This data release documents the location of intersections between roads and streams, referred to as road crossings, and associated basin characteristics to support highway-runoff mitigation analyses using the Stochastic Empirical Loading and Dilution Model (SELDM, Granato, 2013) in Connecticut, Massachusetts, and Rhode Island. The data set of road crossings was generated from the intersections of the U.S. Geological Survey (USGS) National Transportation Dataset (roads) and the StreamStats modified National Hydrography Dataset (streams) and in addition to the three-state study area, includes areas of New York, Vermont, and New Hampshire that are within drainages that cover the three states. Pertinent basin characteristics were defined for sites within CT, MA, and RI and include the following: drainage area, 10-85 slope, longest flow path, number of road crossings by road class, impervious cover, length of roads by road class, and length of streams. Coordinates, street name, and road classification associated with the road crossing point also are included. Users can delineate basins and compute these characteristics, among others, on the USGS StreamStats web application. This data release contains one shapefile in a zipped folder and two tables: RoadCrossingsShapefile.zip, BasinCharacteristics.txt, and BasinCharacteristics_Definitions.txt. The basin characteristics are included in the metadata file and as a separate table for the user’s preference.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. State Legislative Districts (SLDs) are the areas from which members are elected to State legislatures. The SLDs embody the upper (senate) and lower (house) chambers of the state legislature. Nebraska has a unicameral legislature and the District of Columbia has a single council, both of which the Census Bureau treats as upper-chamber legislative areas for the purpose of data presentation; there are no data by SLDL for either Nebraska or the District of Columbia. A unique three-character census code, identified by state participants, is assigned to each SLD within a state. In Connecticut, Illinois, Louisiana, New Hampshire, Wisconsin, and Puerto Rico, the Redistricting Data Program (RDP) participant did not define the SLDs to cover all of the state or state equivalent area. In these areas with no SLDs defined, the code "ZZZ" has been assigned, which is treated as a single SLD for purposes of data presentation. The most recent state legislative district boundaries collected by the Census Bureau are for the 2022 election year and were provided by state-level participants through the RDP.

NOTE: This dataset pertains only to the 2020-2021 school year and is no longer being updated. For additional data on COVID-19, visit data.ct.gov/coronavirus. This dataset includes the leading and secondary metrics identified by the Connecticut Department of Health (DPH) and the Department of Education (CSDE) to support local district decision-making on the level of in-person, hybrid (blended), and remote learning model for Pre K-12 education. Data represent daily averages for two-week periods by date of specimen collection (cases and positivity), date of hospital admission, or date of ED visit. Hospitalization data come from the Connecticut Hospital Association and are based on hospital location, not county of patient residence. COVID-19-like illness includes fever and cough or shortness of breath or difficulty breathing or the presence of coronavirus diagnosis code and excludes patients with influenza-like illness. All data are preliminary. These data are updated weekly and reflect the previous two full Sunday-Saturday (MMWR) weeks (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf). These metrics were adapted from recommendations by the Harvard Global Institute and supplemented by existing DPH measures. For national data on COVID-19, see COVID View, the national weekly surveillance summary of U.S. COVID-19 activity, at https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html DPH note about change from 7-day to 14-day metrics: Prior to 10/15/2020, these metrics were calculated using a 7-day average rather than a 14-day average. The 7-day metrics are no longer being updated as of 10/15/2020 but the archived dataset can be accessed here: https://data.ct.gov/Health-and-Human-Services/CT-School-Learning-Model-Indicators-by-County/rpph-4ysy As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well. With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

This geodatabase contains data from the 2017 State of Narragansett Bay and Its Watershed Technical Report (nbep.org), Chapter 12: "Salt Marsh." The Narragansett Bay Estuary Program compiled a dataset of salt marsh and brackish marsh in Narragansett Bay, Little Narragansett Bay, the Coastal Ponds, and the Sounds using the US Fish and Wildlife Service National Wetlands Inventory (NWI) dataset for Rhode Island, Connecticut, and Massachusetts. This data source was selected because the data collection methods used by the US Fish and Wildlife Service were consistent between both states. The NWI used 2008 imagery for Massachusetts (except for the upper Taunton River imagery from 1995) and 2010 imagery for Rhode Island. Data were summarized at the study area, bay section, and bay segment scales.

Areas of groundwater discharge are hydrologically and ecologically important, and yet are difficult to predict at the river network scale. Thermal infrared imagery can be used to identify areas of groundwater discharge based on an observed temperature anomaly (colder during the late summer or warmer during the late winter). The thermal images, direct temperature measurements (11 cm depth) and discharge zone (seep) _location information in this data release were collected as part of a study to evaluate and improve predicted spatial patterns of groundwater discharge. The data were collected during the late summer / early fall of 2017 along selected river reaches in the Farmington River watershed (Connecticut and Massachusetts). This dataset contains 4 files. 1) Images.zip is a zipped directory containing thermal infrared and real color images. 2) Image_Details.csv contains attribute information for each thermal image. 3) ScannedReaches.shp is a shapefile indicating the river reaches that were surveyed. 4) Seeps.shp is a shapefile of groundwater seep locations and attributes that were identified during the fieldwork.

Project: NOAA Digital Orthophotography and Ancillary Oblique Imagery Collection for the Coasts of Main/New Hampshire, Massachusetts/Rhode Island/Connecticut, and Hudson River/Long Island /NY/NJ Contract No. EA133C11CQ0010 Reference No. NCNP0000-14-00967 Woolpert Order No. 74571 CONTRACTOR: Woolpert, Inc. The project represents the collection of digital oblique imagery for the coasts of Maine and New Hampshire; Massachusetts and Rhode Island; Connecticut, the Hudson River, Long Island and the NY/NJ metro area. The work was requested by the National Oceanic and Atmospheric Administration, Office of Response and Restoration division. The imagery is being acquired for use in the development of the ESI (Environmental Sensitivity Index) data in this same region. The entire project area includes approximately 11,669 square miles. Original contact information: Contact Org: National Oceanic and Atmospheric Administration (NOAA) Phone: 843-740-1200

Analysis of ‘CT School Learning Model Indicators by County (14-day metrics) - ARCHIVE’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/feda0dbb-905d-48c8-81ec-590689a6da8f on 26 January 2022.

--- Dataset description provided by original source is as follows ---

NOTE: This dataset pertains only to the 2020-2021 school year and is no longer being updated. For additional data on COVID-19, visit data.ct.gov/coronavirus.

This dataset includes the leading and secondary metrics identified by the Connecticut Department of Health (DPH) and the Department of Education (CSDE) to support local district decision-making on the level of in-person, hybrid (blended), and remote learning model for Pre K-12 education.

Data represent daily averages for two-week periods by date of specimen collection (cases and positivity), date of hospital admission, or date of ED visit. Hospitalization data come from the Connecticut Hospital Association and are based on hospital location, not county of patient residence. COVID-19-like illness includes fever and cough or shortness of breath or difficulty breathing or the presence of coronavirus diagnosis code and excludes patients with influenza-like illness. All data are preliminary.

These data are updated weekly and reflect the previous two full Sunday-Saturday (MMWR) weeks (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf).

These metrics were adapted from recommendations by the Harvard Global Institute and supplemented by existing DPH measures.

For national data on COVID-19, see COVID View, the national weekly surveillance summary of U.S. COVID-19 activity, at https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html

DPH note about change from 7-day to 14-day metrics: Prior to 10/15/2020, these metrics were calculated using a 7-day average rather than a 14-day average. The 7-day metrics are no longer being updated as of 10/15/2020 but the archived dataset can be accessed here: https://data.ct.gov/Health-and-Human-Services/CT-School-Learning-Model-Indicators-by-County/rpph-4ysy

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

--- Original source retains full ownership of the source dataset ---

This data release includes the results of state agency led electrofishing surveys conducted in lotic habitats in six states in the Northeastern U.S.: Maine, New Hampshire, Vermont, Massachusetts, Connecticut, and Rhode Island. The following state agencies collected the electrofishing data: Massachusetts Division of Fisheries & Wildlife, Vermont Department of Fish and Wildlife, Vermont Department of Environmental Conservation, Maine Department of Inland Fisheries & Wildlife, Rhode Island Department of Environmental Management, Connecticut Department of Energy and Environmental Protection, and New Hampshire Department of Fish and Game. The survey results are consolidated to a Hydrologic Unit Code (HUC)12 scale and includes surveys from November 1949 through December 2021 with most surveys occurring between 1985 and 2021. Fifty-three species and 24,553 surveys are represented in these data.

DPH note about change from 7-day to 14-day metrics: As of 10/15/2020, this dataset is no longer being updated. Starting on 10/15/2020, the school learning model indicator metrics will be calculated using a 14-day average rather than a 7-day average. The new school learning model indicators dataset using 14-day averages can be accessed here: https://data.ct.gov/Health-and-Human-Services/CT-School-Learning-Model-Indicators-by-County-14-d/e4bh-ax24 As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well. With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county). This dataset includes the leading and secondary metrics identified by the Connecticut Department of Health (DPH) and the Department of Education (CSDE) to support local district decision-making on the level of in-person, hybrid (blended), and remote learning model for Pre K-12 education. Data represent daily averages for each week by date of specimen collection (cases and positivity), date of hospital admission, or date of ED visit. Hospitalization data come from the Connecticut Hospital Association and are based on hospital _location, not county of patient residence. COVID-19-like illness includes fever and cough or shortness of breath or difficulty breathing or the presence of coronavirus diagnosis code and excludes patients with influenza-like illness. All data are preliminary. These data are updated weekly; the previous week period for each dataset is the previous Sunday-Saturday, known as an MMWR week (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf). The date listed is the date the dataset was last updated and corresponds to a reporting period of the previous MMWR week. For instance, the data for 8/20/2020 corresponds to a reporting period of 8/9/2020-8/15/2020. These metrics were adapted from recommendations by the Harvard Global Institute and supplemented by existing DPH measures. For national data on COVID-19, see COVID View, the national weekly surveillance summary of U.S. COVID-19 activity, at https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html Notes: 9/25/2020: Data for Mansfield and Middletown for the week of Sept 13-19 were unavailable at the time of reporting due to delays in lab reporting.

The U.S. Geological Survey in cooperation with the Federal Emergency Management Agency has conducted a study to evaluate potential changes to1-percent annual exceedance probability streamflows. The study was conducted using the Precipitation Runoff Modeling System (PRMS). Climate inputs to the model of temperature and precipitation were scaled to anticipated changes that could occur in 2030, 2050, and 2100 based on global climate models. The output from the models were used to characterize the 1-percent AEP streamflows for the years 2030, 2050, and 2100 and compare the results to baseline conditions, 1950-2015. The data include the model input and output and spatial data for model referencing. Scripts for processing PRMS output to obtain final results are also provided.

The U.S. Geological Survey, in cooperation with the Federal Highway Administration (FHWA) and the Connecticut, Massachusetts, and Rhode Island Departments of Transportation (DOTs), gathered geospatial data to facilitate the development of a regional Stochastic Empirical Loading and Dilution Model (SELDM) application (Granato and others, 2023). As part of this study, the surficial geology of Connecticut, Massachusetts, Rhode Island, and contributing areas from neighboring states was compiled from disparate datasets and reclassified into two categories that represent presence or absence of sand and gravel deposits (also referred to as stratified drift). This dataset provides a key basin characteristic for the region that may be used to help FHWA and DOTs to address potential environmental impacts of transportation projects in accordance with the National Environmental Policy Act of 1969 (https://www.epa.gov/nepa). Knowledge of local surficial geology also may support the assessment of green infrastructure as methods to reduce the effect of highway and urban receiving waters. Furthermore, this dataset facilitates the estimation of streamflow statistics at ungaged locations in the regions, these statistics were shown to be among the most sensitive input variables for refining SELDM outputs (Granato and others, 2023). This data release provides the compiled raster dataset of sand and gravel deposits as a Georeferenced Tagged Image File Format (GeoTIFF) raster dataset. The spatial extent includes the entirety of Connecticut, Massachusetts, and Rhode Island, as well as portions of contributing area to these states in New Hampshire, New York, and Vermont. References: Granato, G.E., Spaetzel, A.B., and Jeznach, L.C., 2023, Approaches for assessing flows, concentrations, and loads of highway and urban runoff and receiving-stream stormwater in southern New England with the Stochastic Empirical Loading and Dilution Model (SELDM): U.S. Geological Survey Scientific Investigations Report 2023–5087, 152 p., https://doi.org/10.3133/sir20235087

The U.S. Geological Survey, in cooperation with the Federal Highway Administration (FHWA) and the Connecticut, Massachusetts, and Rhode Island Departments of Transportation (DOTs), gathered geospatial data to facilitate the development of a regional Stochastic Empirical Loading and Dilution Model (SELDM) application (Granato and others, 2023). As part of this study, the surficial geology of Connecticut, Massachusetts, Rhode Island, and contributing areas from neighboring states was compiled from disparate datasets and reclassified into two categories that represent presence or absence of sand and gravel deposits (also referred to as stratified drift). This dataset provides a key basin characteristic for the region that may be used to help FHWA and DOTs to address potential environmental impacts of transportation projects in accordance with the National Environmental Policy Act of 1969 (https://res1wwwd-o-tepad-o-tgov.vcapture.xyz/nepa). Knowledge of local surficial geology also may support the assessment of green infrastructure as methods to reduce the effect of highway and urban receiving waters. Furthermore, this dataset facilitates the estimation of streamflow statistics at ungaged locations in the regions, these statistics were shown to be among the most sensitive input variables for refining SELDM outputs (Granato and others, 2023). This data release provides the compiled raster dataset of sand and gravel deposits as a Georeferenced Tagged Image File Format (GeoTIFF) raster dataset. The spatial extent includes the entirety of Connecticut, Massachusetts, and Rhode Island, as well as portions of contributing area to these states in New Hampshire, New York, and Vermont. References: Granato, G.E., Spaetzel, A.B., and Jeznach, L.C., 2023, Approaches for assessing flows, concentrations, and loads of highway and urban runoff and receiving-stream stormwater in southern New England with the Stochastic Empirical Loading and Dilution Model (SELDM): U.S. Geological Survey Scientific Investigations Report 2023–5087, 152 p., https://res1doid-o-torg.vcapture.xyz/10.3133/sir20235087

This hosted feature layer has been published in RI State Plane Feet NAD 83.This dataset represents coastal waters and the coastline of Rhode Island, as well as portions of neighboring Connecticut, Massachusetts, and New York. Includes Narragansett Bay, salt ponds, and tributaries.

Project: NOAA Digital Orthophotography for the Coasts of Main/New Hampshire, Massachusetts/Rhode Island/Connecticut, and Hudson River/Long Island /NY/NJ Contract No. EA133C11CQ0010 Reference No. NCNP0000-14-00967 Woolpert Order No. 74571 CONTRACTOR: Woolpert, Inc. The project represents the collection of digital orthoimagery for the coasts of Maine and New Hampshire; Massachusetts and Rhode Island; Connecticut, the Hudson River, Long Island and the NY/NJ metro area. The work was requested by the National Oceanic and Atmospheric Administration, Office of Response and Restoration division. The imagery is being acquired for use in the development of the ESI (Environmental Sensitivity Index) data in this same region. The entire project area includes approximately 11,669 square miles. Original contact information: Contact Org: National Oceanic and Atmospheric Administration (NOAA) Phone: 843-740-1200

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.

DPH note about change from 7-day to 14-day metrics: As of 10/15/2020, this dataset is no longer being updated. Starting on 10/15/2020, these metrics will be calculated using a 14-day average rather than a 7-day average. The new dataset using 14-day averages can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/hree-nys2

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

This dataset includes a weekly count and weekly rate per 100,000 population for COVID-19 cases, a weekly count of COVID-19 PCR diagnostic tests, and a weekly percent positivity rate for tests among people living in community settings. Dates are based on date of specimen collection (cases and positivity).

A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case.

These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities.

These data are updated weekly; the previous week period for each dataset is the previous Sunday-Saturday, known as an MMWR week (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf). The date listed is the date the dataset was last updated and corresponds to a reporting period of the previous MMWR week. For instance, the data for 8/20/2020 corresponds to a reporting period of 8/9/2020-8/15/2020.

Notes: 9/25/2020: Data for Mansfield and Middletown for the week of Sept 13-19 were unavailable at the time of reporting due to delays in lab reporting.

Project: NOAA Digital Orthophotography for the Coasts of Main/New Hampshire, Massachusetts/Rhode Island/Connecticut, and Hudson River/Long Island /NY/NJ Contract No. EA133C11CQ0010 Reference No. NCNP0000-14-00967 Woolpert Order No. 74571 CONTRACTOR: Woolpert, Inc. The project represents the collection of digital orthoimagery for the coasts of Maine and New Hampshire; Massachusetts and Rhode Isl...

The NRHP provides a listing of important places for the preservation of American history. This dataset is used to support coastal and ocean planning by displaying sites/areas within 10km of the coastal shoreline.

Airports Polygon is a 1:24,000-scale, feature-based layer that includes all airport features depicted on all of the U.S. Geological Survey (USGS) 7.5 minute topographic quadrangle maps that cover the State of Connecticut and are listed on the Federal Aviation Administration (FAA) "Airport Data (5010) & Contact Information" June 5, 2008 report. Airports in New York, Massachusetts and Rhode Island that are near the Connecticut state boundary are included. Airports that are listed by FAA and are visible on aerial photography (Connecticut 2004 Orthophotos and Connecticut 2006 NAIP Color Orthophotos from National Agriculture Imagery Program) are included. Airports that are listed by FAA but are not visible on aerial photography are not included. All airports listed by FAA are included in a separate point feature-based layer, Airport FAA CT. The airport point locations were generated from latitude and longitude coordinates contained in the FAA report and all the attribute information in the report was included. The airport layer is based partly on information from USGS topographic quadrangle maps published between 1969 and 1984 which does not represent airports in Connecticut at any one particular point in time. The layer does depict current conditions as to airports listed by FAA and having _location identification codes and visible on aerial photography of 2004 and 2006. The layer delineates airports and heliports. It includes airport name, airport _location code, type of facility, public or private use of facility and state the airport is located in. It does not include airport elevation, flight schedule, runway capacity, or ownership information. Features are polygonal and generally depict landing strips and perimeters for large and small airports and helicopter landing pads. Attribute information allows to cartographic representation (symbolize) and labeling of these features on a map. This layer was originally published in 1994 and slightly updated in 2005.

This data release provides a set of Hydrological Simulation Program--Fortran (HSPF) model files representing five EPA-selected future climate change scenarios for the Farmington River Basin in Massachusetts and Connecticut. Output from these models are intended for use as input to EPA Watershed Management Optimization Support Tool (WMOST) modeling. Climate scenarios, based on 2036-2065 changes from 1975-2004 for Representative Concentration Pathways (RCP) 4.5 and 8.5, model the effects of air temperature and precipitation changes (in degrees F for air temperature, in percent for precipitation) made to the input historical meteorological time series for 1975-2004. Meteorological data are from the following climate stations in Connecticut: Hartford Airport, Burlington, and Norfolk. Each set of climate scenario model files are derived from the original calibrated model files created by EPA and the Connecticut Department of Energy and Environmental Protection Bureau of Water Management to evaluate nutrient sources and loadings to Long Island Sound and assessment of impacts of Best-Management Practices (BMP), and later extended by U.S. Geological Survey (USGS) to support WMOST modeling (refer to Source Input fields in this metadata file).