September 2025

City of Boston 2014 parcels created by the Assessing Department. To add ownership information please join the Property Assessment CSV file in Analyze Boston with Parcels 2014 geospatial data using PID_LONG and GIS_ID fields.

This layer contains the 2012 parcel outlines in the City of Boston. Shared publicly

This layer contains the 2009 parcel outlines in the City of Boston. Shared publicly

City of Boston 2019 parcels created by the Assessing Department. To add ownership information please join the Property Assessment CSV file in Analyze Boston with Parcels 2019 geospatial data using PID_LONG and GIS_ID fields.

City of Boston's parcel data created by the Assessing department in 2016. This data combines 2016 geometry data with 2016 ownership data.

This layer contains the polygons representing the parcel outlines in the City of Boston.

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

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

Data, statistics and adopted local options related to property taxes

The map is based on output from a HAZUS flood model for 100 year coastal flood scenario. The data shows the count of buildings by censusblock that could be inundated by 100 year coastal floods. http://www.fema.gov/plan/prevent/hazus/

November 2024

City of Boston 2021 parcels created by the Assessing Department. To add ownership information please join the Property Assessment CSV file in Analyze Boston with Parcels 2021 geospatial data using PID_LONG and GIS_ID fields.

This layer contains the 2004 parcel outlines in the City of Boston. Shared publicly

This layer contains the 2010 parcel outlines in the City of Boston. Shared publicly

This layer contains the 2011 parcel outlines in the City of Boston. Shared publicly

High resolution land cover dataset for City of Boston, MA. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The primary sources used to derive this land cover layer were 2013 LiDAR data, 2014 Orthoimagery, and 2016 NAIP imagery. Ancillary data sources included GIS data provided by City of Boston, MA or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2500 and all observable errors were corrected.

High resolution land cover dataset for City of Boston, MA. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The primary sources used to derive this land cover layer were 2013 LiDAR data, 2014 Orthoimagery, and 2016 NAIP imagery. Ancillary data sources included GIS data provided by City of Boston, MA or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2500 and all observable errors were corrected.

Credits: University of Vermont Spatial Analysis Laboratory in collaboration with the City of Boston, Trust for Public Lands, and City of Cambridge.