ME SALS Patches with Protected lands from the ME Conservation Lands data and NWR interest data.This tool identifies the suite of patches (8680) with potential to support Saltmarsh Sparrows in the near and long term. The patches are ranked according to a formula that assesses the relative importance of a variety of factors known to positively or negatively influence Saltmarsh Sparrow populations (see Influence Factors tab). Patches in darker green colors are assumed to provide higher quality habitat than patches in lighter green colors and indicate important areas to focus conservation attention. This prioritization does not factor current density or abundance of Saltmarsh Sparrows into the results. Rather, it ranks patches according to their suitability to provide high quality Sparrow habitat according to expert opinion. THESE DATA HAVE BEEN REPLCE BY THE https://fws.maps.arcgis.com/home/item.html?id=be7290ec39084f62874dcde6316a0bd3

Statewide polygon feature class of seamlines dissolved by date of flight capture for image used in delievery product from USDA Farm Service Agency. Individual shapefiles provided by USDA were appended into one feature class, then dissolved on the IDATE field and re-projected to NAD83 NJ State Plane Feet in order to overlay easily with other state GIS data. The dissolve tool was run with the multipart polygon option.

This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into country boundaries, administrative 1 boundaries, and 50 km hex bins. The unit of measurement is micrograms per cubic meter.The layer shows the annual average PM 2.5 from 1998 to 2016, highlighting if the overall mean for an area meets the World Health Organization guideline of 10 micrograms per cubic meter annually. Areas that don't meet the guideline and are above the threshold are shown in red, and areas that are lower than the guideline are in grey.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality globally. Some of the things we can learn from this layer:What is the average annual PM 2.5 value over 19 years? (1998-2016)What is the annual average PM 2.5 value for each year from 1998 to 2016?What is the statistical trend for PM 2.5 over the 19 years? (downward or upward)Are there hot spots (or cold spots) of PM 2.5 over the 19 years?How many people are impacted by the air quality in an area?What is the death rate caused by the joint effects of air pollution?Choose a different attribute to symbolize in order to reveal any of the patterns above.A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis, trends, and a 19-year average. The country and administrative 1 layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.Boundaries and population figures:Antarctica is excluded from all maps because it was not included in the original NASA grids.50km hex bins generated using the Generate Tessellation tool - projected to Behrmann Equal Area projection for analysesPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Administrative boundaries from World Administrative Divisions layer from ArcGIS Living Atlas - projected to Behrmann Equal Area projection for analyses and hosted in Web MercatorSources: Garmin, CIA World FactbookPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Country boundaries from Esri 2019 10.8 Data and Maps - projected to Behrmann Equal Area projection for analyses and hosted in Web Mercator. Sources: Garmin, Factbook, CIAPopulation figures attached to the country boundaries come from the World Population Estimate 2016 Sources Living Atlas layer Data processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The layers are hosted in Web Mercator Auxillary Sphere projection, but were processed using an equal area projection: Behrmann. If using this layer for analysis, it is recommended to start by projecting the data back to Behrmann.The country and administrative layer were dissolved and joined with population figures in order to visualize human impact.The dissolve tool ensures that each geographic area is only symbolized once within the map.Country boundaries were generalized post-analysis for visualization purposes. The tolerance used was 700m. If performing analysis with this layer, find detailed country boundaries in ArcGIS Living Atlas. To create the population-weighted attributes on the country and Admin 1 layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and the PM2.5 and population figures were summarized within the country and Admin 1 boundaries.The summation of the PM 2.5 values were then divided by the total population of each geography. This population value was determined by summarizing the population values from the hex bins within each geography.Some artifacts in the hex bin layer as a result of the input NASA rasters. Because the gridded surface is created from multiple satellites, there are strips within some areas that are a result of satellite paths. Some areas also have more of a continuous pattern between hex bins as a result of the input rasters.Within the country layer, an air pollution attributable death rate is included. 2016 figures are offered by the World Health Organization (WHO). Values are offered as a mean, upper value, lower value, and also offered as age standardized. Values are for deaths caused by all possible air pollution related diseases, for both sexes, and all age groups. For more information visit this page, and here for methodology. According to WHO, the world average was 95 deaths per 100,000 people.To learn the techniques used in this analysis, visit the Learn ArcGIS lesson Investigate Pollution Patterns with Space-Time Analysis by Esri's Kevin Bulter and Lynne Buie.

This data set represents the 2025 elk hunt area, herd unit, and regions boundaries for Wyoming. The layer was originally digitized at a scale of 1:100,000, using USGS 1:100,000 DRGs as a backdrop for heads up digitizing. Updates requested by Wyoming Game and Fish Biological Services were completed by selecting needed features from other layers, including roads, streams, HUCs, NAIP rasters and others. Hunt area boundary descriptions are part of hunting regulations, which are approved and published annually by the Wyoming Game and Fish Commission. When needed, the 2008 edition (1st Edition) of the Wyoming Road and Recreation Atlas (Benchmark Maps) was consulted for road and other information.NOTE: A layer of herd units is derived from this hunt area layer by dissolving on the "HERDUNIT" and "HERDNAME" attributes (Dissolve_Fields), and unchecking the box "Create multipart features (optional)". All of the same metadata is used from the hunt area layer except that the citation title is modified so that "Herd Unit" replaces "Hunt Area". The "Dissolve" tool: ArcToolbox > Data Management Tools > Generalization.NOTE: A layer of nonresident regions is derived from this hunt area layer by dissolving on the "Region" attribute (Dissolve_Field), and unchecking the box "Create multipart features (optional)". All of the same metadata is used from the hunt area layer except that the citation title is modified so that "Nonresident Region" replaces "Hunt Area". The "Dissolve" tool: ArcToolbox > Data Management Tools > Generalization.

This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into country boundaries, administrative 1 boundaries, and 50 km hex bins. The unit of measurement is micrograms per cubic meter.The layer shows the annual average PM 2.5 from 1998 to 2016, highlighting if the overall mean for an area meets the World Health Organization guideline of 10 micrograms per cubic meter annually. Areas that don't meet the guideline and are above the threshold are shown in red, and areas that are lower than the guideline are in grey.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality globally. Some of the things we can learn from this layer:What is the average annual PM 2.5 value over 19 years? (1998-2016)What is the annual average PM 2.5 value for each year from 1998 to 2016?What is the statistical trend for PM 2.5 over the 19 years? (downward or upward)Are there hot spots (or cold spots) of PM 2.5 over the 19 years?How many people are impacted by the air quality in an area?What is the death rate caused by the joint effects of air pollution?Choose a different attribute to symbolize in order to reveal any of the patterns above.A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis, trends, and a 19-year average. The country and administrative 1 layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.Boundaries and population figures:Antarctica is excluded from all maps because it was not included in the original NASA grids.50km hex bins generated using the Generate Tessellation tool - projected to Behrmann Equal Area projection for analysesPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Administrative boundaries from World Administrative Divisions layer from ArcGIS Living Atlas - projected to Behrmann Equal Area projection for analyses and hosted in Web MercatorSources: Garmin, CIA World FactbookPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Country boundaries from Esri 2019 10.8 Data and Maps - projected to Behrmann Equal Area projection for analyses and hosted in Web Mercator. Sources: Garmin, Factbook, CIAPopulation figures attached to the country boundaries come from the World Population Estimate 2016 Sources Living Atlas layer Data processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The layers are hosted in Web Mercator Auxillary Sphere projection, but were processed using an equal area projection: Behrmann. If using this layer for analysis, it is recommended to start by projecting the data back to Behrmann.The country and administrative layer were dissolved and joined with population figures in order to visualize human impact.The dissolve tool ensures that each geographic area is only symbolized once within the map.Country boundaries were generalized post-analysis for visualization purposes. The tolerance used was 700m. If performing analysis with this layer, find detailed country boundaries in ArcGIS Living Atlas. To create the population-weighted attributes on the country and Admin 1 layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and the PM2.5 and population figures were summarized within the country and Admin 1 boundaries.The summation of the PM 2.5 values were then divided by the total population of each geography. This population value was determined by summarizing the population values from the hex bins within each geography.Some artifacts in the hex bin layer as a result of the input NASA rasters. Because the gridded surface is created from multiple satellites, there are strips within some areas that are a result of satellite paths. Some areas also have more of a continuous pattern between hex bins as a result of the input rasters.Within the country layer, an air pollution attributable death rate is included. 2016 figures are offered by the World Health Organization (WHO). Values are offered as a mean, upper value, lower value, and also offered as age standardized. Values are for deaths caused by all possible air pollution related diseases, for both sexes, and all age groups. For more information visit this page, and here for methodology. According to WHO, the world average was 95 deaths per 100,000 people.To learn the techniques used in this analysis, visit the Learn ArcGIS lesson Investigate Pollution Patterns with Space-Time Analysis by Esri's Kevin Bulter and Lynne Buie.

This data set represents the 2025 deer hunt area, herd unit, and regions boundaries for Wyoming. The layer was originally digitized at a scale of 1:100,000, using USGS 1:100,000 DRGs as a backdrop for heads up digitizing. Updates requested by Wyoming Game and Fish Biological Services were completed by selecting needed features from other layers, including roads, streams, HUCs, NAIP 2009 rasters and others. Hunt area boundary descriptions are part of hunting regulations, which are approved and published annually by the Wyoming Game and Fish Commission. When needed, the 2008 edition (1st Edition) of the Wyoming Road and Recreation Atlas (Benchmark Maps) was consulted for road and other information.NOTE: A layer of herd units is derived from this hunt area layer by dissolving on the "MD_HERDUNIT" or ""WD_HERDUNIT" and "MD_HERDNAME" or ""WD_HERDNAME" attributes (Dissolve_Fields), and unchecking the box "Create multipart features (optional)". All of the same metadata is used from the hunt area layer except that the citation title is modified so that "Mule Deer Herd Unit" or "White-tailed Deer Herd Unit" replaces "Hunt Area". The "Dissolve" tool: ArcToolbox > Data Management Tools > Generalization.NOTE: A layer of nonresident regions is derived from this hunt area layer by dissolving on the "Region" attribute (Dissolve_Field), and unchecking the box "Create multipart features (optional)". All of the same metadata is used from the hunt area layer except that the citation title is modified so that "Nonresident Region" replaces "Hunt Area". The "Dissolve" tool: ArcToolbox > Data Management Tools > Generalization.

This data set represents the 2023 moose huntarea and herdunit boundaries for Wyoming. It was digitized at a scale of 1:100,000, using USGS 1:100,000 DRGs as a backdrop for heads up digitizing. Updates requested by Wyoming Game and Fish Biological Services Department, were completed by selecting needed features from other layers, including roads, streams, HUCs, NAIP 2009 rasters. Huntarea boundary descriptions are part of hunting regulations, which are approved and published annually by the Wyoming Game and Fish Commission. When needed, the 2008 edition (1st Edition) of the Wyoming Road and Recreation Atlas (Benchmark Maps) was consulted for road and other information. NOTE: A layer of herd units is derived from this hunt area layer by dissolving on the "HERDUNIT" and "HERDNAME" attributes (Dissolve_Fields), and unchecking the box "Create multipart features (optional)". All of the same metadata is used from the hunt area layer except that the citation title is modified so that "Herd Unit" replaces "Hunt Area". The "Dissolve" tool: ArcToolbox > Data Management Tools > Generalization.

Whole grids from the v3.3 Michigan Great Lakes Grids GIS layer were assigned to statistical districts to match boundaries in existing GIS layers (see maps 1-3 in the 2000 Consent Decree). The Dissolve tool in ArcGIS 10.5 was used to dissolve the Michigan Great Lakes Grid layer (v3.3) to create statistical district boundaries that were then bounded by waterbody descriptions as follows: 1) The MI-8 and MH-1 statistical districts were clipped to end at the boundary of the St. Mary’s River as described in the 2000 Consent Decree. 2) MH-6 was clipped to end at the Blue Water Bridge as described in Fisheries Order 200, and 3) “MICH”in Lake Erie was modified to end at the Lake Erie/Detroit River boundary as described in the 2016-2017 Michigan Fishing Guide. Note that all districts consist of whole or clippped (as described above) 10-minute grids except for Lake Erie which consists of whole aggregated 10-minute grids and two clipped (as described above) 5-minute grids (le_602 and le_603). Also note that statistical district boundaries are almost identical to those of the lake trout management units (where they exist) as described in FO-200.GIS layer last updated 10/02/2019. Metadata last updated 10/2/2019. SourcesConsent Decree, United States of America, and Bay Mills Indian Community, Sault Ste. Marie Tribe of Chippewa Indians, Grand Travers Band of Ottawa and Chippewa Indians, Little River Band of Ottawa Indians, and Little Travers Bay Bands of Odawa Indians v. State of Michigan et al. (Case No. 2:73 CV 26, August, 2000). https://www.michigan.gov/documents/dnr/consent_decree_2000_197687_7.pdfFisheries Order 200. Statewide Trout, Salmon, Whitefish, Lake Herring, and Smelt Regulations. Michigan Natural Resources Commission and the Michigan Department of Natural Resources. https://www.michigan.gov/documents/dnr/FO_200.10_317498_7.pdf?111313Hansen, M. J., 1996. A lake trout restoration plan for Lake Superior. Great Lakes Fishery Commission. Michigan Fishing Guide. Department of Natural Resources, State of Michigan. https://www.michigan.gov/documents/dnr/2019MIFishingGuide-Feb26_647890_7.pdfSmith, S. H., Buettner, H. J., and R. Hile. 1961. Fishery Statistical Districts of the Great Lakes. Great Lakes Fishery Commission Technical Report No. 2, September 1961. https://www.glfc.org/pubs/TechReports/Tr02.pdf

Seafloor geomorphology is the study of physical features on the seafloor. This layer represents the characterizations of geomorphic features and the zones within the ocean where they occur. The data for this layer are from the first map of seafloor geomorphology ever published; this map was published 2014 by GRID Arendal.“The new seafloor features map provides a foundation on which to build an understanding of the living and non-living resources of the ocean and to improve decision making on a range of global issues like food security, resource use and conservation.” - Dr. Peter Harris, the project leader and Managing Director of GRID-Arendal.Dataset SummaryThe geomorphic features in this layer were created by automated and manual processes over the course of many months. The source data for the process is a modified 30-arc second (~1 km) resolution version of SRTM30_PLUS global bathymetry produced in 2009. The following features and zones are included as sub-layers:FeaturesCanyons - Submarine canyons are defined as steep-walled, sinuous valleys with V-shaped cross sections, axes sloping outward as continuously as river-cut land canyons and relief comparable to even the largest of land canyons.Seamounts - Seamounts are a single or group of peaks, greater than 1,000 meters in relief above the sea floor, characteristically of conical form.Guyots - Guyots are isolated or a group of seamount having a comparatively smooth flat top. Also called tablemounts.Troughs - Troughs are long depressions of the sea floor characteristically flat bottomed and steep sided and normally shallower than a trench. In this study we found that troughs are also commonly open at one end (i.e. not defined by closed bathymetric contours) and their broad, flat floors may exhibit a continuous gradient. Troughs may originate from glacial erosion processes or have form through tectonic processes.Glacial Troughs - Glacial troughs are the largest of the shelf valleys at high latitudes incised by glacial erosion during the Pleistocene ice ages to form elongate troughs, typically trending across the continental shelf and extending inland as fjord complexes. Glacial troughs are characterized by depths of over 100 m (often exceeding 1,000 m depth) and are distinguished from shelf valleys by an over-deepened longitudinal profile that reaches a maximum depth inboard of the shelf break, thus creating a perched basin on the shelf with an associated sill.Trenches - Trenches are long narrow very deep asymmetrical depressions of the sea floor, with relatively steep sides. Trenches are generally distinguished from troughs by their “V” shape in cross section (in contrast with flat-bottomed troughs). Bridges - Bridge features are blocks of material that partially infill Trenches forming a “bridge”across the trench.Sills - Sills are a sea floor barrier of relatively shallow depth restricting water movement between basins. Thus every basin has a sill, over which fluid would escape if the basin were filled to overflowing. Shelf Valleys - Shelf valleys are greater than 10 km in length and greater than 10 m in depth overall with an elongate shape more than 4 times greater in length than width.Rift Valleys - Rift valleys are confined to the central axis of mid-ocean spreading ridges; they are elongate, local depressions flanked generally on both sides by ridges.Ridges - Ridges are isolated or a group of elongated narrow elevations of varying complexity with steep sides, often separating basin features. Ridges have greater than 1,000 meters of relief.Spreading Ridges - Spreading ridges are mid-oceanic mountain systems of global extent.Terraces - Terraces an isolated or a group of relatively flat horizontal or gently inclined surface(s), sometimes long and narrow, which is (are) bounded by a steeper ascending slope on one side and by a steeper descending slope on the opposite side. Fans - Fans are relatively smooth, fan-like, depositional featured normally sloping away from the outer termination of a canyon or canyon system. Fans overlay and comprise part of the continental rise and are located offshore from the base of the continental slope. Fans are inter-related with submarine canyons and sediment drift deposits; in cases where canyon axes extend across the rise, the canyon-channels may be flanked by sediment drift deposits, which have been grouped with fans in this study. Fans are defined in the present study by 100 m isobaths that form a concentric series exhibiting an expanding spacing in a seaward direction away from the base of the slope, sometimes clearly associated with a canyon mouth, but also comprising low-relief ridges between canyon-channels on the abyssal plain.Rises - Continental rises are areas with sediment thickness greater than 300 meters and the occurrence of a smooth sloping seabed as indicated by evenly-spaced, slope-parallel contours. In this study, the term “Rise” was restricted to features that abut continental margins and does not include the mid-ocean ridge.Plateaus - Plateaus are flat or nearly flat elevations of considerable areal extent, dropping off abruptly on one or more sides. TerrainMountains - Greater than 1,000 meters of local relief within ~25 kilometers.Hills - Between 300 and 1,000 meters of local relief within ~25 kilometers.Plains - Less than 300 meters of local relief within ~25 kilometers.Basins - Basins are depressions in the sea floor that are more or less equi-dimensional in plan, of variable extent, and are restricted to seafloor depressions defined by closed bathymetric contours.Escarpments - Escarpments are “an elongated, characteristically linear, steep slope separating horizontal or gently sloping sectors of the sea floor in non-shelf areas. Also abbreviated to scarp” (IHO, 2008). Escarpments, like basins, overlay other features (i.e. other individual features may be partly or wholly covered by escarpments). Thus features like the continental slope, seamounts, guyots, ridges and submarine canyons (for example) may be sub-classified in terms of their area of overlain escarpment.ZonesShelf - The zone adjacent to the continents or islands. Slope - The deepening seafloor from the edge of the shelf to the top of the continental rise.Abyss - Areas below the foot of the continental rise and includes all depths up to 6,000 meters.Hadal - Depths greater than 6,000 metersNote that the above definitions are brief summarizations of the definitions contained in Geomorphology of the Oceans.Esri staff edited several of the layers: Zones, Terrain, Basins, and Glacial Troughs to improve drawing performance. All of these edits were split polygon operations; no vertexes were moved, only at cut points were vertexes introduced. If these layers are downloaded, these edits can be removed by using the Dissolve tool, with all fields, including shape, and producing no multi-part polygons in the output.For metadata info, please see Bluehabitats.org.What can you do with this layer?This layer is based on a dynamic map service, which means there are several sub-layers of vector features that can be used for visualization and analysis throughout the ArcGIS Platform. This layer is not editable.This layer is part of a larger collection of Oceans layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about oceans layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.

The region is the top tier of local government in New Zealand. There are 16 regions of New Zealand (Part 1 of Schedule 2 of the Local Government Act 2002). Eleven are governed by an elected regional council, while five are governed by territorial authorities (the second tier of local government) who also perform the functions of a regional council and thus are known as unitary authorities. These unitary authorities are Auckland Council, Nelson City Council, Gisborne, Tasman, and Marlborough District Councils. The Chatham Islands Council also perform some of the functions of a regional council, but is not strictly a unitary authority. Unitary authorities act as regional councils for the purposes of a wide range of Acts and regulations. Regional council areas are based on water catchment areas. Regional councils are responsible for the administration of many environmental and public transport matters.Regional Councils were established in 1989 after the abolition of the 22 local government regions. The local government act 2002, requires the boundaries of regions to confirm as far as possible to one or more water catchments. When determining regional boundaries, the local Government commission gave consideration to regional communities of interest when selecting water catchments to included in a region. It also considered factors such as natural resource management, land use planning and environmental matters. Some regional boundaries are conterminous with territorial authority boundaries but there are many exceptions. An example is Taupo District, which is split between four regions, although most of its area falls within the Waikato Region. Where territorial local authorities straddle regional council boundaries, the affected area have been statistically defined in complete area units. Generally regional councils contain complete territorial authorities. The unitary authority of the Auckland Council was formed in 2010, under the Local Government (Tamaki Makarau Reorganisation) Act 2009, replacing the Auckland Regional Council and seven territorial authorities.The seaward boundary of any costal regional council is the twelve mile New Zealand territorial limit. Regional councils are defined at meshblock and area unit level.Regional Councils included in the 2013 digital pattern are:Regional Council CodeRegional Council Name01Northland Region02Auckland Region03Waikato Region04Bay of Plenty Region05Gisborne Region06Hawke's Bay Region07Taranaki Region08Manawatu-Wanganui Region09Wellington Region12West Coast Region13Canterbury Region14Otago Region15Southland Region16Tasman Region17Nelson Region18Marlborough Region99Area Outside RegionAs at 1stJuly 2007, Digital Boundary data became freely available.Deriving of Output FilesThe original vertices delineating the meshblock boundary pattern were digitised in 1991 from 1:5,000 scale urban maps and 1:50,000 scale rural maps. The magnitude of error of the original digital points would have been in the range of +/- 10 metres in urban areas and +/- 25 metres in rural areas. Where meshblock boundaries coincide with cadastral boundaries the magnitude of error will be within the range of 1–5 metres in urban areas and 5 - 20 metres in rural areas. This being the estimated magnitude of error of Landonline.The creation of high definition and generalised meshblock boundaries for the 2013 digital pattern and the dissolving of these meshblocks into other geographies/boundaries were completed within Statistics New Zealand using ESRI's ArcGIS desktop suite and the Data Interoperability extension with the following process: 1. Import data and all attribute fields into an ESRI File Geodatabase from LINZ as a shapefile2. Run geometry checks and repairs.3. Run Topology Checks on all data (Must Not Have Gaps, Must Not Overlap), detailed below.4. Generalise the meshblock layers to a 1m tolerance to create generalised dataset. 5. Clip the high definition and generalised meshblock layers to the coastline using land water codes.6. Dissolve all four meshblock datasets (clipped and unclipped, for both generalised and high definition versions) to higher geographies to create the following output data layers: Area Unit, Territorial Authorities, Regional Council, Urban Areas, Community Boards, Territorial Authority Subdivisions, Wards Constituencies and Maori Constituencies for the four datasets. 7. Complete a frequency analysis to determine that each code only has a single record.8. Re-run topology checks for overlaps and gaps.9. Export all created datasets into MapInfo and Shapefile format using the Data Interoperability extension to create 3 output formats for each file. 10. Quality Assurance and rechecking of delivery files.The High Definition version is similar to how the layer exists in Landonline with a couple of changes to fix topology errors identified in topology checking. The following quality checks and steps were applied to the meshblock pattern:Translation of ESRI Shapefiles to ESRI geodatabase datasetThe meshblock dataset was imported into the ESRI File Geodatabase format, required to run the ESRI topology checks. Topology rules were set for each of the layers. Topology ChecksA tolerance of 0.1 cm was applied to the data, which meant that the topology engine validating the data saw any vertex closer than this distance as the same location. A default topology rule of “Must Be Larger than Cluster Tolerance” is applied to all data – this would highlight where any features with a width less than 0.1cm exist. No errors were found for this rule.Three additional topology rules were applied specifically within each of the layers in the ESRI geodatabase – namely “Must Not Overlap”, “Must Not Have Gaps” and “"Area Boundary Must Be Covered By Boundary Of (Meshblock)”. These check that a layer forms a continuous coverage over a surface, that any given point on that surface is only assigned to a single category, and that the dissolved boundaries are identical to the parent meshblock boundaries.Topology Checks Results: There were no errors in either the gap or overlap checks.GeneralisingTo create the generalised Meshblock layer the “Simplify Polygon” geoprocessing tool was used in ArcGIS, with the following parameters:Simplification Algorithm: POINT_REMOVEMaximum Allowable Offset: 1 metreMinimum Area: 1 square metreHandling Topological Errors: RESOLVE_ERRORSClipping of Layers to CoastlineThe processed feature class was then clipped to the coastline. The coastline was defined as features within the supplied Land2013 with codes and descriptions as follows:11- Island – Included12- Mainland – Included21- Inland Water – Included22- Inlet – Excluded23- Oceanic –Excluded33- Other – Included.Features were clipped using the Data Interoperability extension, attribute filter tool. The attribute filter was used on both the generalised and high definition meshblock datasets creating four meshblock layers. Each meshblock dataset also contained all higher geographies and land-water data as attributes. Note: Meshblock 0017001 which is classified as island, was excluded from the clipped meshblock layers, as most of this meshblock is oceanic. Dissolve meshblocks to higher geographiesStatistics New Zealand then dissolved the ESRI meshblock feature classes to the higher geographies, for both the full and clipped dataset, generalised and high definition datasets. To dissolve the higher geographies, a model was built using the dissolver, aggregator and sorter tools, with each output set to include geography code and names within the Data Interoperability extension. Export to MapInfo Format and ShapfilesThe data was exported to MapInfo and Shapefile format using ESRI's Data Interoperability extension Translation tool. Quality Assurance and rechecking of delivery filesThe feature counts of all files were checked to ensure all layers had the correct number of features. This included checking that all multipart features had translated correctly in the new file.

This data set represents the 2025 game warden district boundaries for Wyoming. For updates, the Wyoming Atlas and Gazetteer (DeLorme, Inc.) is consulted for road and other information. In addition, a streams layer, a roads layer, and a HUC12 layer (converted from polygons to lines) are used to capture new linework. Other layers may be used as needed.District boundaries follow a variety of features including watershed boundaries (4th - 6th-level HUCs), streams, rivers, roads, topographic features, property ownership boundaries, and county boundaries. Delineated districts were digitized based on written descriptions provided by wardens and regional coordinators at scales no smaller than 1:100,000. Boundaries were digitized using 1:100,000 and 1:24,000 scale USGS topographic maps, 4th-6th level HUC layers, TIGER road and stream data, landownership, and county boundary data. Where written descriptions were in conflict with regional boundaries or adjacent warden districts, resolution was attempted through contact with supervisors/coordinators and the Biological Services section of the Wildlife Division. In some cases this meant that digitized boundaries were adjusted to coincide with existing regional boundaries even if written boundary descriptions were not exactly identical. Personnel changes occur frequently; instances of making such updates are not recorded in the metadata.NOTE: A layer of regional boundaries is derived from this warden district layer by dissolving on the "REGION" attribute (Dissolve_Field), and unchecking the box "Create multipart features (optional)". The "Dissolve" tool: ArcToolbox > Data Management Tools > Generalization.

This data set represents the 2025 antelope hunt area and herd unit boundaries for Wyoming. It was digitized at a scale of 1:100,000, using USGS 1:100,000 DRGs as a backdrop for heads up digitizing. Updates requested by Wyoming Game and Fish Biological Services Department, were completed by selecting needed features from other layers, including roads, streams, HUCs, NAIP 2009 rasters. Hunt area boundary descriptions are part of hunting regulations, which are approved and published annually by the Wyoming Game and Fish Commission. When needed, the 2008 edition (1st Edition) of the Wyoming Road and Recreation Atlas (Benchmark Maps) was consulted for road and other information.NOTE: This layer of herd units is derived from the hunt area layer by dissolving on the "HERDUNIT" and "HERDNAME" attributes (Dissolve_Fields), and unchecking the box "Create multipart features (optional)". All of the same metadata is used from the hunt area layer except that the citation title is modified so that "Herd Unit" replaces "Hunt Area". The "Dissolve" tool: ArcToolbox > Data Management Tools > Generalization.

This layer shows Household Pulse Survey data on gender identity and sexual orientation. Gender identity is the internal perception of gender, and how one identifies based on how one aligns or doesn’t align with cultural options for gender. This is a different concept than sex assigned at birth. Sexual orientation is the type of sexual attraction one has the capacity to feel for others, generally labeled based on the gender relationship between the person and the people they are attracted to. This is not the same as sexual behavior or preference.Learn more about how the Census Bureau survey measures sexual orientation and gender identity. This page includes nation-wide characteristics such as age, Hispanic origin and race, and educational attainment. Also read some of their findings about experiences during the COVID-19 pandemic, such as lesbian, gay, bisexual, or transgender (LGBT) adults experiencing higher rates of both economic hardship and mental health hardship. See the questionnaire used in phase 3.2 of the Household Pulse Survey.Source: Household Pulse Survey Data Tables. Data values in this layer are from Week 34 (July 21 - August 2, 2021), the first week that gender identity and sexual orientation questions were part of this survey. Top 15 metros are based on total population and are the same 15 metros available for all Household Pulse Data Tables.This layer is symbolized to show the percent of adults who are lesbian, gay, bisexual, or transgender (LGBT) as well as adults whose gender or sexual orientation was not listed on the survey (LGBTQIA+). The color of the symbol depicts the percentage and the size of the symbol depicts the count. *Percent calculations do not use those who did not report either their gender or sexual orientation in either the numerator or denominator, consistent with methodology used by the source.*Data Prep Steps:Data prep used Table 1 (Child Tax Credit Payment Status and Use, by Select Characteristics) to perform tabular data transformation. SAS to Table conversion tool was used to bring the tables into ArcGIS Pro.The data is joined to 2019 TIGER boundaries from the U.S. Census Bureau.Using the counties in each metro according to the Metropolitan and Micropolitan Statistical Area Reference Files, metro boundaries created via Merge and Dissolve tools in ArcGIS Pro.In preparing the field aliases and long descriptions, "none of these" and "something else" were generally modified to "not listed."

This data set represents the 2022 mountain lion hunt area boundaries for Wyoming. It was digitized at a scale of 1:100,000, using USGS 1:100,000 DRGs as a backdrop for heads up digitizing. Hunt area boundary descriptions are part of hunting regulations, which are approved and published by the Wyoming Game and Fish Commission. When needed, the 1998 edition of the Wyoming Atlas and Gazetteer (DeLorme, Inc.) was consulted for road information.NOTE: A layer of management units is derived from this hunt area layer by dissolving on the "MGMTUNIT" attribute (Dissolve_Field), and unchecking the box "Create multipart features (optional)". All of the same metadata is used from the hunt area layer except that the citation title is modified so that "Management Units" replaces "Hunt Areas". The "Dissolve" tool: ArcToolbox > Data Management Tools > Generalization.

This data set represents the 2020 black bear hunt area and management unit boundaries for Wyoming. It was originally digitized at a scale of 1:100,000, using USGS 1:100,000 DRGs as a backdrop for heads up digitizing. Hunt area boundary descriptions are part of hunting regulations, which are approved and published by the Wyoming Game and Fish Commission. For updates, the Wyoming Atlas and Gazetteer (DeLorme, Inc.) is consulted for road and other information. In addition, a streams layer, a roads layer, and a HUC12 layer (converted from polygons to lines) are used to capture new linework. Other layers may be used as needed.NOTE: A layer of management units is derived from this hunt area layer by dissolving on the "MGMTUNIT" attribute (Dissolve_Fields), and unchecking the box "Create multipart features (optional)". All of the same metadata is used from the hunt area layer except that the citation title is modified so that "Management Units" replaces "Hunt Areas". The "Dissolve" tool: ArcToolbox > Data Management Tools > Generalization.

This data set represents the 2021 Rocky Mountain goat hunt area and herd unit boundaries for Wyoming. The layer was originally digitized at a scale of 1:100,000, using USGS 1:100,000 DRGs as a backdrop for heads up digitizing. Updates are currently done by selecting needed features from other layers, including roads, streams, HUCs, etc. Huntarea boundary descriptions are part of hunting regulations, which are approved and published annually by the Wyoming Game and Fish Commission. When needed, the 2008 edition (First Edition) of the Wyoming Road and Recreation Atlas (Benchmark Maps) was consulted for road information.NOTE: A layer of herd units is derived from this hunt area layer by dissolving on the "HERDUNIT" and "HERDNAME" attributes (Dissolve_Fields), and unchecking the box "Create multipart features (optional)". All of the same metadata is used from the hunt area layer except that the citation title is modified so that "Herd Unit" replaces "Hunt Area". The "Dissolve" tool: ArcToolbox > Data Management Tools > Generalization.

Feature layer generated from running the Dissolve Boundaries analysis tool.

Input Data

NOAA Continuously Updated Shoreline Product (CUSP), accessed 1-11-2023; read a 1-page factsheet about CUSP; view and download CUSP data in the NOAA Shoreline Data Explorer (to download, select “Download CUSP by Region” and select Southeast Caribbean)
Southeast Blueprint 2023 subregions: Caribbean

Mapping Steps

Make a copy of the Southeast Caribbean CUSP feature line dataset and reproject it to ESPG 5070.
For the big island of Puerto Rico, special steps were required to deal with CUSP shorelines that did not connect across large rivers.
  Add and calculate a field to use to dissolve the lines.
    Dissolve the lines using the dissolve function, which reveals where there are gaps in the shoreline.
    Use the integrate tool to snap together nearby nodes, using a tolerance of 8 m. This connects the disconnected lines on the big island of Puerto Rico.
    Convert these modified shorelines to a polygon.
    Add and calculate a dissolve field, then dissolve using the dissolve tool. This is necessary because interior waterbodies on the big island of Puerto Rico also have shorelines in the CUSP data. This step produces a layer where inland waterbodies are included as a part of the island where they occur.
    From the resulting layer, select the big island of Puerto Rico and create a separate polygon feature layer from it. This extracts a modified shoreline boundary for the big island of Puerto Rico only. We don’t want to use the modified shorelines created above for other islands that didn’t have an issue of disconnected shoreline segments near large rivers.

Go back to the original Caribbean CUSP lines and convert them to polygons.
Add a dissolve field and dissolve using the dissolve tool. This produces a layer where all inland waterbodies are included as a part of the island where they occur.
From the island boundaries derived from the original CUSP data, remove the polygons that overlap with the big island of Puerto Rico derived from the modified CUSP data. This produces a layer representing all U.S. Caribbean islands except the big island of Puerto Rico.
Merge the modified big island of Puerto Rico layer with the layer for all other islands.
Create and populate a field that has unique IDs for all islands.
Convert the island polygon to a raster using the ArcPy Feature to Raster function. This makes a raster that correctly represents the interior of the islands. However, because the Feature to Raster function for polygons works differently than the Line to Raster function, the shoreline doesn’t perfectly match the result we get when we convert the CUSP lines to a raster. 
Because the Caribbean coastal shoreline condition indicator is created from the CUSP lines, we need the shorelines to match exactly. To reconcile this, go back to the original Caribbean CUSP line data and use the Feature to Raster function again, this time converting the lines to a raster. 
Use the ArcPy Cell Statistics “MAXIMUM” function to combine the two rasters above (one created from the CUSP lines and one created from the CUSP-derived polygons).
Export the raster that represents the extent of Caribbean islands.
Use the Region Group function to give unique values to each island.
Reclassify to make 3 island size classes. The big island of Puerto Rico is the only island in the highest class. The medium island class contains the following islands: Isla Mona, Isla de Vieques, Isla de Culebra, St. Thomas, St. John, and St. Croix. All other islands were put in the smaller class. All other non-island pixels in the Caribbean were given a value of marine.

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint 2023 Data Download or Caribbean-only Southeast Blueprint 2023 Data Download under > 6_Code. Literature Cited National Oceanic and Atmospheric Administration (NOAA), National Ocean Service, National Geodetic Survey. NOAA Continually Updated Shoreline Product (CUSP): Southeast Caribbean. [https://coast.noaa.gov/digitalcoast/data/cusp.html].

The boundary between different legal land use zone areas (zoning), as defined in Chapter 24 of the City of Gaithersburg Code. Zoning boundaries generally follow property boundaries but do occasionally split properties. Zoning areas do not include public right-of-ways, which are considered to only allow transportation and similar uses for the public benefit. The zoning feature class is created by using the DISSOLVE tool to merge parcel fabric parcels by similar ZONING field types. Effective as of April 24, 2022.

This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into country boundaries, administrative 1 boundaries, and 50 km hex bins. The unit of measurement is micrograms per cubic meter.The layer shows the annual average PM 2.5 from 1998 to 2016, highlighting if the overall mean for an area meets the World Health Organization guideline of 10 micrograms per cubic meter annually. Areas that don't meet the guideline and are above the threshold are shown in red, and areas that are lower than the guideline are in grey.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality globally. Some of the things we can learn from this layer:What is the average annual PM 2.5 value over 19 years? (1998-2016)What is the annual average PM 2.5 value for each year from 1998 to 2016?What is the statistical trend for PM 2.5 over the 19 years? (downward or upward)Are there hot spots (or cold spots) of PM 2.5 over the 19 years?How many people are impacted by the air quality in an area?What is the death rate caused by the joint effects of air pollution?Choose a different attribute to symbolize in order to reveal any of the patterns above.A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis, trends, and a 19-year average. The country and administrative 1 layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.Boundaries and population figures:Antarctica is excluded from all maps because it was not included in the original NASA grids.50km hex bins generated using the Generate Tessellation tool - projected to Behrmann Equal Area projection for analysesPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Administrative boundaries from World Administrative Divisions layer from ArcGIS Living Atlas - projected to Behrmann Equal Area projection for analyses and hosted in Web MercatorSources: Garmin, CIA World FactbookPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Country boundaries from Esri 2019 10.8 Data and Maps - projected to Behrmann Equal Area projection for analyses and hosted in Web Mercator. Sources: Garmin, Factbook, CIAPopulation figures attached to the country boundaries come from the World Population Estimate 2016 Sources Living Atlas layer Data processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The layers are hosted in Web Mercator Auxillary Sphere projection, but were processed using an equal area projection: Behrmann. If using this layer for analysis, it is recommended to start by projecting the data back to Behrmann.The country and administrative layer were dissolved and joined with population figures in order to visualize human impact.The dissolve tool ensures that each geographic area is only symbolized once within the map.Country boundaries were generalized post-analysis for visualization purposes. The tolerance used was 700m. If performing analysis with this layer, find detailed country boundaries in ArcGIS Living Atlas. To create the population-weighted attributes on the country and Admin 1 layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and the PM2.5 and population figures were summarized within the country and Admin 1 boundaries.The summation of the PM 2.5 values were then divided by the total population of each geography. This population value was determined by summarizing the population values from the hex bins within each geography.Some artifacts in the hex bin layer as a result of the input NASA rasters. Because the gridded surface is created from multiple satellites, there are strips within some areas that are a result of satellite paths. Some areas also have more of a continuous pattern between hex bins as a result of the input rasters.Within the country layer, an air pollution attributable death rate is included. 2016 figures are offered by the World Health Organization (WHO). Values are offered as a mean, upper value, lower value, and also offered as age standardized. Values are for deaths caused by all possible air pollution related diseases, for both sexes, and all age groups. For more information visit this page, and here for methodology. According to WHO, the world average was 95 deaths per 100,000 people.To learn the techniques used in this analysis, visit the Learn ArcGIS lesson Investigate Pollution Patterns with Space-Time Analysis by Esri's Kevin Bulter and Lynne Buie.