Northeastern United States County Boundary data are intended for geographic display of state and county boundaries at statewide and regional levels. Use it to map and label counties on a map. These data are derived from Northeastern United States Political Boundary Master layer. This information should be displayed and analyzed at scales appropriate for 1:24,000-scale data. The State of Connecticut, Department of Environmental Protection (CTDEP) assembled this regional data layer using data from other states in order to create a single, seamless representation of political boundaries within the vicinity of Connecticut that could be easily incorporated into mapping applications as background information. More accurate and up-to-date information may be available from individual State government Geographic Information System (GIS) offices. Not intended for maps printed at map scales greater or more detailed than 1:24,000 scale (1 inch = 2,000 feet.)

The New England Fishery Management Council protected a set of seven habitat closure areas in 2004, and Lydonia and Oceanographer Canyons in 2005. The Northeast Canyons and Seamounts Marine National Monument was established in 2016.Note that this map is not an authoritative source for protected area boundaries, and that web maps within this story map are not intended to be used in isolation. The authoritative source is the NOAA Greater Atlantic Regional Fisheries Office GIS data page.

Light detection and ranging (LiDAR) has become a common tool for generating remotely sensed forest inventories. However, regional modeling of forest attributes using LiDAR has remained challenging due to varying parameters between LiDAR datasets, such as pulse density. Here we develop a regional model using a three dimensional convolutional neural network (CNN). We then apply our model to publicly available data over New England, generating maps of fourteen forest attributes at a 10 m resolution over 85 % of the region. Attributes include aboveground biomass (kg), total biomass (kg), tree count (#), percent conifer (%), basal area (m^2), mean height (m), quadratic mean diameter (cm), percent spruce/fir (%), percent white pine (%), inner bark volume (m^3), merchantable volume (m^3), and spruce/fir volume (m^3. All values correspond to the amount per pixel cell (I.E. kg of biomass found within that pixel). Map/model performance was assessed using the USFS’s FIA inventory, which constituted an independent dataset free from spatial autocorrelation. More data can be found in the following pre-print: Ayrey, E., Hayes, D. J., Kilbride, J. B., Fraver, S., Kershaw, J. A., Cook, B. D., & Weiskittel, A. R. (2019). Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Regional Forest Inventories. bioRxiv , 580514.

Understanding the impacts of landscape change on species distributions can help inform decision-making and conservation planning. Unfortunately, empirical data that span large spatial extents across multiple taxa are limited. In this study, we used expert elicitation techniques to develop species distribution models (SDMs) for harvested wildlife species (n = 10) in the New England region of the northeastern United States. We administered an online survey that elicited opinions from wildlife experts on the probability of species occurrence throughout the study region. We collected 3396 probability of occurrence estimates from 46 experts, and used linear mixed-effects methods and landcover variables at multiple spatial extents to develop SDMs. We applied models to rasters (30 × 30 m pixles) of the New England region to map each species’ distribution. Details of the project can be found in the following publication: Pearman-Gillman SB, Katz JE, Mickey R, Murdoch JD, and Donovan TM. 2020. Predicting wildlife distribution patterns in New England USA with expert elicitation techniques. Global Ecology and Conservation 21:e00853. doi:10.1016/j.gecco.2019.e00853

Northeastern United States Town Boundary data are intended for geographic display of state, county and town (municipal) boundaries at statewide and regional levels. Use it to map and label towns on a map. These data are derived from Northeastern United States Political Boundary Master layer. This information should be displayed and analyzed at scales appropriate for 1:24,000-scale data. The State of Connecticut, Department of Environmental Protection (CTDEP) assembled this regional data layer using data from other states in order to create a single, seamless representation of political boundaries within the vicinity of Connecticut that could be easily incorporated into mapping applications as background information. More accurate and up-to-date information may be available from individual State government Geographic Information System (GIS) offices. Not intended for maps printed at map scales greater or more detailed than 1:24,000 scale (1 inch = 2,000 feet.)

This geographic information system (GIS) data layer shows the dominant lithology and geochemical, termed lithogeochemical, character of near-surface bedrock in the New England region covering the states of Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont. The bedrock units in the map are generalized into groups based on their lithological composition and, for granites, geochemistry. Geologic provinces are defined as time-stratigraphic groups that share common features of age of formation, geologic setting, tectonic history, and lithology. This data set incorporates data from digital maps of two NAWQA study areas, the New England Coastal Basin (NECB) and the Connecticut, Housatonic, and Thames River Basins (CONN) areas and extends data to cover the states of Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont. The result is a regional dataset for the lithogeochemical characterization of New England (the layer named NE_LITH). Polygons in the final coverage are attributed according to state, drainage area, geologic province, general rock type, lithogeochemical characteristics, and specific bedrock map unit.

A regression model that estimates monthly temperature and precipitation as a function of latitude, longitude, and elevation for the New England area was used to estimate annual growing degree days and precipitation for the state of Massachusetts. For details of the regression model please see the published paper (Ollinger, S.V., Aber, J.D., Federer, C.A., Lovett, G.M., Ellis, J.M., 1995. Modeling Physical and Chemical Climate of the Northeastern United States for a Geographic Information System. US Dept of Agriculture, Forest Service, Radnor, PA, USA).

Wildlands in New England is the first U.S. study to map and characterize within one region all conserved lands that, by design, allow natural processes to unfold with no active management or intervention. These “forever wild lands” include federal Wilderness areas along with diverse public and private natural areas and reserves. Knowing the precise locations of Wildlands, their characteristics, and their protection status is important as both a baseline for advancing conservation initiatives and an urgent call to action for supporting nature and society. Wildlands play a unique role in the integrated approach to conservation and land planning advanced by the Wildlands, Woodlands, Farmlands & Communities (WWF&C) initiative, which calls for: at least 70 percent of the region to be protected forest; Wildlands to occupy at least 10 percent of the land; and all existing farmland to be permanently conserved. This research was conducted by WWF&C partners Harvard Forest (Harvard University), Highstead Foundation, and Northeast Wilderness Trust, in collaboration with over one hundred conservation organizations and municipal, state, and federal agencies. This dataset contains the Geographical Information System (GIS) polygon layer of Wildlands created by this project and used in all analyses for the 2023 report. Another GIS layer will be updated as new Wildlands are brought to our attention or created and will be available at https://wildlandsandwoodlands.org/ for researchers.

This data package contains 3 GIS layers showing generalized forest types across New England as delineated in older forestry publications. These were digitized so that they can be used to illustrate broad vegetation patterns across the region in modern publications. These GIS layers include maps drawn by Hawley and Hawes (1912), RT Fisher (1933), and Westveld and the Committee on Silviculture, New England Section, Society of American Foresters (1956).

These data were collected for use in of the RI Coastal Resource Management Council's Ocean Special Area Management Plan planning process and were also intended as an update and refinement to a similar set of maps created in 2004 by New England regional Sea Grant. This data layer was developed from September 2008 - January 2009, published in February 2009 and reviewed by fishermen and updated a final time in September - October 2009. Data were collected through interviews and mapping exercises conducted in person, both one-on-one and in small groups, with representatives of the RI Fishermen's Alliance, independent fishermen, and unaffiliated fishermen. Other RI fishermen's associations participated in the Ocean SAMP stakeholder process but, to date, have not yet participated in the data collection effort.

In each interview, fishermen were first given a brief introduction to the RI Ocean SAMP planning process and shown NOAA nautical charts of the SAMP area. Researchers then asked the fishermen to describe where they fish, and to draw polygons encompassing these areas on the nautical charts. Fishermen were then asked follow-up questions about these areas, including (1) During which seasons do you fish in each area?; (2) With what gear?; and in some cases (3) What are your target species in each area? Following these meetings, data were aggregated onto one set of charts, which were then compared with the 2004 maps to corroborate the current information. Charts were then scanned and georeferenced and polygons were digitized in order to create Geographic Information Systems (GIS) shapefiles. Attribute fields were created for the data layers to record available information about seasonality and gear type. It should be noted that this dataset has some limitations and data may be incomplete. In addition, these data do not include out-of-state fisheries which may be conducted within the SAMP area, such as the herring mid-water trawling fishery based out of other New England ports.

I. SNEP HRU Project Background The Southeast New England Program (SNEP) region consists of watersheds in Massachusetts and Rhode Island that primarily drain into Narragansett Bay, Buzzards Bay, or Nantucket Sound. It encompasses all or portions of 134 municipalities many of which are highly developed. The region faces multiple water quality issues with stormwater being previously identified a major contributor. These maps have been generated for all 134 Municipalities including 81 subwatersheds in the SNEP region to provide organizations and municipalities a way to understand where significant stormwater pollution may be originating. For organizations or municipalities with GIS capabilities the data that created these maps is available as well. II. What are HRUs? Hydrologic Response Units (HRUs) describe a landscape through unique combinations of land use and land cover (residential, commercial, forest, etc.), soil types (A, B, C, D), and additional characteristics such as slope, and impervious cover. These landscape characteristics, or HRUs, provide the building block to quantify stormwater pollutant loads (nitrogen, phosphorus, and total suspended solids (TSS)) originating from a given land area. The HRUs and nutrient pollutant loads in stormwater provides a baseline from which reduction targets can be created. III. How can HRUs be used? These maps and their underlying data can provide critical information to municipalities, watershed organizations, EPA, and others to assess stormwater pollutant loads in SNEP watersheds. EPA expects that this information will facilitate further understanding of the distribution of stormwater pollutant load source areas throughout the watersheds. This information serves to advance a broader understanding of stormwater impacts and potential management options by the public and direct stakeholders. Consistent HRUs may help municipalities implement MS4 permitting requirements and facilitate stormwater management strategies, such as land use conversion, stormwater Control Measure (SCM) siting, and targeting areas for conservation. HRU mapping can identify best locations for SCMs and can be utilized with additional stormwater planning tools (such as EPA’s Opti-Tool) to develop a cost-effective stormwater management plan. By providing a consistent HRU map for the SNEP region, practitioners can focus their efforts on implementation of SCM strategies rather than mapping their landscape. Hotspot mapping is a tool that integrates the HRU analysis and stormwater runoff pollutant load outputs to indicate areas where pollutant loads are highest and areas that stormwater controls may be best implemented. The HRUs and pollutant loads can be overlayed with parcel analysis to determine which parcels have high loads/areas of large impervious cover. The parcel data can help towns prioritize their efforts by determining the properties with highest potential to reduce pollutant loads through stormwater controls. Similarly, it can help determine which properties have large stormwater pollutant loads. IV. Other Resources HRUs That have been completed by EPA - Taunton River Watershed FDC Project and Tisbury, MA IC Disconnection Project The Cape Cod Commission developed HRUs for Barnstable County (CCC: Barnstable County HRUs). The UNH Stormwater Center developed parcel level hotspot mapping in New Hampshire for municipalities to prioritize where new BMPs should be placed (UNHSC: NH Hotspot Mapping).

The 2022 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Divisions are groupings of states within a census geographic region, established by the Census Bureau for the presentation of census data. The current nine divisions (East North Central, East South Central, Middle Atlantic, Mountain, New England, Pacific, South Atlantic, West North Central, and West South Central) are intended to represent relatively homogeneous areas that are subdivisions of the four census geographic regions.

This data release provides a generalized lithology look-up table for the lithogeochemical classification of Vermont's bedrock geologic map units. The table is defined from the mapped bedrock geologic units published by Ratcliffe and others (2011) and the generalized lithology of rock group A and rock group B for lithogeochemical classification as defined by Robinson and Kapo (2003). The 2003 classification was created fro all six New England states and Vermont's geologic units were based on an older, less detailed, bedrock map of Vermont by Doll and others (1961). The new data table in this data release is designed to be joined with the published attribute table from the 2011 map database, as part of the bedrock geologic map unit polygons. The join attribute is the item called "Lith" in the 2011 map database. The data table is non-interpretive and the 2011 map data were not modified. The data release contains two files, including one metadata file and one comma-delimited (CSV) file: VTcontax_attrib_lithology.csv. References: Doll, C.G., Cady, W.M., Thompson, J.B., and Billings, M.P., 1961, Centennial geologic map of Vermont: Vermont Geological Survey, Miscellaneous Map MISCMAP-01, scale 1:250,000. Ratcliffe, N.M., Stanley, R.S., Gale, M.H., Thompson, P.J., and Walsh, G.J., 2011, Bedrock geologic map of Vermont: U.S. Geological Survey Scientific Investigations Map 3184, 3 sheets, scale 1:100,000, https://pubs.usgs.gov/sim/3184/ Robinson, G.R., Jr., and Kapo, K.E., 2003, Generalized lithology and lithogeochemical character of near-surface bedrock in the New England region: U.S. Geological Survey Open-File Report 03-225, https://pubs.usgs.gov/of/2003/of03-225/

These data represent a digital form of the geologic map of Cape Cod and the islands.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Mapping vegetation of the Appalachian National Scenic Trail (APPA, also referred to as the “AT corridor” for the Appalachian Trail corridor) involved the following six primary steps: (1) preliminary map classification with a vegetation primer for each APPA project area, (2) field reconnaissance for each APPA project area, (3) map classification by each APPA project area, (4) aerial image interpretation and mapping by each APPA project area, (5) compilation of a final classification and map layer covering the entire AT corridor following accuracy assessment (AA), and (6) database development of the map layer. Although these steps proceeded sequentially, they overlap to some degree. Steps 1–4 proceeded sequentially by APPA project area starting in the Southern Blue Ridge (SBR) project area in 2010, moving north to the Central Appalachian (CAP) project area in 2011, then to the Lower New England (LNE) project area in 2012, and ending in the Northern Appalachian (NAP) project area in 2013. (See Figures 6–9 in the “Introduction and Project Overview” section of this report for detailed locations of the four APPA project areas.) Steps 5 and 6 compiled all APPA project areas into a contiguous map classification and map layer. Summary reports generated from the vegetation map layer of the map classes representing USNVC natural (including ruderal) vegetation types apply to 28,242 polygons (92.9% of polygons) and cover 106,413.0 ha (95.9%) of the map extent for APPA. The map layer indicates APPA to be 92.4% forest and woodland (102,480.8 ha), 1.7% shrubland (1866.3 ha), and 1.8% herbaceous cover (2,065.9 ha). Map classes representing park-special vegetation (undefined in the USNVC) apply to 58 polygons (0.2% of polygons) and cover 404.3 ha (0.4%) of the map extent. Map classes representing USNVC cultural types apply to 1,777 polygons (5.8% of polygons) and cover 2,516.3 ha (2.3%) of the map extent. Map classes representing nonvegetated water (non-USNVC) apply to 332 polygons (1.1% of polygons) and cover 1,586.2 ha (1.4%) of the map extent.

Surficial geologic maps play and important role in understanding the present sea floor and the processes that shape it. Between 1984 and 1991, over 1,700 bottom sample stations were occupied in the northwestern Gulf of Maine. Although the data were originally collected for a variety of research projects, contracts, and graduate student theses, they were eventually compiled as part of a Maine Geological Survey and University of Maine program to map the inner continental shelf of this region.

This dataset contains data from a process-oriented research cruise aboard the R/V Neil Armstrong from June 18th to July 2nd. The goal of this cruise was to map the three-dimensional structure of a mid-depth salinity maximum intrusion of warm salinity slope water extending onto the continental shelf south of New England. This was done through the use of Autonomous Underwater Vehicles (two REMUS 100 vehicles and one Tethys class AUV (Long Range AUV or LRAUV)), a towed Rockland Scientific Vertical Microstructure Profiler (VMP 250), and ship-board CTD and ADCP measurements. More details about the processing, data coverage, and usage can be found in the accompanying manuscript. This cruise took place on the shelf waters south of Cape Cod, MA, extending to the shelf break, with all of the data collected between 40°N to 41°N and 71.5°W to 70°W. Attached is a data map showing the location of all data included within this dataset.

File Descriptions: Datamap.jpg A map of the locations of all data included within this dataset.

CTD_summer2021.mat
This file contains profiles from the ship-board CTD (SeaBird 911+). Raw data was processed and gridded into 1 decibar bins using standard procedures in Seasave V 7.26.7.121 (Look at cnv file header for details about processing). This file is organized as a structure, with each variable in the data being a different field called by dot notation and each row with the structure being a different CTD profile. Biooptical variables are not quality-controlled.
CTD.time: the time of each profile in the MATLAB datetime format (from the processed SeaBird header file) in GMT CTD.lon: degrees longitude of the profile (from the processed SeaBird header file) CTD.lat: degrees latitude of the profile (from the processed SeaBird header file) CTD.pres: the pressure in decibar at each location of the profile
CTD.sal: the seawater practical salinity in psu
CTD.temp: the seawater in-situ temperature in °C
CTD.flor: seawater fluorescence in mg/ m3 CTD.depth: depth at each location within the profile in meters CTD.density: sigmatheta (the potential seawater density with respect to a reference pressure of 0 db) in kg.m3 minus 1,000kg/m3

CTD_Darter_MMMdd.mat and CTD_Edgar_MMMdd.mat These files contain the data from the REMUS 100 missions, with Darter and Edgar being the two different REMUS 100 vehicles.
Conductivity: conductivity in mS/cm
Depth: depth in meters Latitude: degrees latitude
Longitude: degrees longitude Mission_number: the number of the REMUS mission Mission_time: time during the mission in seconds since midnight in GMT
Salinity: the seawater practical salinity in psu
Sound_speed: the sound speed in m/s
Temperature: the seawater temperature in °C

LRAUV_20210623T194917.mat and LRAUV_20210624T145829.mat These files contain data from the Tethys Class LRAUV (Long Range AUV) missions. Each file contains 10 structure variables.
CTD_Seabird: structure containing the bin median temperature in °C and salinity in PSU.
depth: the depth at each data point in meters.
fix_residual_percent_distance_traveled: underwater dead-reckoned navigation error (based on GPS fix when on surface) as a percentage of distance traveled latitude: Latitude at each data point (not corrected for vehicle drift in underwater current)
latitude_fix: latitude of GPS fix (vehicle surfaced) longitude: Longitude at each data point (not corrected for vehicle drift in underwater current) longitude_fix: longitude of GPS fix (vehicle surfaced) platform_battery_charge: The battery charge in ampere-hour
time_fix: time in seconds since January 1, 1970 (epoch time)

VMPtransact_YYYYMMdd.mat Vertical Microstructure Profiler (Rockland Scientific VMP 250) These files contain the processed data for each Vertical Microstructure Profiler (Rockland Scientific VMP 250) transect, consisting of multiple profiles. Data has been gridded on a 1 decibar equidistant grid using standard procedures in Rockland Scientific’s processing software. Note: Bio-optical variables and dissipation rates have not been quality-controlled.
Time: Time in MATLAB datenum format (days since 0000-00-00 00:00:00) in GMT z: Pressure in decibar T: in-situ temperature in degC cnd: conductivity in mS/cm
Chl: Chlorophyll from fluorescence in mg/ m3 turb: Turbidity in NTU eps: dissipation rate inferred from microstructure shear in m^2/s^3. (Note: Dissipation estimates come from standard fitting of microstructure data within a 1 decibar bin to a turbulence spectrum within Rockland Scientific’s standard processing. The dissipation data in the provided files has not been quality-controlled. VMP-data was georeferenced by comparing the time stamps of VMP and processed ADCP files.

ADCP_ar50_wh300.mat This file contains the data from the shipboard ADCP (Teledyne WH300 kHz). ADCP data was processed aboard using standard procedures in UHDAS/CODAS (University of Hawaii Technical Services Program, s…

This polygon shape file is a 1:25,000 – 1: 50 000 environmental dataset combining 16 input data sources. These are outlined in the lineage statement. The input data sets identify agreed HEV criteria for the purposes of regional strategic planning and other environmental assessment projects that require identification of significant biodiversity values. This project was undertaken by Planning Services Unit, OEH with input from all the Planning teams in Regional Operations, OEH. The New England North West High Environmental Values dataset covers the New England North West Regional Growth Planning area.

This map shows the boundaries of regions, shires and municipalities as from 1 January 1967. The map is annotated to show the areas transferred from the Namoi to the New England Region.

The scale is 48 miles = 7/8 inch.


(SR Map No.52734). 1 map.

Note:
This description is extracted from Concise Guide to the State Archives of New South Wales, 3rd Edition 2000.

The 2020 TIGER/Line Shapefiles contain current geographic extent and boundaries of both legal and statistical entities (which have no governmental standing) for the United States, the District of Columbia, Puerto Rico, and the Island areas. This vintage includes boundaries of governmental units that match the data from the surveys that use 2020 geography (e.g., 2020 Population Estimates and the 2020 American Community Survey). In addition to geographic boundaries, the 2020 TIGER/Line Shapefiles also include geographic feature shapefiles and relationship files. Feature shapefiles represent the point, line and polygon features in the MTDB (e.g., roads and rivers). Relationship files contain additional attribute information users can join to the shapefiles. Both the feature shapefiles and relationship files reflect updates made in the database through September 2020. To see how the geographic entities, relate to one another, please see our geographic hierarchy diagrams here.Census Urbanized Areashttps://www2.census.gov/geo/tiger/TIGER2020/UACCensus Urban/Rural Census Block Shapefileshttps://www.census.gov/cgi-bin/geo/shapefiles/index.php2020 TIGER/Line and Redistricting shapefiles:https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.2020.htmlTechnical documentation:https://www2.census.gov/geo/pdfs/maps-data/data/tiger/tgrshp2020/TGRSHP2020_TechDoc.pdfTIGERweb REST Services:https://tigerweb.geo.census.gov/tigerwebmain/TIGERweb_restmapservice.htmlTIGERweb WMS Services:https://tigerweb.geo.census.gov/tigerwebmain/TIGERweb_wms.htmlThe legal entities included in these shapefiles are:American Indian Off-Reservation Trust LandsAmerican Indian Reservations – FederalAmerican Indian Reservations – StateAmerican Indian Tribal Subdivisions (within legal American Indian areas)Alaska Native Regional CorporationsCongressional Districts – 116th CongressConsolidated CitiesCounties and Equivalent Entities (except census areas in Alaska)Estates (US Virgin Islands only)Hawaiian Home LandsIncorporated PlacesMinor Civil DivisionsSchool Districts – ElementarySchool Districts – SecondarySchool Districts – UnifiedStates and Equivalent EntitiesState Legislative Districts – UpperState Legislative Districts – LowerSubminor Civil Divisions (Subbarrios in Puerto Rico)The statistical entities included in these shapefiles are:Alaska Native Village Statistical AreasAmerican Indian/Alaska Native Statistical AreasAmerican Indian Tribal Subdivisions (within Oklahoma Tribal Statistical Areas)Block Groups3-5Census AreasCensus BlocksCensus County Divisions (Census Subareas in Alaska)Unorganized Territories (statistical county subdivisions)Census Designated Places (CDPs)Census TractsCombined New England City and Town AreasCombined Statistical AreasMetropolitan and Micropolitan Statistical Areas and related statistical areasMetropolitan DivisionsNew England City and Town AreasNew England City and Town Area DivisionsOklahoma Tribal Statistical AreasPublic Use Microdata Areas (PUMAs)State Designated Tribal Statistical AreasTribal Designated Statistical AreasUrban AreasZIP Code Tabulation Areas (ZCTAs)Shapefiles - Features:Address Range-FeatureAll Lines (called Edges)All RoadsArea HydrographyArea LandmarkCoastlineLinear HydrographyMilitary InstallationPoint LandmarkPrimary RoadsPrimary and Secondary RoadsTopological Faces (polygons with all geocodes)Relationship Files:Address Range-Feature NameAddress RangesFeature NamesTopological Faces – Area LandmarkTopological Faces – Area HydrographyTopological Faces – Military Installations