NYC Building Footprints

I have seen and received quite a number of emails, and have even seen applications that confuse MapPluto for a building data set. To clarify what the building footprint’s represent as well as to remove any confusion between the two very different data sets, I decided to write this post.

MapPluto
MapPluto is a compilation of City agency data at the tax lot (aka parcel) level produced and distributed by the Department of City Planning. A tax lot defines the basic unit of land ownership. Much has been written about MapPluto, so I do not intend to cover this data set in detail. However, it is important to understand that a tax lot can encompass multiple buildings.

A NYC tax lot uses Borough Block and Lot (BBL) as a unique parcel identifier. DCP compiles a variety of City data sets at the parcel level into MapPluto. One of the main data sources is the Department of Finance’s (DOF) Real Property and Assessment Data (RPAD). One of the attributes in RPAD is Number of floors, which is included in MapPluto as NumFloors. DCP defines this column in the metadata as being for “…the primary building on the tax lot, the number of full and partial stories starting from the ground floor.” This is due to the fact, as previously stated, that there can be multiple buildings on a tax lot. Since only one value is possible, DCP elected to go with the number of floors of the ‘primary’ building.

An example of a tax lot with multiple buildings is the community of Breezy Point, Queens. Originally a gated community of summer bungalows that are now permanent homes, Breezy Point spans 12 tax lots and encompasses 3,017 buildings. one of the parcels (BBL 4163400050) includes 424 buildings and has a value of 1 for the number of floors. Although the houses in Breezy Point are of similar housing stock, the number of floors is for the ‘primary’ building and thus not an exact figure. Another common example are NYC Housing Authority (NYCHA) developments. Although buildings within a development are often the same number of floors, this is not always the case.

In general, it is the responsibility of the person working with the data to read the metadata to get an understanding of the data and its limitations and constraints. There are cases where values are estimated, imputed or no longer actively maintained. In the case of MapPluto, building height applies to only one of potentially many buildings on a tax lot. This is not an error but a limitation of the data.

Building Footprints
Building footprints represent the ground-level perimeter outline of a building (i.e., footprint) greater than or equal to 400 square feet and greater than or equal to 12 feet in height unless they were previously captured and have a Building Identification Number (BIN). The purpose for the size and height constraint is to prevent the capture of non-buildings (e.g., containers, tents), which we have seen in the past. The specifications to which built features are captured and which are not can be found in the metadata.

The building footprints include ground and roof height elevations. These values are in feet and are derived photogrammetrically using stereo imagery, LiDAR and a TIN model.

There are cases where there will be no value in these columns. The reason for this is how the building footprints are maintained. To understand this, we need to revisit the past.

The building footprints were first captured as part of the first NYC Planimetrics in 1997 based on 1996 imagery. The NYC Planimetrics came to be called NYCMap. An excellent article on this effort can be found in the New Yorker (unfortunately a subscription is required to access the full article). In the beginning there was no plan for the periodic update of the planimetrics. Since 2006 the planimetrics have been updated on a four-year cycle.

Utilizing the Department of Buildings (DOB) permit data (new construction, major alterations and demolitions), it was determined that the building footprints could be updated on a more frequent basis. Since 2004 the buildings have been updated regularly and since the NYC Open Data Portal launched have been updated on a quarterly basis. These updates are done on-screen using heads up digitizing. Since these updates are not done photogrammetrically elevations values are not available and thus not in the database. With each planimetric update, buildings that are digitized on-screen are replaced with photogrammetrically-captured buildings and elevation values are assigned.

Lastly the Building Identification Numbers (BIN) assigned by DCP are inserted into the corresponding building footprint. The BIN is the unique identifier used by City agencies to identify buildings. Many agencies utilize the BIN to associate additional data to a building. BIN is returned by Geoclient API geocoding service provided by DoITT.

3 thoughts on “NYC Building Footprints

  1. Along with the content you provided here, you might want to add a note about the existence and meaning of non-unique BIN values within the building footprint data, as well notes about some of the inconsistencies users might find between versions of that dataset. These likewise should be considered limitations of the dataset, as you have as well framed some of the limitations of the parcel dataset.

    Some examples of issues users will encounter aside from the non-unique BIN numbers include the reassigning of BIN values, as well as the splitting and merging of footprints between vintages resulting also in the reassigning or even migration of BIN field values. Of note, it was once brought to my attention by the city that the building footprints available for download from DOITT are not always of the same vintage as those appearing in city mapping applications which are apparently updated more often. Additionally, the BBL values appearing on the footprints can sometimes be very wrong. All of these known issues need to be considered when using these datasets for use in spatial analyses, especially when there is any plan to update analyses over time, or to make comparisons against other vintages using the cardinality established within the datasets themselves.

    • Yes, I will cover these areas in part two of the post. Check back in two weeks.

      As far as ‘mapping applications’, NYCityMap and our other web applications the buildings are not updated more frequently than the quarterly extract.

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