To access parcel information:Enter an address or zoom in by using the +/- tools or your mouse scroll wheel. Parcels will draw when zoomed in.Click on a parcel to display a popup with information about that parcel.Click the "Basemap" button to display background aerial imagery.From the "Layers" button you can turn map features on and off.Complete Help (PDF)Parcel Legend:Full Map LegendAbout this ViewerThis viewer displays land property boundaries from assessor parcel maps across Massachusetts. Each parcel is linked to selected descriptive information from assessor databases. Data for all 351 cities and towns are the standardized "Level 3" tax parcels served by MassGIS. More details ...Read about and download parcel dataUpdatesV 1.1: Added 'Layers' tab. (2018)V 1.2: Reformatted popup to use HTML table for columns and made address larger. (Jan 2019)V 1.3: Added 'Download Parcel Data by City/Town' option to list of layers. This box is checked off by default but when activated a user can identify anywhere and download data for that entire city/town, except Boston. (March 14, 2019)V 1.4: Data for Boston is included in the "Level 3" standardized parcels layer. (August 10, 2020)V 1.4 MassGIS, EOTSS 2021

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

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.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. After the field sampling was complete, aerial photograph signatures were verified for all of the associations using the classification plot data, Bell et al. (2002), and Elliman (2004) and (2005) data. These signatures were extrapolated to other areas within the park boundary that were not sampled. Using ARCGIS 9.1, polygon boundaries in the preliminary vegetation map were further edited and refined to develop a draft association-level vegetation map. Polygons were updated with USNVC association names and codes based on the classification plot data. Polygons that were attributed with land use - land cover categories in the preliminary vegetation map retained their attributes. The aerial photointerpretation key was updated. The thematic accuracy of this 2006 draft association-level vegetation association and land use map was then assessed for accuracy.

Learn more about the project and how to use the canopy assessment data by visiting the StoryMap!Data DictionaryProject generalized the LU codes in their FY 2019 parcels dataset to create a new attribute LU_general as shown below. Open space was added as a land use category by overlaying Boston’s Open_space dataset onto the parcels (open space taking precedence over underlying parcel land use designation). Dissolve on generalized LU codes. Overlay with city boundary (BostonCity_AOI) to identify ROW (anything not covered by parcels or open space polygons). FY19 parcels label--> LU_general- UnknownA ResidentialAH CommercialC CommercialCL CommercialCM ResidentialCP ResidentialE InstitutionalEA InstitutionalI IndustrialR1 ResidentialR2 ResidentialR3 ResidentialR4 ResidentialRC Mixed UseRL Residential

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

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.

January 2002

This layer is a high-resolution tree canopy change-detection layer for City of Boston, MA. It contains three tree-canopy classes for the period 5 years: (1) No Change; (2) Gain; and (3) Loss. It was created by extracting tree canopy from existing high-resolution land-cover maps for 2014 and 2019 and then comparing the mapped trees directly. Tree canopy that existed during both time periods was assigned to the No Change category while trees removed by development, storms, or disease were assigned to the Loss class. Trees planted during the interval were assigned to the Gain category, as were the edges of existing trees that expanded noticeably. Direct comparison was possible because both the 2014 and 2019 maps were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). 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. No accuracy assessment was conducted, but the dataset will be subjected to manual review and correction.

Data, statistics and adopted local options related to property taxes