Boston Neighborhood Boundaries represent a combination of zoning neighborhood boundaries, zip code boundaries and 2010 census tract boundaries. These boundaries are used in the broad sense for visualization purposes, research analysis and planning studies. However these boundaries are not official neighborhood boundaries for the City of Boston. The BPDA is not responsible for any districts or boundaries within the City of Boston except for the districts we use for planning 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.

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

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.

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.

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

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.

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.Feature service also available.

This map compares the number of households living above the poverty line to the number of households living below. In the U.S. overall, there are 6.2 households living above the poverty line for every 1 household living below. Green areas on the map have a higher than normal number of households living above compared to below poverty. Orange areas on the map have a higher than normal number of households living below the poverty line compared to those above in that same area.In this map you see the ratio of households living above the poverty line to households living below the poverty line. For the U.S. overall, there are 6.2 households living above the poverty line for every household living below. This map is shaded to clearly show which areas have about the same ratio as the U.S. overall, and which areas have far more families living above poverty or far more families living below poverty than "normal.""The poverty rate is one of several socioeconomic indicators used by policy makers to evaluate economic conditions. It measures the percentage of people whose income fell below the poverty threshold. Federal and state governments use such estimates to allocate funds to local communities. Local communities use these estimates to identify the number of individuals or families eligible for various programs." Source: U.S. Census BureauThe map shows the ratio for states, counties, tracts and block groups, using data from the U.S. Census Bureau's American Community Survey (ACS) for 2013 for the previous 12 months. -------------------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.

The Department of Neighborhood Development (DND) takes care of city owned property, including the maintanence of buildings and vacant land. This is a legacy dataset that provides information on these properties including the size, location, potential use, and more.

Please see the current visualization of this data, provided by the Department of Neighborhood Development, for the most up to date information. Available @ http://property.boston.gov

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

Feature layer generated from running the Plan Routes solution.

This layer represents City of Boston Zoning Subdistrict boundaries indicating geographic areas subject to specific zoning guidelines. Developed and maintained by the Planning Department GIS in accordance with the Boston Zoning Code.

The TIGER/Line Files are shapefiles and related database files (.dbf) that 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 File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

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

Residential yards are a significant component of urban socio-ecological systems; residential land covers 11% of the United States and is often the dominant land use within urban areas. Residential yards also play an important role in the sustainability of urban socio-ecological systems, affecting biogeochemical cycles, water, and the climate via individual- and household-level behaviors. Vegetation, such as trees and grasses, are unevenly distributed across front and back yards, and we sought to understand how similar yards are to each other when compared to their neighboring yards and neighborhoods using aerial imagery. There are many ways to measure yard similarity, and we compared several measures to account for different definitions of ‘neighborness’. We examined the spatial autocorrelation of several yard vegetation characteristics in both front and backyards in Boston, MA, USA. Our study area included 1,027 Census block groups (sub-neighborhood areas) and 175,576 parcels with matched front-backyard pairings (n = 351,152 yards in total) across Boston’s metropolitan area. This data package contains 1) 351,152 yard spatially-referenced yard polygons with five measures of vegetation summarized, 2) the containing block groups, and 3) and *.R script that replicates the analyses reported in Locke, D. H., Ossola, A., Minor, E., & Lin, B. B. (2021). Spatial contagion structures urban vegetation from parcel to landscape. People and Nature, 00, 1–15. https://doi.org/10.1002/pan3.10254

Methods

1. Study Area This study focused on the Boston, MA, metropolitan region (42°21′29″N 71°03′49″W), an area of approximately 703 km2. The region has a humid continental climate (mean annual temperature = 9.6 °C; mean annual precipitation = 1233 mm) (PRISM Climate Group 2015) and was historically covered with mesic forests. Forty-four percent of the land area is residential (Ossola et al., 2019a), which is consistent with other urban areas in western countries such as Baltimore, MD (Avolio et al., 2020), Chicago, IL (Lewis et al., 2019), Adelaide, (Australia)(Ossola et al., 2021), Edinburgh (Scotland), Belfast (Northern Ireland), Cardiff (Wales), and Leicester and Oxford (England) (Loram et al., 2007), and represents more than twice as much land area as parks and open spaces (18.43%) (Ossola et al., 2019b). Backyards compose 14% of all urban land area and contain ~21% of all tree canopy cover; front yards cover ~8% of the area and have ~8% of the study area’s tree canopy cover (Ossola et al., 2019b).

2. Open Data Classified LiDAR point cloud data (year 2014) were obtained from the US Geological Survey (“MA Post-Sandy CMPG 2013–14”, NPS = 0.7 m, vertical and horizontal accuracy = 0.05 m and 0.35 m, respectively). High-resolution RBG-NIR imagery (1 m ground resolution, year 2014) were obtained from the National Agriculture Imagery Program (NAIP, USDA). Residential parcel polygons, building footprints, and road centerline data were downloaded from the open data portals of the Commonwealth of Massachusetts (2017) and the City of Boston (2017).

3. Geospatial analyses All front, corner, and backyards contained in all residential parcels with a house were located and classified in ArcGIS Desktop 10.5 (ESRI, Redlands, CA) by using the workflow described in Ossola and others (2019a, 2019b). Briefly, each house centroid was identified to fit an offset line perpendicular to the closest street centerline. Front and backyards were then located by splitting each parcel polygon with a dividing segment, perpendicular to the offset line, passing through the house centroid, and extending to the parcel’s border. Yards were classified by attributing the front yard as the closest unit to the respective road centerline. Corner yards, which lack clear front/back sides, were assigned to all parcels located within 15 m from street intersections and were excluded from analyses. The workflow used to locate and classify yards exceeded 98% accuracy (Ossola et al., 2019a). Vegetation maps detailing tree height, canopy volume, and tree and grass covers were modelled and validated for their accuracy based on the LiDAR and RBG-NIR imagery as detailed in previous papers (Ossola et al., 2019a, 2019b). Briefly, tree canopy height was extracted from a canopy height model (1.5 m ground resolution) interpolated from the LiDAR data in ArcGIS Desktop 10.5 (ESRI, Redlands, CA). Tree and grass covers were modelled at 1.5 m resolution by using maximum likelihood supervised classification of ~100,000 pixels manually attributed to one of three land cover classes (i.e., tree, grass and non-vegetated cover), and based on the tree canopy height map and the RGB-NIR imagery (Singh et al., 2012). The average vertical accuracy of the tree height data, as recorded by the LiDAR point cloud, is 5.3 cm. The accuracy of the grass and tree canopy cover classification is 91.7% and 98.9%, respectively (Ossola & Hopton, 2018a). Canopy volume was calculated as the product of tree canopy cover and height within each pixel, assuming this volume to be completely occupied by vegetation (Ossola & Hopton, 2018a, 2018b), which overestimates total volume. Because these remotely sensed data view the earth from above, and tree canopy overhangs turf, the turf estimates are plausibly underestimates (Akbari et al., 2003).

References

Akbari, H., Rose, L. S., & Taha, H. (2003). Analyzing the land cover of an urban environment using high-resolution orthophotos. Landscape and Urban Planning, 63(1), 1–14. https://doi.org/10.1016/S0169-2046(02)00165-2 Avolio, M. L., Blanchette, A., Sonti, N. F., & Locke, D. H. (2020). Time Is Not Money: Income Is More Important Than Lifestage for Explaining Patterns of Residential Yard Plant Community Structure and Diversity in Baltimore. Frontiers in Ecology and Evolution, 8(April), 1–14. https://doi.org/10.3389/fevo.2020.00085 Lewis, A. D., Bouman, M. J., Winter, A. M., Hasle, E. A., Stotz, D. F., Johnston, M. K., Klinger, K. R., Rosenthal, A., & Czarnecki, C. A. (2019). Does nature need cities? Pollinators reveal a role for cities in wildlife conservation. Frontiers in Ecology and Evolution, 7(JUN), 1–8. https://doi.org/10.3389/fevo.2019.00220 Loram, A., Tratalos, J., Warren, P. H., & Gaston, K. J. (2007). Urban domestic gardens (X): The extent & structure of the resource in five major cities. Landscape Ecology, 22(4), 601–615. https://doi.org/10.1007/s10980-006-9051-9 Ossola, A., & Hopton, M. E. (2018a). Climate differentiates forest structure across a residential macrosystem. Science of the Total Environment, 639, 1164–1174. https://doi.org/10.1016/j.scitotenv.2018.05.237 Ossola, A., & Hopton, M. E. (2018b). Measuring urban tree loss dynamics across residential landscapes. Science of The Total Environment, 612, 940–949. https://doi.org/10.1016/j.scitotenv.2017.08.103 Ossola, A., Jenerette, G. D., McGrath, A., Chow, W., Hughes, L., & Leishman, M. R. (2021). Small vegetated patches greatly reduce urban surface temperature during a summer heatwave in Adelaide, Australia. Landscape and Urban Planning, 209. https://doi.org/10.1016/j.landurbplan.2021.104046 Ossola, A., Locke, D. H., Lin, B., & Minor, E. (2019a). Greening in style: Urban form, architecture and the structure of front and backyard vegetation. Landscape and Urban Planning, 185(November 2018), 141–157. https://doi.org/10.1016/j.landurbplan.2019.02.014 Ossola, A., Locke, D. H., Lin, B., & Minor, E. S. (2019b). Yards increase forest connectivity in urban landscapes. Landscape Ecology, 7(12). https://doi.org/10.1007/s10980-019-00923-7 Singh, K. K., Vogler, J. B., Shoemaker, D. A., & Meentemeyer, R. K. (2012). LiDAR-Landsat data fusion for large-area assessment of urban land cover: Balancing spatial resolution, data volume and mapping accuracy. ISPRS Journal of Photogrammetry and Remote Sensing, 74(November), 110–121. https://doi.org/10.1016/j.isprsjprs.2012.09.009