NYC Projected

NYC Projected

Much has been written about map projections and coordinate systems. I will not attempt to add to what has already been well covered by those with more technical expertise and writing skills than myself. If you are interested though the USGS is an excellent resource. You can start here.

I recently had a thoughtful email discussion with a colleague who questioned our (NYC DoITT) use of the New York State Plane coordinate system in this era of web mapping. This got my thinking. Did I really know why the choice was made? Do we just continue to use this coordinate system because we always have?

Unfortunately, none of the individuals that were involved in the first digital mapping effort to fly the City and capture planimetric data (more here) are around anymore. And there is no documentation on the reasoning that went into the decision. However it is fairly straightforward to work backwards to understand within reason why the coordinate system was chosen. The New York State Plane Long Island Zone (EPSG 2263) is a local coordinate system that provides a high degree of accuracy and balances size and shape well. I know with certainty that the flyover and basemap were developed in support of the NYC Department of Environmental Protection (DEP) sewer and water mapping initiatives. Taking a leap of faith one can presume that DEP required a degree of accuracy that was best supported by the linear accuracy of the New York State Plane coordinate system.

epsg-2263-labels

To maintain consistency, all subsequent flyovers and planimetric captures have used this same coordinate system. But the story does not end there. Updates have been made to the coordinate system over the intervening years. The High Accuracy Reference Network (HARN) introduced an upgrade to North American Datum of 1988 which was subsequently upgraded by Continuously Operating Reference Station CORS in 2011. These very slight adjustments lead us to a current EPSG 6539. We have yet to adopt this new coordinate reference system (CRS) due to the disruptive nature of the change for minimal difference, but may move to it in the future.

epsg-3857-label

State Plane is popular within state and local governments across the US but is that what the geo and open data communities are using? Most likely not. Google developed and popularized the ubiquitous web mapping coordinate system Web Mercator for its online maps. GPS coordinates and OpenStreetMap use WGS 84.

Thankfully transforming between coordinate systems is not as cumbersome and as problematic as it was in the past. Many systems can reproject  data on the fly. But is there a real difference between these popular coordinate systems used for NYC?

epsg-4326-labels

The three maps illustrate how the NYC landmass appears in the three coordinate systems discussed. State Plane and Web Mercator maintain the landmass shape well but the Web Mercator size is slightly distorted. This is most evident in Staten Island. The most extreme difference can be seen in WGS 84. The entire landmass seems stretched and flattened.

To answer my previous questions, I think the right choice was initially made and staying with the choice was an equally good decision. That said, we can support more than one coordinate system.

In the coming months, we plan on publishing our aerial imagery (digital orthophotography) as map tiles (WMTS) in both 2263 and 4326. So please check back. And feel free to leave us a comment with your thoughts on the topic.

NYC Planimetric Update 2014/16

Background

For those wondering exactly what planimetrics are and why they should care, read on. Otherwise, you can skip to the last paragraph and download the data here.

Planimetric mapping is the capture of geographic features from aerial survey (i.e., capture of aerial photography) that are traditionally mapped in two dimensions and are therefore exclusive of elevation.  Quite simply these are the visible features that can be digitized from aerial photography. Often referred to as planimetric features or simply planimetics, these geographic features in their sum total essentially represent the base map data (i.e., layers) for a specific area.

NYC DoITT first developed a planimetric database in 2000. The data was captured from the first ‘modern’ aerial survey of the New York City that took place in 1996. Referred to as the NYC Landbase, components of this effort were the establishment of:

  • a ‘database design’ (the delivery was ArcInfo coverages);
  • coverage parameters (e.g., scale, projection, precision, fuzzy tolerance and dangle length);
  • the specific features to be captured;
  • and a classification scheme (i.e., feature codes).

The delivery of the data was by 2,500′ x 2,500′ tiles, which directly corresponded to the orthophotography tiles.

Updates

A subsequent ad-hoc update to the planimetric database was done in 2004. This update was based on aerial surveys from 2001 (Manhattan and Staten Island) and 2002 (Bronx, Brooklyn and Queens). This update conformed to the previous database design. In 2006, DoITT formalized the update frequency of  the planimetric database and aerial surveys. The aerial photography would be captured on a two-year cycle and the planimetrics a four-year cycle. With the first regularly-scheduled planimetric update to be based on the 2006 aerial photography.

With each subsequent update, refinements have been made. New features and domains have been added,  obsolete features have been removed, features are captured in three dimensions, a seamless database is produced and the time between aerial capture and delivery of planimetrics shortened.

Year of Update

A lot of work goes in to producing the planimetrics. The orthophotography takes from nine to twelve months to deliver. A spring capture in 2014 is therefore delivered in 2015. The planimetric features themselves also take from nine to twleve months. All of this is a long way of explaining why data from 2014 is published in 2016. It takes time.

2014 Update

For the 2014 update, additional refinements were made. Skybridges were captured as separate features (sub type within building footprints). Below is an example of a skybridge connecting 3 and 4 MetroTech Center, Brooklyn.

skybridge

Previous (2010) and current (2014) representation of skybridges.

Cooling towers are a new feature capture – see example below. This data will be published in the next couple weeks.

coolingtower

Cooling towers – black rectangles

 

 

Curblines are a new separate feature. Previously curblines were a subtype in Pavement Edge. Pavement Edge features were segmented at the apex of each edge and a unique ID was assigned. These IDs were then transferred to the Citywide Centerline.

pavementedge

Pavement edge with Blockface ID

As with the previous update, all of the individual data sets are on the open data portal. New with this update is a comprehensive database that contains all of the data sets. Additionally, the data were tagged with ‘planimetrics’ and ‘doitt gis’ to simplify search and discovery. Lastly, previous blog posts will be updated with any new or updated data urls. Happy mapping!

Step Streets: An Unusual Means of Network Connectivity

For accurate routing, network connectivity is essential. This is the case regardless of one’s mode of travel. A person may start a journey on foot, move to a bicycle, then to subway and finish off her trip again on foot. Nevertheless, all legs (e.g., street, bike lane, subway line) of the journey, regardless of the mode of travel, need to be connected.

In the course of a trip, we may on occasion encounter unusual means of ‘network’ connectivity. For example, we may need to carry our bicycle up a set of stairs to enter onto a bridge. In this post, I will cover what I find to be an unusual means of urban connectivity: step streets – more on the name later. Quite simply, a step street can be considered an outdoor stairway or a series of steps that connect two different elevations.

When I think of ‘step streets’ as access ways in urban environments, older European cities come to mind first. Cities with narrow streets and sometimes steep grades that were laid out well before the advent of the automobile. One of the most famous examples, albeit not of a narrow passageway, is the Spanish Steps in Rome that connects Piazza di Spagna and Piazza Trinità dei Monti.


Image via Wikipedia

A more classic example, of a steep narrow passageway comprised of steps, can be found in Lago D’Iseo, Italy. You could probably schlep a bicycle up these steps but not much more.

Narrow Step Street

Narrow Step Street

Image via Martha’s Marvelous Munchies

In older city settings, stairs or paths were probably the only means for getting pedestrians from point A to point B. I do not profess to know the history of steps in urban settings, but I imagine they were built out of necessity. In most cases, one could imagine, due to limited remaining space and a large difference in elevation. And in these parts of a city, cars are often not permitted for obvious reasons.

Stateside, San Francisco comes to mind first due to its hilly terrain and a street layout that works with, not against, the terrain. Although an amazing site with beautiful landscaping and a notable tourist destination, Lombard Street demonstrates the impractical nature, and some might argue, misuse of space, in trying to move automobiles through extremely steep spaces.

Lombard St, San Francisco

Lombard St, San Francisco

Image via Wikipedia

In contrast there are a series of pedestrian-only ‘stairways’ that connect Jones and Taylor Streets in the Russian Hill section of San Francisco. In OpenStreetMap, these ways are tagged highway = steps. For more in San Francisco stariways see here.

San Francisco Stairways

San Francisco Stairways

Back here in New York City, where the terrain is considerably flatter and the rents slightly lower, there are a series of narrow passageways comprised entirely of steps that are limited to pedestrians and are referred to in the local vernacular as ‘step streets.’

Many step streets are official streets and therefore can be found on the official City Map. For background context, the official City Map is a collection of Alteration maps. Alteration maps record changes to the City Map including public streets, parks and public places – see here for more information. Alteration maps of a corresponding area supersede all previous Alteration Maps from previous dates).

Below is a portion of an Alteration map from 1955 recording the elimination (de-mapping) of West 230th Street in the Bronx. This map shows both West 230th and West 231st Street labeled ‘STEPS.’ This label is most likely how the term step street started: as one might say as a cartographer’s annotation.

Alteration Map: Elimination of W 230 St

Alteration Map: Elimination of W 230 St


Alteration Map courtesy of the Department of City Planning. To see the complete map click on the image.

Step streets can also be found in NYC Open Data where the Roadway Type (RW_TYPE) is 7.
LION
CSCL

One of the hilliest areas in NYC is northern Manhattan and the Bronx. And as one might expect that is where the greatest number of step streets are found. This is where you will find the densest clustering of step streets. And these are the ‘classic’ step streets (i.e., connect two streets). The example below connects two sections of Pinehurst Street in Manhattan north of W 181st Street.

Pinehurst Ave & W 181 St, Manhattan

Based on the data, the longest step street is West 230th Street between Netherland and Johnson Avenue in the Bronx. A length of approximately 295′ with an elevation difference of 38′ for a grade of 12.9 percent. A relatively modest slope compared to the 26.8 percent grade of W 187 Street between Overlook Terrace and Fort Washington Ave also in the Bronx.

The definition of a step street seems to have been expanded to include steps that connect streets to the boardwalks in the Rockaways. In any case, go ahead and download the data to explore these and other interesting anomalies or check out the map below.

Additional reading:
Forgotten New York
Sister Betty
Boredpanda

NYC Streets on Paper

As is usually the case with development projects, pen must be put to paper first followed by a series of reviews and sign-offs before a shovel is put to the ground. That is also the case with street construction. What is unique, however, is that a street must be added to a map before it is constructed.

In New York City, a newly proposed street must be added to the official ‘City Map’ (not to be confused with NYCityMap) through the Uniformed Land Use Review Procedure (ULURP) before it can be constructed. Thus a street will exist on paper before it becomes a reality. These streets are what have become to be known as paper streets. Paper streets are not unique to NYC but ULURP is.

Paper streets may exist on paper only for many years before they are ever constructed. The street’s configuration or name may change before construction takes place. There are even situations whereby a street could halted (de-mapped) – see definition below – before it ever becomes a reality.

The dashed lines on the map below represent paper streets in the Midland Beach section of Staten Island. It is clear from the area that these infill streets are intended to complete the planned street grid when fully built out. However there could be circumstances (e.g., being in a flood zone) that prevent the streets from being constructed.

Paper Streets: Midland Beach, Staten Island

Paper Streets: Midland Beach, Staten Island

Although originally on paper only, paper streets can be found in NYC digital data. The NYC Street Centerline (CSCL) data set on the NYC Open Data Portal and City Planning’s LION data set include paper streets. For those wondering, LION is an extract of CSCL that includes both single-line (generic) and dual-line (roadbed) representation of the street network plus additional geographies. Additionally, LION has more fine-grained segmentation (breaks occur whenever geographies cross or there are unique address range breaks). Whereas, CSCL is focused specifically on the actual street (roadbed) representation with segmentation by block. More on these data sets in a later dedicated post.

Paper streets can be found as follows:
LION – featuretyp values of 5 and 9;
CSCL – STATUS values 3 and 9.

The inverse of a paper street is a de-mapped street. As the name would apply, this is a case where a street was officially removed from the City Map. And as with paper streets, the street will appear on paper (City Map) as being de-mapped before they are actually removed.

De-mapped Street

De-mapped Street: Melrose Crescent, Bronx

De-mapped streets can be found in LION where status equals 5.

State of the Map US 2015 and the role of Governments in OpenStreetMap

A little over two weeks had passed since the closing of the State of the Map US (SOTMUS) conference in NYC. For those not familiar, SOTMUS is the yearly conference for the US chapter of OpenStreetMap (OSM). This period offered some much needed time to reflect on the conference as a whole: setting, presentations and sessions, exhibitors, organization and execution. On all points, I felt SOTMUS hit the mark and was a resounding success.

United Nations

Yes, I’m sure there were some minor shortcomings as evident by some of the tweets I saw (#SOTMUS). Nonetheless, for a conference organized and executed by volunteers coupled with the comments I heard, it was clearly a success. I have a new found appreciation for the hard work that goes into organizing such a large event after having assisted the organizers in securing the Surrogate Court space for the opening night (NYC DoITT sponsored IT). Kudos to the organizers! But alas I digress.

This post is not intended to be a review of the event. Many others I’m sure have already covered that and a better positioned to do so. My objective was to dive further into the role local government could play in OpenStreetMap. This post can be seen as an extension of the panel I was on at SOTMUS, which as a demonstration of interest in the topic, was the second of two panels on OSM and government. I’m sure that many would even question whether government has a role at all. To that I would say, duplicating or recreating what has already been mapped and increasingly is available on open data sites, is time consuming and wasteful. On the international landscape that is often not the case but here in the US it is.

Consider the NYC building footprint (with height) and address import. To manually digitize approximately one million buildings would have been a labor intensive and lengthy process. On-screen digitizing over aerial photography of a lower resolution then NYC possess would have also resulted in lower quality and less consistent data. Contrast that with a careful import utilizing high quality preexisting *authoritative* data that resulted in nearly complete and consistent coverage of NYC is in my opinion hard to argue against. A bulk import then frees up the community to focus on keeping OSM current and filling in the gaps where needed. Certainly a less daunting task then starting – with respect to buildings – from a nearly blank canvas.

The NYC buildings and address import was largely undertaken by Mapbox. NYC DoITT assisted with planning and answering questions (NYC addressing is a challenge) throughout the effort and of course providing the data. Part of the effort included a change notification email that gets sent out each night. The email shows the changesets from the previous day. Since a changeset can be comprised of multiple edits, wading through numerous unrelated edits (primary focus is on buildings and addresses) can be time consuming; however the change notification has proven useful and has resulted in hundreds of edits to NYC data.

Each changeset comes with a map (see example below) to guide the reviewer to the specific location of the edit. NYC DoITT staff review the changeset and apply any valid changes to the internal repository. Due to schema differences and ODbL license restrictions, the OSM data is not imported into the internal repository. The changesets are used as a guide.

OSM Change Set

Tools such as MapRoulette can also be used to bring in changes made to *authoritative* data sets. This is the method being used by the local NYC OSM community to incorporate missing bike lane data into OSM (see Eric Brelsford’s lightning talk here).

I think it is undeniable that *authoritative* government data can further enrich OSM to the benefit of many. You may then be asking yourself, what is the benefit to a local government? To me there are both direct and indirect benefits.

From a strategic perspective, it is important to have options when making decisions. In the case of data, not all local governments can afford or have the technical capabilities to manage their own geospatial data. And even when they do, there are cases where governments use external data sources for routing and logistics. To have only a couple proprietary commercial options limits choice and drives up cost. Having a robust and complete open data set provides governments alternatives. And the benefit is twofold: direct cost savings and indirect alternatives.

OSM can also benefit a much wider audience. Open data is great. And the movement towards more open data is fantastic. What is often not discussed is the barrier to enter the open data space. Not only specific to geospatial data, a person needs a variety of skills and software (there are open source options in geo such as QGIS) to work with and analyze the data. This is not an intended barrier but a result of the complexity within the current geospatial technology space. This greatly reduces the number of people downloading and working with open data. Conversely with OSM, there is a platform and an ecosystem of tools already in place. There are tools for viewing, editing, analyzing, rendering and even downloading OSM data. This allows people to focus on what they want to do with the data (e.g., make or view a map) and less about the intricacies of setting up the data to work with it. And there are an amazing set of tools from independent open source developers to commercial entities. From the elegant and simple ID editor to the Tangram map renderer. OSM can open up a wealth of possibilities and can be a viable alternative.

NYC Addressing: A Primer

There is no mystery and intrigue when defining the primary function of an address is to locate or identify a property. And although we often take addressing (hereto defined as the process of assigning and using addresses) for granted, addressing provides an essential function to all. This is evident in daily life where addresses are used by individuals, corporations and governments as they interact and conduct business. Common examples across this spectrum are the delivery of mail and packages, police and fire departments responding to 911 emergency service calls, and generally navigating the areas we inhabit or visit. Addressing is the fuel that make our cities, towns and villages run clean.

The mystery and intrigue comes from improper or confusing addresses that can cause problems or delays with the delivery of services and response to emergency incidents at an address. Numerous stories exist of problems encountered by first responders to problematic addresses. Standardized and predictable addressing makes locating an address quicker and easier for all parties. When and where possible it is best to assign addresses:

  • in logical numeric sequence and
  • consistently across a single block (all with or without hyphens);
  • with odd and even house numbers on separate street sides;
  • to the street a property fronts;
  • that are not duplicates of existing addresses.

There is no single authority overseeing address within NYC. Addresses are assigned in New York City by the Topographical Units of the respective Borough President’s (BP) Offices. That is, the Queens BP assigns addresses only within Queens and so forth. NYC DoITT provides a secure web-based application for BP’s to make address and street name assignments. The application ensures centralized storage of address assignments; notification to responsible parties (911) and consistency of address assignments across boroughs.

Addresses are assigned to buildings for the following general cases:

  • new construction;
  • additional entrance to an existing business;
  • change an address of an existing building;
  • storefront business.

Unique Cases

It sometimes seems as if NYC has each and every possible address anomaly although that is most certainly not the case. Below are just a few types of the address anomalies in NYC.

Vanity Address: an address for a building that uses a street or place name on which the building does not front. The figure below provides an excellent an example as well as the challenges vanity addresses pose. Imagine trying to find 16 Penn Plaza while standing in front of 2 Penn Plaza.

Penn Plaza Area

Hyphenated Address: often referred to as Queens-style addresses, a hyphenated address has a hyphen in the house number (e.g., 70-111). The left side of the hyphen represents the nearest cross street exclusive of avenues and the right side of the hyphen represents the house number.

Edgewater Park: a gated community in the Bronx, Edgewater Park is divided into alphabetic sectors (A, B, C…) which are used in lieu of a street name for addressing. Geosupport uses Edewater Park as the street name to avoid confusion to the extent possible. An example of an address 111C Edgewater Park. See figure below.

Edgewater Park

Miscellaneous

House number containing fraction and letter: 138 1/2 B Edgewater Park, Bronx.

Odd and even house numbers on the same side of street: Park Row, Manhattan (see figure below)

Hyphenated and non-hyphenated address on the same block: Ann Street, Manhattan (see figure below)

Park Row Addresses

Address Data

There are two primary methods for modeling and managing addresses in a geospatial database. The first is by street, which is commonly referred to as a street centerline. This method models the high and low house numbers on a street segment (i.e., block) for each side of the street. Geocoders then interpolate an input address proportionately between the high and low house number range on the respective side of the street. Geocoded addresses using this method are approximations of actual addresses and include hypothetical non-existent addresses.

The second, and more recent approach, is to represent each individual address, which is referred to as address points. For this method, each and every address is modeled generally within the building the address falls. Both methods are used by NYC and both data sets are available to the public.

Address points is a geospatial dataset that models the approximate entrance of a building and includes the properties signed address (house number, street name). Address points were developed by NYC DoITT and completed in 2012. The data were subsequently released to the public in 2013. Since that time the data has been released on a quarterly basis.

Data sources

CSCL, Citywide Street Centerline, models only physical streets and does not have duplicate segments for cases where there are alternate street names.

LION – an extract from CSCL that includes both roadbed (modeling of dual carriage ways) and generic (modeling a single line to represent dual carriage ways). LION provides both to support legacy use of the data. In addition, LION has duplicate segments for each alternate name of a street segment.

Address points – a point representing all known addresses.

Other Resources

Manhattan BP – http://manhattanbp.nyc.gov/downloads/pdf/address-assignments-v-web.pdf

NYC Building Footprints Part II

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.

Change
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.

BIN
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.

BBL
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.

DOF uses the Billing BBL for RPAD and the Base BBL (also referred to as the FKA [Formerly Known As]) for the Digital Tax Map and ACRIS. DCP uses the billing BBL for MapPluto.

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…

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.

Make your own map of NYC

So you want to make a map of NYC. You are familiar with the NYC Open Data Portal but have had some difficulty in finding all the data you need. You have the requisite software to construct the map (e.g., QGIS, ArcGIS, MapBox Studio, etc.) and the basic skills to do so. Perfect, read on.

This post is intended to provide a single source for the map ingredients. It is not intended to be the map cookbook. It’s up to the reader (i.e., mapper) to decide on data to include, colors, symbols, labels, fonts, etc.

NYC Planimetrics

The table below lists NYC DoITT geospatial data on the Open Data Portal and provides a very basic description of each data set. For more detailed information, see the included metadata. The table is grouped into core basemap features and other DoITT data.

The first grouping of data comprises the data used to construct the above map. These data are referred to as the planimetrics (aka NYCMap). Planimetrics are features captured from aerial photography and represent the City’s basemap. These features are updated on a four year cycle. Most but not all features are provided to the public. Utility structures is one that is not due to security concerns. For an historical perspective and to see how far we have come, read the following article on NYCMap. *Note the full article requires a subscription.

The second group represents other NYC DoITT data that may be of interest but is not necessary for producing a basemap. Some of these data are maintained directly by DoITT and others are compiled from agency sources. We do our best to keep these data current.

The last group includes notable geospatial data sets not managed by DoITT and agency sites where spatial data can be found.

Feature Name Notes/Alternate name Download URL
DoITT Boardwalks Boardwalks http://bit.ly/1AgMGek
Building footprint Permiter of base of building with height http://bit.ly/2gj5p1m
Contours Two-foot contours http://bit.ly/1xzWbBJ
Hydrography Water bodies http://bit.ly/1GXHvSp
Hydrography Structures Manmade features at the waterfront http://bit.ly/1qVgBAn
Medians Physical separation between travel lanes. http://bit.ly/2gsQPHZ
Miscellaneous Structures Billboards, sign gantrys, etc. http://bit.ly/2gsY2HS
Open Space City and Non-City parks and fields http://bit.ly/1xp4KSu
Parking Lots Paved parking areas http://bit.ly/1rZDCof
Pavement Edge Perimeter edge of pavement http://bit.ly/1q69NxX
Retaining Walls Where elev difference >= 10 feet http://bit.ly/2hc1V2i
Roadbed Roadway (polygon) http://bit.ly/1sNPqbn
Sidewalks Sidewalks in the right-of-way http://bit.ly/1s33RLE
Shoreline features NYC shoreline http://bit.ly/1BJo2EL
Swimming pools Inground only http://bit.ly/2hdo9nt
Cooling towers Ventilation and cooling towers > 4′ in diameter. http://bit.ly/2hdrGlH
Additional DoITT Spatial Data
Address Points Point representing addresses. http://bit.ly/2hbXu7H
Citywide Street Centerline CSCL; includes Bike lanes http://bit.ly/1zVbRmk
Digital Elevation Model One-foot DEM http://bit.ly/1sNY0GQ
NYC Wi-Fi Hotspot Locations Open and fee-based public wifi http://bit.ly/1wOcEiX
After-School Programs City-funded programs http://bit.ly/1BJnv5S
Agency Service Center Walk-in service centers http://bit.ly/1xxtqYZ
Greenthumb Community garden program http://bit.ly/1uEYXWv
Business Improvement Districts BIDS http://bit.ly/1qpzSiP
Individual Landmarks Individual, scenic and interior landmarks http://bit.ly/1ClycWj
Historic Districts Designated historic districts http://bit.ly/1G9V6HA
NYCHA Developments New York City Housing Authority http://bit.ly/1vkNxZJ
Zip Code Boundaries Zip code polygons http://bit.ly/1Ha4JVx
Data Services / Ready-to-use data
Building footprints and subway lines, stations and entrances Auto-synched on CARTO http://bit.ly/2gtoIs3
Notable Non-DoITT Spatial Data
Digital Tax Map DOF tax map; inlcudes blocks, lots, air rights, etc. http://bit.ly/1gfX6gs
Bytes of the Big Apple Various data sets including MapPLUTO http://on.nyc.gov/1wBowrp
Dept. of Transportation Various data sets & feeds http://on.nyc.gov/1yiQTtX

Every effort will be made to ensure the above table is complete and the links are current. If you find an error or omission, please feel free to add a comment below to let us know. Lastly, check back periodically as this table will be expanded as newer data sets are published.

Last updated: 12/09/2016

Happy mapping!