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.
On September 25, 2013, New York City released the 2012 energy and water use data for all properties required to annually benchmark under Local Law 84. New York City is the first in the nation to publicly disclose data for large multifamily buildings. Approximately a million New Yorkers can now see how much energy and water their apartment buildings consumed in 2012.
The new data set includes more than 9,000 self-reported multifamily properties, effectively more than tripling the size of the first year's list. The data also represents the first year's results of both manual and automatic water benchmarking, with more than 6,800 properties reporting water data.
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.
GIS data: A waterfront access plan (WAP) is a specific plan, set forth in the Zoning Resolution that tailors waterfront bulk regulations and public access requirements to the specific conditions of a particular waterfront area.
Locations from the NYC Green Infrastructure initiative which presents an alternative approach to improving water quality that integrates “green infrastructure,” such as swales and green roofs, with investments to optimize the existing system and to build targeted, cost-effective “grey” or traditional infrastructure
This is a polygon shapefile of publicly accessible waterfront spaces in NYC containing jurisdictional and construction status information. The majority of these spaces were constructed in conjunction with private waterfront developments, pursuant to the NYC Zoning Resolution. This feature class also includes spaces that are under construction (in progress), or have been approved through ULURP (Uniform Land Use Review Procedure) but construction of the waterfront spaces has not commenced. The publicly owned spaces in this database do not include parks or open spaces under the jurisdiction of NYC Department of Parks and Recreation, NYS Office of Parks, Recreation & Historic Preservation, or the National Park Service. This shapefile was used to create the City Waterfront Interactive Maps at http://www.nyc.gov/html/dcp/html/cwp/cw.shtml .
Data collected to fulfill the requirements of the SWTR (Surface Water Treatment Rule) and FAD (Filtration Avoidance Determination). Data is collected via grab sampling, analysis, LIMS data capture and reporting. Each record represents either a four hour turbidity result, a 24 hour average turbidty result, or a daily fecal coliform result from DEL18DT (Delaware Shaft 18 downtake). Data is used to monitor compliance with the requirements above. There are no limitations for the data.
Water quality sample results collected by the Department of Health and Mental Hygiene at all New York City Beaches. These water quality results are used by the Department to determine the status (open, advisory, closed) of Beaches.
GIS data: Community Districts (Water areas included)
Community Districts are mandated by the city charter to review and monitor quality of life issues for New York City (NYC) neighborhoods. NYC is currently comprised of 59 community districts. The first byte is a borough code and the second and third bytes are the community district number. There are also 12 Joint Interest Areas (JIAs). The JIAs are major parks and airports and are not contained within any community district. This dataset is being provided by the Department of City Planning (DCP) for informational purposes only. DCP does not warranty the completeness, accuracy, content, or fitness for any particular purpose or use of the dataset, nor are any such warranties to be implied or inferred with respect to the dataset as furnished on the website. DCP and the City are not liable for any deficiencies in the completeness, accuracy, content, or fitness for any particular purpose or use the dataset, or applications utilizing the dataset, provided by any third party.
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.
Each record is either an individual drinking fountain or multiple drinking fountains that are attached to each other. NYC Parks created the dataset using collector for arcgis. All outdoor drinking fountains in parks were collected and a rigorous QA/QC process followed. In addition to the GPS coordinates the dataset includes descriptions of its physical environment and attributes about the drinking fountain itself.
This dataset captures all water features within NYC Parks. It combines the State’s hydrography layer with known water fountains. Made for cartographic purposes, the attribute data isn’t as maintained as the geometry.