The Census Bureau does not recognize or release data for Boston neighborhoods. However, Census block groups can be aggregated to approximate Boston neighborhood boundaries to allow for reporting and visualization of Census data at the neighborhood level. Census block groups are created by the U.S. Census Bureau as statistical geographic subdivisions of a census tract defined for the tabulation and presentation of data from the decennial census and the American Community Survey. The 2020 Census block group boundary files for Boston can be found here. These block group-approximated neighborhood boundaries are used for work with Census data. Work that does not rely on Census data generally uses the Boston neighborhood boundaries found here.

The population density picture of Boston is generally a story of two Bostons: the high density central and northern neighborhoods, and the low density southern neighborhoods.The highest density areas of Boston are particularly concentrated in Brighton, Allston, and the Fenway area, areas of the city with large numbers of college students and young adults. There is also high population density in areas such as the Back Bay, the South End, Charlestown, the North End, and South Boston. These are all relatively small areas geographically, but have housing stock conducive to population density (e.g. multi-family dwelling units, row housing, large apartment buildings). The southern neighborhoods, specifically Hyde Park and West Roxbury, have significant numbers of people living in them, but lots sizes tend to be much larger. These areas of the city also tend to have more single family dwelling units. In that, there are fewer people per square mile than places north in the city. Census data reveals that population density varies noticeably from area to area. Small area census data do a better job depicting where the crowded neighborhoods are. In this map, areas of highest density exceed 30,000 persons per square kilometer. Very high density areas exceed 7,000 persons per square kilometer. High density areas exceed 5,200 persons per square kilometer. The last categories break at 3,330 persons per square kilometer, and 1,500 persons per square kilometer.How to make this map for your city

Neighborhood boundaries are created based on zip code, zoning district boundaries and census tract boundaries. This GIS data layer was produced by the BRA Office of Digital Cartography and GIS. These boundaries should not be considered official neighborhood boundaries for the City of Boston. The locations of features and boundaries shown on this map are approximate and are intended for planning and visualization purposes only. This is not intended for survey, engineering, or legal purposes.

The Census Bureau does not recognize or release data for Boston neighborhoods. However, Census tracts can be aggregated to approximate Boston neighborhood boundaries to allow for reporting and visualization of Census data at the neighborhood level. Census tracts are created by the U.S. Census Bureau as statistical geographic subdivisions of a county defined for the tabulation and presentation of data from the decennial census and the American Community Survey. The 2020 Census tract boundary files for Boston can be found here. These tract-approximated neighborhood boundaries are used for work with Census data. Work that does not rely on Census data generally uses the Boston neighborhood boundaries found here.

Between 1935 and 1940 the federal government’s Home Owners’ Loan Corporation (HOLC) classified the neighborhoods of 239 cities according to their perceived investment risk. This practice has since been referred to as “redlining,” as the neighborhoods classified as being the highest risk for investment were often colored red on the resultant maps. The Mapping Inequality project, a collaboration of faculty at the University of Richmond’s Digital Scholarship Lab, the University of Maryland’s Digital Curation Innovation Center, Virginia Tech, and Johns Hopkins University has digitized and georectified all 239 HOLC maps and made them publicly available, including the HOLC map of Boston from 1938. The Boston Area Research Initiative has coordinated (i.e., spatial joined) the districts from the 1938 HOLC map of Boston with census tracts from the 2010 U.S. Census. This dataset contains the original shapefile and the spatially joined tract-level data.

2020 Census data for the city of Boston, Boston neighborhoods, census tracts, block groups, and voting districts. In the 2020 Census, the U.S. Census Bureau divided Boston into 207 census tracts (~4,000 residents) made up of 581 smaller block groups. The Boston Planning and Development Agency uses the 2020 tracts to approximate Boston neighborhoods. The 2020 Census Redistricting data also identify Boston’s voting districts.

For analysis of Boston’s 2020 Census data including graphs and maps by the BPDA Research Division and Office of Digital Cartography and GIS, see 2020 Census Research Publications

For a complete official data dictionary, please go to 2020 Census State Redistricting Data (Public Law 94-171) Summary File, Chapter 6. Data Dictionary. 2020 Census State Redistricting Data (Public Law 94-171) Summary File

2020 Census Tracts In Boston

2020 Census Block Groups In Boston

Boston Neighborhood Boundaries Approximated By 2020 Census Tracts

Boston Voting District Boundaries

Boston Main Street districts are a network of 20 Main Street Organizations that use a comprehensive revitalization approach to create, build, and sustain healthy commercial districts.

Neighborhood boundaries are created based on zip code, zoning district boundaries and census tract boundaries. This GIS data layer was produced by the BRA Office of Digital Cartography and GIS. These boundaries should not be considered official neighborhood boundaries for the City of Boston. The locations of features and boundaries shown on this map are approximate and are intended for planning and visualization purposes only. This is not intended for survey, engineering, or legal purposes. March 2011

Crosswalk numbers for the city of Boston. Generated in July 2008 from original maps dated 1951-1962 and related sketches. Placed using centerlines from Water and Sewer as well as block defintions. Shapefiles for individual districts were merged into this one file but do not have an active connection. Some discrepency between centerlines and earlier maps was allowed, though obvious problems were marked in red. The legend is consistent between all districts, except for Roxbury where the map did not distinguish mid-block or school crosswalks.

This map service contains boundaries for the following types of public school districts:Local School - administered by a city or town school committee.Regional Academic - administered by a regional school committee.Regional Vocational Technical - administered by a regional vocational school committee.Independent Vocational and County Agricultural - administered by a board of trustees.Independent Public, including Commonwealth Charter Schools and Horace Mann Charter SchoolsDistrict information as of December 2, 2014, was obtained from the Massachusetts Department of Elementary and Secondary Education (ESE).For full metadata see https://www.mass.gov/info-details/massgis-data-public-school-districtsMap service also available.

Authoritative police districts dataset for the City of Boston.

Noise pollution in cities has major negative effects on the health of both humans and wildlife. Using iPhones, we collected sound-level data at hundreds of locations in four areas of Boston, Massachusetts (USA) before, during, and after the fall 2020 pandemic lockdown, during which most people were required to remain at home. These spatially dispersed measurements allowed us to make detailed maps of noise pollution that are not possible when using standard fixed sound equipment. The four sites were: the Boston University campus (which sits between two highways), the Fenway/Longwood area (which includes an urban park and several hospitals), Harvard Square (home of Harvard University), and East Boston (a residential area near Logan Airport). Across all four sites, sound levels averaged 6.4 dB lower during the pandemic lockdown than after. Fewer high noise measurements occurred during lockdown as well. The resulting sound maps highlight noisy locations such as traffic intersections and qui..., We collected sound measurements within four different urban sites in Boston, Massachusetts. Working in small teams of 2-4 people, we used the mobile app SPLnFFT to collect sound level data in A-weighted decibel readings using smartphones. We exclusively used iPhones for data collection for consistency in hardware and software. Before each collection, we calibrated each iPhone to the same standard, which was used for every collection outing. We recorded the L50 value (the median sound level) for each recording because the L50 value is less affected by short bursts of loud sound than the mean reading. Recordings ran for approximately 20 seconds each. We recorded all sound measurements between 9 am and 5 pm on workdays to avoid the influence of rush-hour traffic, and only collected data on days without rain, snow, or strong wind to prevent inaccuracies due to weather. Within these conditions, we collected sound measurements over multiple days and at different times to ensure representative..., , # Data from: Maps made with smartphones highlight lower noise pollution during COVID-19 pandemic lockdown at four locations in Boston

https://doi.org/10.5061/dryad.ncjsxkt35

Dataset contents include csv files of all data (each file describes collection year and site of data), R script used to create noise maps, and kml files needed to run the map creation code.

Description of the data and file structure

Each csv file contains the L50 values (median sound level) taken from hundreds of 20 second recordings over multiple collection days. The SPLnFFT application exports the latitude and longitude of where the recording was taken, which is also included in the csv files and is used to create the noise maps. The csv files are used as data frames for the R script to create noise maps for each collection site. The R script contains comments and instructions to clearly indicate each step of the map creation. The kml files are used to create bound...

Areas that are within 10 minutes of an exit are emphasized on this map, to give an indication of how accessible neighborhoods are by highway. The colors represent 1, 3, 5 and 10 minute increments from the exits, based on posted exit speeds and local road speeds in ideal conditions. The areas were calculated using ready to use services hosted in ArcGIS which feature a road network from HERE. A simple geoprocessing tool sent 40,000+ exit locations to the service, which returned the 160,000+ polygons. ---------------------The Civic Analytics Network collaborates on shared projects that advance the use of data visualization and predictive analytics in solving important urban problems related to economic opportunity, poverty reduction, and addressing the root causes of social problems of equity and opportunity. For more information see About the Civil Analytics Network.

Census tracts are created by the U.S. Census Bureau to be small, relatively permanent statistical subdivisions of a county. Census tracts average about 4,000 inhabitants: minimum population –1,200 and maximum population –8,000. Census tracts are split or merged every 10 years, depending on population change, with local feedback through the Participant Statistical Areas Program (PSAP).

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 had received quarterly updates of these data as part of its license for the HERE (Navteq) core map release (streets and related data); however, that license has expired. These ZIP Code boundaries are aligned to the street centerlines of the Q2 2018 HERE product (with a release date of April 1, 2018) and use a then-recent USPS source file.In March 2024, MassGIS modified the boundaries for all ZIP Code areas in Boston based on the U.S. Postal Service's ZIP Code Look Up by Address website. MassGIS also added polygons for ZIP Codes 02199 and 02203.Five-digit ZIP Codes were developed by the USPS and first introduced in 1963 for efficient mail delivery (the term ZIP stands for Zone Improvement Plan) but are difficult to map with complete certainty. In most cases, addresses in close proximity to each other are grouped in the same ZIP Code, which gives the appearance that ZIP Codes are defined by a clear geographic boundary. However, even when ZIP Codes appear to be geographically grouped, a clear ZIP Code boundary cannot always be drawn because ZIP Codes are only assigned to a point of delivery and not the spaces between delivery points. In areas without a regular postal route or no mail delivery, ZIP Codes may not be defined or have unclear boundaries.The USPS does not maintain an official ZIP Code map. The Census Bureau and many other commercial services will interpolate the data to create polygons to represent the approximate area covered by a ZIP code, but none of these maps are official or entirely accurate. Please see this good discussion of the issues of mapping ZIP Codes.See full metadata.Map service also available.

This dataset underlies a choropleth map of Boston area communities in which areas are shaded according to the percentage of the population that was foreign-born during each decade. The data was drawn from the US Census of Population, as well as the American Community Survey.

Throughout history, government and industries have neglected investments in some neighborhoods, especially communities of color, who are more likely to have fewer resources.

This map includes shoreline change data for the state of Massachusetts hosted by the Massachusetts Office of Coastal Zone Management.The active data layer in this map is Massachusetts Shoreline Change Transect (1970-2014) with short-term shoreline change rates. To view long-term rates, open map in Map Viewer to turn on layer.The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color aerial orthoimagery and 2007 topographic lidar datasets obtained from the National Oceanic and Atmospheric Administration's Ocean Service, Coastal Services Center. In 2018 two new mean high water (MHW) shorelines for Massachusetts were extracted from lidar collected between 2010 and 2014 (described below). 2018 addition shoreline 1The North Shore and South Coast uses 2010 lidar data collected by the U.S. Army Corps of Engineers (USACE) Joint Airborne Lidar Bathymetry Technical Center of Expertise. The South Shore and Outer Cape uses 2011 lidar data collected by the U.S. Geological Survey's (USGS) National Geospatial Program Office. Nantucket and Martha’s Vineyard uses 2012 lidar data collected by the USACE (post Sandy)from a 2012 USACE Post Sandy Topographic lidar survey. 2018 addition shoreline 2The North Shore, Boston, South Shore, Cape Cod Bay, Outer Cape, South Cape, Nantucket, Martha’s Vineyard, and the South Coast (around Buzzards Bay to the Rhode Island Border) is from 2013-14 lidar data collected by the (USGS) Coastal and Marine Geology Program. This 2018 update of the rate of shoreline change in Massachusetts includes two types of rates. Some of the rates include a proxy-datum bias correction, this is indicated in the filename with “PDB”. The rates that do not account for this correction have “NB” in their file names. The proxy-datum bias is applied because in some areas a proxy shoreline (like a High Water Line shoreline) has a bias when compared to a datum shoreline (like a Mean High Water shoreline). In areas where it exists, this bias should be accounted for when calculating rates using a mix of proxy and datum shorelines. This issue is explained further in Ruggiero and List (2009) and in the process steps of the metadata associated with the rates. This release includes both long-term (~150 years) and short term (~30 years) rates. Files associated with the long-term rates have “LT” in their names, files associated with short-term rates have “ST” in their names.