This data, maintained by the Mayor’s Office of Housing (MOH), is an inventory of all income-restricted units in the city. This data includes public housing owned by the Boston Housing Authority (BHA), privately- owned housing built with funding from DND and/or on land that was formerly City-owned, and privately-owned housing built without any City subsidy, e.g., created using Low-Income Housing Tax Credits (LIHTC) or as part of the Inclusionary Development Policy (IDP). Information is gathered from a variety of sources, including the City's IDP list, permitting and completion data from the Inspectional Services Department (ISD), newspaper advertisements for affordable units, Community Economic Development Assistance Corporation’s (CEDAC) Expiring Use list, and project lists from the BHA, the Massachusetts Department of Housing and Community Development (DHCD), MassHousing, and the U.S. Department of Housing and Urban Development (HUD), among others. The data is meant to be as exhaustive and up-to-date as possible, but since many units are not required to report data to the City of Boston, MOH is constantly working to verify and update it. See the data dictionary for more information on the structure of the data and important notes. The database only includes units that have a deed-restriction. It does not include tenant-based (also known as mobile) vouchers, which subsidize rent, but move with the tenant and are not attached to a particular unit. There are over 22,000 tenant-based vouchers in the city of Boston which provide additional affordability to low- and moderate-income households not accounted for here. The Income-Restricted Housing report can be directly accessed here:
https://www.boston.gov/sites/default/files/file/2023/04/Income%20Restricted%20Housing%202022_0.pdf

Learn more about income-restricted housing (as well as other types of affordable housing) here: https://www.boston.gov/affordable-housing-boston#income-restricted

The American Community Survey Education Tabulation (ACS-ED) is a custom tabulation of the ACS produced for the National Center of Education Statistics (NCES) by the U.S. Census Bureau. The ACS-ED provides a rich collection of social, economic, demographic, and housing characteristics for school systems, school-age children, and the parents of school-age children. In addition to focusing on school-age children, the ACS-ED provides enrollment iterations for children enrolled in public school. The data profiles include percentages (along with associated margins of error) that allow for comparison of school district-level conditions across the U.S. For more information about the NCES ACS-ED collection, visit the NCES Education Demographic and Geographic Estimates (EDGE) program at: https://nces.ed.gov/programs/edge/Demographic/ACSAnnotation values are negative value representations of estimates and have values when non-integer information needs to be represented. See the table below for a list of common Estimate/Margin of Error (E/M) values and their corresponding Annotation (EA/MA) values.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data. -9 An '-9' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small. -8 An '-8' means that the estimate is not applicable or not available. -6 A '-6' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution. -5 A '-5' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. -3 A '-3' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate. -2 A '-2' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.

Click here to check Short-Term Rental Eligibility

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

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 and is refreshed nightly:

• Live Street Address Management (SAM) Addresses.
• Assessing Online.
• Building and Property Violations
• 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.
     
     

2. 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.
     
     

3. No violations of laws regarding short-term rental use

   • A “yes” in the “Legally Restricted” column tells you that there is a complaint against the unit that finds

     • A legal restriction that prohibits the use of the unit as a Short-Term Rental under local, state, or federal law, OR

     • 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.
       
       

4. Owner-occupied

   • A “no” in the “Unit Owner-Occupied” column tells you that there is NO Residential Tax Exemption filed for that unit via the Assessing Department, and that unit is automatically categorized as NOT eligible for the following Short-Term Rental types:

     • Home-Share
     • Limited-Share
       
       
     • Residential Tax Exemption indicates that a unit is owner-occupied and generates a “yes” in the “Unit Owner-Occupied” column.
       
       

     • Owners are not required to file a Residential Tax Exemption in order to be eligible to register a unit as a Short-Term Rental.
       
       

     • If you would like to apply for Residential Tax Exemption, you can apply here.
       
       

     • 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

The American Community Survey Education Tabulation (ACS-ED) is a custom tabulation of the ACS produced for the National Center of Education Statistics (NCES) by the U.S. Census Bureau. The ACS-ED provides a rich collection of social, economic, demographic, and housing characteristics for school systems, school-age children, and the parents of school-age children. In addition to focusing on school-age children, the ACS-ED provides enrollment iterations for children enrolled in public school. The data profiles include percentages (along with associated margins of error) that allow for comparison of school district-level conditions across the U.S. For more information about the NCES ACS-ED collection, visit the NCES Education Demographic and Geographic Estimates (EDGE) program at: https://nces.ed.gov/programs/edge/Demographic/ACSAnnotation values are negative value representations of estimates and have values when non-integer information needs to be represented. See the table below for a list of common Estimate/Margin of Error (E/M) values and their corresponding Annotation (EA/MA) values.All information contained in this file is in the public _domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.-9An '-9' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small.-8An '-8' means that the estimate is not applicable or not available.-6A '-6' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.-5A '-5' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate.-3A '-3' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate.-2A '-2' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.

The Low-Income Energy Affordability Data (LEAD) Tool was created by the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA) to help state and local partners understand housing and energy characteristics for the low- and moderate-income (LMI) communities they serve. The LEAD Tool provides estimated LMI household energy data based on income, energy expenditures, fuel type, housing type, and geography, which stakeholders can use to make data-driven decisions when planning for their energy goals. From the LEAD Tool website, users can also create and download customized heat-maps and charts for various geographies, housing, and energy characteristics.

Datasets are available for 50 states plus Puerto Rico and Washington D.C., along with their cities, counties, and census tracts. The file below, "1. Description of Files," provides a list of all files included in this dataset. A description of the abbreviations and units used in the LEAD Tool data can be found in the file below titled "2. Data Dictionary 2018". The Low-Income Energy Affordability Data comes primarily from the 2018 U.S. Census American Community Survey 5-Year Public Use Microdata Samples and is calibrated to 2018 U.S. Energy Information Administration electric utility (Survey Form-861) and natural gas utility (Survey Form-176) data. The methodology for the LEAD Tool can viewed below (3. Methodology Document).

For more information, and to access the interactive LEAD Tool platform, please visit the "4. LEAD Tool Platform" resource link below.

For more information on the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA), please visit the "5. CELICA Website" resource below.

This map service is based on the Long Term Care Residences point datalayer and contains the locations of licensed nursing homes, rest homes and assisted living residences in Massachusetts.Long-term care residences provide housing and services for individuals who are managing illness and/or disability attributed to physical and/or mental health conditions. While terminology may vary, generally long-term care facilities are distinguished by the type of medical and custodial (non-medical services such as dressing, bathing, etc.) care they provide, the relative independence of their residents, and the types of on-site amenities. Furthermore, some facilities cater to specific patient populations (e.g. Alzheimer's patients).For the purposes of this datalayer, a nursing home is defined as a residential facility that provides 24-hour nursing care, rehabilitative services and activities of daily living to the chronically ill who require a relatively high level of institutional support. A rest home provides 24-hour supervision and supportive services for individuals who do not routinely need nursing or medical care. Similarly, assisted living residences provide residents with housing and various daily living support services, but usually do not offer medical care. Assisted living residences often emphasize greater autonomy and privacy for residents through individual apartment-style rentals. Other residential facilities that provide long term care such as group homes (i.e. boarding homes or congregate housing) and hospice facilities are not explicitly specified in this datalayer. Many locations in this datalayer, however, may offer additional services ranging from independent retirement living to intensive skilled nursing and palliative care. Non-residential care locations such as adult day health, rehabilitation, and senior centers are omitted.See the datalayer's full metadata for more information.A Map Service also is available.

This data set, compiled by the Fraunhofer Center for Sustainable Energy Systems, includes long-term 10-minute temperature and relative humidity data, and HVAC system state data for 79 apartments in a low-income housing complex in Revere, MA. The monitoring period spans two winters and one summer between 2011 and 2013. Data were collected as part of a project sponsored by the U.S. Department of Energy Building America program to evaluate the impact of programmable thermostat usability on occupant behavior. This project was done in conjunction with NREL as part of the US Department of Energy's Building America program.

The CAMA (Computer Assisted Mass Appraisal) Database is created on a yearly basis using data obtained from the State Department of Assessments and Taxation (SDAT). Each yearly download contains additional residential housing characteristics as available for parcels included in the CAMA Database and the CAMA supplementary databases for each jurisdiction.. Documentation for CAMA, including thorough definitions for all attributes is enclosed. Complete Property View documentation can be found at http://www.mdp.state.md.us/data/index.htm under the "Technical Background" tab. It should be noted that the CAMA Database consists of points and not parcel boundaries. For those areas where parcel polygon data exists the CAMA Database can be joined using the ACCTID or a concatenation of the BLOCK and LOT fields, whichever is appropriate. (Spaces may have to be excluded when concatenating the BLOCK and LOT fields). A cursory review of the 2004 version of the CAMA Database indicates that it has more accurate data when compared with the 2003 version, particularly with respect to dwelling types. However, for a given record it is not uncommon for numerous fields to be missing attributes. Based on previous version of the CAMA Database it is also not unlikely that some of the information is inaccurate. This layer was edited to remove points that did not have a valid location because they failed to geocode. There were 897 such points. A listing of the deleted points is in the table with the suffix "DeletedRecords." This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

Violations on Boston buildings or properties issued by inspectors from the Building and Structures Division of the Inspectional Services Department.

Note: property_id is equivalent to sam_id.

Looking for Public Works violations? Check out this dataset: https://data.boston.gov/dataset/public-works-violations

The CAMA (Computer Assisted Mass Appraisal) Database is created on a yearly basis using data obtained from the State Department of Assessments and Taxation (SDAT). Each yearly download contains additional residential housing characteristics as available for parcels included in the CAMA Database and the CAMA supplementary databases for each jurisdiction.. Documentation for CAMA, including thorough definitions for all attributes is enclosed. Complete Property View documentation can be found at http://www.mdp.state.md.us/data/index.htm under the "Technical Background" tab. It should be noted that the CAMA Database consists of points and not parcel boundaries. For those areas where parcel polygon data exists the CAMA Database can be joined using the ACCTID or a concatenation of the BLOCK and LOT fields, whichever is appropriate. (Spaces may have to be excluded when concatenating the BLOCK and LOT fields). A cursory review of the 2004 version of the CAMA Database indicates that it has more accurate data when compared with the 2003 version, particularly with respect to dwelling types. However, for a given record it is not uncommon for numerous fields to be missing attributes. Based on previous version of the CAMA Database it is also not unlikely that some of the information is inaccurate. This layer was edited to remove points that did not have a valid location because they failed to geocode. There were 236 such points. A listing of the deleted points is in the table with the suffix "DeletedRecords." This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

Citations issued by the Public Works Department's Code Enforcement Division.

Looking for Building and Property violations? Check out this dataset: https://data.boston.gov/dataset/building-and-property-violations1

The American Community Survey Education Tabulation (ACS-ED) is a custom tabulation of the ACS produced for the National Center of Education Statistics (NCES) by the U.S. Census Bureau. The ACS-ED provides a rich collection of social, economic, demographic, and housing characteristics for school systems, school-age children, and the parents of school-age children. In addition to focusing on school-age children, the ACS-ED provides enrollment iterations for children enrolled in public school. The data profiles include percentages (along with associated margins of error) that allow for comparison of school district-level conditions across the U.S. For more information about the NCES ACS-ED collection, visit the NCES Education Demographic and Geographic Estimates (EDGE) program at: https://nces.ed.gov/programs/edge/Demographic/ACSAnnotation values are negative value representations of estimates and have values when non-integer information needs to be represented. See the table below for a list of common Estimate/Margin of Error (E/M) values and their corresponding Annotation (EA/MA) values.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.-9An '-9' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small.-8An '-8' means that the estimate is not applicable or not available.-6A '-6' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.-5A '-5' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate.-3A '-3' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate.-2A '-2' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.

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

The CAMA (Computer Assisted Mass Appraisal) Database is created on a yearly basis using data obtained from the State Department of Assessments and Taxation (SDAT). Each yearly download contains additional residential housing characteristics as available for parcels included in the CAMA Database and the CAMA supplementary databases for each jurisdiction.. Documentation for CAMA, including thorough definitions for all attributes is enclosed. Complete Property View documentation can be found at http://www.mdp.state.md.us/data/index.htm under the "Technical Background" tab. It should be noted that the CAMA Database consists of points and not parcel boundaries. For those areas where parcel polygon data exists the CAMA Database can be joined using the ACCTID or a concatenation of the BLOCK and LOT fields, whichever is appropriate. (Spaces may have to be excluded when concatenating the BLOCK and LOT fields). A cursory review of the 2004 version of the CAMA Database indicates that it has more accurate data when compared with the 2003 version, particularly with respect to dwelling types. However, for a given record it is not uncommon for numerous fields to be missing attributes. Based on previous version of the CAMA Database it is also not unlikely that some of the information is inaccurate. This layer was edited to remove points that did not have a valid location because they failed to geocode. There were 235 such points. A listing of the deleted points is in the table with the suffix "DeletedRecords." This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

The map was developed using available parcel polygons attributed with tax assessment data as of project initiation in early 2020, Computer-Assisted Mass Appraisal (CAMA) data dated February 2020, and the Chesapeake Bay Program’s 2017/18 Land Use Land Cover data (2022 edition), subsequently referred to as “CBP LULC.” The map also incorporates land use datasets provided by county and municipal jurisdictions to the extent possible while maintaining standard statewide classification definitions and rules. The product was developed to be consistent with the 2018 National Agriculture Imagery Program (NAIP) imagery and CBP LULC dataset. MDP’s draft updated land use classification scheme is available as a separate document. This product is a beta release for public use and further testing. Methods for developing subsequent releases beyond this 2018 baseline will be refined based on feedback from the user community. Urban Land Uses 11 Low-density residential - Detached single-family/duplex dwelling units, yards, and associated areas. Includes generalized areas with lot sizes of less than five acres but at least one-half acre (0.2 to 2 dwelling units/acre). 12 Medium-density residential - Detached single-family/duplex, attached single-unit row housing, yards, and associated areas Includes generalized areas with lot sizes of less than one-half acre but at least one-eighth acre (2 to 8 dwelling units/acre). 13 High-density residential - Attached single-unit row housing, garden apartments, high-rise apartments/condominiums, mobile home and trailer parks, yards, and associated areas. Includes generalized areas with more than eight dwelling units per acre. This may include subsidized housing. 14 Commercial - Retail and wholesale services. Areas used primarily for the sale of products and services, including associated yards and parking areas. This category may include airports, welcome houses, telecommunication towers, and boat marinas. 15 Industrial - Manufacturing and industrial parks, including associated warehouses, storage yards, research laboratories, and parking areas. Warehouses that are returned by a commercial query should be categorized as industrial. This also includes power plants. 16 Institutional - Elementary and secondary schools, middle schools, junior and senior high schools, public and private colleges and universities, military installations (built-up areas only, including buildings and storage, training, and similar areas), churches, medical and health facilities, correctional facilities, government offices and facilities that are clearly separable from any surrounding natural or agricultural land cover, and other non-profit uses. 17 Extractive - Surface mining operations, including sand and gravel pits, quarries, coal surface mines, and deep coal mines. Status of activity (active vs. abandoned) is not distinguished. 18 Open urban land - Includes parks, open spaces, recreational areas not classified as institutional, golf courses, and cemeteries. Includes only built-up and turf-dominated areas that are clearly separable from any surrounding natural or agricultural land cover. 190 – Very Low Density Residential – Clustered residential parcels that have lot sizes less than 20 acres but at least five acres (0.2 to 0.05 dwelling units/acre) 50 – Water 80 Transportation - Transportation features include impervious roads, roadway rights-of-way, and parcels primarily containing light rail or metro stations and park-and-ride lots. 99 – Other Land - Remaining land not covered under another category. Examples include but are not limited to unbuilt lots, rural land, single-family residential parcels greater than or equal to 20 acres in size, and undeveloped portions of large parcels containing urban uses. May include undeveloped land that is either developable or constrained from further development.Note: Urban Land Use classifications encompass the entire parcel on parcels less than five acres that contain a structure as of 2018 based on the Maryland Department of Planning and Maryland State Department of Assessment and Taxation’s Computer-Assisted Mass Appraisal (CAMA) Building dataset. Elsewhere, the Chesapeake Bay Program’s 2017/18 Land Use Land Cover dataset (2022 edition) is used to delineate the extent of development on a parcel. For more information, see Methodology Documentation.Feature Service Link: https://geodata.md.gov/imap/rest/services/PlanningCadastre/MD_LandUse/MapServer/1This copy has been projected to "WGS 1984 Web Mercator (auxiliary sphere)" and, therefore, is for illustrative purposes only. To use the data for geospatial analysis or area calculations, please download the copy projected to "NAD_1983 StatePlane Maryland FIPS 1900" from MDP's website at https://planning.maryland.gov/pages/ourproducts/downloadfiles.aspx.