The political boundary datalayer is a polygon representation of town boundaries created from arcs developed from survey coordinates extracted from the 68-volume Harbor and Lands Commission Town Boundary Atlas for the 351 communities (cities and towns) in Massachusetts. The Atlas was published in the early 1900's and is maintained by the Survey Section of Massachusetts Highway Department. For communities with a coastal boundary, MassGIS has collaborated with Massachusetts Water Resources Authority and the Department of Environmental Protection to complete a 1:12000 scale coastline. The boundary for the coastline was defined as being the upland side of tidal flats and rocky inter-tidal zones. Note that the 351 communities are the official municipal names, not including "villages" or other sections of towns.This datalayer was created for the purposes of providing an up-to-date polygon version of the town boundaries for the 351 cities and towns of the Commonwealth of Massachusetts. The legislative intent for some boundaries could not be mapped. Boundaries where that is true are identified in the attribute information. This layer contains multi-part polygons, one for each municipality. The coastline on this layer has been generalized for small-scale cartography and faster display in web map services.See the layer metadata for details.

This feature service from MassGIS displays the year in which cities and towns in Massachusetts were first settled by Europeans. The data were gathered by the Secretary of the Commonwealth of Massachusetts. Sources include: 2010 Census Report; Community Profiles, Department of Housing and Community Development; Historic Atlas of Massachusetts, University of Massachusetts Press 1991.Data source: https://www.sec.state.ma.us/divisions/cis/historical/incorporation-settlement.htmMap service also available.

The Community of Interest Map Collection Project aims to collect COI maps submitted to legislative and congressional redistricting bodies and organizations during the 2021 redistricting cycle.

Find Massachusetts health data by community, county, and region, including population demographics. Build custom data reports with over 100 health and social determinants of health data indicators and explore over 28,000 current and historical data layers in the map room.

MassGIS' standardized ("Level 3") property tax parcel mapping data set was developed through a competitive procurement funded by MassGIS. Each community in the Commonwealth was bid on by one or more vendors and the unit of work awarded was a city or town. The specification for this work was Level 3 of the MassGIS Digital Parcel Standard. Standardization of assessor parcel mapping is complete for all 351 Massachusetts' cities and towns. MassGIS is now incorporating updates from municipalities into the database. This hosted feature layer is exported from MassGIS' internal database of the feature class GISDATA.L3_TAXPAR_POLY_ASSESS, which links L3_TAXPAR_POLY and L3_ASSESS. The export includes the expression: (POLY_TYPE IN ('FEE', 'TAX')) OR (POLY_TYPE IN ('ROW', 'PRIV_ROW', 'RAIL_ROW', 'WATER') AND PROP_ID IS NOT NULL) It contains several fields from GISDATA.L3_ASSESS and stacked polygons where multiple assessor records link to a parcel. It contains features that do not have an associated record in GISDATA.L3_ASSESS, except for rights of way and water bodies. ROWs and water bodies with a non-null PROP_ID are included. The data in this feature layer is used for the popups in the Massachusetts Interactive Property Map. See full data descriptionA hosted tile layer will draw very quickly at map scale of 1:18,056 (level 15) to 1:564 (level 20).

Background and Data Limitations The Massachusetts 1830 map series represents a unique data source that depicts land cover and cultural features during the historical period of widespread land clearing for agricultural. To our knowledge, Massachusetts is the only state in the US where detailed land cover information was comprehensively mapped at such an early date. As a result, these maps provide unusual insight into land cover and cultural patterns in 19th century New England. However, as with any historical data, the limitations and appropriate uses of these data must be recognized: (1) These maps were originally developed by many different surveyors across the state, with varying levels of effort and accuracy. (2) It is apparent that original mapping did not follow consistent surveying or drafting protocols; for instance, no consistent minimum mapping unit was identified or used by different surveyors; as a result, whereas some maps depict only large forest blocks, others also depict small wooded areas, suggesting that numerous smaller woodlands may have gone unmapped in many towns. Surveyors also were apparently not consistent in what they mapped as ‘woodlands’: comparison with independently collected tax valuation data from the same time period indicates substantial lack of consistency among towns in the relative amounts of ‘woodlands’, ‘unimproved’ lands, and ‘unimproveable’ lands that were mapped as ‘woodlands’ on the 1830 maps. In some instances, the lack of consistent mapping protocols resulted in substantially different patterns of forest cover being depicted on maps from adjoining towns that may in fact have had relatively similar forest patterns or in woodlands that ‘end’ at a town boundary. (3) The degree to which these maps represent approximations of ‘primary’ woodlands (i.e., areas that were never cleared for agriculture during the historical period, but were generally logged for wood products) varies considerably from town to town, depending on whether agricultural land clearing peaked prior to, during, or substantially after 1830. (4) Despite our efforts to accurately geo-reference and digitize these maps, a variety of additional sources of error were introduced in converting the mapped information to electronic data files (see detailed methods below). Thus, we urge considerable caution in interpreting these maps. Despite these limitations, the 1830 maps present an incredible wealth of information about land cover patterns and cultural features during the early 19th century, a period that continues to exert strong influence on the natural and cultural landscapes of the region.

    Acknowledgements
    Financial support for this project was provided by the BioMap Project of the Massachusetts Natural Heritage and Endangered Species Program, the National Science Foundation, and the Andrew Mellon Foundation. This project is a contribution of the Harvard Forest Long Term Ecological Research Program.

MassGIS has processed Massachusetts municipalities (cities and towns) from the U.S. Census Bureau's 2020 data release for Massachusetts to assist GIS users who may need access to these value-added datasets. These data are suitable for use with Census 2020 products and certain Census publications and demographics surveys created after 2020.See datalayer metadata.Map service also available.

This layer is sourced from maps.coast.noaa.gov.

This map service presents spatial information developed as part of the National Oceanic and Atmospheric Administration (NOAA) Office for Coastal Management’s Coastal Flood Exposure Mapper. The purpose of the online mapping tool is to provide coastal managers, planners, and stakeholders a preliminary look at exposures to coastal flooding hazards. The Mapper is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help communities initiate resilience planning efforts. Currently the extent of the Coastal Flood Exposure Mapper covers U.S. coastal areas along the Gulf of Mexico and Atlantic Ocean. NOAA provides the information “as-is” and shall incur no responsibility or liability as to the completeness or accuracy of this information. NOAA assumes no responsibility arising from the use of this information. For additional information, please contact the NOAA Office for Coastal Management (coastal.info@noaa.gov).

© NOAA Office for Coastal Management

Massachusetts Counties, based on Survey Towns. Contains the 14 county polygons and a detailed coastline. Published as a map service from MassGIS' ArcGIS Server platform.See full metadata

The 2022 cartographic boundary KMLs 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. The cartographic boundary files include both incorporated places (legal entities) and census designated places or CDPs (statistical entities). An incorporated place is established to provide governmental functions for a concentration of people as opposed to a minor civil division (MCD), which generally is created to provide services or administer an area without regard, necessarily, to population. Places always nest within a state, but may extend across county and county subdivision boundaries. An incorporated place usually is a city, town, village, or borough, but can have other legal descriptions. CDPs are delineated for the decennial census as the statistical counterparts of incorporated places. CDPs are delineated to provide data for settled concentrations of population that are identifiable by name, but are not legally incorporated under the laws of the state in which they are located. The boundaries for CDPs often are defined in partnership with state, local, and/or tribal officials and usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity. CDP boundaries often change from one decennial census to the next with changes in the settlement pattern and development; a CDP with the same name as in an earlier census does not necessarily have the same boundary. The only population/housing size requirement for CDPs is that they must contain some housing and population. The generalized boundaries of most incorporated places in this file are based on those as of January 1, 2022, as reported through the Census Bureau's Boundary and Annexation Survey (BAS). The generalized boundaries of all CDPs are based on those delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.

The geographic extent of the town of Easton, MA and surrounding cities and townsTown boundaries were copied from MassGIS Data - Community Boundaries (Towns) from Survey Points (last update November 2015). Edits were made to remove coast lines of internal waterways. The boundaries of for Taunton, Raynam and Bridgewater were copied from MassGIS - Community Boundaries (Towns) Without Coast (February 2014). Boundaries were edited to match those from Community Boundaries from Survey Points.

This point datalayer shows the locations of community behavioral health centers across the Commonwealth of Massachusetts. Centers appearing in this layer are those that provide mental health crisis care as listed by the Executive Office of Health and Human Services (EOHHS) as of December 2024. Locations were scraped from the EOHHS Find a CBHC tool and were geocoded to MassGIS' address points and verified using current ortho imagery and individual websites where needed.More information available here...Map Service also available here...

This dataset is no longer being updated as of 6/30/2022. It is being retained on the Open Data Portal for its potential historical interest.

In November 2020, the City of Cambridge began collecting and analyzing COVID-19 data from municipal wastewater, which can serve as an early indicator of increased COVID-19 infections in the city. The Cambridge Public Health Department and Cambridge Department of Public Works are using technology developed by Biobot, a Cambridge based company, and partnering with the Massachusetts Water Resources Authority (MWRA). This Cambridge wastewater surveillance initiative is funded through a $175,000 appropriation from the Cambridge City Council.

This dataset indicates the presence of the COVID-19 virus (measured as viral RNA particles from the novel coronavirus per ml) in municipal wastewater. The Cambridge site data here were collected as a 24-hour composite sample, which is taken weekly. The MWRA site data ere were collected as a 24-hour composite sample, which is taken daily. MWRA and Cambridge data are listed here in a single table.

An interactive graph of this data is available here: https://cityofcambridge.shinyapps.io/COVID19/?tab=wastewater

All areas within the City of Cambridge are captured across four separate catchment areas (or sewersheds) as indicated on the map viewable here: https://cityofcambridge.shinyapps.io/COVID19/_w_484790f7/BioBot_Sites.png. The North and West Cambridge sample also includes nearly all of Belmont and very small areas of Arlington and Somerville (light yellow). The remaining collection sites are entirely -- or almost entirely -- drawn from Cambridge households and workplaces.

Data are corrected for wastewater flow rate, which adjusts for population in general. Data listed are expected to reflect the burden of COVID-19 infections within each of the four sewersheds. A lag of approximately 4-7 days will occur before new transmissions captured in wastewater data would result in a positive PCR test for COVID-19, the most common testing method used. While this wastewater surveillance tool can provide an early indication of major changes in transmission within the community, it remains an emerging technology. In assessing community transmission, wastewater surveillance data should only be considered in conjunction with other clinical measures, such as current infection rates and test positivity.

Each location is selected because it reflects input from a distinct catchment area (or sewershed) as identified on the color-coded map. Viral data collected from small catchment areas like these four Cambridge sites are more variable than data collected from central collection points (e.g., the MWRA facility on Deer Island) where wastewater from dozens of communities are joined and mixed. Data from each catchment area will be impacted by daily activity among individuals living in that area (e.g., working from home vs. traveling to work) and by daytime activities that are not from residences (businesses, schools, etc.) As such, the Regional MWRA data provides a more stable measure of regional viral counts. COVID wastewater data for Boston North and Boston South regions is available at https://www.mwra.com/biobot/biobotdata.htm

The CT Municipalities layer consists of individual polygons representing each of the 169 municipalities that make up the state of Connecticut. This feature class is based on the Towns layer originally created by CTDEEP from USGS maps. The towns from the CTDEEP data were dissolved to create 169 records (one for each town). Fields were added and deleted to create a generic schema.The CT Municipalities feature class was created in (municipality) alphabetical order. Fields were added to identify the municipality number and the CTDOT Municipality number, which differ from each other in some cases. In 1947 the town of Saybrook officially changed its name to Deep River. Other State agencies and municipalities changed their numbering systems to reflect this name change, however, most of what is now CTDOT kept their existing numbering system. This is why the CTDOT town number for Deep River is 122, the number formerly assigned to Saybrook.The square miles associated with each town are for their interior land mass area. Coastal communities have boundaries that extend into Long Island Sound. These town boundary extensions into Long Island Sound are not included in the square miles field.CTDOT has created and will maintain a cartographic rendering of the geometric shape of Municipal boundaries. Official Town and City designations as incorporated areas consisting of an authorized governing body are managed by CT's Office of Policy and Management (OPM).CTDOT has undertaken a good faith effort to represent the boundaries cartographically in a fair and equitable fashion, from the best available data compiled from existing state, regional, and local resources including - existing historical cartographic renderings of the boundary locations, supplemental survey information, and map submissions. Corrections can be submitted to the CTDOT for incorporation and correction where applicable.Attribution was assigned to designations managed by a variety of entities that strictly follow Municipal boundaries and additional designations will be added as requested by State, regional, and local partners.

Reporting of Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

This archived public use dataset has 11 data elements reflecting United States COVID-19 community levels for all available counties.

The COVID-19 community levels were developed using a combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days. The COVID-19 community level was determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge.

Using these data, the COVID-19 community level was classified as low, medium, or high.

COVID-19 Community Levels were used to help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals.

For the most accurate and up-to-date data for any county or state, visit the relevant health department website. COVID Data Tracker may display data that differ from state and local websites. This can be due to differences in how data were collected, how metrics were calculated, or the timing of web updates.

Archived Data Notes:

This dataset was renamed from "United States COVID-19 Community Levels by County as Originally Posted" to "United States COVID-19 Community Levels by County" on March 31, 2022.

March 31, 2022: Column name for county population was changed to “county_population”. No change was made to the data points previous released.

March 31, 2022: New column, “health_service_area_population”, was added to the dataset to denote the total population in the designated Health Service Area based on 2019 Census estimate.

March 31, 2022: FIPS codes for territories American Samoa, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands were re-formatted to 5-digit numeric for records released on 3/3/2022 to be consistent with other records in the dataset.

March 31, 2022: Changes were made to the text fields in variables “county”, “state”, and “health_service_area” so the formats are consistent across releases.

March 31, 2022: The “%” sign was removed from the text field in column “covid_inpatient_bed_utilization”. No change was made to the data. As indicated in the column description, values in this column represent the percentage of staffed inpatient beds occupied by COVID-19 patients (7-day average).

March 31, 2022: Data values for columns, “county_population”, “health_service_area_number”, and “health_service_area” were backfilled for records released on 2/24/2022. These columns were added since the week of 3/3/2022, thus the values were previously missing for records released the week prior.

April 7, 2022: Updates made to data released on 3/24/2022 for Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands to correct a data mapping error.

April 21, 2022: COVID-19 Community Level (CCL) data released for counties in Nebraska for the week of April 21, 2022 have 3 counties identified in the high category and 37 in the medium category. CDC has been working with state officials to verify the data submitted, as other data systems are not providing alerts for substantial increases in disease transmission or severity in the state.

May 26, 2022: COVID-19 Community Level (CCL) data released for McCracken County, KY for the week of May 5, 2022 have been updated to correct a data processing error. McCracken County, KY should have appeared in the low community level category during the week of May 5, 2022. This correction is reflected in this update.

May 26, 2022: COVID-19 Community Level (CCL) data released for several Florida counties for the week of May 19th, 2022, have been corrected for a data processing error. Of note, Broward, Miami-Dade, Palm Beach Counties should have appeared in the high CCL category, and Osceola County should have appeared in the medium CCL category. These corrections are reflected in this update.

May 26, 2022: COVID-19 Community Level (CCL) data released for Orange County, New York for the week of May 26, 2022 displayed an erroneous case rate of zero and a CCL category of low due to a data source error. This county should have appeared in the medium CCL category.

June 2, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a data processing error. Tolland County, CT should have appeared in the medium community level category during the week of May 26, 2022. This correction is reflected in this update.

June 9, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a misspelling. The medium community level category for Tolland County, CT on the week of May 26, 2022 was misspelled as “meduim” in the data set. This correction is reflected in this update.

June 9, 2022: COVID-19 Community Level (CCL) data released for Mississippi counties for the week of June 9, 2022 should be interpreted with caution due to a reporting cadence change over the Memorial Day holiday that resulted in artificially inflated case rates in the state.

July 7, 2022: COVID-19 Community Level (CCL) data released for Rock County, Minnesota for the week of July 7, 2022 displayed an artificially low case rate and CCL category due to a data source error. This county should have appeared in the high CCL category.

July 14, 2022: COVID-19 Community Level (CCL) data released for Massachusetts counties for the week of July 14, 2022 should be interpreted with caution due to a reporting cadence change that resulted in lower than expected case rates and CCL categories in the state.

July 28, 2022: COVID-19 Community Level (CCL) data released for all Montana counties for the week of July 21, 2022 had case rates of 0 due to a reporting issue. The case rates have been corrected in this update.

July 28, 2022: COVID-19 Community Level (CCL) data released for Alaska for all weeks prior to July 21, 2022 included non-resident cases. The case rates for the time series have been corrected in this update.

July 28, 2022: A laboratory in Nevada reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate will be inflated in Clark County, NV for the week of July 28, 2022.

August 4, 2022: COVID-19 Community Level (CCL) data was updated on August 2, 2022 in error during performance testing. Data for the week of July 28, 2022 was changed during this update due to additional case and hospital data as a result of late reporting between July 28, 2022 and August 2, 2022. Since the purpose of this data set is to provide point-in-time views of COVID-19 Community Levels on Thursdays, any changes made to the data set during the August 2, 2022 update have been reverted in this update.

August 4, 2022: COVID-19 Community Level (CCL) data for the week of July 28, 2022 for 8 counties in Utah (Beaver County, Daggett County, Duchesne County, Garfield County, Iron County, Kane County, Uintah County, and Washington County) case data was missing due to data collection issues. CDC and its partners have resolved the issue and the correction is reflected in this update.

August 4, 2022: Due to a reporting cadence change, case rates for all Alabama counties will be lower than expected. As a result, the CCL levels published on August 4, 2022 should be interpreted with caution.

August 11, 2022: COVID-19 Community Level (CCL) data for the week of August 4, 2022 for South Carolina have been updated to correct a data collection error that resulted in incorrect case data. CDC and its partners have resolved the issue and the correction is reflected in this update.

August 18, 2022: COVID-19 Community Level (CCL) data for the week of August 11, 2022 for Connecticut have been updated to correct a data ingestion error that inflated the CT case rates. CDC, in collaboration with CT, has resolved the issue and the correction is reflected in this update.

August 25, 2022: A laboratory in Tennessee reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate may be inflated in many counties and the CCLs published on August 25, 2022 should be interpreted with caution.

August 25, 2022: Due to a data source error, the 7-day case rate for St. Louis County, Missouri, is reported as zero in the COVID-19 Community Level data released on August 25, 2022. Therefore, the COVID-19 Community Level for this county should be interpreted with caution.

September 1, 2022: Due to a reporting issue, case rates for all Nebraska counties will include 6 days of data instead of 7 days in the COVID-19 Community Level (CCL) data released on September 1, 2022. Therefore, the CCLs for all Nebraska counties should be interpreted with caution.

September 8, 2022: Due to a data processing error, the case rate for Philadelphia County, Pennsylvania,

This point datalayer shows the locations of community health centers across the Commonwealth of Massachusetts. Centers appearing in this layer are those that provide primary, dental, or eye care as listed by the Massachusetts League of Community Health Centers as of December 2024. Locations were scraped from the Massachusetts League of Community Health Centers Find a Community Health Center tool and were geocoded to MassGIS' address points and verified using current ortho imagery and individual websites where needed.More information available here...Map Service also available here...

The four adjacent Outer Cape communities of Eastham, Truro, Provincetown, and Wellfleet have built an intermunicipal partnership to pursue a regional approach to shoreline management. This partnership promotes short- and long-term science-based decisions that will maximize the effectiveness and efficiency of community responses to the increased threat of coastal hazards. This map set is a product of that partnership, the Intermunicipal Shoreline Management Project, a project first initiated in 2019 with funding from CZM's Coastal Resilience Grant Program.Maps showing the general location of littoral cells, the sediment transport system and ISM management cells along the eastern shoreline of Cape Cod Bay.Management Cells: The spatial base map upon which to implement a regional shoreline management framework for the ISM planning area. Recognizing that nearshore and shoreline characteristics drive coastal change, management cells are organized around the concept of littoral cells or natural coastal compartments that contain a complete cycle of sedimentation including sources, transport paths, and sinks. Management cells can be used to determine a shoreline project’s location within the littoral cell and to aid in the identification of key management considerations for a given project. Ignoring municipal boundaries should enhance each town’s ability to work with the natural processes of coastal change and help facilitate a uniform, science-based regional shoreline management approach. Littoral Cells / Sediment Transport System: Although represented as discrete points and lines, features are not intended to imply point specific locations. Rather the information provided is intended to visualize generally the areas of sediment sources and sinks, the locations of null points, and the directions of net sediment transport along the eastern shore of Cape Cod Bay.DefinitionsLittoral Cell: A coastal compartment that contains a complete cycle of sedimentation including sources, transport paths, and sinks. Net Longshore Sediment Transport (Q): Annual net flow of sediment along the coast expressed as the volume rate of wave-produced sediment transport. Null Point: A point along the shore that defines the updrift or down drift boundary of a littoral cell, (Q=0). Sediment Sink: An area where sediment is removed from a littoral cell (an area of deposition). Sediment Source: An area where sediment in added to a littoral cell (an area of erosion). For more information seeBerman, G.A., 2011, Longshore Sediment Transport, Cape Cod, Massachusetts. Marine Extension Bulletin, Woods Hole Sea Grant & Cape Cod Cooperative Extension. 48 p.Giese, G.S., Borrelli, M., Mague, S.T., Barger, P., McFarland, S., 2018. Assessment of the Century-Scale Sediment Budget for the Eastham and Wellfleet Coasts of Cape Cod Bay. A Report Submitted to the Towns of Eastham and Wellfleet, Center for Coastal Studies, Provincetown, MA. 32p. Giese, G.S., M. Borrelli, S.T. Mague, T. Smith and P. Barger, 2014, Assessment of Multi- Decadal Coastal Change: Provincetown Harbor to Jeremy Point, Wellfleet. A Report Submitted to the Massachusetts Bays Program, .Center for Coastal Studies, Provincetown, MA. 23 p. Giese, G.S., Borrelli, M., Mague, S.T., Smith, T.L., Barger, P., Hughes, P., 2013. Evaluating century-scale coastal change: Provincetown/Truro line to Provincetown Harbor. No. 14- 1, Center for Coastal Studies. 11p.

To access parcel information:Enter an address or zoom in by using the +/- tools or your mouse scroll wheel. Parcels will draw when zoomed in.Click on a parcel to display a popup with information about that parcel.Click the "Basemap" button to display background aerial imagery.From the "Layers" button you can turn map features on and off.Complete Help (PDF)Parcel Legend:Full Map LegendAbout this ViewerThis viewer displays land property boundaries from assessor parcel maps across Massachusetts. Each parcel is linked to selected descriptive information from assessor databases. Data for all 351 cities and towns are the standardized "Level 3" tax parcels served by MassGIS. More details ...Read about and download parcel dataUpdatesV 1.1: Added 'Layers' tab. (2018)V 1.2: Reformatted popup to use HTML table for columns and made address larger. (Jan 2019)V 1.3: Added 'Download Parcel Data by City/Town' option to list of layers. This box is checked off by default but when activated a user can identify anywhere and download data for that entire city/town, except Boston. (March 14, 2019)V 1.4: Data for Boston is included in the "Level 3" standardized parcels layer. (August 10, 2020)V 1.4 MassGIS, EOTSS 2021

The Communities at Sea maps use Vessel Trip Report location point data as input to create density polygons representing visitation frequency ("fisherdays"). The data show total labor including crew time and the time spent in transit to and from fishing locations. They do not show other variables such as vessel value or number of pounds landed. The results can be interpreted as maps of "community presence." This layer shows data for the dredge fishing gear group for Chatham, MA from 2011-2015.

The Communities at Sea maps use Vessel Trip Report location point data as input to create density polygons representing visitation frequency ("fisherdays"). The data show total labor including crew time and the time spent in transit to and from fishing locations. They do not show other variables such as vessel value or number of pounds landed. The results can be interpreted as maps of "community presence." This layer shows data for the gillnet fishing gear group for New Bedford, MA from 2011-2015.