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 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.

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 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. After the field sampling was complete, aerial photograph signatures were verified for all of the associations using the classification plot data, Bell et al. (2002), and Elliman (2004) and (2005) data. These signatures were extrapolated to other areas within the park boundary that were not sampled. Using ARCGIS 9.1, polygon boundaries in the preliminary vegetation map were further edited and refined to develop a draft association-level vegetation map. Polygons were updated with USNVC association names and codes based on the classification plot data. Polygons that were attributed with land use - land cover categories in the preliminary vegetation map retained their attributes. The aerial photointerpretation key was updated. The thematic accuracy of this 2006 draft association-level vegetation association and land use map was then assessed for accuracy.

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

These ESRI shape files are of National Park Service tract and boundary data that was created by the Land Resources Division. Bounds of the tracts and islands are photo interpreted from 1996 ortho photo mosaics created by the University of Rhode Island for the park. Tracts and islands are consistent with the legislated boundaries defined by PL 104-333 which also references map number BOHA 80,002. Tracts are numbered and created by the regional cartographic staff at the Land Resources Program Centers and are associated to the Land Status Maps. This data should be used to display properties that NPS owns and properties that NPS may have some type of interest such as scenic easements or right of ways.

This map contains demographic variables for block groups in the Boston area. The map shows a comparison of two variables: per capita income growth from 2015-2020 and unemployment. Size and color are used to show the two variables.The size of the circle represents the unemployed population over the age of 16, so the largest circles show the areas with the most unemployment. The colors showcase a range of personal income growth from 2015 to 2020. Green areas have the least projected growth, and yellow areas have the highest projected income growth.The data comes from Esri's ArcGIS Online data enrichment using the Living Atlas Block Group Analysis layer. The vintage of the data is 2015.

This reference contains the imagery data used in the completion of the baseline vegetation inventory project for the NPS park unit. Orthophotos, raw imagery, and scanned aerial photos are common files held here. High-quality existing photography housed by the Commonwealth of Massachusetts Office of Geographic and Environmental Information (MassGIS) was used as the base for the BOHA vegetation map. A true color orthophotomosaic was developed from a set of digital 1:5,000 scale medium resolution true color aerial images that are considered the new "basemap" for the Commonwealth of Massachusetts by MassGIS and the Executive Office of Environmental Affairs (EOEA) until 2005 DOQs became available in 2006 (MassGIS 2007). The photography for the entire commonwealth was captured in April 2005 when deciduous trees were mostly bare and the ground was generally free of snow. The image type is 4-band (RGBN) natural color (Red, Green, Blue) and Near infrared in 8 bits (values ranging 0-255) per band format.

Wicked Free WiFi locations around the City of Boston as determined by the Meraki API.

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

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 quiet locations such as parks. This project demonstrates that changes in human activity can reduce noise pollution and that simple smartphone technology can be used to make highly detailed maps of noise pollution that identify sources of high sound levels potentially harmful to humans in urban environments. Methods 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. We followed these procedures for both collection cycles (2020 during lockdown and 2021 after lockdown had been lifted). The 2017 data were collected for an unrelated noise pollution project conducted by previous members of the Primack Lab and were not collected with the exact parameters established for the 2020 and 2021 collections. However, we found these noise data to be valuable given that they could be used to compare lockdown sound levels to the soundscape before the COVID-19 pandemic. We used R Studio to create sound maps from the individual data points in a way that allows for spatial visualization of the soundscape before, during, and after the pandemic lockdown. To test for statistically significant differences in sound level between years, we performed Welch’s t-tests on the raw data for all sites comparing lockdown (2020) measurements to pre (2017) and post (2021) lockdown measurements. Given the hypothesis that 2020 would have lower sound levels at each site, we report the results of one-tailed t-tests.

This file contains the 65 cities and towns in Massachusetts for which MBTA bus or rapid transit service is provided. This data is based off of the 2010 census. The legislative intent for some boundaries could not be mapped. Boundaries where that is true are identified in the attribute information. Name Description Data Type Example town_name Full name for the MA town or city identification. String Boston town_id MassGIS Town-ID Code (alphabetical, 1-351) Numeric 34 sum_acres Area covered by the town or city in acres. Double 31304.22 sum_square Area covered by the town or city in square miles. Double 48.91 Use constraints: This data set, like all other cartographic products may contain inherent aberrations in geography or thematical errors. The boundaries included in this data set were developed using accepted GIS methodology. Cartographic products can never truly represent real-world conditions due to several factors. These factors can include, but are not limited to: human error upon digitizing, computational tolerance of the computer, or the distortion of map symbology. Because of these factors MassGIS cannot be held legally responsible for personal or property damages resulting from any type of use of the data set. These boundaries are suitable for map display and planning purposes. They cannot be used as a substitute for the work of a professional land surveyor.MassDOT/MBTA shall not be held liable for any errors in this data. This includes errors of omission, commission, errors concerning the content of the data, and relative and positional accuracy of the data. This data cannot be construed to be a legal document. Primary sources from which this data was compiled must be consulted for verification of information contained in this data.

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.

The 1:100,000-scale geologic map of the South Boston 30' x 60' quadrangle, Virginia and North Carolina, provides geologic information for the Piedmont along the I-85 and U.S. Route 58 corridors and in the Roanoke River watershed, which includes the John H. Kerr Reservoir and Lake Gaston. The Raleigh terrane (located on the eastern side of the map) contains Neoproterozoic to early Paleozoic(?) polydeformed, amphibolite-facies gneisses and schists. The Carolina slate belt of the Carolina terrane (located in the central part of the map) contains Neoproterozoic metavolcanic and metasedimentary rocks at greenschist facies. Although locally complicated, the slate-belt structure mapped across the South Boston map area is generally a broad, complex anticlinorium of the Hyco Formation (here called the Chase City anticlinorium) and is flanked to the west and east by synclinoria, which are cored by the overlying Aaron and Virgilina Formations. The western flank of the Carolina terrane (located in the western-central part of the map) contains similar rocks at higher metamorphic grade. This terrane includes epidote-amphibolite-facies to amphibolite-facies gneisses of the Neoproterozoic Country Line complex, which extends north-northeastward across the map. The Milton terrane (located on the western side of the map) contains Ordovician amphibolite-facies metavolcanic and metasedimentary gneisses of the Cunningham complex. Crosscutting relations and fabrics in mafic to felsic plutonic rocks constrain the timing of Neoproterozoic to late Paleozoic deformations across the Piedmont. In the eastern part of the map, a 5- to 9-kilometer-wide band of tectonic elements that contains two late Paleozoic mylonite zones (Nutbush Creek and Lake Gordon) and syntectonic granite (Buggs Island pluton) separates the Raleigh and Carolina terranes. Amphibolite-facies, infrastructural metaigneous and metasedimentary rocks east of the Lake Gordon mylonite zone are generally assigned to the Raleigh terrane. In the western part of the map area, a 5- to 8-kilometer-wide band of late Paleozoic tectonic elements includes the Hyco and Clover shear zones, syntectonic granitic sheets, and amphibolite-facies gneisses along the western margin of the Carolina terrane at its boundary with the Milton terrane. This band of tectonic elements is also the locus for early Mesozoic extensional faults associated with the early Mesozoic Scottsburg, Randolph, and Roanoke Creek rift basins. The map shows fluvial terrace deposits of sand and gravel on hills and slopes near the Roanoke and Dan Rivers. The terrace deposits that are highest in altitude are the oldest. Saprolite regolith is spatially associated with geologic source units and is not shown separately on the map. Mineral resources in the area include gneiss and granite quarried for crushed stone, tungsten-bearing vein deposits of the Hamme district, and copper and gold deposits of the Virgilina district. Surface-water resources are abundant and include rivers, tributaries, the John H. Kerr Reservoir, and Lake Gaston. Groundwater flow is concentrated in saprolite regolith, along fractures in the crystalline bedrock, and along fractures and bedding-plane partings in the Mesozoic rift basins.

This dataverse repository contains two datasets: 1. A one square meter resolution map of biomass for the City of Boston. Units are Mg biomass per hectare (Mg/ha). 2. A one square meter resolution map of canopy cover for the City of Boston. Units are binary: 0 = no canopy, 1 = canopy Both datasets are derived from LiDAR and high resolution remote sensing imagery. Details of the methodology are provided in the following publications: Raciti, SM, Hutyra, LR, Newell, JD, 2014. Mapping carbon storage in urban trees withmulti-source remote sensing data: Relationships between biomass, land use, and demographics in Boston neighborhoods,Science of the Total Environment, 500-501, 72-83. http://dx.doi.org/10.1016/j.scitotenv.2014.08.070 Raciti, SM, Hutyra, LR, Newell, JD, 2015. Corrigendum to “Mapping carbon storage in urban trees with multi-source remote sensing data: Relationships between biomass, land use, and demographics in Boston neighborhoods”, Science of the Total Environment, 538, 1039-1041. http://dx.doi.org/10.1016/j.scitotenv.2015.07.154

This layer is a digital raster graphic of the historical 15-minute USGS topographic quadrangle maps of coastal towns in Massachusetts. These quadrangles were mosaicked together to create a single data layer of the coast of Massachusetts and a large portion of the southeastern area of the state. The Massachusetts Office of Coastal Zone Management (CZM) obtained the map images from the Harvard Map Collection. The maps were produced in the late 1890s and early 20th century at a scale of 1:62,500 or 1:63,360 and are commonly known as 15-minute quadrangle maps because each map covers a four-sided area of 15 minutes of latitude and 15 minutes of longitude. A digital raster graphic (DRG) is a scanned image of a U.S. Geological Survey (USGS) standard series topographic map. In ArcSDE the image is named IMG_USGS_HIST_COASTAL.

In spring 2008, the U.S. Geological Survey, as part of its Boston 133 Cities Urban Area mapping program, contracted for true-color imagery covering the metropolitan Boston area and beyond. Image type for the entire region (more than 1.7 million acres) is 24-bit, 3-band (red, green, blue) natural color. Each band has pixel values ranging 0-255. Pixel resolution is 30 cm., or approximately one foot. In spring 2009, USGS continued the project and 4-band 30cm imagery was obtained for the remainder of the state.This digital orthoimagery can serve a variety of purposes, from general planning, to field reference for spatial analysis, to a tool for data development and revision of vector maps. It can also serve as a reference layer or basemap for myriad applications inside geographic information system (GIS) software.The data are served from MassGIS' ArcGIS Online account as a tiled cached map service for fast display.For full metadata and links to download the imagery visit https://www.mass.gov/info-details/massgis-data-20082009-aerial-imagery.

This public map service contains points and polygons representing information from the Massachusetts Cultural Resource Information System (MACRIS) database and related records on file at the Massachusetts Historical Commission (MHC), including the Inventory of Historic Assets of the Commonwealth, National Register of Historic Places nomination forms, local historic district study reports, local landmark reports, and other materials. The MACRIS database and the layers within the MACRIS Maps web application are updated regularly as new information is submitted and added, and as the accuracy of earlier versions of the datalayer is improved. Three datalayers are being made available to the public: The Inventory Points layer contains the locations of buildings, burial grounds, structures, and objects (e.g. statues, monuments, walls). The points layer is symbolized to indicate the most common historic designation types: 1) National Register of Historic Places, 2) local historic district, 3) both National Register and local historic district, 4) Preservation Restriction, 5) Massachusetts Historic Landmark (MA/HL) and 6) inventoried but not designated with one of the previous designations. Less common designations are not symbolized in MACRIS, but are included in the Designations attribute field.The Inventory Areas polygon layer includes areas and districts symbolized in MACRIS in a similar manner to Inventory Points. Another polygon layer, Towns, possesses a binary “y” or blank field to indicate whether a town has a survey pending digitization. Please note that new and updated information is added to MHC files daily, and that there may be considerable lag time before this information is reflected in MACRIS or in MACRIS Maps. Map information for “completed” towns may not reflect the most current information on file with MHC. For additional information, users may consult the source records, forms and maps that make up the official Inventory of Historic and Archaeological Assets of the Commonwealth, on file at the MHC, Massachusetts Archives Building, 220 Morrissey Boulevard, Boston, during weekday business hours. No appointment is needed. For directions, see https://www.sec.state.ma.us/mhc/.See the metadata for more details.

Social vulnerability is defined as the disproportionate susceptibility of some social groups to the impacts of hazards, including death, injury, loss, or disruption of livelihood. In this dataset from Climate Ready Boston, groups identified as being more vulnerable are older adults, children, people of color, people with limited English proficiency, people with low or no incomes, people with disabilities, and people with medical illnesses. Source:The analysis and definitions used in Climate Ready Boston (2016) are based on "A framework to understand the relationship between social factors that reduce resilience in cities: Application to the City of Boston." Published 2015 in the International Journal of Disaster Risk Reduction by Atyia Martin, Northeastern University.Population Definitions:Older Adults:Older adults (those over age 65) have physical vulnerabilities in a climate event; they suffer from higher rates of medical illness than the rest of the population and can have some functional limitations in an evacuation scenario, as well as when preparing for and recovering from a disaster. Furthermore, older adults are physically more vulnerable to the impacts of extreme heat. Beyond the physical risk, older adults are more likely to be socially isolated. Without an appropriate support network, an initially small risk could be exacerbated if an older adult is not able to get help.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for population over 65 years of age.Attribute label: OlderAdultChildren: Families with children require additional resources in a climate event. When school is cancelled, parents need alternative childcare options, which can mean missing work. Children are especially vulnerable to extreme heat and stress following a natural disaster.Data source: 2010 American Community Survey 5-year Estimates (ACS) data by census tract for population under 5 years of age.Attribute label: TotChildPeople of Color: People of color make up a majority (53 percent) of Boston’s population. People of color are more likely to fall into multiple vulnerable groups aswell. People of color statistically have lower levels of income and higher levels of poverty than the population at large. People of color, many of whom also have limited English proficiency, may not have ready access in their primary language to information about the dangers of extreme heat or about cooling center resources. This risk to extreme heat can be compounded by the fact that people of color often live in more densely populated urban areas that are at higher risk for heat exposure due to the urban heat island effect.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract: Black, Native American, Asian, Island, Other, Multi, Non-white Hispanics.Attribute label: POC2Limited English Proficiency: Without adequate English skills, residents can miss crucial information on how to preparefor hazards. Cultural practices for information sharing, for example, may focus on word-of-mouth communication. In a flood event, residents can also face challenges communicating with emergency response personnel. If residents are more sociallyisolated, they may be less likely to hear about upcoming events. Finally, immigrants, especially ones who are undocumented, may be reluctant to use government services out of fear of deportation or general distrust of the government or emergency personnel.Data Source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract, defined as speaks English only or speaks English “very well”.Attribute label: LEPLow to no Income: A lack of financial resources impacts a household’s ability to prepare for a disaster event and to support friends and neighborhoods. For example, residents without televisions, computers, or data-driven mobile phones may face challenges getting news about hazards or recovery resources. Renters may have trouble finding and paying deposits for replacement housing if their residence is impacted by flooding. Homeowners may be less able to afford insurance that will cover flood damage. Having low or no income can create difficulty evacuating in a disaster event because of a higher reliance on public transportation. If unable to evacuate, residents may be more at risk without supplies to stay in their homes for an extended period of time. Low- and no-income residents can also be more vulnerable to hot weather if running air conditioning or fans puts utility costs out of reach.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for low-to- no income populations. The data represents a calculated field that combines people who were 100% below the poverty level and those who were 100–149% of the poverty level.Attribute label: Low_to_NoPeople with Disabilities: People with disabilities are among the most vulnerable in an emergency; they sustain disproportionate rates of illness, injury, and death in disaster events.46 People with disabilities can find it difficult to adequately prepare for a disaster event, including moving to a safer place. They are more likely to be left behind or abandoned during evacuations. Rescue and relief resources—like emergency transportation or shelters, for example— may not be universally accessible. Research has revealed a historic pattern of discrimination against people with disabilities in times of resource scarcity, like after a major storm and flood.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for total civilian non-institutionalized population, including: hearing difficulty, vision difficulty, cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty. Attribute label: TotDisMedical Illness: Symptoms of existing medical illnesses are often exacerbated by hot temperatures. For example, heat can trigger asthma attacks or increase already high blood pressure due to the stress of high temperatures put on the body. Climate events can interrupt access to normal sources of healthcare and even life-sustaining medication. Special planning is required for people experiencing medical illness. For example, people dependent on dialysis will have different evacuation and care needs than other Boston residents in a climate event.Data source: Medical illness is a proxy measure which is based on EASI data accessed through Simply Map. Health data at the local level in Massachusetts is not available beyond zip codes. EASI modeled the health statistics for the U.S. population based upon age, sex, and race probabilities using U.S. Census Bureau data. The probabilities are modeled against the census and current year and five year forecasts. Medical illness is the sum of asthma in children, asthma in adults, heart disease, emphysema, bronchitis, cancer, diabetes, kidney disease, and liver disease. A limitation is that these numbers may be over-counted as the result of people potentially having more than one medical illness. Therefore, the analysis may have greater numbers of people with medical illness within census tracts than actually present. Overall, the analysis was based on the relationship between social factors.Attribute label: MedIllnesOther attribute definitions:GEOID10: Geographic identifier: State Code (25), Country Code (025), 2010 Census TractAREA_SQFT: Tract area (in square feet)AREA_ACRES: Tract area (in acres)POP100_RE: Tract population countHU100_RE: Tract housing unit countName: Boston Neighborhood

Locations within the City of Boston of all small cell antenna/DAS approved by the City prior to 01/01/2017