphibians with county borders in the United States, 2) dragonflies and river basins globally and 3) gridding of distributions of reptiles. In these cases, species boundary density maps were used as a basis to identify potential biases which were then explored empirically using appropriate methods.

For amphibians, counties in the United States (US) were digitized using a county map from the US (https://gadm.org/), then buffered by with 2.5km either side. Amphibian species range boundary density maps were reclassified showing where species range boundaries existed (with other non-range boundary areas reclassified as “no data,”) and all species boundaries numerically indicated (i.e. values of 1 indicates one species range boundary, values of 10 indicates ten species range boundaries). Percentages of species boundary areas falling on county and in the buffers, in addition to species range boundaries which did not overlap with county boundaries were calculated to give measures of what percentage of the species boundaries fell within 2.5km of county boundaries.

For Odonata, many species were mapped to river basin borders. We used river basins of levels 6-8 (sub-basin to basin) in the river hierarchy (https://hydrosheds.org) to assess the relationship between Odonata boundaries and river boundaries. Two IUCN datasets exist for Odonata; the IUCN Odonata specialist group spatial dataset

This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows median earnings by occupational group. This is shown by tract, county, and state boundaries. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Only full-time year-round workers included. Median earnings is based on earnings in past 12 months of survey. Occupation Groups based on Bureau of Labor Statistics (BLS)' Standard Occupation Classification (SOC). This layer is symbolized to show median earnings of the full-time, year-round civilian employed population. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintage: 2010-2014ACS Table(s): B24021 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 28, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This layer was created as part of Esri’s Green Infrastructure Initiative and is one of five newly generated companion datasets that can be used for Green Infrastructure (GI) planning at national, regional, and more local scales. If used together, these layers should have corresponding date-based suffixes (YYYYMMDD). The corresponding layer names are: Intact Habitat Cores, Habitat Connectors, Habitat Fragments, Habitat Cost Surface, and Intact Habitat Cores by Betweenness. These Esri derived data, and additional data central to GI planning from other authoritative sources, are also available as Map Packages for each U.S. State and can be downloaded from the Green Infrastructure Data Gallery.

This layer represents modeled Intact Habitat Cores, or minimally disturbed natural areas at least 100 acres in size and greater than 200 meters wide. Esri created these data following a methodology outlined by the Green Infrastructure Center Inc. These data were generated using 2011 National Land Cover Data. Cores were derived from all “natural” land cover classes and excluded all “developed” and “agricultural” classes including crop, hay and pasture lands. The resulting cores were tested for size and width requirements (at least 100 acres in size and greater than 200 meters wide) and then converted into unique polygons. This process resulted in the generation of over 550,000 cores. Cores were then overlaid with a diverse assortment of physiographic, biologic and hydrographic layers to populate each core with attributes (53 in total) related to the landscape characteristics found within. These data were also compiled to compute a “core quality index”, or score related to the perceived ecological value of each core, to provide users with additional insight related to the importance of each core when compared to all others. See this map image layer for a version that includes popups and ability to query the data. The source data used to derive this attribution is as follows: Number of endemic species (Mammals, Fish, Reptiles, Amphibians, Trees) (Jenkins, Clinton N., et. al, (April 21, 2015) US protected lands mismatch biodiversity priorities, PNAS vol.112, no. 16)

Priority Index areas: Endemic species, small home range size and low protection status. (Jenkins, Clinton N., et. al, (April 21, 2015) US protected lands mismatch biodiversity priorities, PNAS vol.112, no. 16)

Unique ecological systems (based upon work by Aycrig, Jocelyn L, et. al. (2013) Representation of Ecological Systems within the Protected Areas Network of the Continental United States. PLos One 8(1):e54689). New data constructed by Esri staff, using TNC Ecological Regions as summary areas.

Ecologically relevant landforms (Theobald DM, Harrison-Atlas D, Monahan WB, Albano CM (2015) Ecologically-Relevant Maps of Landforms and Physiographic Diversity for Climate Adaptation Planning. PLoS ONE 10(12): e0143619. doi:10.1371/journal.pone.0143619 ,

Local Landforms (produced 3/2016) by Deniz Basaran and Charlie Frye, Esri, 30 m* resolution. "Improved Hammond’s Landform Classification and Method for Global 250-m Elevation Data" by Karagulle, Deniz; Frye, Charlie; Sayre, Roger; Breyer, Sean; Aniello, Peter; Vaughan, Randy; Wright, Dawn, has been successfully submitted online and is presently being given consideration for publication in Transactions in GIS. *We scaled the neighborhood windows from the 250-meter method described in the paper, and then applied that to 30-meter data in the U.S.

National Elevation Dataset, USGS, 30 m resolution

NWI – National Wetlands Inventory “ Classification of Wetlands and Deepwater Habitats of the United States”. U.S. Department of the Interior, Fish and Wildlife Service, Washington, DC. FWS/OBS-79/31 , U.S. Fish and Wildlife Service, Division of Habitat and Resource Conservation (prepared 10/2015)

NLCD 2011 – National LandCover Database 2011 (downloaded 1/2016) Homer, C.G., et. al. 2015,Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing, v. 81, no. 5, p. 345-354

NHDPlusV2 Received from Charlie Frye, Esri 3/2016. Produced by the EPA with support from the USGS.

gSSURGO –Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. Web Soil Survey. Accessed 3/2016, 30 m resolution

GAP Level 3 Ecological System Boundaries (downloaded 4/ 2016) NOAA CCAP Coastal Change Analysis Program Regional Land Cover and Change–downloaded by state (3/2016) from: C-CAP FTP Tool, see Description of this 30 m resolution, 2010 edition of data

NHD USGS National Hydrography Dataset

TNC Terrestrial Ecoregions (downloaded 3/2016)

2015 LCC Network Areas

Evaluation:

The creation of a national core quality index is a very ambitious objective, given the extreme variability in ecosystem conditions across the United States. The additional attributes were intended to provide flexibility in accommodating regional or local environmental differences across the U.S.

Scripts for constructing local cores and scoring them using the Green Infrastructure Center’s methodology are available on Esri's Green Infrastructure web site.

Two general approaches were used in the developing core quality index values. The first (default) follows the guidance of the Green Infrastructure Center’s scoring approach developed for the southeastern US where size of the core is the primary determinant of quality. The second; Bio-Weights puts more emphasis on bio-diversity and uniqueness ecosystem type and de-emphasizes slightly the importance of core size. This is to compensate for the very large intact core habitat areas in the west and southwest which also have comparatively low biodiversity values.

Scoring values:

Default Weights

0.4, # Acres0.1, # THICKNESS0.05, # TOPOGRAPHIC DIVERSITY (Standard Deviation)0.1, # Biodiversity Priority Index (SPECIES RICHNESS in GIC original version)0.05, # PERCENTAGE WETLAND COVER0.03, # Ecological Land Unit – Shannon-Weaver Index (SOIL VARIETY in GIC original version)0.02, # COMPACTNESS RATIO (AREA RELATIVE TO THE AREA OF A CIRCLE WITH THE SAME PERIMETER LENGTH)0.1, # STREAM DENSITY (LINEAR FEET/ACRE)0.05, # Ecological System Redundancy (RARE/THREATENED/ENDANGERED SPECIES ABUNDANCE (Number of occurrences) in GIC original version) 0.1, # Endemic Species Max (RARE/THREATENED/ENDANGERED SPECIES DIVERSITY (Number of unique species in a core) in GIC original version)

Bio-Weights

0.2, # Acres0.1, # THICKNESS0.05, # TOPOGRAPHIC DIVERSITY (Standard Deviation)0.25, # Biodiversity Priority Index (SPECIES RICHNESS in GIC original version)0.05, # PERCENTAGE WETLAND COVER0.03, # Ecological Land Unit – Shannon-Weaver Index (SOIL VARIETY in GIC original version)0.02, # COMPACTNESS RATIO (AREA RELATIVE TO THE AREA OF A CIRCLE WITH THE SAME PERIMETER LENGTH)0.1, # STREAM DENSITY (LINEAR FEET/ACRE)0.1, # Ecological System Redundancy (RARE/THREATENED/ENDANGERED SPECIES ABUNDANCE (Number of occurrences) in GIC original version) 0.1, # Endemic Species Max (RARE/THREATENED/ENDANGERED SPECIES DIVERSITY (Number of unique species in a core) in GIC original version)

Source MXD: GICores_03_05_All_Cores.mxd

Landfire ver 1.3 Vector Map of Existing Vegetation Types (EVT) for the USA. Part of the ECP vegetation cartography project, in this case to transform the existing Landfire national EVT raster into vector format for broader utility in visualization and analysis. A primary goal of this project was to develop ecological layers to use as overlays on top of high-resolution imagery, in order to help interpret and better understand the natural landscape. You can see the source national raster by clicking on the "Landfire Raw Rasters v130" at the bottom of the contents list.Using polygons has several advantages: Polygons are how most conservation plans and land decisions/managment are done so polygon-based outputs are more directly useable in management and planning. Unlike rasters, Polygons permit webmaps with clickable links to provide additional information about that ecological community. At the analysis level, polygons allow vegetation/ecological systems depicted to be enriched with additional ecological attributes for each polygon from multiple overlay sources be they raster or vector. These polygons were enriched with ecological data from the following sources:1. Landfire Slope, Aspect, Elevation, Percent Cover for Tree, Shrub and Herb layer, Height for Tree, Shrub and Herb Layer. These attributes are all statistically sampled from the source 30m rasters and assigned to each polygon as follows: Min value, Max value, Mean Value and Standard Deviation. The intent is to include all meaningful upper and lower habitat limits for each polygon, and a mean+std to allow further statistics.2. Vegbank.org site links, and classification and descriptive information, retrieved Jan 2015.3. Natureserve Ecological Systems classification and descriptive information as of Jan 2015 and site links to the Natureserve Explorer site for authoritative data on each ecological system4. Landfire EVT original attributes such as fuel code and Society of American Foresters classification5. NVCS higher level classification information from both Landfire and Natureserve. NOTE, To see these attributes you have to be zoomed in to 36k or beyond, (the scale bar will show 0.6 miles or less) before you click on a polygon, and the full attribute set is only available for natural systems, agriculture is a more limited set . This enrichment effort has been completed for most of the western sections, and is still in progress for the eastern US.The National Vegetation Classification System that helped on these national efforts has 8 hierarchical levels of classification, from Class and Subclass at the top, to Alliance and Association at the bottom. The Ecological Systems upon which this map is based are roughly equivalent to the "Group" level of the NVCS. This version of the vector service is laid out in 3 scales: "Base scale" EVT is a feature service on the core ecological systems class for use from 1k to 36k scales, with a separate labelling service of tan labels. Both are organized into 7 national project sections (more details below). You can turn any labels off or on at these section levels. Within each section are the actual tiles used to organize this 500-million-feature dataset into manageable chunks. Turn on the top "Boundaries, Sections, Tiles" layer to see all of all the tile names and outlines."Landfire Subcl Base Scale" is an NVCS subclass feature service, with one set of sections at 72k cartography, and another heavier boundary line for use at 1k-36k cartography as an overlay with the core ecological systems. There is also a separate set of subclass labels organized by section that you can turn off or on."Landfire Subcl mid-out scale" is a national map cached for speed as polygon boundary overlay cartography from 144k to 2 million, then as solid color polygons (for orientation only) at the 4 to 36 million scale.As mentioned above the base service contains about 500 million vector features partitioned in 300 tiles (light-blue 4-digit labels in the "Boundaries.." service) containing 500,000 to 1m features each. These are grouped into 7 map/feature services for 7 regional sections covering the continental US, (Dark blue 2-letter codes: NW=northwest, SW=southwest, NC=north central, SC=south central, NE=northeast, SE=southeast, EA=eastAlso included as a reference layer for this webmap is a set of USGS PADUS (Protected Areas Database for USA) Protected Area Boundaries and Classifications.Also included is the raw source data used to create these vectors, from the USFS raster service version 1.3, called "Landfire Raw Rasters v130" in the map. It contains the following national rasters:(Raster Attributes incorporated into the vector overlay)US_130EVT: Existing Vegetation Type, source material for the polygons in the overlay map servicesUS_130EVC: Existing Vegetation Cover - vertically projected percent cover of the live canopy layer for a specific areaUS_130EVH: Existing Vegetation Height - average height of the dominant vegetationOther rasters included in the image service:US_130CBD: Forest Canopy Bulk Density - density of available canopy fuel in a stand, kg m‐3 * 100US_130CBH: Forest Canopy Base Height - average height from the ground to a forest stand's canopy bottom at which there is a sufficient amount of forest canopy fuel to propagatefire vertically into the canopy, meters * 10US_130CC: Forest Canopy Cover - proportion of the forest floor covered by the vertical projection of the tree crownsUS_130CH: Forest Canopy Height - average height of the top of the vegetated canopy, meters * 10US_130FBFM13: 13 Fire Behavior Fuel Models from Anderson, Hal E. 1982. Aids to determining fuel models for estimating fire behavior. Gen. Tech. Rep. INT-122. Ogden, UT: U.S. Dept. of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. 22 p.US_130FBFM40: 40 Fire Behavior Fuel Models from Scott, J.H.; Burgan, R.E. 2005. Standard fire behavior fuel models: a comprehensive set for use with Rothermel’s surface fire spread model. Gen. Tech. Rep. RMRS-GTR-153. For Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 72 pUS_DIST##: Disturbance for given year (1999‐2010) spatial, temporal and severity information related to landscape change to model vegetation transitionsUS_FDIST##: Fuel Disturbance for given yearUS_VDIST##: Vegetation Disturbance for given yearUS_VTM##: Vegetation Transition Magnitude for given year - summary of the relationship between disturbance types and resulting effects on the vegetation in terms of changes in life‐form andcanopy coverUS_130BPS: Biophysical Settings - vegetation that may have been dominant on the landscape pre Euro‐American settlementUS_130ESP: Environmental Site Potential - vegetation that could be supported at a given site based on the biophysical environmentUS_130FRG: Fire Regime Groups - presumed historical fire regimes within landscapes based on interactions between vegetation dynamics, fire spread, fire effects, and spatial context,US_130MFRI: Mean Fire Return Interval - average period between fires under the presumed historical fire regimeUS_130PLS: Percent Low Severity Fire - low‐severity fires relative to mixed‐ and replacement‐severity fires under the presumed historical fire regimeUS_130PMS: Percent Mixed-Severity Fire - mixed‐severity fires relative to low‐ and replacement‐severity fires under the presumed historical fire regimeUS_130PRS: Percent Replacement-Severity Fire - replacement‐severity fires relative to low‐ and mixed‐severity fires under the presumed historical fire regimeThe legends are long and detailed so the webmap is best navigated using the middle "contents" icon in the left panel rather than the "Legend" icon. The Legends for each of the component tiles in the region are identical.Redraw times are not supposed to exceed 1-2 seconds per layer on this project, but it will be a bit slower the first time you load up the map, and the 144k & 72k feature service scales are currently slower in this test version.

This layer was created as part of Esri’s Green Infrastructure Initiative and is one of five newly generated companion datasets that can be used for Green Infrastructure (GI) planning at national, regional, and more local scales. If used together, these layers should have corresponding date-based suffixes (YYYYMMDD). The corresponding layer names are: Intact Habitat Cores, Habitat Connectors, Habitat Fragments, Habitat Cost Surface, and Intact Habitat Cores by Betweenness. These Esri derived data, and additional data central to GI planning from other authoritative sources, are also available as Map Packages for each U.S. State and can be downloaded from the Green Infrastructure Data Gallery.

This layer represents modeled Intact Habitat Cores, or minimally disturbed natural areas at least 100 acres in size and greater than 200 meters wide. Esri created these data following a methodology outlined by the Green Infrastructure Center Inc. These data were generated using 2011 National Land Cover Data. Cores were derived from all “natural” land cover classes and excluded all “developed” and “agricultural” classes including crop, hay and pasture lands. The resulting cores were tested for size and width requirements (at least 100 acres in size and greater than 200 meters wide) and then converted into unique polygons. This process resulted in the generation of over 550,000 cores. Cores were then overlaid with a diverse assortment of physiographic, biologic and hydrographic layers to populate each core with attributes (53 in total) related to the landscape characteristics found within. These data were also compiled to compute a “core quality index”, or score related to the perceived ecological value of each core, to provide users with additional insight related to the importance of each core when compared to all others. See this map image layer for a version that includes popups and ability to query the data. The source data used to derive this attribution is as follows: Number of endemic species (Mammals, Fish, Reptiles, Amphibians, Trees) (Jenkins, Clinton N., et. al, (April 21, 2015) US protected lands mismatch biodiversity priorities, PNAS vol.112, no. 16)

Priority Index areas: Endemic species, small home range size and low protection status. (Jenkins, Clinton N., et. al, (April 21, 2015) US protected lands mismatch biodiversity priorities, PNAS vol.112, no. 16)

Unique ecological systems (based upon work by Aycrig, Jocelyn L, et. al. (2013) Representation of Ecological Systems within the Protected Areas Network of the Continental United States. PLos One 8(1):e54689). New data constructed by Esri staff, using TNC Ecological Regions as summary areas.

Ecologically relevant landforms (Theobald DM, Harrison-Atlas D, Monahan WB, Albano CM (2015) Ecologically-Relevant Maps of Landforms and Physiographic Diversity for Climate Adaptation Planning. PLoS ONE 10(12): e0143619. doi:10.1371/journal.pone.0143619 ,

Local Landforms (produced 3/2016) by Deniz Basaran and Charlie Frye, Esri, 30 m* resolution. "Improved Hammond’s Landform Classification and Method for Global 250-m Elevation Data" by Karagulle, Deniz; Frye, Charlie; Sayre, Roger; Breyer, Sean; Aniello, Peter; Vaughan, Randy; Wright, Dawn, has been successfully submitted online and is presently being given consideration for publication in Transactions in GIS. *We scaled the neighborhood windows from the 250-meter method described in the paper, and then applied that to 30-meter data in the U.S.

National Elevation Dataset, USGS, 30 m resolution

NWI – National Wetlands Inventory “ Classification of Wetlands and Deepwater Habitats of the United States”. U.S. Department of the Interior, Fish and Wildlife Service, Washington, DC. FWS/OBS-79/31 , U.S. Fish and Wildlife Service, Division of Habitat and Resource Conservation (prepared 10/2015)

NLCD 2011 – National LandCover Database 2011 (downloaded 1/2016) Homer, C.G., et. al. 2015,Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing, v. 81, no. 5, p. 345-354

NHDPlusV2 Received from Charlie Frye, Esri 3/2016. Produced by the EPA with support from the USGS.

gSSURGO –Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. Web Soil Survey. Accessed 3/2016, 30 m resolution

GAP Level 3 Ecological System Boundaries (downloaded 4/ 2016) NOAA CCAP Coastal Change Analysis Program Regional Land Cover and Change–downloaded by state (3/2016) from: C-CAP FTP Tool, see Description of this 30 m resolution, 2010 edition of data

NHD USGS National Hydrography Dataset

TNC Terrestrial Ecoregions (downloaded 3/2016)

2015 LCC Network Areas

Evaluation:

The creation of a national core quality index is a very ambitious objective, given the extreme variability in ecosystem conditions across the United States. The additional attributes were intended to provide flexibility in accommodating regional or local environmental differences across the U.S.

Scripts for constructing local cores and scoring them using the Green Infrastructure Center’s methodology are available on Esri's Green Infrastructure web site.

Two general approaches were used in the developing core quality index values. The first (default) follows the guidance of the Green Infrastructure Center’s scoring approach developed for the southeastern US where size of the core is the primary determinant of quality. The second; Bio-Weights puts more emphasis on bio-diversity and uniqueness ecosystem type and de-emphasizes slightly the importance of core size. This is to compensate for the very large intact core habitat areas in the west and southwest which also have comparatively low biodiversity values.

Scoring values:

Default Weights

0.4, # Acres0.1, # THICKNESS0.05, # TOPOGRAPHIC DIVERSITY (Standard Deviation)0.1, # Biodiversity Priority Index (SPECIES RICHNESS in GIC original version)0.05, # PERCENTAGE WETLAND COVER0.03, # Ecological Land Unit – Shannon-Weaver Index (SOIL VARIETY in GIC original version)0.02, # COMPACTNESS RATIO (AREA RELATIVE TO THE AREA OF A CIRCLE WITH THE SAME PERIMETER LENGTH)0.1, # STREAM DENSITY (LINEAR FEET/ACRE)0.05, # Ecological System Redundancy (RARE/THREATENED/ENDANGERED SPECIES ABUNDANCE (Number of occurrences) in GIC original version) 0.1, # Endemic Species Max (RARE/THREATENED/ENDANGERED SPECIES DIVERSITY (Number of unique species in a core) in GIC original version)

Bio-Weights

0.2, # Acres0.1, # THICKNESS0.05, # TOPOGRAPHIC DIVERSITY (Standard Deviation)0.25, # Biodiversity Priority Index (SPECIES RICHNESS in GIC original version)0.05, # PERCENTAGE WETLAND COVER0.03, # Ecological Land Unit – Shannon-Weaver Index (SOIL VARIETY in GIC original version)0.02, # COMPACTNESS RATIO (AREA RELATIVE TO THE AREA OF A CIRCLE WITH THE SAME PERIMETER LENGTH)0.1, # STREAM DENSITY (LINEAR FEET/ACRE)0.1, # Ecological System Redundancy (RARE/THREATENED/ENDANGERED SPECIES ABUNDANCE (Number of occurrences) in GIC original version) 0.1, # Endemic Species Max (RARE/THREATENED/ENDANGERED SPECIES DIVERSITY (Number of unique species in a core) in GIC original version)

Source MXD: GICores_03_05_All_Cores.mxd

View on MapView ArcGIS Service

In order to support ongoing marine spatial planning in New Zealand, a numerical environmental classification using Gradient Forest (GF) models, was developed using a broad suite of biotic and high-resolution environmental predictor variables. A total of 630,997 records of 1,716 taxa living on, or near the seafloor and occurring at 39,766 unique locations were used to inform the transformation of 20 gridded environmental variables to represent spatial patterns of compositional turnover in 4 biotic groups (demersal fish, benthic invertebrates, macroalgae, and reef fish) and the overall seafloor community. Compositional turnover of the overall community was classified using a hierarchical procedure to define groups at different levels of classification detail and at two resolutions: a 250 m resolution grid from the coastline to the edge of the territorial sea (12 NM from shore), and a 1 km resolution grid from the edge of the territorial sea to the edge of the New Zealand exclusive economic zone. The 75-group level classification was assessed as representing the highest number of groups that captured the majority of the variation across the New Zealand marine environment. This classification is referred to as the New Zealand ‘Seafloor Community Classification’ (SCC). Associated spatially explicit measures of uncertainty for compositional turnover for the overall community (measured as the standard deviation of the mean (SD) compositional turnover averaged across each environmental variable) are also available, as is an added measure of uncertainty – coverage of the environmental space, which highlights geographic areas where predictions may be less certain due to low sampling. The full report and description of classes (at the 75 group level) are available at: https://www.doc.govt.nz/globalassets/documents/conservation/marine-and-coastal/marine-protected-areas/development-of-new-zealand-seafloor-community-classification.pdfhttps://www.doc.govt.nz/globalassets/documents/conservation/marine-and-coastal/marine-protected-areas/seafloor-community-classification-supplementary-information.pdf

Explore your Community’s Potential for Green Infrastructure. View the remaining intact habitat near you, and other measures of natural and man-made assets that connect us.The habitat cores shown were derived using a model built by the Green Infrastructure Center Inc. and adapted by Esri.This app includes an easily accessible layer of intact core habitat areas across the continental United States, appropriate in scale to support Green Infrastructure Planning at local, regional and national scales, using the best available national data. The results are intended to be supplemented or replaced with more current or higher resolution data when available, while still supporting Green Infrastructure planning initiatives at the regional level.Using a methodology outlined by the Green Infrastructure Center, Inc. Esri staff created a national intact habitat cores database for the lower 48 United States.The methodology identified, using nationally available datasets, intact or minimally disturbed areas at least 100 acres in size and with a minimum width of 200 meters.The identification of intact areas relied upon the 2011 National Land Cover Database. Potential cores areas were selected from land cover categories not containing the word “developed” or those categories associated with agriculture uses (crop, hay and pasture lands). The resulting areas were tested for size and width requirements, and then converted into unique polygons.These polygons were then overlaid with a diverse assortment of physiographic, biologic and hydrographic layers to use in computing a “core quality index”.These layers included:Number of endemic species (Mammals, Fish, Reptiles, Amphibians, Trees) (Jenkins, Clinton N., et. al, (April 21, 2015) US protected lands mismatch biodiversity priorities, PNAS vol.112, no. 16, www.pnas.org/cgi/doi/10.1073/pnas.1418034112)Priority Index areas: Endemic species, small home range size and low protection status. (Jenkins, Clinton N., et. al, (April 21, 2015) US protected lands mismatch biodiversity priorities, PNAS vol.112, no. 16, www.pnas.org/cgi/doi/10.1073/pnas.1418034112)Unique ecological systems (based upon work by Aycrig, Jocelyn L, et. al. (2013) Representation of Ecological Systems within the Protected Areas Network of the Continental United States. PLos One 8(1):e54689). New data constructed by Esri staff, using TNC Ecological Regions as summary areas.Ecologically relevant landforms (Theobald DM, Harrison-Atlas D, Monahan WB, Albano CM (2015) Ecologically-Relevant Maps of Landforms and Physiographic Diversity for Climate Adaptation Planning. PLoS ONE 10(12): e0143619. doi:10.1371/journal.pone.0143619 ,http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0143619Local Landforms (produced 3/2016) by Deniz Basaran and Charlie Frye, Esri, 30 m* resolution."Improved Hammond’s Landform Classification and Method for Global 250-m Elevation Data" by Karagulle, Deniz; Frye, Charlie; Sayre, Roger; Breyer, Sean; Aniello, Peter; Vaughan, Randy; Wright, Dawn, has been successfully submitted online and is presently being given consideration for publication in Transactions in GIS.*we scaled the neighborhood windows from the 250-meter method described in the paper, and then applied that to 30-meter data in the U.S.National Elevation Dataset, USGS, 30 m resolution, http://viewer.nationalmap.gov/launch/NWI – National Wetlands Inventory “ Classification of Wetlands and Deepwater Habitats of the United States”. U.S. Department of the Interior, Fish and Wildlife Service, Washington, DC. FWS/OBS-79/31 , U.S. Fish and Wildlife Service, Division of Habitat and Resouce Conservation (prepared 10/2015)NLCD 2011 – National LandCover Database 2011http://www.mrlc.gov/nlcd2011.php (downloaded 1/2016) Homer, C.G., et. al. 2015,Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing, v. 81, no. 5, p. 345-354 NHDPlusV2 –https://www.epa.gov/waterdata/nhdplus-national-hydrography-dataset-plusReceived from Charlie Frye, ESRI 3/2016. Produced by the EPA with support from the USGS.gSSURGO –Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. Web Soil Survey. Available online at http://websoilsurvey.nrcs.usda.gov/. Accessed 3/2016, 30 m resolutionGAP Level 3 Ecological System Boundaries (downloaded 4/ 2016)http://gapanalysis.usgs.gov/gaplandcover/data/download/NOAA CCAP Coastal Change Analysis Program Regional Land Cover and Change–downloaded by state (3/2016) from: https://coast.noaa.gov/ccapftp/#/ Description: https://coast.noaa.gov/dataregistry/search/collection/info/ccapregional30 m resolution, 2010 edition of dataNHD USGS National Hydrography Dataset http://nhd.usgs.gov/data.htmlTNC Terrestrial Ecoregionshttp://maps.tnc.org/gis_data.html#TNClands (downloaded 3/2016)2015 LCC Network Areashttps://www.sciencebase.gov/catalog/item/55b943ade4b09a3b01b65d78Evaluation:The creation of a national core quality index is a very ambitious objective, given the extreme variability in ecosystem conditions across the United States. The additional attributes were intended to provide flexibility in accommodating regional or local environmental differences across the U.S.Scripts for constructing local cores and scoring them using the Green Infrastructure Center’s methodology are available on esri.com/greeninfrastructureTwo general approaches were used in the developing core quality index values. The first (default) follows the guidance of the Green Infrastructure Center’s scoring approach developed for the southeastern US where size of the core is the primary determinant of quality. The second; Bio-Weights puts more emphasis on bio-diversity and uniqueness ecosystem type and de-emphasizes slightly the importance of core size. This is to compensate for the very large intact core habitat areas in the west and southwest which also have comparatively low biodiversity values.Scoring values:Default Weights0.4, # Acres0.1, # THICKNESS0.05, # TOPOGRAPHIC DIVERSITY (Standard Deviation)0.1, # Biodiversity Priority Index (SPECIES RICHNESS in GIC original version)0.05, # PERCENTAGE WETLAND COVER0.03, # Ecological Land Unit – Shannon-Weaver Index (SOIL VARIETY in GIC original version)0.02, # COMPACTNESS RATIO (AREA RELATIVE TO THE AREA OF A CIRCLE WITH THE SAME PERIMETER LENGTH)0.1, # STREAM DENSITY (LINEAR FEET/ACRE)0.05, # Ecological System Redundancy (RARE/THREATENED/ENDANGERED SPECIES ABUNDANCE (Number of occurrences) in GIC original version) 0.1, # Endemic Species Max (RARE/THREATENED/ENDANGERED SPECIES DIVERSITY (Number of unique species in a core) in GIC original version)Bio-Weights0.2, # Acres0.1, # THICKNESS0.05, # TOPOGRAPHIC DIVERSITY (Standard Deviation)0.25, # Biodiversity Priority Index (SPECIES RICHNESS in GIC original version)0.05, # PERCENTAGE WETLAND COVER0.03, # Ecological Land Unit – Shannon-Weaver Index (SOIL VARIETY in GIC original version)0.02, # COMPACTNESS RATIO (AREA RELATIVE TO THE AREA OF A CIRCLE WITH THE SAME PERIMETER LENGTH)0.1, # STREAM DENSITY (LINEAR FEET/ACRE)0.1, # Ecological System Redundancy (RARE/THREATENED/ENDANGERED SPECIES ABUNDANCE (Number of occurrences) in GIC original version) 0.1, # Endemic Species Max (RARE/THREATENED/ENDANGERED SPECIES DIVERSITY (Number of unique species in a core) in GIC original version)

In constructing the overall Boulder County Human Services Index (using Census Tracts as the geographical unit), we transform each indicator so that we can measure them on a similar scale. We express the data in standardized (or z-score) form, which indicates how far a Census Tract's raw score is from the mean of all Census Tracts. After the transformation process, all indicators have a mean (µ) of zero (0) and a standard deviation (σ) of one (1). The indicators thus can be expressed in the same units of measurement. The z-scores for each indicator were summed to get a total score for the index. The total score was classified into 5 categories using the natural breaks (Jenks) classification. Then they were ranked from very low to very high based on the natural breaks. We used the same methodology as developed by Dr. Lisa Piscopo, Executive Strategist for Denver Human Services.

Following a methodology outlined by the Green Infrastructure Center Inc., Esri staff created a national intact habitat cores database for the lower 48 United States. These data were generated using 2011 National Land Cover Data. Cores were derived from all “natural” land cover classes and excluded all “developed” and “agricultural” classes including crop, hay and pasture lands. The resulting cores were tested for size and width requirements (at least 100 acres in size and greater than 200 meters) and then converted into unique polygons. This process resulted in the generation of over 550,000 intact habitat cores. These polygons were then overlaid with a diverse assortment of physiographic, biologic and hydrographic layers to populate each core with attributes (53 in total) related to the landscape characteristics found within. These data were also compiled to compute a “core quality index”, or score related to the perceived ecological value of each core, to provide users with additional insight related to the importance of each core when compared to all others. The source data used to derive this attribution is as follows:Source data:Number of endemic species:(Mammals, Fish, Reptiles, Amphibians, Trees) Jenkins, Clinton N., et. al, (April 21, 2015) US protected lands mismatch biodiversity priorities, PNAS vol.112, no. 16, www.pnas.org/cgi/doi/10.1073/pnas.1418034112)Priority Index areas: Endemic species, small home range size and low protection status. Jenkins, Clinton N., et. al, (April 21, 2015) US protected lands mismatch biodiversity priorities, PNAS vol.112, no. 16, www.pnas.org/cgi/doi/10.1073/pnas.1418034112)Unique ecological systems:Based upon work by Aycrigg, Jocelyn L, et. al. (2013) Representation of Ecological Systems within the Protected Areas Network of the Continental United States. PLos One 8(1):e54689). New data constructed by Esri staff, using TNC Ecological Regions as summary areas.Ecologically relevant landforms:Theobald DM, Harrison-Atlas D, Monahan WB, Albano CM (2015) Ecologically-Relevant Maps of Landforms and Physiographic Diversity for Climate Adaptation Planning.PLoS ONE 10(12): e0143619. doi:10.1371/journal.pone.0143619http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0143619Local Landforms:Produced 3/2016 by Deniz Karagulle and Charlie Frye, Esri, 30 m* resolution. "Improved Hammond’s Landform Classification and Method for Global 250-m Elevation Data" by Karagulle, Deniz; Frye, Charlie; Sayre, Roger; Breyer, Sean; Aniello, Peter; Vaughan, Randy; Wright, Dawn, In Review, Transactions in GIS.*The neighborhood window was scaled from the 250-meter method described in the paper to 30-meter data in the U.S.Ecological Land Units: Sayre, R., J. Dangermond, C. Frye, R. Vaughan, P. Aniello, S. Breyer, D. Cribbs, D. Hopkins, R. Nauman, W. Derrenbacher, D. Wright, C. Brown, C. Convis, J. Smith, L. Benson, D. Paco VanSistine, H. Warner, J. Cress, J. Danielson, S. Hamann, T. Cecere, A. Reddy, D. Burton, A. Grosse, D. True, M. Metzger, J. Hartmann, N. Moosdorf, H. Dürr, M. Paganini, P. DeFourny, O. Arino, S. Maynard, M. Anderson, and P. Comer. 2014. A New Map of Global Ecological Land Units — An Ecophysiographic Stratification Approach. Washington, DC: Association of American Geographers. 46 pages http://www.aag.org/cs/global_ecosystems. 2015 updated data accessed 3/2016, 250 m resolution.National Elevation Dataset:USGS, 30 m resolution, http://viewer.nationalmap.gov/launch/NWI – National Wetlands Inventory:Classification of Wetlands and Deepwater Habitats of the United States. U.S. Department of the Interior, Fish and Wildlife Service, Washington, DC. FWS/OBS-79/31, U.S. Fish and Wildlife Service, Division of Habitat and Resource Conservation (prepared 10/2015)NLCD 2011 – National LandCover Database 2011:http://www.mrlc.gov/nlcd2011.php(downloaded 1/2016) Homer, C.G., et. al. 2015,Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing, v. 81, no. 5, p. 345-354 NHDPlusV2:https://www.epa.gov/waterdata/nhdplus-national-hydrography-dataset-plusReceived from Charlie Frye, Esri 3/2016. Produced by the EPA with support from the USGS.gSSURGO:Soil Survey Staff. Gridded Soil Survey Geographic Database for the Conterminous United States. Natural Resources Conservation Service, United States Department of Agriculture. Available online at http://gdg.sc.egov.usda.gov/. Accessed 3/2016, 30 m resolutionSSurgo:Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. Web Soil Survey. Available online at http://websoilsurvey.nrcs.usda.gov/. Accessed 3/2016GAP Level 3 Ecological System Boundaries:http://gapanalysis.usgs.gov/gaplandcover/data/download/(downloaded 4/ 2016) NOAA CCAP Coastal Change Analysis Program Regional Land Cover and Change:https://coast.noaa.gov/ccapftp/#/Description: https://coast.noaa.gov/dataregistry/search/collection/info/ccapregional30 m resolution, 2010 edition of data (downloaded by state 3/2016)NHD USGS National Hydrography Dataset:http://nhd.usgs.gov/data.htmlTNC Terrestrial Ecoregions:http://maps.tnc.org/gis_data.html#TNClands(downloaded 3/2016)2015 LCC Network Areas:https://www.sciencebase.gov/catalog/item/55b943ade4b09a3b01b65d78Detailed Description of the Core Quality Index Methodology:The creation of a national core quality index was a challenging undertaking given the extreme heterogeneity of ecosystem conditions across the United States. As a result, 9 separate index scores were generated for each core, each placing varying weights on landscape characteristics of regional or local significance. This was done to account for variation across the U.S. and to provide users with additional flexibility in accommodating regional or local environmental priorities.Two general approaches were used in developing the core quality index values. The first, Default Weights, uses core size as the primary determinant of quality, following the guidance of the Green Infrastructure Center’s scoring approach developed for the southeastern US. The second, Bio-Weights, puts more emphasis on characteristics associated with bio-diversity and uniqueness of ecosystem types and de-emphasizes slightly the importance of core size. This alternative was developed to compensate for the very large intact core habitat areas in the west and southwest which also have comparatively low biodiversity values.Scoring values:Default Weights0.4, # Acres0.1, # Thickness0.05, # Topographic Diversity (Standard Deviation)0.1, # Biodiversity Priority Index (Species Richness in GIC original version)0.05, # Percentage Wetland Cover0.03, # Ecological Land Unit – Shannon-Weaver Index (Soil Variety in GIC original version)0.02, # Compactness Ratio (Area relative to the area of a circle with the same perimeter length)0.1, # Stream Density (Linear Feet/Acre)0.05, # Ecological System Redundancy (Rare/Threatened/Endangered Species Abundance (Number of occurrences) in GIC original version) 0.1, # Endemic Species Max (Rare/Threatened/Endangered Species Abundance (Number of unique species in a core) in GIC original version) Bio-Weights0.2, # Acres0.1, # Thickness0.05, # Topographic Diversity (Standard Deviation)0.25, # Biodiversity Priority Index (Species Richness in GIC original version)0.05, # Percentage Wetland Cover0.03, # Ecological Land Unit – Shannon-Weaver Index (Soil Variety in GIC original version)0.02, # Compactness Ratio (Area Relative To The Area Of A Circle With The Same Perimeter Length)0.1, # Stream Density (Linear Feet/Acre)0.1, # Ecological System Redundancy (Rare/Threatened/Endangered Species Abundance (Number of occurrences) in GIC original version) 0.1, # Endemic Species Max (Rare/Threatened/Endangered Species Diversity (Number of unique species in a core) in GIC original version) Scripts for constructing local cores and scoring them using the Green Infrastructure Center’s methodology are available on http://www.esri.com/about-esri/greeninfrastructure

Landfire ver 1.3 Vector Map of Existing Vegetation Types (EVT) for the USA, with ecological attributes for each polygon from the following sources:1. Landfire Slope, Aspect, Elevation, Percent Cover for Tree, Shrub and Herb layer, Height for Tree, Shrub and Herb Layer. These attributes are all statistically sampled from the source 30m rasters and assigned to each polygon as follows: Min value, Max value, Mean Value and Standard Deviation. The intent is to include all meaningful upper and lower habitat limits for each polygon, and a mean+std to allow further statistics.2. Vegbank.org site links, and classification and descriptive information, retrieved Jan 2015.3. Natureserve Ecological Systems classification and descriptive information as of Jan 2015 and site links to the Natureserve Explorer site for authoritative data on each ecological system4. Landfire EVT original attributes such as fuel code and Society of American Foresters classification5. NVCS higher level classification information from both Landfire and Natureserve.The service is designed at 3 scales: Base scale EVT is a feature service for use from 1k to 36k scales, with a separate labelling serviceMidscale Subclasses are a feature service for use at 72k to 288k, with an integrated labelling layer in the serviceOuter scale Subclasses are a cached service of boundaries only for orientation at 500k to 18 million scale.The base service contains about 300 million vector features, so for useability it is partitioned in 300 tiles (light-blue 4-digit labels) containing 500,000 to 1m features each. These are grouped into 7 map/feature services for 7 regional sections covering the continental US, (Dark blue 2-letter codes: NW=northwest, SW=southwest, NC=north central, SC=south central, NE=northeast, SE=southeast, EA=eastAlso included is the raw source raster service version 1.3 from USFS, called "Landfire Raw Rasters v130" containing the following national rasters:(Raster Attributes incorporated into the vector overlay)US_130EVT: Existing Vegetation Type, source material for the polygons in the overlay map servicesUS_130EVC: Existing Vegetation Cover - vertically projected percent cover of the live canopy layer for a specific areaUS_130EVH: Existing Vegetation Height - average height of the dominant vegetationOther rasters included in the image service:US_130CBD: Forest Canopy Bulk Density - density of available canopy fuel in a stand, kg m‐3 * 100US_130CBH: Forest Canopy Base Height - average height from the ground to a forest stand's canopy bottom at which there is a sufficient amount of forest canopy fuel to propagatefire vertically into the canopy, meters * 10US_130CC: Forest Canopy Cover - proportion of the forest floor covered by the vertical projection of the tree crownsUS_130CH: Forest Canopy Height - average height of the top of the vegetated canopy, meters * 10US_130FBFM13: 13 Fire Behavior Fuel Models from Anderson, Hal E. 1982. Aids to determining fuel models for estimating fire behavior. Gen. Tech. Rep. INT-122. Ogden, UT: U.S. Dept. of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. 22 p.US_130FBFM40: 40 Fire Behavior Fuel Models from Scott, J.H.; Burgan, R.E. 2005. Standard fire behavior fuel models: a comprehensive set for use with Rothermel’s surface fire spread model. Gen. Tech. Rep. RMRS-GTR-153. For Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 72 pUS_DIST##: Disturbance for given year (1999‐2010) spatial, temporal and severity information related to landscape change to model vegetation transitionsUS_FDIST##: Fuel Disturbance for given yearUS_VDIST##: Vegetation Disturbance for given yearUS_VTM##: Vegetation Transition Magnitude for given year - summary of the relationship between disturbance types and resulting effects on the vegetation in terms of changes in life‐form andcanopy coverUS_130BPS: Biophysical Settings - vegetation that may have been dominant on the landscape pre Euro‐American settlementUS_130ESP: Environmental Site Potential - vegetation that could be supported at a given site based on the biophysical environmentUS_130FRG: Fire Regime Groups - presumed historical fire regimes within landscapes based on interactions between vegetation dynamics, fire spread, fire effects, and spatial context,US_130MFRI: Mean Fire Return Interval - average period between fires under the presumed historical fire regimeUS_130PLS: Percent Low Severity Fire - low‐severity fires relative to mixed‐ and replacement‐severity fires under the presumed historical fire regimeUS_130PMS: Percent Mixed-Severity Fire - mixed‐severity fires relative to low‐ and replacement‐severity fires under the presumed historical fire regimeUS_130PRS: Percent Replacement-Severity Fire - replacement‐severity fires relative to low‐ and mixed‐severity fires under the presumed historical fire regimeThe legends are long and detailed so the webmap is best navigated using the middle "contents" icon in the left panel rather than the "Legend" icon. The Legends for each of the component tiles in the region are identical.Redraw times are supposed to be 1-2 seconds per layer, it will be a bit slower the first time you load up the map, and the 144k & 72k scales are slower in this test version

This layer shows median earnings by occupational group broken down by sex. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Only full-time year-round workers included. Median earnings is based on earnings in past 12 months of survey. Occupation Groups based on Bureau of Labor Statistics (BLS)' Standard Occupation Classification (SOC). This layer is symbolized to show median earnings of the full-time, year-round civilian employed population. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B24022 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows median earnings by occupational group. This is shown by tract, county, and state boundaries. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Only full-time year-round workers included. Median earnings is based on earnings in past 12 months of survey. Occupation Groups based on Bureau of Labor Statistics (BLS)' Standard Occupation Classification (SOC). This layer is symbolized to show median earnings of the full-time, year-round civilian employed population. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintage: 2010-2014ACS Table(s): B24021 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 28, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows median earnings by occupational group broken down by sex. This is shown by tract, county, and state boundaries. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Only full-time year-round workers included. Median earnings is based on earnings in past 12 months of survey. Occupation Groups based on Bureau of Labor Statistics (BLS)' Standard Occupation Classification (SOC). This layer is symbolized to show median earnings of the full-time, year-round civilian employed population. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintage: 2010-2014ACS Table(s): B24022 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 28, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This layer shows median earnings by occupational group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Only full-time year-round workers included. Median earnings is based on earnings in past 12 months of survey. Occupation Groups based on Bureau of Labor Statistics (BLS)' Standard Occupation Classification (SOC). This layer is symbolized to show median earnings of the full-time, year-round civilian employed population. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B24021Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This layer shows median earnings by occupational group. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Only full-time year-round workers included. Median earnings is based on earnings in past 12 months of survey. Occupation Groups based on Bureau of Labor Statistics (BLS)' Standard Occupation Classification (SOC). This layer is symbolized to show median earnings of the full-time, year-round civilian employed population. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B24021Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This layer shows median earnings by occupational group broken down by sex. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Only full-time year-round workers included. Median earnings is based on earnings in past 12 months of survey. Occupation Groups based on Bureau of Labor Statistics (BLS)' Standard Occupation Classification (SOC). This layer is symbolized to show median earnings of the full-time, year-round civilian employed population. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B24022 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.