Graph and download economic data for Resident Population in Suffolk County, NY (NYSUFF0POP) from 1970 to 2024 about Suffolk County, NY; New York; NY; residents; population; and USA.

Detailed chemical, station (source and documentation, sample locations), and texture data are provided for sediments in Long Island Sound and New York Bight. The sediment data are provided as spreadsheet (Microsoft Excel) and tab-delimited files on the web site. These data are in the form of sections within the web site, which provides extensive supporting data, interpretive diagrams, and discussion. The data were obtained from a variety of sources: published reports, theses, unpublished data from agencies and organizations in the Long Island Sound and New York Bight area and Federal agencies such as the U.S. Army Corps of Engineers, U.S. Environmental Protection Agency, NOAA, National Status and Trends Benthic Surveillance program, and the U.S. Geological Survey.

The depth to water table was measured at 276 groundwater monitoring wells (observation and supply) screened in the upper glacial and Magothy aquifers during April and May of 2016. This raster data set was interpolated from the water level data collected at those sites and represents a continuous surface of the estimated depth to water for hydrologic conditions for Nassau and Suffolk Counties, Long Island, New York. These data are presented in Sheet 4 of Scientific Investigations Map 3398.

Graph and download economic data for Resident Population in Nassau County, NY (NYNASS9POP) from 1970 to 2024 about Nassau County, NY; New York; NY; residents; population; and USA.

These data were automated to provide an accurate high-resolution historical shoreline of South Shore of Long Island, New York suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

A dataset listing New York counties by population for 2024.

These data were automated to provide an accurate high-resolution historical shoreline of Long Island, NY suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attributi...

This dataset contains information submitted by New York State Article 28 Hospitals as part of the New York Statewide Planning and Research Cooperative (SPARCS) and Institutional Cost Report (ICR) data submissions. The dataset contains information on the volume of discharges, All Payer Refined Diagnosis Related Group (APR-DRG), the severity of illness level (SOI), medical or surgical classification the median charge, median cost, average charge and average cost per discharge. When interpreting New York’s data, it is important to keep in mind that variations in cost may be attributed to many factors. Some of these include overall volume, teaching hospital status, facility specific attributes, geographic region and quality of care provided. For more information, check out: http://www.health.ny.gov/statistics/sparcs/ or go to the "About" tab.

About this dataset

Context

The dataset tabulates the Long Lake town population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Long Lake town across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Long Lake town was 788, a 0.63% decrease year-by-year from 2022. Previously, in 2022, Long Lake town population was 793, an increase of 0.38% compared to a population of 790 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Long Lake town decreased by 62. In this period, the peak population was 850 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Long Lake town is shown in this column.
• Year on Year Change: This column displays the change in Long Lake town population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Long Lake town Population by Year. You can refer the same here

Background

This data set was engineered for the purpose of modeling apartment rent prices. See my pluto-modeling repository for more information on how this data was used for modeling, and why the target variable was chosen as a proxy for rent prices. For more information on how the data were engineered from the PLUTO data set, see my pluto-database repository.

If you have requests or suggestions for improving this data set, please reach out to me on LinkedIn. I'm always happy to hear from people who use my creations and I'm glad to help you get what you need.

Variables

Identifiers

These variables are primarily used to identify records in the data set. With the exception of year, they are not reccommended for use in the modeling process.

NOTE: This data set only contains BBL-identified records from residential buildings. Other building types are excluded, such as commercial, industrial, and parking lots.

• year
  • the year of the record
  • (year, BBL) uniquely identify records in this data set and can be used as the primary key if the CSV files are imported into a database
• bbl
  • BBL stands for "Borough, Block, Lot"
  • the BBL is a unique numeric identifier for each lot in the NYC building dataset
  • individual buildings are not identified directly in this data-set, but most lots contain only one building, and those that contain more usually contain only a few buildings
• block
  • a code that identifies a block; unique up to the boroough
• zipcode
  • postal code

Building (Lot) Level Features

These variables are used to building features up to the lot level of precision. In most cases, they are an adequate substitute for direct building-level data, which are not available.

Location

• xcoord
  • gives the x-coordinate of the building in New York and Long Island Projection units
• ycoord
  • gives the y-coordinate of the building in New York and Long Island Projection units

Age and Alteration

• yearbuilt
  • the year the building was built
• yearalter
  • the year the building was last altered
  • an alteration is defined as a major rennovation such as gut rennovation, core structural change, etc.
  • this variable is equal to year built if a building has not been altered
• age
  • the age of the building in years (equal to year - yearbuilt)
• build_alter_gap
  • the difference between the year built and the most recent alteration
• alterage
  • the age of the most recent alteration in years (equal to year - yearalter)
  • this variable is equal to age if a building has not been altered (the same caveat applies to the squared and cubed variants of this variable)
• alterage_squared
  • the age of the most recent alteration in years squared
  • the square of age has been added to the data set for linear modeling purposes (see note below)
• alterage_cubed
  • the age of the most recent alteration in years cubed
  • the cube of age has been added to the data set for linear modeling purposes (see note below)

NOTE: Regression models can account for non-linear effects by squaring and/or cubing continuous variables. The intuition behind including squared and cubed alterage variants is that the deterioration of a building matters most when it is either new or older. In general, if the influence of a variable X has a quadratic significance pattern, then we include the squared and cubed versions of X in the model. The reason for this is that d/dX B_1*X + B_2*X^2 + B_3*X^3 = B_1 + 2*B_2*X + 3*B_3 X^2.

Building Class Features

• elevator
  • 1 if the building has an elevator, 0 otherwise
• commercial
  • 1 if the residential building also has stores or offices on premesis, 0 otherwise
• garage
  • 1 if the building has a garage, 0 otherwise
• storage
  • 1 if the building has a storage space, 0 otherwise
• basement
  • 1 if the building has a basement, 0 otherwise
• waterfront
  • 1 if the building is on the waterfront, 0 otherwise
• frontage
  • 1 if the building has a frontage (abbutts at least one street), 0 otherwise
• block_assmeblage
  • 1 if the building is in a block assmeblage, 0 otherwise
• cooperative
  • 1 if the building is managed as cooperative, 0 otherwise
• conv_loft_wh
  • 1 if the building is converted from a loft or warehouse, 0 otherwise

walk-up building features

• tenament
  • 1 if the building was originally constructed as a tenament, 0 otherwise
• garden
  • 1 if the building is a garden community, 0 otherwise
    • garden communities are low-sitting buildings with a wide footprint
    • these buildings often have a couryard with a garden and a large number of residential units

elevator building featu...

Summary:

This repository contains spatial data files representing the density of vegetation cover within a 200 meter radius of points on a grid across the land area of New York City (NYC), New York, USA based on 2017 six-inch resolution land cover data, as well as SQL code used to carry out the analysis. The 200 meter radius was selected based on a study led by researchers at the NYC Department of Health and Mental Hygiene, which found that for a given point in the city, cooling benefits of vegetation only begin to accrue once the vegetation cover within a 200 meter radius is at least 32% (Johnson et al. 2020). The grid spacing of 100 feet in north/south and east/west directions was intended to provide granular enough detail to offer useful insights at a local scale (e.g., within a neighborhood) while keeping the amount of data needed to be processed for this manageable.

The contained files were developed by the NY Cities Program of The Nature Conservancy and the NYC Environmental Justice Alliance through the Just Nature NYC Partnership. Additional context and interpretation of this work is available in a blog post.

References:

Johnson, S., Z. Ross, I. Kheirbek, and K. Ito. 2020. Characterization of intra-urban spatial variation in observed summer ambient temperature from the New York City Community Air Survey. Urban Climate 31:100583. https://doi.org/10.1016/j.uclim.2020.100583

Files in this Repository:

Spatial Data (all data are in the New York State Plane Coordinate System - Long Island Zone, North American Datum 1983, EPSG 2263):

Points with unique identifiers (fid) and data on proportion tree canopy cover (prop_canopy), proportion grass/shrub cover (prop_grassshrub), and proportion total vegetation cover (prop_veg) within a 200 meter radius (same data made available in two commonly used formats, Esri File GeoDatabase and GeoPackage):

nyc_propveg2017_200mbuffer_100ftgrid_nowater.gdb.zip

nyc_propveg2017_200mbuffer_100ftgrid_nowater.gpkg

Raster Data with the proportion total vegetation within a 200 meter radius of the center of each cell (pixel centers align with the spatial point data)

nyc_propveg2017_200mbuffer_100ftgrid_nowater.tif

Computer Code:

Code for generating the point data in PostgreSQL/PostGIS, assuming the data sources listed below are already in a PostGIS database.

nyc_point_buffer_vegetation_overlay.sql

Data Sources and Methods:

We used two openly available datasets from the City of New York for this analysis:

Borough Boundaries (Clipped to Shoreline) for NYC, from the NYC Department of City Planning, available at https://www.nyc.gov/site/planning/data-maps/open-data/districts-download-metadata.page

Six-inch resolution land cover data for New York City as of 2017, available at https://data.cityofnewyork.us/Environment/Land-Cover-Raster-Data-2017-6in-Resolution/he6d-2qns

All data were used in the New York State Plane Coordinate System, Long Island Zone (EPSG 2263). Land cover data were used in a polygonized form for these analyses.

The general steps for developing the data available in this repository were as follows:

Create a grid of points across the city, based on the full extent of the Borough Boundaries dataset, with points 100 feet from one another in east/west and north/south directions

Delete any points that do not overlap the areas in the Borough Boundaries dataset.

Create circles centered at each point, with a radius of 200 meters (656.168 feet) in line with the aforementioned paper (Johnson et al. 2020).

Overlay the circles with the land cover data, and calculate the proportion of the land cover that was grass/shrub and tree canopy land cover types. Note, because the land cover data consistently ended at the boundaries of NYC, for points within 200 meters of Nassau and Westchester Counties, the area with land cover data was smaller than the area of the circles.

Relate the results from the overlay analysis back to the associated points.

Create a raster data layer from the point data, with 100 foot by 100 foot resolution, where the center of each pixel is at the location of the respective points. Areas between the Borough Boundary polygons (open water of NY Harbor) are coded as "no data."

All steps except for the creation of the raster dataset were conducted in PostgreSQL/PostGIS, as documented in nyc_point_buffer_vegetation_overlay.sql. The conversion of the results to a raster dataset was done in QGIS (version 3.28), ultimately using the gdal_rasterize function.

This dataset displays the results of averaged ecological analyses within sample blocks for infaunal diversity as of Spring, 2013 within the pilot project area. This file contains ecological characteristics of infaunal Fishers diversity (prediction of the number of species at different levels of abundance) in the central Long Island Sound Pilot Area based on grab samples and analyses of high definition photos and video. These data are averages of data processed from individual samples within the large sample block areas. This dataset represents data collected in Spring (May) 2013.View Dataset on the Gateway

This data set contains vector lines and polygons representing coastal hydrography used in the creation of the Environmental Sensitivity Index (ESI) for Long Island, New York. The HYDRO data layer contains all annotation used in producing the atlas. The annotation features are categorized into two subclasses in order to simplify the mapping and quality control procedures: SOC, for socioeconomic...

Detailed chemical, station (source and documentation, sample locations), and texture data are provided for sediments in Long Island Sound and New York Bight. The sediment data are provided as spreadsheet (Microsoft Excel) and tab-delimited files on the web site. These data are in the form of sections within the web site, which provides extensive supporting data, interpretive diagrams, and discussion. The data were obtained from a variety of sources: published reports, theses, unpublished data from agencies and organizations in the Long Island Sound and New York Bight area and Federal agencies such as the U.S. Army Corps of Engineers, U.S. Environmental Protection Agency, NOAA, National Status and Trends Benthic Surveillance program, and the U.S. Geological Survey.

Summary:

The files contained herein represent green roof footprints in NYC visible in 2016 high-resolution orthoimagery of NYC (described at https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_AerialImagery.md). Previously documented green roofs were aggregated in 2016 from multiple data sources including from NYC Department of Parks and Recreation and the NYC Department of Environmental Protection, greenroofs.com, and greenhomenyc.org. Footprints of the green roof surfaces were manually digitized based on the 2016 imagery, and a sample of other roof types were digitized to create a set of training data for classification of the imagery. A Mahalanobis distance classifier was employed in Google Earth Engine, and results were manually corrected, removing non-green roofs that were classified and adjusting shape/outlines of the classified green roofs to remove significant errors based on visual inspection with imagery across multiple time points. Ultimately, these initial data represent an estimate of where green roofs existed as of the imagery used, in 2016.

These data are associated with an existing GitHub Repository, https://github.com/tnc-ny-science/NYC_GreenRoofMapping, and as needed and appropriate pending future work, versioned updates will be released here.

Terms of Use:

The Nature Conservancy and co-authors of this work shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any sale, distribution, loan, or offering for use of these digital data, in whole or in part, is prohibited without the approval of The Nature Conservancy and co-authors. The use of these data to produce other GIS products and services with the intent to sell for a profit is prohibited without the written consent of The Nature Conservancy and co-authors. All parties receiving these data must be informed of these restrictions. Authors of this work shall be acknowledged as data contributors to any reports or other products derived from these data.

Associated Files:

As of this release, the specific files included here are:

• GreenRoofData2016_20180917.geojson is in the human-readable, GeoJSON format, in geographic coordinates (Lat/Long, WGS84; EPSG 4263).
• GreenRoofData2016_20180917.gpkg is in the GeoPackage format, which is an Open Standard readable by most GIS software including Esri products (tested on ArcMap 10.3.1 and multiple versions of QGIS). This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.
• GreenRoofData2016_20180917_Shapefile.zip is a zipped folder containing a Shapefile and associated files. Please note that some field names were truncated due to limitations of Shapefiles, but columns are in the same order as for other files and in the same order as listed below. This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.
• GreenRoofData2016_20180917.csv is a comma-separated values file (CSV) with coordinates for centroids for the green roofs stored in the table itself. This allows for easily opening the data in a tool like spreadsheet software (e.g., Microsoft Excel) or a text editor.

Column Information for the datasets:

Some, but not all fields were joined to the green roof footprint data based on building footprint and tax lot data; those datasets are embedded as hyperlinks below.

• fid - Unique identifier
• bin - NYC Building ID Number based on overlap between green roof areas and a building footprint dataset for NYC from August, 2017. (Newer building footprint datasets do not have linkages to the tax lot identifier (bbl), thus this older dataset was used). The most current building footprint dataset should be available at: https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh. Associated metadata for fields from that dataset are available at https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md.
• bbl - Boro Block and Lot number as a single string. This field is a tax lot identifier for NYC, which can be tied to the Digital Tax Map (http://gis.nyc.gov/taxmap/map.htm) and PLUTO/MapPLUTO (https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page). Metadata for fields pulled from PLUTO/MapPLUTO can be found in the PLUTO Data Dictionary found on the aforementioned page. All joins to this bbl were based on MapPLUTO version 18v1.
• gr_area - Total area of the footprint of the green roof as per this data layer, in square feet, calculated using the projected coordinate system (EPSG 2263).
• bldg_area - Total area of the footprint of the associated building, in square feet, calculated using the projected coordinate system (EPSG 2263).
• prop_gr - Proportion of the building covered by green roof according to this layer (gr_area/bldg_area).
• cnstrct_yr - Year the building was constructed, pulled from the Building Footprint data.
• doitt_id - An identifier for the building assigned by the NYC Dept. of Information Technology and Telecommunications, pulled from the Building Footprint Data.
• heightroof - Height of the roof of the associated building, pulled from the Building Footprint Data.
• feat_code - Code describing the type of building, pulled from the Building Footprint Data.
• groundelev - Lowest elevation at the building level, pulled from the Building Footprint Data.
• qa - Flag indicating a positive QA/QC check (using multiple types of imagery); all data in this dataset should have 'Good'
• notes - Any notes about the green roof taken during visual inspection of imagery; for example, it was noted if the green roof appeared to be missing in newer imagery, or if there were parts of the roof for which it was unclear whether there was green roof area or potted plants.
• classified - Flag indicating whether the green roof was detected image classification. (1 for yes, 0 for no)
• digitized - Flag indicating whether the green roof was digitized prior to image classification and used as training data. (1 for yes, 0 for no)
• newlyadded - Flag indicating whether the green roof was detected solely by visual inspection after the image classification and added. (1 for yes, 0 for no)
• original_source - Indication of what the original data source was, whether a specific website, agency such as NYC Dept. of Parks and Recreation (DPR), or NYC Dept. of Environmental Protection (DEP). Multiple sources are separated by a slash.
• address - Address based on MapPLUTO, joined to the dataset based on bbl.
• borough - Borough abbreviation pulled from MapPLUTO.
• ownertype - Owner type field pulled from MapPLUTO.
• zonedist1 - Zoning District 1 type pulled from MapPLUTO.
• spdist1 - Special District 1 pulled from MapPLUTO.
• bbl_fixed - Flag to indicate whether bbl was manually fixed. Since tax lot data may have changed slightly since the release of the building footprint data used in this work, a small percentage of bbl codes had to be manually updated based on overlay between the green roof footprint and the MapPLUTO data, when no join was feasible based on the bbl code from the building footprint data. (1 for yes, 0 for no)

For GreenRoofData2016_20180917.csv there are two additional columns, representing the coordinates of centroids in geographic coordinates (Lat/Long, WGS84; EPSG 4263):

• xcoord - Longitude in decimal degrees.
• ycoord - Latitude in decimal degrees.

Acknowledgements:

This work was primarily supported through funding from the J.M. Kaplan Fund, awarded to the New York City Program of The Nature Conservancy, with additional support from the New York Community Trust, through New York City Audubon and the Green Roof Researchers Alliance.

This USGS data release contains 7Q10 and 30Q10 [lowest annual 7-day and 30-day average streamflow that occurs (on average) once every 10 years] statistics at 292 USGS streamgages in or adjacent to New York State excluding Long Island. all_sites_wstats.csv - includes 7Q10 and 30Q10 values for all sites and includes information on results from the trend analysis and which sites have daily exceedance probability values available. site_regulated_7day_exc_perc#.csv and site_regulated_30day_exc_perc#.csv files include daily exceedance probability values for all altered sites that were not suitable for calculating low flow statistics. R scripts used to compile and screen streamgage datasets of daily flow, perform trend analysis, and calculate the low streamflow statistics 7Q10 and 30Q10 are included in processing_scripts.zip. Users are encouraged to read the readme file in this zipped file for details on the scripts and associated files used to generate the statistics.

Long Island Sound is one of the largest estuaries along the Atlantic coast of the United States. It is a glacially produced, semi-enclosed, northeast-southwest-trending embayment, which is 150 km long and 30 km across at its widest point. Its mean water depth is approximately 24 m. The eastern end of the Sound opens to the Atlantic Ocean through several large passages between islands, whereas the western end is connected to New York Harbor through a narrow tidal strait. Long Island Sound abuts the New York-Connecticut metropolitan area and contains more than 8 million people within its watershed. A study of the modern sedimentary environments on the sea floor within the Long Island Sound estuarine system was undertaken as part of a larger research program by the U.S. Geological Survey (Coastal and Marine Geology Program) conducted in cooperation with the State of Connecticut Department of Environmental Protection and the U.S. Environmental Protection Agency. Knowledge of the botto ...

Summary

This repository contains spatial datasets with metadata on land cover, tree canopy change, and estimated tree points and crown polygons for New York City (NYC; New York, USA) as of 2021, made available by The Nature Conservancy, New York Cities Program and developed under contract by the University of Vermont Spatial Analysis Lab. The datasets are provided herein with high-level background and information; additional analysis, particularly on tree canopy change and distribution across NYC considering various geogrpahic units are planned for release in a forthcoming report by The Nature Conservancy. For questions about these data, contact Michael Treglia, Lead Scientist with The Nature Conservancy, New York Cities Program, at michael.treglia@tnc.org.

Datasets included here are as follows (file names in italics):

Land cover as of 2021 (landcover_nyc_2021_6in.tif):

Raster dataset with six-inch (15.24 centimeter) pixel resolution, delineating land covers as: 1) tree canopy (with crowns greater than eight feet [2.44 meters] tall; 2) grass/shrub (including vegetation less than or equal to eight feet [2.44 feet] tall; 3) bare ground; 4) open water; 5) building; 6) road; 7) other impervious; and 8) railroad. This is intended to serve as an update to high-resolution land cover data for 2010 and 2017 made available by the City of New York.

Tree canopy change during 2017-2021 (treecanopychange_nyc_2017_2021_6in.tif):

Raster dataset with six-inch (15.24 centimeter) pixel resolution, with pixels that were estimated tree canopy in 2017 (based on 2017 land cover data) or 2021 delineated as: 1) canopy that did not change (“no change”); 2) canopy that was gained (“gain”); 3) canopy that was lost (“loss”). This is intended to be an updated tree canopy change dataset, analogous to a canopy change dataset for 2010-2017 made available by the City of New York.

Estimated tree points, crown polygons, and objects as of 2021 (Trees_Centroids_Crown_Objects_2021.gdb.zip):

The approximated locations (centroids) and approximated tree crowns as circles (shapes), and tree objects themselves based on canopy data (objects) for individual trees with crowns taller than eight feet (2.44 meters); in cases where there are trees with overlapping crowns, only the top trees are captured. These data are based on automated processing of the tree canopy class from the land cover data; additional methodological details are included in the metadata for this dataset. Given the height cutoff, that this dataset only captures the trees seen from above, and the large number of understory trees in some areas (e.g., forested natural areas), and limits in the automated processing this is not intended to be a robust census of trees in NYC, but may serve as useful for some purposes. Unlike the land cover and tree canopy change datasets, no directly comparable datasets for NYC from past years that we are aware of.

These datasets were based on object-based image analysis of a combination of 2021 Light Detection and Ranging (LiDAR; data available from the State of New York) for tree canopy and tree location/crown data in particular) along with high-resolution aerial imagery (from 2021 via the USDA National Agriculture Inventory Program and from 2022 via the New York State GIS Clearinghouse), followed by manual corrections. The general methods used to develop the land cover and tree canopy datasets are described in MacFaden et al. (2012). A per-pixel accuracy assessment of the land cover data with 1,999 points estimated an overall accuracy of 95.52% across all land cover classes, and 99.06% for tree canopy specifically (a critical focal area for this project). Iterative review of the data and subject matter expertise were contributed by from The Nature Conservancy and the NYC Department of Parks and Recreation.

While analyses of tree canopy and tree canopy change across NYC are pending, those interested can review a report that includes analyses of the most recent data (2010-2017) and a broad consideration of the NYC urban forest, The State of the Urban Forest in New York City (Treglia et al 2021).

References

MacFaden, S. W., J. P. M. O’Neil-Dunne, A. R. Royar, J. W. T. Lu, and A. G. Rundle. 2012. High-resolution tree canopy mapping for New York City using LIDAR and object-based image analysis. Journal of Applied Remote Sensing 6(1):063567.

Treglia, M.L., Acosta-Morel, M., Crabtree, D., Galbo, K., Lin-Moges, T., Van Slooten, A., & Maxwell, E.N. (2021). The State of the Urban Forest in New York City. The Nature Conservancy. doi: 10.5281/zenodo.5532876

Terms of Use

© The Nature Conservancy. This material is provided as-is, without warranty under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 (CC BY-NC-SA 4.0) license.

The Nature Conservancy (TNC) oversaw development of these data and reserves all rights in the data provided.

TNC makes no guarantee of accuracy or completeness.

Data are for informational purposes and are not suitable for legal, engineering, or surveying purposes. Data do not represent an on-the-ground survey and represent only the approximate relative location of feature boundaries.

TNC is not obligated to update/maintain the data to reflect changing conditions.

Commercial use is not allowed.

Redistribution (sublicensing) is allowed, provided all accompanying metadata as well as these Terms of Use are provided, unaltered, alongside the data.

TNC should be credited as the data source in derivative works, following the recommended citation provided herein.

Users are advised to pay attention to the contents of this metadata document.

Recommended Citation

If using any of these datasets, please cite the work according to the following recommended citation:

The Nature Conservancy. 2024. New York City Land Cover (2021), Tree Canopy Change (2017-2021), and Estimated Tree Location and Crown Data (2021). Developed under contract by the University of Vermont Spatial Analysis Laboratory. doi: 10.5281/zenodo.14053441.

Technical Notes about the Spatial Data

All spatial data are provided in the New York State Plan Long Island Zone (US survey foot) coordinate reference system, EPSG 2263. The land cover and tree canopy change datasets are made available as raster data in Cloud Optimized GeoTIFF format (.tif), with associated metadata files as .xml files. The vector data of estimated tree locations and crown objects and shapes are made available in a zipped Esri File Geodatabase, with metadata stored within the File Geodatabase.

These data were automated to provide an accurate high-resolution historical shoreline of Long Island Sound, New York suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The N...

The last update to this data was completed on 6/22/2020, this update focused on reviewing existing waterbodies and folding in missing waterbodies for the Chemung HUC (02050105) and the Tioga HUC (02050104). A History of edits is listed below. The full statewide dataset can be downloaded from: https://www.usgs.gov/national-hydrography/access-national-hydrography-productsWeb Service url - https://gisservices.its.ny.gov/arcgis/rest/services/NYS_Hydrography_HollowFill/MapServerThis web service is a subset for New York State of the National Hydrography Dataset (NHD), there is more information about each layer in the description of the groups and specific layers. The NHD is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes and the Atlantic Ocean. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee. -- History of edits 02/18/2020 -- Waterbody Updates to Southern Long Island HUC (02030202) & Owego-Wappasening HUC (02050103) 10/04/2019 – Waterbody Updates to Upper Susquehanna HUC (02050101) 08/19/2019 – Waterbody Updates to Chenango HUC (02050102) & Northern Long Island HUC (02030201)Please contact NYS ITS Geospatial Services at nysgis@its.ny.gov if you have any questions.