Provides information on buildings and structures within designated historic districts and individual landmark sites.

This dataset is part of the DOB NOW Electrical Permit Data Collection: https://data.cityofnewyork.us/browse?Data-Collection_Data-Collection=DOB+NOW+Electrical+Permits+Data
This dataset contains general information about the job filing, including location, applicant, type of work and fee information. Each instance of electrical work might have one row/record for the Initial application, as well as one or more Post Approval Amendment or Subsequent Filing. (See Filing_Type field.)

This dataset contains information for buildings that have completed the certification process. These buildings are all publicly viewable on the WiredNYC website (www.wiredscore.com)

Tree Canopy (TC) Assessment metrics for New York City. This dataset consists of TC metrics summarized to several different sets of geographic base layers. The metrics presented in this table are based on 2010 high resolution land cover dataset.

The TC Assessment is a top-down approach to analyzing the forest. Its purpose is to integrate high resolution land cover data with other GIS datasets to produce a set of detailed metrics on the forest that allow decision makers to know how much tree canopy currently exists (termed Existing TC) and amount of land where is it biophysically feasible to establish tree canopy on (termed Possible TC).

Existing TC is determined by extracting all features classified as tree canopy from a high resolution land cover dataset. Possible TC is determined by identifying land where canopy could possibly exist. Possible TC in a GIS context is determined by overlaying high resolution land cover with cadastral and planimetric datasets to include building polygons and road polygons.

Possible TC is queried out from this overlay and consists of all land that was not existing canopy, not water, not a building, and not a road. Possible TC is further divided into two subcategories: Possible-impervious and Possible-vegetation. Possible-impervious consists of all impervious land that, through modification, could support tree canopy. Examples of such features are parking lots, driveways (through overhanging coverage) and playgrounds. Possible-vegetation consists of all land that is low-lying vegetation, primarily grass or shrubs, which could conceivably be converted to support tree canopy. Examples of such features include residential lawns and playing fields. TC metrics do not serve to address the issues of where it is socially desirable or financially feasible to plant trees. Rather, the TC metrics serve as the basis for beginning to form answers to these questions.

TC metrics are presented in the attribute table as both absolute area (in map units) and relative area (percentage of land area) per parcel. For example, an Existing TC Area (TC_E_A) value of 13,677 and an Existing TC Percentage (TC_E_P) of 21.8 indicate that for the parcel in question the area of Existing TC is 13,677 (in map units) and 21.8% of that feature is tree canopy. This assessment was completed by the University of Vermont's Spatial Analysis Laboratory with funding from National Urban and Community Forestry Advisory Council (NUCFAC) and the National Science Fundation (NSF) and in cooperation with the USDA Forest Service's Northern Research Station.

The TC Assessment protocols were developed by the USDA Forest Service's Northern Research Station and the University of Vermont's Spatial Analysis Laboratory in collaboration with the Maryland Department of Natural Resources. TC assessments have been conducted for numerous communities throughout the U.S. where the results have been instrumental in helping to establishing TC goals.

This collection contains elevator applications, aka job filings, submitted online through the "DOB NOW: BUILD" portal. An approved application allows the applicant to pull a permit and begin work on an elevator project.

DOB NOW: BUILD is an online portal that allows users to file for permit applications, check the status of an application, make payments, schedule appointments, and print permits.

Data in this collection is limited to elevator applications (also known as ELV1) submitted through DOB NOW, and includes Initial (New), Subsequent, and Post Approval Amendment (PAA) filing types. The collection includes all electrical applications submitted to the Department of Buildings since December 11, 2017, when the applications were launched in DOB NOW and were no longer accepted through other submission processes.

This dataset contains details for each device on the application. There should be one row per device/Job Filing Number in this dataset. For example, an Initial Permit Application for Device 1234 and Device 5678 would have 2 rows (one for 1234 and one for 5678). If a Post Approval Amendment (PAA) was submitted for this application, then the Device rows would be repeated but the Job Filing Number would have a P1 suffix to indicate the PAA.

This collection contains applications submitted online via "DOB NOW: BUILD" portal by Design Professionals to get an authorization to begin work on an elevator project.

Data in this collection is limited to applications (job filings) related to elevator applications only (also known as ELV1) submitted on DOB NOW, and includes Initial (New), subsequent, and Post Approval Amendment (PAA) filing types.

DOB NOW: BUILD is an online portal that allows users to file for permit applications, check the status of an application, make payments, schedule appointments, and print permits.

This dataset contains general information about the job filing, including location, applicant, type of work and fee information. Each instance of elevator work might have one row/record for the Initial application, as well as one or more Post Approval Amendment. (See Filing_Type field.)

A 6-in resolution 8-class land cover dataset derived from the 2017 Light Detection and Ranging (LiDAR) data capture. This dataset was developed as part of an updated urban tree canopy assessment and therefore represents a ''top-down" mapping perspective in which tree canopy overhanging features is assigned to the tree canopy class. The eight land cover classes mapped were: (1) Tree Canopy, (2) Grass\Shrubs, (3) Bare Soil, (4) Water, (5) Buildings, (6) Roads, (7) Other Impervious, and (8) Railroads. The primary sources used to derive this land cover layer were 2017 LiDAR (1-ft post spacing) and 2016 4-band orthoimagery (0.5-ft resolution). Object based image analysis was used to automate land-cover features using LiDAR point clouds and derivatives, orthoimagery, and vector GIS datasets --
City Boundary (2017, NYC DoITT)
Buildings (2017, NYC DoITT)
Hydrography (2014, NYC DoITT)
LiDAR Hydro Breaklines (2017, NYC DoITT)
Transportation Structures (2014, NYC DoITT)
Roadbed (2014, NYC DoITT)
Road Centerlines (2014, NYC DoITT)
Railroads (2014, NYC DoITT)
Green Roofs (date unknown, NYC Parks)
Parking Lots (2014, NYC DoITT)
Parks (2016, NYC Parks)
Sidewalks (2014, NYC DoITT)
Synthetic Turf (2018, NYC Parks)
Wetlands (2014, NYC Parks)
Shoreline (2014, NYC DoITT)
Plazas (2014, NYC DoITT)
Utility Poles (2014, ConEdison via NYCEM)
Athletic Facilities (2017, NYC Parks)

For the purposes of classification, only vegetation > 8 ft were classed as Tree Canopy. Vegetation below 8 ft was classed as Grass/Shrub.

To learn more about this dataset, visit the interactive "Understanding the 2017 New York City LiDAR Capture" Story Map -- https://maps.nyc.gov/lidar/2017/
Please see the following link for additional documentation on this dataset -- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_LandCover.md

This data is collected annually via EPA Portfolio Manager.

The data collection requires building owners to measure their energy and water consumption and compare it against that of similar buildings in the city and country.

The data is useful for policy analysts as it provides transparency into energy and water consumption for the city's largest buildings.

Please visit https://www1.nyc.gov/site/buildings/codes/benchmarking.page for additional information.

This dataset is part of the DOB NOW Electrical Permit Data Collection: https://data.cityofnewyork.us/browse?Data-Collection_Data-Collection=DOB+NOW+Electrical+Permits+Data
This dataset contains details of the electrical scope of work. For each Job Filing Number, there can be multiple rows/records in this dataset. For example, there might be electrical work being performed in the basement as well as on the 4th floor. One row/record for Basement and one for the floor. The job might have some 101 to 200 amps Service Switches as well as some Up to 100 amps Service Switches, and there would be one row/record for each.

This dataset contains information for certified and non-certified buildings that are participating in the WiredNYC program. These buildings are all publicly viewable on the WiredNYC website.

High resolution land cover data set for New York City. This is the 3ft version of the high-resolution land cover dataset for New York City. 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 minimum mapping unit for the delineation of features was set at 3 square feet. The primary sources used to derive this land cover layer were the 2010 LiDAR and the 2008 4-band orthoimagery. Ancillary data sources included GIS data (city boundary, building footprints, water, parking lots, roads, railroads, railroad structures, ballfields) provided by New York City (all ancillary datasets except railroads); UVM Spatial Analysis Laboratory manually created railroad polygons from manual interpretation of 2008 4-band orthoimagery. The tree canopy class was considered current as of 2010; the remaining land-cover classes were considered current as of 2008. Object-Based Image Analysis (OBIA) techniques 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. More than 35,000 corrections were made to the classification. Overall accuracy was 96%. This dataset was developed as part of the Urban Tree Canopy (UTC) Assessment for New York City. As such, it represents a 'top down' mapping perspective in which tree canopy over hanging other features is assigned to the tree canopy class. At the time of its creation this dataset represents the most detailed and accurate land cover dataset for the area. This project was funded by National Urban and Community Forestry Advisory Council (NUCFAC) and the National Science Fundation (NSF), although it is not specifically endorsed by either agency. The methods used were developed by the University of Vermont Spatial Analysis Laboratory, in collaboration with the New York City Urban Field Station, with funding from the USDA Forest Service.

The Department of Finance (DOF) is required by NY State law to value condominiums or cooperatives as if they were residential rental apartment buildings. DOF uses income information from rental properties similar in physical features and location to the condominiums or cooperatives. DOF applies this income data to the condominium or cooperative and determine its value in the same way DOF values rental apartment buildings.

The Department of Finance (DOF) is required by NY State law to value condominiums or cooperatives as if they were residential rental apartment buildings. DOF uses income information from rental properties similar in physical features and location to the condominiums or cooperatives. DOF applies this income data to the condominium or cooperative and determine its value in the same way DOF values rental apartment buildings.
Update Schedule: Annually

Condominiums and cooperatives are valued as if they were residential rental apartments. Income information from similar rental properties is applied to determine value.
The Department of Finance (DOF) chooses similar properties to value condos and coops. Properties are selected based on a combination of factors such as: land location, income levels, building age and construction and exemptions and subsidies.

The Department of Finance (DOF) is required by NY State law to value condominiums or cooperatives as if they were residential rental apartment buildings. DOF uses income information from rental properties similar in physical features and location to the condominiums or cooperatives. DOF applies this income data to the condominium or cooperative and determine its value in the same way DOF values rental apartment buildings.

The Department of Finance (DOF) is required by NY State law to value condominiums or cooperatives as if they were residential rental apartment buildings. DOF uses income information from rental properties similar in physical features and location to the condominiums or cooperatives. DOF applies this income data to the condominium or cooperative and determine its value in the same way DOF values rental apartment buildings.
Update Frequency: Annually

Local Law 84 of 2009 (LL84) requires annual energy and water benchmarking data to be submitted by owners of buildings with more than 50,000 square feet. This data is collected via the Environmental Protection Agency's (EPA) Portfolio Manager website

Each property is identified by it's EPA assigned property ID, and can contain one or more tax lots identified by one or more BBLs (Borough, Block, Lot) or one or more buildings identified by one or more building identification numbers (BIN)

Please visit DOB's Benchmarking and Energy Efficiency Rating page
for additional information.

Condominiums and cooperatives are valued as if they were residential rental apartments. Income information from similar rental properties is applied to determine value.
The Department of Finance (DOF) chooses similar properties to value condos and coops. Properties are selected based on a combination of factors such as: land location, income levels, building age and construction and exemptions and subsidies.
Update Schedule: Annually

The Department of Finance (DOF) is required by NY State law to value condominiums or cooperatives as if they were residential rental apartment buildings. DOF uses income information from rental properties similar in physical features and location to the condominiums or cooperatives. DOF applies this income data to the condominium or cooperative and determine its value in the same way DOF values rental apartment buildings.
Update Frequency: Annually

The Department of Finance (DOF) is required by NY State law to value condominiums or cooperatives as if they were residential rental apartment buildings. DOF uses income information from rental properties similar in physical features and location to the condominiums or cooperatives. DOF applies this income data to the condominium or cooperative and determine its value in the same way DOF values rental apartment buildings.
Update Frequency: Annually

The Department of Finance (DOF) is required by NY State law to value condominiums or cooperatives as if they were residential rental apartment buildings. DOF uses income information from rental properties similar in physical features and location to the condominiums or cooperatives. DOF applies this income data to the condominium or cooperative and determine its value in the same way DOF values rental apartment buildings.
Update Frequency: Annually

The Department of Finance (DOF) is required by NY State law to value condominiums or cooperatives as if they were residential rental apartment buildings. DOF uses income information from rental properties similar in physical features and location to the condominiums or cooperatives. DOF applies this income data to the condominium or cooperative and determine its value in the same way DOF values rental apartment buildings.
Update Schedule: Annually

Condominiums and cooperatives are valued as if they were residential rental apartments. Income information from similar rental properties is applied to determine value.
The Department of Finance (DOF) chooses similar properties to value condos and coops. Properties are selected based on a combination of factors such as: land location, income levels, building age and construction and exemptions and subsidies.
Update Frequency: Annually

Condominiums and cooperatives are valued as if they were residential rental apartments. Income information from similar rental properties is applied to determine value.
The Department of Finance (DOF) chooses similar properties to value condos and coops. Properties are selected based on a combination of factors such as: land location, income levels, building age and construction and exemptions and subsidies.
Update Frequency: Annually

The Department of Finance (DOF) is required by NY State law to value condominiums or cooperatives as if they were residential rental apartment buildings. DOF uses income information from rental properties similar in physical features and location to the condominiums or cooperatives. DOF applies this income data to the condominium or cooperative and determine its value in the same way DOF values rental apartment buildings.
Update Frequency: Annually