This post is a follow-up to the previous building footprint post. It expands upon some topics, and covers some new areas. And as with most everything, a bit of background is necessary to understand where we have come from and, in some cases, why things are the way they are. Progress is made incrementally. The current state of NYC geospatial data has improved immensely but certainly further improvements are warranted.
Although NYC is largely a ‘built’ city, construction activity is continually taking place. As such, building footprint edits are made to account for these changes in the non-digital world and differences will be seen from extract to extract. Additionally, as errors and omissions are encountered in the data, corrections are made. The building footprints is a dynamic data set, extracted quarterly and we hope to move to a continuous update stream in the near future. Nonetheless, change will still need to be handled. More on that to come in the next year.
In the case of demolished buildings, these building geometries are archived and provided as a separate historical buildings file on the NYC Open Data portal.
The Building Identification Number (BIN) provides a unique identifier for the buildings to which they are assigned. Not every building within the building footprints database has been assigned a BIN. For those building not yet assigned a BIN or where a BIN has yet to be inserted into the building footprints, a placeholder is inserted. These placeholders have been referred to as ‘million’ BINs. They are identified by a borough code plus six zeros.
The borough codes are as follows:
Manhattan = 1
Brooklyn = 2
Bronx = 3
Queens = 4
Staten Island = 5
BINs are assigned by the Department of City Planning (DCP). BINs originated from the Property Address Directory (PAD), one of the data sources of Geosupport. PAD predated the building footprints; therefore PAD relied on other sources to define buildings. With the advent of the building footprints, many more buildings needed to be assigned a BIN. This work is ongoing. As DCP assigns BINs, they are provided to DoITT and inserted into the corresponding building footprints. At present there are only 27,792 ‘million’ BINs remaining in the December 2014 building footprint extract. That represents 2.5% of the 1,082,483 building footprints. The majority of these are detached garages or minor buildings on lots. This number will continue to decrease until we reach complete coverage.
For all tax lots, except condominiums (condos), there is a single representative BBL across all City agencies. Condos are the exception due to the fact that each individual unit (i.e., apartment) within a condo building has its own BBL. Therefore, condos have multiple BBLs per tax lot. It is my understanding that the Billing BBL was created by The Department of Finance (DOF) as a way of representing a condo’s management entity for the purpose of correspondence and record keeping. Billing BBLs always have 75 as the first two digits in the block portion of the BBL (e.g., 7501.). Unfortunately there does not seem to be agreement across all City agencies, or even within an agency, on a unique BBL for condo lots.
The building footprints use the Billing BBL. The building footprints carry the BBL as a means of providing a way of associating buildings to tax lots. Since the BBLs are managed outside of the building footprints, the BBLs are synchronized periodically. Due to the different update frequency of MapPluto and the building footprints, inconsistencies can be present. In the December 2014 extract there were 5,199 BBL mismatches representing 0.4% of the total.
There are also cases where buildings do not fall within an official tax lot. For these, DCP assigns a ‘dummy’ lot number of 9999. An example is the Subway station at 96th and Broadway (BIN 1089286, BBL 10124399990). These ‘dummy’ lots are in PAD but do not exist in MapPluto.
A reminder to always read the metadata. To borrow from the Ancient Greek aphorism “know thyself”, know thy data. In addition to improving the data, we look to continually improve the metadata.
Finally, to the data editors that work in relative obscurity at DoITT, DCP and DOF I say thank you for a job well done. To all I wish you a Happy Holidays. Till next year…