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

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

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

Graph and download economic data for Resident Population in Boston-Cambridge-Newton, MA-NH (MSA) (BOSPOP) from 2000 to 2024 about Boston, NH, MA, residents, population, and USA.

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.

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.

This record contains the underlying research data for the publication "People in more racially diverse neighborhoods are more prosocial" and the full-text is available from: https://ink.library.smu.edu.sg/lkcsb_research/5359Five studies tested the hypothesis that people living in more diverse neighborhoods would have more inclusive identities, and would thus be more prosocial. Study 1 found that people residing in more racially diverse metropolitan areas were more likely to tweet prosocial concepts in their everyday lives. Study 2 found that following the 2013 Boston Marathon bombings, people in more racially diverse neighborhoods were more likely to spontaneously offer help to individuals stranded by the bombings. Study 3 found that people living in more ethnically diverse countries were more likely to report having helped a stranger in the past month. Providing evidence of the underlying mechanism, Study 4 found that people living in more racially diverse neighborhoods were more likely to identify with all of humanity, which explained their greater likelihood of having helped a stranger in the past month. Finally, providing causal evidence for the relationship between neighborhood diversity and prosociality, Study 5 found that people asked to imagine that they were living in a more racially diverse neighborhood were more willing to help others in need, and this effect was mediated by a broader identity. The studies identify a novel mechanism through which exposure to diversity can influence people, and document a novel consequence of this mechanism.

Direct link: Short-Term Rental Eligibility Dataset

DATASET CONTEXT

Boston's ordinance on short-term rentals is designed to incorporate the growth of the home-share industry into the City's work to create affordable housing for all residents. We want to preserve housing for residents while allowing Bostonians to benefit from this new industry. Starting on on January 1, 2019, short-term rentals in Boston will need to register with the City of Boston.

Eligibility for every unit in the City of Boston is dependant on the following six criteria:

• No affordability covenant restrictions
• Compliance with housing laws and codes
• No violations of laws regarding short-term rental use
• Owner occupied
• Two- or three-family dwelling
• Residential use classification

The Short-Term Rental Eligibility Dataset leverages information, wherever possible, about these criteria. For additional details and information about these criteria, please visit https://www.boston.gov/short-term-rentals.

ABOUT THIS DATASET

ATTENTION: The Short-Term Rental Eligibility Dataset is now available for residents and landlords to determine their registration eligibility.

NOTE: These data are refreshed on a nightly basis.

In June 2018, a citywide ordinance established new guidelines and regulations for short-term rentals in Boston. Registration opened January 1, 2019. The Short-Term Rental Eligibility Dataset was created to help residents, landlords, and City officials determine whether a property is eligible to be registered as a short-term rental.

The Short-Term Rental Eligibility Dataset currently joins data from the following datasets:

• Live Street Address Management (SAM) Addresses
• Assessing Online
• Code Enforcement - Building and Property
• Problem Properties
• Income-Restricted Properties
• STR Registration Data

HOW TO DETERMINE ELIGIBILITY FOR SHORT-TERM RENTAL REGISTRATION

1. ** Open** the Short-Term Rental Eligibility Dataset. In the dataset's search bar, enter the address of the property you are seeking to register.

2. Find the row containing the correct address and unit of the property you are seeking. This is the information we have for your unit.

3. Look at the columns marked as “Home-Share Eligible,” “Limited-Share Eligible,” and “Owner-Adjacent Eligible.”

   A “yes” under any of these columns means your unit IS eligible for registration under that short-term rental type. Click here for a description of short-term rental types.

   A “no” under any of these columns means your unit is NOT eligible for registration under that short-term rental type. Click here for a description of short-term rental types.

4. If your unit has a “yes” under “Home-Share Eligible,” “Limited-Share Eligible,” or “Owner-Adjacent Eligible,” you can register your unit here.

WHY IS MY UNIT LISTED AS “NOT ELIGIBLE”?

If you find that your unit is listed as NOT eligible, and you would like to understand more about why, you can use the Short-Term Rental Eligibility Dataset to learn more. The following columns measure each of the six eligibility criteria in the following ways:

1. No affordability covenant restrictions

   • A “yes” in the “Income Restricted” column tells you that the unit is marked as income restricted and is NOT eligible.

The “Income Restricted” column measures whether the unit is subject to an affordability covenant, as reported by the Department of Neighborhood Development and/or the Boston Planning and Development Agency.
For questions about affordability covenants, contact the Department of Neighborhood Development.

1. Compliance with housing laws and codes

   • A “yes” in the “Problem Properties” column tells you that this unit is considered a “Problem Property” by the Problem Properties Task Force and is NOT eligible.

Learn more about how “Problem Properties” are defined here.

* A **“yes”** in the **“Problem Property Owner”** column tells you that the owner of this unit also owns a “Problem Property,” as reported by the Problem Properties Task Force. 

Owners with any properties designated as a Problem Property are NOT eligible.

No unit owned by the owner of a “Problem Property” may register a short-term rental.
Learn more about how “Problem Properties” are defined here.

* The **“Open Violation Count”** column tells you how many open violations the unit has. Units with **any open** violations are NOT eligible. Violations counted include: violations of the sanitary, building, zoning, and fire code; stop work orders; and abatement orders. 

NOTE: Violations written before 1/1/19 that are still open will make a unit NOT eligible until these violations are resolved.
If your unit has an open violation, visit these links to appeal your violation(s) or pay your code violation fine(s).

* The **“Violations in the Last 6 Months”** column tells you how many violations the unit has received in the last six months. Units with **three or more** violations, whether open or closed, are NOT eligible. 

NOTE: Only violations written on or after 1/1/19 will count against this criteria.
If your unit has an open violation, visit these links to appeal your violation(s) or pay your code violation fine(s).

How to comply with housing laws and codes:
Have an open violation? Visit these links to appeal your violation(s) or pay your code violation fine(s).
Have questions about problem properties? Visit Neighborhood Service’s Problem Properties site.
a legal restriction that prohibits the use of the unit as a Short-Term Rental under condominium bylaws.
Units with legal restrictions found upon investigation are NOT eligible.

If the investigation of a complaint against the unit yields restrictions of the nature detailed above, we will mark the unit with a “yes” in this column. Until such complaint-based investigations begin, all units are marked with “no.”
NOTE: Currently no units have a “legally restricted” designation.
Limited-Share
If you are the owner-occupant of a unit and you have not filed for Residential Tax Exemption, you can still register your unit by proving owner-occupancy. It is recommended that you submit proof of residency in your short-term rental registration application to expedite the process of proving owner-occupancy (see “Primary Residence Evidence” section).

* **“Building Owner-Occupied”** measures whether the building has a single owner AND is owner occupied. A “no” in this column indicates that the unit is NOT eligible for an owner-adjacent short-term rental. 

If you believe your building occupancy data is incorrect, please contact the Assessing Department.

1. Two- or three-family dwelling

   • The “Units in Building” column tells you how many units are in the building. Owner-Adjacent units are only allowed in two- to three-family buildings; therefore, four or more units in this column will mark the unit as NOT eligible for an Owner-Adjacent Short-Term Rental.

   • A “no” in the “Building Single Owner” column tells you that the owner of this unit does not own the entire building and is NOT eligible for an Owner-Adjacent Short-Term Rental.

   If you believe your building occupancy data is incorrect, please contact the Assessing Department.
   R4

   If you believe your building occupancy data is incorrect, please contact the Assessing Department.

Visit this site for more information on unit eligibility criteria.

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.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. 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.

Resident Population in Federal Reserve District 1: Boston was 14413.40600 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, Resident Population in Federal Reserve District 1: Boston reached a record high of 14413.40600 in January of 2024 and a record low of 11054.43000 in January of 1970. Trading Economics provides the current actual value, an historical data chart and related indicators for Resident Population in Federal Reserve District 1: Boston - last updated from the United States Federal Reserve on July of 2025.

This data contains the scores from the Residential Displacement Risk Map, created by the Mayor’s Office of Housing (MOH) and released in March of 2025. The Residential Displacement Risk Map is Boston’s first interactive map measuring current displacement pressures and levels of residential displacement risk across Boston. The map aims to increase understanding of this challenge, and will be updated every couple of years to keep track of changing patterns.

This map is part of Boston’s first ever Anti-Displacement Action Plan. The Action Plan responds to residential, small business, and cultural displacement with new tools to fill gaps in Boston’s existing anti-displacement toolkit. It will also better position the City to target resources to people, places, and spaces at greatest risk of displacement, and it includes recommendations for how to use this map in planning, policy, and development decision making.

The Residential Displacement Risk Map can also be used to raise awareness of displacement and housing instability challenges and provide a data-driven understanding of displacement risk. It is meant to be used by the City, residents, community organizations, academics, housing advocates, and more.

The Residential Displacement Risk Map measures community-level displacement, meaning how likely it is for high numbers of households to be displaced from an area, changing its fundamental demographic makeup. The Residential Displacement Risk Map does not measure household- or individual-level displacement risk, or how likely it is for any one household or individual to be displaced. Those who live in a high-risk area will not necessarily be displaced. The map only paints a general picture of an area’s sensitivity to displacement pressures. A higher score indicates a higher risk of displacement.

The Residential Displacement Risk Map measures direct displacement (when residents are forced to move from their homes, such as in an eviction or a foreclosure) and estimates economic displacement (when current residents of an area can no longer afford to live there). The map uses direct displacement as a guidepost for predicting where economic displacement is likely to occur, based on a variety of characteristics that are associated with direct displacement. If an area has high direct displacement (evictions and foreclosures), then it is likely to also have high economic displacement. More detail on how the Residential Displacement Risk Map measures risk can be found in the technical documentation linked below.

The Displacement Risk Map can be directly accessed here: https://experience.arcgis.com/experience/177e64a85f4041d2b4655d7cd1991c56/

Learn more about the City’s Anti-Displacement Action Plan here: https://www.boston.gov/departments/planning-advisory-council/anti-displacement-action-plan#:~:text=It%20lays%20out%20priority%20policies,and%20preserving%20existing%20affordable%20housing

Technical documentation for the map can be accessed here: https://docs.google.com/document/d/1ctv0S67Rx5GA46GbY_Glo_y-JYoQRCMS336yPDw_18o/edit?usp=sharing

Graph and download economic data for All-Transactions House Price Index for Boston, MA (MSAD) (ATNHPIUS14454Q) from Q3 1977 to Q1 2025 about Boston, MA, appraisers, HPI, housing, price index, indexes, price, and USA.

This dataset offers a comparative snapshot of the social composition in two transatlantic port districts by documenting over 5,000 individuals who lived in the main thoroughfares of Antwerp (Schippersstraat, Vingerlingstraat, and Oudemansstraat, located in the Schipperskwartier) and Boston (North Street, situated in the North End) in 1880 and 1900. Based on Belgian population registers and U.S. census records, it includes data on name, sex, year of birth and age, birthplace, marital status, relationship to the household head, occupation, race, and birthplaces of parents. As the two sources sometimes contain different variables or use differing classifications, the available data may vary between the Belgian and American cases.

The dataset was created in the context of the postdoctoral research project at the University of Antwerp (Belgium), Rethinking “Sailortown:” Comparing the Socioeconomic Dynamics of Harbour Districts in Antwerp and Boston, 1850-1930, funded by the Research Foundation – Flanders, grant number 12X1822N. It was used in the peer-reviewed article 'Unpacking the Town within Sailortown: The Port District Neighborhoods of Antwerp and Boston, c. 1880-1900', Journal of Urban History (forthcoming).

Further details on the dataset's structure, methodology and use can be found in the accompanying data paper: Kristof Loockx, 'Residents of Late Nineteenth-Century Port Districts: A Comparative Dataset from Antwerp and Boston, 1880-1900', Journal of Open Humanities Data 11, no. 39 (2025): 1-9. DOI: https://doi.org/10.5334/johd.335

This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

Graph and download economic data for Housing Inventory: Active Listing Count in Boston-Cambridge-Newton, MA-NH (CBSA) (ACTLISCOU14460) from Jul 2016 to Jun 2025 about Boston, NH, MA, active listing, listing, and USA.

This dataset contains tabular data at three scales (city, tract, and synoptic site) and related vector shapefiles (for watersheds or buffers around synoptic sites) for areas included in the Carbon in Urban River Biogeochemistry Project (CURB) to assess how social, built, and biophysical factors shape aquatic functions. The city scale included 486 urban areas in the continental United States with greater than 50,000 residents. Tabular data are provided for each urban area (CURB_CensusUrbanArea.csv) and all U.S. Census tracts within seven urban areas (Atlanta, GA, Boston, MA, Miami, FL, Phoenix, AZ, Portland, OR, Salt Lake City, UT, and San Francisco, CA; CURB_CensusTract.csv) to characterize a range of social, built, and biophysical factors. In six focal cities (Baltimore, MD, Boston, MA, Atlanta, GA, Miami, FL, Salt Lake City, UT, and Portland, OR) up to 100 sites were selected for synoptic water quality sampling. For each synoptic site tabular data (CURB_SynopticSite.csv) are provided to characterize a range of social, built, and biophysical factors within the watershed (Atlanta, Baltimore, Boston, Portland, Salt Lake City) or within a buffer of the site (Miami). Vector shapefiles are provided for the watershed boundaries (CURB_Synoptic_Watersheds.zip) for all synoptic sites in each city except Miami, FL where 400-m buffers (CURB_Miami_Synoptic_Buffers.zip) around the synoptic site were used.

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. Block groups generally contain between 600 and 3,000 people. Census block groups are split or merged every 10 years, depending on population change, with local feedback through the Participant Statistical Areas Program (PSAP). These shapefiles provide the boundaries for the 2020 block groups for Boston.

Microsatellite (SSR) data for Boston-area population genetics study of dandelionsAdditional dendrograms corresponding to Figure 4 in the paper of the full datasets of all three populations showing similar results of panmixia (CG=Cedar Grove; MF=Moon Farm; PH=Prospect Hill)