The New York City Community Health Survey (CHS) is a telephone survey conducted annually by the DOHMH, Division of Epidemiology, Bureau of Epidemiology Services. CHS provides robust data on the health of New Yorkers, including neighborhood, borough, and citywide estimates on a broad range of chronic diseases and behavioral risk factors.

The New York City Community Mental Health Survey (CMHS) was a one-time telephone survey conducted by the DOHMH. The CMHS was conducted in conjunction with the annual 2012 Community health Survey (CHS). The CMHS provides robust data on the mental health of New Yorkers, including neighborhood, borough, and citywide estimates. The data are analyzed and disseminated to influence mental health program decisions, and increase the understanding of the mental health among New Yorkers.

Summary results from NYC Community Health Survey 2010-2016: adults ages 18 years and older Source: NYC Community Health Survey (CHS) 2010-16. The Community Health Survey (CHS) includes self-reported data from adults, years 18 and older. CHS has included adults with landline phones since 2002 and, starting in 2009, also has included adults who can be reached by cell-phone. Starting in 2011, CHS weighting methods were updated to use Census 2010 and additional demographic characteristics (http://www1.nyc.gov/assets/doh/downloads/pdf/epi/epiresearch-chsmethods.pdf ).

Data are age-adjusted to the US 2000 Standard Population.
Data prepared by Bureau of Epidemiology Services, New York City Department of Health and Mental Hygiene

The New York City Community Health Survey (CHS) is a telephone survey conducted annually by the DOHMH, Division of Epidemiology, Bureau of Epidemiology Services. CHS provides robust data on the health of New Yorkers, including neighborhood, borough, and citywide estimates on a broad range of chronic diseases and behavioral risk factors. The data are analyzed and disseminated to influence health program decisions, and increase the understanding of the relationship between health behavior and health status. For more information see EpiQuery, https://a816-healthpsi.nyc.gov/epiquery/CHS/CHSXIndex.html

"TARGET POPULATION The target population of the CHS includes non-institutionalized adults aged 18 and older who live in a household with a landline telephone in New York City (the five borough area). Starting in 2009, adults living in households with only cell phones have also been included in the survey.

HEALTH TOPICS Most years the CHS includes approximately 125 questions, covering the following health topics: general health status and mental health, health care access, cardiovascular health, diabetes, asthma, immunizations, nutrition and physical activity, smoking, HIV, sexual behavior, alcohol consumption, cancer screening and other health topics. A core group of demographics variables are included every year to facilitate weighting and comparisons among different groups of New Yorkers.

SAMPLING The CHS uses a stratified random sample to produce neighborhood and citywide estimates. Neighborhoods are defined using the United Hospital Fund's (UHF) neighborhood designation, which assigns neighborhood based on the ZIP code of the respondent. New ZIP codes have been added since the UHF's were originally defined. There are 42 UHF neighborhoods in NYC. However, to avoid small sample sizes for CHS estimates, UHF estimates are generally collapsed into 34 UHFs/groups.

Starting in 2009, a second sample consisting of cell-only households with New York City exchanges was added. This design is non-overlapping because in the cell-only sample, adults living in households with landline telephones were screened out.

A computer-assisted telephone interviewing (CATI) system is used to collect the survey data. The CHS sampling frame was constructed with a list of telephone numbers provided by a commercial vendor. Upon agreement to participate in the survey, one adult is randomly selected from the household to complete the interview.

Interviewing is conducted in a variety of languages. Every year, the questionnaire is translated from English into Spanish, Russian, and Chinese. Some years, live translation services are provided by Language Line (including Hindi, Arabic, Farsi, and Haitian Creole). Typically, data collection begins in March of the study year and ends in December. The average length of the survey is 25 minutes.

LIMITATIONS The survey sampling methodology does not capture the following groups: households without any telephone service and (prior to 2009) households that only have a cell phone. The CHS also excludes adults living in institutional group housing, such as college dormitories.

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Community Health Survey Dataset

This dataset includes annual survey data on the health of New York City adults, covering health behaviors, access to care, and social determinants of health. This datasets does not have geographical information. Data is collected and published by the NYC Department of Health and Mental Hygiene (DOHMH). More information about how this dataset was generated can be found here.

The New York City Health Opinion Poll (HOP) is a periodic rapid online poll conducted by New York City Department of Health and Mental Hygiene. The goals of the poll are to measure adult New Yorkers’ awareness, acceptance and use — or barriers to use — of our programs; knowledge, opinions and attitudes about health care and practices; and opinions about public events that are related to health. The data collected through public health polling are rapidly analyzed and disseminated. This real-time community input informs programming and policy development at the Health Department to better meet the needs of New Yorkers.

The Heat Vulnerability Index (HVI) shows neighborhoods whose residents are more at risk for dying during and immediately following extreme heat. It uses a statistical model to summarize the most important social and environmental factors that contribute to neighborhood heat risk. The factors included in the HVI are surface temperature, green space, access to home air conditioning, and the percentage of residents who are low-income or non-Latinx Black. Differences in these risk factors across neighborhoods are rooted in past and present racism. Neighborhoods are scored from 1 (lowest risk) to 5 (highest risk) by summing the following factors and assigning them into 5 groups (quintiles):

Median Household Income (American Community Survey 5 year estimate, 2016-2020) Percent vegetative cover (trees, shrubs or grass) (2017 LiDAR, NYC DOITT) Percent of population reported as Non-Hispanic Black on Census 2020 Average surface temperature Fahrenheit from ECOSSTRESS thermal imaging, August 27,2020 Percent of households reporting Air Conditioning access, Housing ad Vacancy Survey, 2017

Citywide raster files of annual average predicted surface for nitrogen dioxide (NO2), fine particulate matter (PM2.5), black carbon (BC), and nitric oxide (NO); summer average for ozone (O3) and winter average for sulfure dioxide (SO2).

Description: Annual average predicted surface for nitrogen dioxide (NO2), fine particulate matter (PM2.5), black carbon (BC), and nitric oxide (NO); summer average for ozone (O3) and winter average for sulfure dioxide (SO2). File type is ESRI grid raster files at 300 m resolution, NAD83 New York Long Island State Plane FIPS, feet projection. Prediction surface generated from Land Use Regression modeling of December 2008- December 2019 (years 1-11) New York Community Air Survey monitoring data.As these are estimated annual average levels produced by a statistical model, they are not comparable to short term localized monitoring or monitoring done for regulatory purposes. For description of NYCCAS design and Land Use Regression Modeling process see: nyc-ehs.net/nyccas

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.