NYC Open Data

	Name	Popularity
1.	DOHMH School Cafeteria inspections (2020 - Present) DOHMH School Cafeteria inspections (2020 - Present) Health public, private, parochial, school, cafeteria, ... The dataset contains current inspection data for cafeterias permitted in public, private, and parochial schools in NYC. All school cafeterias are required to be in compliance with NYS and NYC Food Safety Regulations, found in New York City Health Code Article 81. School cafeteria inspections are conducted at least annually to ensure compliance with food safety regulations. This dataset includes information obtained as part of the permitting process and data collected during inspections. This data includes inspection results for active school cafeterias for the last three years. Data for cafeterias that have ceased operations and any violations cited during the inspection that were dismissed during adjudication are excluded from this dataset	575 views
2.	Permitted Nonpotable Wells Permitted Nonpotable Wells Health well, well water, nonpotable water well A permit is required to install, drill, replace or operate a nonpotable water well in New York City used to supply water to any buildings or affiliated structures. This dataset contains a list of current permits for nonpotable water wells located in New York City. All nonpotable water wells located in New York City need a permit to operate. Data is collected to track permit information of currently permitted nonpotable wells in New York City. Data is collected online through the permitting web application. Each record represents a permit record for all currently permitted nonpotable wells in New York City. This data can be used to verify the location and permit expiration date of currently permitted nonpotable wells in New York City.	320 views
3.	Youth Risk Behavior Survey (Middle School) Youth Risk Behavior Survey (Middle School) Health health, survey, youth, risk behavior The NYC Middle School Youth Risk Behavior Survey (MS YRBS) is conducted through an ongoing collaboration between the New York City Department of Health and Mental Hygiene (DOHMH), the Department of Education (DOE), and the National Centers for Disease Control and Prevention (CDC). The New York City's YRBS is part of the CDC's National Youth Risk Behavior Surveillance System (YRBSS). The survey's primary purpose is to monitor priority health risk behaviors that contribute to the leading causes of mortality, morbidity, and social problems among youth in New York City. For more information, visit https://www1.nyc.gov/site/doh/data/data-sets/nyc-youth-risk-behavior-survey.page	742 views
4.	NYC Pool Inspections NYC Pool Inspections Health pool, indoor pool, outdoor pool A permit is required to install, operate or construct any indoor or outdoor bathing establishment with a pool in New York City. This permit may also include saunas, steam rooms, or spray grounds that are at the same location as the pool(s). This permit applies to bathing establishments owned or operated by city agencies, commercial interests or private entities including, but not limited to, public or private schools, corporations, hotels, motels, camps, apartment houses, condominiums, country clubs, gymnasia and health establishments. This dataset contains results of indoor and outdoor pool inspections. Due to the COVID-19 public health emergency, there were periods of time in 2020 when facilities were subject to mandatory closure orders or chose not to open, and inspections were subsequently paused or modified.	1,896 views
5.	Hyperlocal Temperature Monitoring Hyperlocal Temperature Monitoring extreme temperature, temperature, weather, warming, ... As a part of the Cool Neighborhoods Initiative, the NYC Parks Department, Mayor’s Office of Resilience and NYC Department of Health and Mental Hygiene monitored street level temperature on a subset of city blocks in some of the neighborhoods with highest heat mortality risk during the summers of 2018 and 2019. The dataset includes hourly average values at approximately 475 locations in degrees Fahrenheit.	4,155 views
6.	2017-2021 NYC KIDS Survey 2017-2021 NYC KIDS Survey Health public health, surveys, new york city, children The NYC KIDS Survey is a population-based telephone survey conducted by the Health Department. The survey provides robust data on the health of children aged 13 years or younger (2017: children aged 0-13 years; 2019: children aged 1-13 years) in New York City, including citywide and borough estimates, on a broad range of topics including physical and mental health, health care access, and school and childcare enrollment and learning. For more information, visit https://www1.nyc.gov/site/doh/data/data-sets/child-chs.page	539 views
7.	DOHMH Covid-19 Milestone Data: Daily Number of People Admitted to NYC hospitals for Covid-19 like Illness DOHMH Covid-19 Milestone Data: Daily Number of People Admitted to NYC hospitals for Covid-19 like Illness citywide, percent, positivity, covid-19, dohmh, ... This dataset shows the number of hospital admissions for influenza-like illness, pneumonia, or include ICD-10-CM code (U07.1) for 2019 novel coronavirus. Influenza-like illness is defined as a mention of either: fever and cough, fever and sore throat, fever and shortness of breath or difficulty breathing, or influenza. Patients whose ICD-10-CM code was subsequently assigned with only an ICD-10-CM code for influenza are excluded. Pneumonia is defined as mention or diagnosis of pneumonia. Baseline data represents the average number of people with COVID-19-like illness who are admitted to the hospital during this time of year based on historical counts. The average is based on the daily avg from the rolling same week (same day +/- 3 days) from the prior 3 years. Percent change data represents the change in count of people admitted compared to the previous day. Data sources include all hospital admissions from emergency department visits in NYC. Data are collected electronically and transmitted to the NYC Health Department hourly. This dataset is updated daily. All identifying health information is excluded from the dataset.	685 views
8.	DOHMH Covid-19 Milestone Data: New Cases of Covid-19 (7 Day Average) DOHMH Covid-19 Milestone Data: New Cases of Covid-19 (7 Day Average) Health citywide, percent, positivity, covid-19, dohmh This dataset shows daily confirmed and probable cases of COVID-19 in New York City by date of specimen collection. Total cases has been calculated as the sum of daily confirmed and probable cases. Seven-day averages of confirmed, probable, and total cases are also included in the dataset. A person is classified as a confirmed COVID-19 case if they test positive with a nucleic acid amplification test (NAAT, also known as a molecular test; e.g. a PCR test). A probable case is a person who meets the following criteria with no positive molecular test on record: a) test positive with an antigen test, b) have symptoms and an exposure to a confirmed COVID-19 case, or c) died and their cause of death is listed as COVID-19 or similar. As of June 9, 2021, people who meet the definition of a confirmed or probable COVID-19 case >90 days after a previous positive test (date of first positive test) or probable COVID-19 onset date will be counted as a new case. Prior to June 9, 2021, new cases were counted ≥365 days after the first date of specimen collection or clinical diagnosis. Any person with a residence outside of NYC is not included in counts. Data is sourced from electronic laboratory reporting from the New York State Electronic Clinical Laboratory Reporting System to the NYC Health Department. All identifying health information is excluded from the dataset. These data are used to evaluate the overall number of confirmed and probable cases by day (seven day average) to track the trajectory of the pandemic. Cases are classified by the date that the case occurred. NYC COVID-19 data include people who live in NYC. Any person with a residence outside of NYC is not included.	1,025 views
9.	DOHMH Covid-19 Milestone Data: Percent of NYC residents tested who tested positive DOHMH Covid-19 Milestone Data: Percent of NYC residents tested who tested positive Health citywide, percent, positivity, covid-19, dohmh This dataset shows daily citywide counts of persons tested by nucleic acid amplification tests (NAAT, also known as a molecular test; e.g. a PCR test) for SARS-CoV-2 , counts of persons with positive tests, and the percent positivity. Also included is a calculation of the average percent positivity over a 7-day period. NAAT tests work through direct detection of the virus’s genetic material, and typically involve collecting a nasal swab. These tests are highly accurate and recommended for diagnosing current COVID-19 infection. After specimen collection, molecular tests are processed in a laboratory, and results are electronically reported to the New York State (NYS) Electronic Clinical Laboratory Results System (ECLRS). Test results for NYC residents are then sent electronically to NYC DOHMH. There is typically a lag of a few days between when a specimen is collected and when a result is reported to NYC DOHMH. Data is sourced from electronic laboratory reporting from NYS ECLRS. All identifying health information is excluded from the dataset.	474 views
10.	DOHMH COVID-19 Antibody-by-Modified ZIP Code Tabulation Area DOHMH COVID-19 Antibody-by-Modified ZIP Code Tabulation Area This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by modified ZIP Code Tabulation Area (ZCTA) of residence. Modified ZCTA reflects the first non-missing address within NYC for each person reported with an antibody test result. This unit of geography is similar to ZIP codes but combines census blocks with smaller populations to allow more stable estimates of population size for rate calculation. It can be challenging to map data that are reported by ZIP Code. A ZIP Code doesn’t refer to an area, but rather a collection of points that make up a mail delivery route. Furthermore, there are some buildings that have their own ZIP Code, and some non-residential areas with ZIP Codes. To deal with the challenges of ZIP Codes, the Health Department uses ZCTAs which solidify ZIP codes into units of area. Often, data reported by ZIP code are actually mapped by ZCTA. The ZCTA geography was developed by the U.S. Census Bureau. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-modzcta.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level. These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents. In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders) Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning. Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020. Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis will almost certainly be miscalculating or counting the same values multiple times. To analyze the most current data, only use the latest extract date. Antibody tests that are missing dates are not included in the dataset; as dates are identified, these events are added. Lags between occurrence and report of cases and tests can be assessed by comparing counts and rates across multiple data extract dates. For further details, visit: • https://www1.nyc.gov/site/doh/covid/covid-19-data.page • https://github.com/nychealth/coronavirus-data • https://data.cityofnewyork.us/Health/Modified-Zip-Code-Tabulation-Areas-MODZCTA-/pri4-ifjk	795 views
11.	DOHMH COVID-19 Antibody-by-Neighborhood Poverty DOHMH COVID-19 Antibody-by-Neighborhood Poverty Health sars-cov-2, covid-19, coronavirus This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by ZIP Code Tabulation Area (ZCTA) neighborhood poverty group. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-poverty.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level. These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents. In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders.) Neighborhood-level poverty groups were classified in a manner consistent with Health Department practices to describe and monitor disparities in health in NYC. Neighborhood poverty measures are defined as the percentage of people earning below the Federal Poverty Threshold (FPT) within a ZCTA. The standard cut-points for defining categories of neighborhood-level poverty in NYC are: • Low: <10% of residents in ZCTA living below the FPT • Medium: 10% to <20% • High: 20% to <30% • Very high: ≥30% residents living below the FPT The ZCTAs used for classification reflect the first non-missing address within NYC for each person reported with an antibody test result. Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning. Rates for poverty were calculated using direct standardization for age at diagnosis and weighting by the US 2000 standard population. Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020. Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis will almost certainly be miscalculating or counting the same values multiple times. To analyze the most current data, only use the latest extract date. Antibody tests that are missing dates are not included in the dataset; as dates are identified, these events are added. Lags between occurrence and report of cases and tests can be assessed by comparing counts and rates across multiple data extract dates. For further details, visit: • https://www1.nyc.gov/site/doh/covid/covid-19-data.page • https://github.com/nychealth/coronavirus-data • https://data.cityofnewyork.us/Health/Modified-Zip-Code-Tabulation-Areas-MODZCTA-/pri4-ifjk	1,197 views
12.	DOHMH COVID-19 Antibody-by-Sex DOHMH COVID-19 Antibody-by-Sex This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by sex. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-sex.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level. These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents. In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders.) Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning. Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020. Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis will almost certainly be miscalculating or counting the same values multiple times. To analyze the most current data, only use the latest extract date. Antibody tests that are missing dates are not included in the dataset; as dates are identified, these events are added. Lags between occurrence and report of cases and tests can be assessed by comparing counts and rates across multiple data extract dates. For further details, visit: • https://www1.nyc.gov/site/doh/covid/covid-19-data.page • https://github.com/nychealth/coronavirus-data	593 views
13.	DOHMH COVID-19 Antibody-by-Week DOHMH COVID-19 Antibody-by-Week This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by week of testing. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/trends/antibody-by-week.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level. These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents. In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders.) Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning. Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020. Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis will almost certainly be miscalculating or counting the same values multiple times. To analyze the most current data, only use the latest extract date. Antibody tests that are missing dates are not included in the dataset; as dates are identified, these events are added. Lags between occurrence and report of cases and tests can be assessed by comparing counts and rates across multiple data extract dates. For further details, visit: • https://www1.nyc.gov/site/doh/covid/covid-19-data.page • https://github.com/nychealth/coronavirus-data	649 views
14.	New York City Health Opinion Poll New York City Health Opinion Poll Health public health, surveys, polls, ... 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.	786 views
15.	DOHMH COVID-19 Antibody-by-Borough DOHMH COVID-19 Antibody-by-Borough Health sars-cov-2, covid-19, coronavirus This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by borough of residence. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-boro.csv	2,213 views
16.	DOHMH COVID Antibody-by-Age DOHMH COVID Antibody-by-Age Health sars-cov-2; covid-19; coronavirus This file contains person-level information on antibody testing: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by age group.	1,414 views
17.	Pregnancy-Associated Mortality Pregnancy-Associated Mortality Health maternal mortality, pregnancy-associated mortality Maternal mortality is widely considered an indicator of overall population health and the status of women in the population. DOHMH uses multiple methods including death certificates, vital records linkage, medical examiner records, and hospital discharge data to identify all pregnancy-associated deaths (deaths that occur during pregnancy or within a year of the end of pregnancy) of New York state residents in NYC each year. DOHMH convenes the Maternal Mortality and Morbidity Review Committee (M3RC), a multidisciplinary and diverse group of 40 members that conducts an in-depth, expert review of each pregnancy-associated death of New York state residents occurring in NYC from both clinical and social determinants of health perspectives. The data in this table come from vital records and the M3RC review process. Data are not cross-classified on all variables: cause of death data are available by the relation to pregnancy (pregnancy-related, pregnancy-associated but not related, unable to determine), race/ethnicity and borough of residence data are each separately available for the total number of pregnancy-associated deaths and pregnancy-related deaths only.	3,484 views
18.	DOHMH Severe Maternal Morbidity DOHMH Severe Maternal Morbidity Health maternal morbidity, pregnancy complications Severe maternal morbidity (SMM) refers to life-threatening complications of labor and delivery that result in significant short- or long-term health consequences. SMM data are derived from linking NYC birth certificates for births occurring at NYC facilities with the mother's delivery hospitalization record from the New York Statewide Planning and Research Cooperative System. SMM is identified using an established algorithm developed by the Centers for Disease Control and Prevention that comprises 21 indicators that represent diagnoses of serious complications of pregnancy or delivery or procedures used to manage serious conditions. Each record represents the aggregated number and rate of SMM events in the population group specified for the year specified.	1,196 views
19.	DOHMH School Cafeteria inspections DOHMH School Cafeteria inspections Health public, private, parochial, school, cafeteria, ... The dataset contains current inspection data for cafeterias permitted in public, private, and parochial schools in NYC. All school cafeterias are required to be in compliance with NYS and NYC Food Safety Regulations, found in New York City Health Code Article 81. School cafeteria inspections are conducted at least annually to ensure compliance with food safety regulations. This dataset includes information obtained as part of the permitting process and data collected during inspections. This data includes inspection results for active school cafeterias for the last three years. Data for cafeterias that have ceased operations and any violations cited during the inspection that were dismissed during adjudication are excluded from this dataset	1,503 views
20.	Mosquito control events in NYC – Adult Mosquito Truck Spraying and Aerial Larviciding Events Mosquito control events in NYC – Adult Mosquito Truck Spraying and Aerial Larviciding Events Health mosquito, pesticide application, ... This dataset shows the mosquito spraing events in NYC. For the historical data, please visit this page.	564 views
21.	Modified Zip Code Tabulation Areas (MODZCTA) Map Modified Zip Code Tabulation Areas (MODZCTA) Map Health A shapefile for mapping data by Modified Zip Code Tabulation Areas (MODZCTA) in NYC, based on the 2010 Census ZCTA shapefile. MODZCTA are being used by the NYC Department of Health & Mental Hygiene (DOHMH) for mapping COVID-19 Data.	4,075 views
22.	Modified Zip Code Tabulation Areas (MODZCTA) Modified Zip Code Tabulation Areas (MODZCTA) Health modzcta, mzcta, covid, covid-19, coronavirus, ... A shapefile for mapping data by Modified Zip Code Tabulation Areas (MODZCTA) in NYC, based on the 2010 Census ZCTA shapefile. MODZCTA are being used by the NYC Department of Health & Mental Hygiene (DOHMH) for mapping COVID-19 Data.	16,154 views
23.	COVID-19 Daily Counts of Cases, Hospitalizations, and Deaths COVID-19 Daily Counts of Cases, Hospitalizations, and Deaths Health sars-cov-2, coronavirus, covid-19 Daily count of NYC residents who tested positive for SARS-CoV-2, who were hospitalized with COVID-19, and deaths among COVID-19 patients. Note that this dataset currently pulls from https://raw.githubusercontent.com/nychealth/coronavirus-data/master/trends/data-by-day.csv on a daily basis.	102,246 views
24.	COVID-19 Outcomes by Testing Cohorts: Cases, Hospitalizations, and Deaths COVID-19 Outcomes by Testing Cohorts: Cases, Hospitalizations, and Deaths Health sars-cov-2, covid-19, coronavirus The dataset shows outcomes (confirmed cases, hospitalizations, and deaths) for cohorts defined by each date of specimen collection (specimen_date). For example, if a NYC resident tested positive for SARS-CoV-2 and was subsequently hospitalized, both events would show under the same specimen_date, indicating the date of specimen collection for the positive test and not the date of the hospitalization. For a comparable dataset showing diagnosis dates for confirmed cases, admission dates for hospitalized patients, and death dates for decedents, see https://data.cityofnewyork.us/Health/COVID-19-Daily-Counts-of-Cases-Hospitalizations-an/rc75-m7u3	21,134 views
25.	Emergency Department Visits and Admissions for Influenza-like Illness and/or Pneumonia Emergency Department Visits and Admissions for Influenza-like Illness and/or Pneumonia Health emergency, emergency department, ... Total emergency department visits, and visits and admissions for influenza-like and/or pneumonia illness by modified ZIP code tabulation area of patient residence.	6,140 views

1.

DOHMH School Cafeteria inspections (2020 - Present) Health public, private, parochial, school, cafeteria, ...

The dataset contains current inspection data for cafeterias permitted in public, private, and parochial schools in NYC. All school cafeterias are required to be in compliance with NYS and NYC Food Safety Regulations, found in New York City Health Code Article 81. School cafeteria inspections are conducted at least annually to ensure compliance with food safety regulations. This dataset includes information obtained as part of the permitting process and data collected during inspections. This data includes inspection results for active school cafeterias for the last three years. Data for cafeterias that have ceased operations and any violations cited during the inspection that were dismissed during adjudication are excluded from this dataset

575 views

2.

Permitted Nonpotable Wells Health well, well water, nonpotable water well

A permit is required to install, drill, replace or operate a nonpotable water well in New York City used to supply water to any buildings or affiliated structures. This dataset contains a list of current permits for nonpotable water wells located in New York City.

All nonpotable water wells located in New York City need a permit to operate. Data is collected to track permit information of currently permitted nonpotable wells in New York City.

Data is collected online through the permitting web application.

Each record represents a permit record for all currently permitted nonpotable wells in New York City.

This data can be used to verify the location and permit expiration date of currently permitted nonpotable wells in New York City.

320 views

3.

Youth Risk Behavior Survey (Middle School) Health health, survey, youth, risk behavior

The NYC Middle School Youth Risk Behavior Survey (MS YRBS) is conducted through an ongoing collaboration between the New York City Department of Health and Mental Hygiene (DOHMH), the Department of Education (DOE), and the National Centers for Disease Control and Prevention (CDC). The New York City's YRBS is part of the CDC's National Youth Risk Behavior Surveillance System (YRBSS). The survey's primary purpose is to monitor priority health risk behaviors that contribute to the leading causes of mortality, morbidity, and social problems among youth in New York City. For more information, visit https://www1.nyc.gov/site/doh/data/data-sets/nyc-youth-risk-behavior-survey.page

742 views

4.

NYC Pool Inspections Health pool, indoor pool, outdoor pool

A permit is required to install, operate or construct any indoor or outdoor bathing establishment with a pool in New York City. This permit may also include saunas, steam rooms, or spray grounds
that are at the same location as the pool(s). This permit applies to bathing establishments owned or operated by city agencies, commercial interests or private entities including, but not limited to, public or private schools, corporations, hotels, motels, camps, apartment houses, condominiums, country clubs, gymnasia and health establishments. This dataset contains results of indoor and outdoor pool inspections.

Due to the COVID-19 public health emergency, there were periods of time in 2020 when facilities were subject to mandatory closure orders or chose not to open, and inspections were subsequently paused or modified.

1,896 views

5.

Hyperlocal Temperature Monitoring extreme temperature, temperature, weather, warming, ...

As a part of the Cool Neighborhoods Initiative, the NYC Parks Department, Mayor’s Office of Resilience and NYC Department of Health and Mental Hygiene monitored street level temperature on a subset of city blocks in some of the neighborhoods with highest heat mortality risk during the summers of 2018 and 2019. The dataset includes hourly average values at approximately 475 locations in degrees Fahrenheit.

4,155 views

6.

2017-2021 NYC KIDS Survey Health public health, surveys, new york city, children

The NYC KIDS Survey is a population-based telephone survey conducted by the Health Department. The survey provides robust data on the health of children aged 13 years or younger (2017: children aged 0-13 years; 2019: children aged 1-13 years) in New York City, including citywide and borough estimates, on a broad range of topics including physical and mental health, health care access, and school and childcare enrollment and learning. For more information, visit https://www1.nyc.gov/site/doh/data/data-sets/child-chs.page

539 views

7.

DOHMH Covid-19 Milestone Data: Daily Number of People Admitted to NYC hospitals for Covid-19 like Illness citywide, percent, positivity, covid-19, dohmh, ...

This dataset shows the number of hospital admissions for influenza-like illness, pneumonia, or include ICD-10-CM code (U07.1) for 2019 novel coronavirus. Influenza-like illness is defined as a mention of either: fever and cough, fever and sore throat, fever and shortness of breath or difficulty breathing, or influenza. Patients whose ICD-10-CM code was subsequently assigned with only an ICD-10-CM code for influenza are excluded. Pneumonia is defined as mention or diagnosis of pneumonia.
Baseline data represents the average number of people with COVID-19-like illness who are admitted to the hospital during this time of year based on historical counts. The average is based on the daily avg from the rolling same week (same day +/- 3 days) from the prior 3 years. Percent change data represents the change in count of people admitted compared to the previous day.
Data sources include all hospital admissions from emergency department visits in NYC. Data are collected electronically and transmitted to the NYC Health Department hourly. This dataset is updated daily.
All identifying health information is excluded from the dataset.

685 views

8.

DOHMH Covid-19 Milestone Data: New Cases of Covid-19 (7 Day Average) Health citywide, percent, positivity, covid-19, dohmh

This dataset shows daily confirmed and probable cases of COVID-19 in New York City by date of specimen collection. Total cases has been calculated as the sum of daily confirmed and probable cases. Seven-day averages of confirmed, probable, and total cases are also included in the dataset.
A person is classified as a confirmed COVID-19 case if they test positive with a nucleic acid amplification test (NAAT, also known as a molecular test; e.g. a PCR test). A probable case is a person who meets the following criteria with no positive molecular test on record: a) test positive with an antigen test, b) have symptoms and an exposure to a confirmed COVID-19 case, or c) died and their cause of death is listed as COVID-19 or similar.
As of June 9, 2021, people who meet the definition of a confirmed or probable COVID-19 case >90 days after a previous positive test (date of first positive test) or probable COVID-19 onset date will be counted as a new case. Prior to June 9, 2021, new cases were counted ≥365 days after the first date of specimen collection or clinical diagnosis.
Any person with a residence outside of NYC is not included in counts.
Data is sourced from electronic laboratory reporting from the New York State Electronic Clinical Laboratory Reporting System to the NYC Health Department.
All identifying health information is excluded from the dataset.

These data are used to evaluate the overall number of confirmed and probable cases by day (seven day average) to track the trajectory of the pandemic. Cases are classified by the date that the case occurred. NYC COVID-19 data include people who live in NYC. Any person with a residence outside of NYC is not included.

1,025 views

9.

DOHMH Covid-19 Milestone Data: Percent of NYC residents tested who tested positive Health citywide, percent, positivity, covid-19, dohmh

This dataset shows daily citywide counts of persons tested by nucleic acid amplification tests (NAAT, also known as a molecular test; e.g. a PCR test) for SARS-CoV-2 , counts of persons with positive tests, and the percent positivity. Also included is a calculation of the average percent positivity over a 7-day period.

NAAT tests work through direct detection of the virus’s genetic material, and typically involve collecting a nasal swab. These tests are highly accurate and recommended for diagnosing current COVID-19 infection. After specimen collection, molecular tests are processed in a laboratory, and results are electronically reported to the New York State (NYS) Electronic Clinical Laboratory Results System (ECLRS). Test results for NYC residents are then sent electronically to NYC DOHMH. There is typically a lag of a few days between when a specimen is collected and when a result is reported to NYC DOHMH.
Data is sourced from electronic laboratory reporting from NYS ECLRS.
All identifying health information is excluded from the dataset.

474 views

10.

DOHMH COVID-19 Antibody-by-Modified ZIP Code Tabulation Area

This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by modified ZIP Code Tabulation Area (ZCTA) of residence. Modified ZCTA reflects the first non-missing address within NYC for each person reported with an antibody test result. This unit of geography is similar to ZIP codes but combines census blocks with smaller populations to allow more stable estimates of population size for rate calculation. It can be challenging to map data that are reported by ZIP Code. A ZIP Code doesn’t refer to an area, but rather a collection of points that make up a mail delivery route. Furthermore, there are some buildings that have their own ZIP Code, and some non-residential areas with ZIP Codes. To deal with the challenges of ZIP Codes, the Health Department uses ZCTAs which solidify ZIP codes into units of area. Often, data reported by ZIP code are actually mapped by ZCTA. The ZCTA geography was developed by the U.S. Census Bureau. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-modzcta.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level.

These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents.

In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders)

Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning.

Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020.

Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis will almost certainly be miscalculating or counting the same values multiple times. To analyze the most current data, only use the latest extract date. Antibody tests that are missing dates are not included in the dataset; as dates are identified, these events are added. Lags between occurrence and report of cases and tests can be assessed by comparing counts and rates across multiple data extract dates.

For further details, visit:
• https://www1.nyc.gov/site/doh/covid/covid-19-data.page
• https://github.com/nychealth/coronavirus-data
• https://data.cityofnewyork.us/Health/Modified-Zip-Code-Tabulation-Areas-MODZCTA-/pri4-ifjk

795 views

11.

DOHMH COVID-19 Antibody-by-Neighborhood Poverty Health sars-cov-2, covid-19, coronavirus

This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by ZIP Code Tabulation Area (ZCTA) neighborhood poverty group. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-poverty.csv

Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level.

These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents.

In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders.)

Neighborhood-level poverty groups were classified in a manner consistent with Health Department practices to describe and monitor disparities in health in NYC. Neighborhood poverty measures are defined as the percentage of people earning below the Federal Poverty Threshold (FPT) within a ZCTA. The standard cut-points for defining categories of neighborhood-level poverty in NYC are:
• Low: <10% of residents in ZCTA living below the FPT
• Medium: 10% to <20%
• High: 20% to <30%
• Very high: ≥30% residents living below the FPT
The ZCTAs used for classification reflect the first non-missing address within NYC for each person reported with an antibody test result.

Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning. Rates for poverty were calculated using direct standardization for age at diagnosis and weighting by the US 2000 standard population.
Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020.

Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis will almost certainly be miscalculating or counting the same values multiple times. To analyze the most current data, only use the latest extract date. Antibody tests that are missing dates are not included in the dataset; as dates are identified, these events are added. Lags between occurrence and report of cases and tests can be assessed by comparing counts and rates across multiple data extract dates.

For further details, visit:
• https://www1.nyc.gov/site/doh/covid/covid-19-data.page
• https://github.com/nychealth/coronavirus-data
• https://data.cityofnewyork.us/Health/Modified-Zip-Code-Tabulation-Areas-MODZCTA-/pri4-ifjk

1,197 views

12.

DOHMH COVID-19 Antibody-by-Sex

This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by sex. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-sex.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level.

These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents.

In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders.)

Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning.

Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020.

Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis will almost certainly be miscalculating or counting the same values multiple times. To analyze the most current data, only use the latest extract date. Antibody tests that are missing dates are not included in the dataset; as dates are identified, these events are added. Lags between occurrence and report of cases and tests can be assessed by comparing counts and rates across multiple data extract dates.

For further details, visit:
• https://www1.nyc.gov/site/doh/covid/covid-19-data.page
• https://github.com/nychealth/coronavirus-data

593 views

13.

DOHMH COVID-19 Antibody-by-Week

This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by week of testing. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/trends/antibody-by-week.csv

Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level. These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents.

In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders.)

Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning.

Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020.

Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis will almost certainly be miscalculating or counting the same values multiple times. To analyze the most current data, only use the latest extract date. Antibody tests that are missing dates are not included in the dataset; as dates are identified, these events are added. Lags between occurrence and report of cases and tests can be assessed by comparing counts and rates across multiple data extract dates.

For further details, visit:
• https://www1.nyc.gov/site/doh/covid/covid-19-data.page
• https://github.com/nychealth/coronavirus-data

649 views

14.

New York City Health Opinion Poll Health public health, surveys, polls, ...

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.

786 views

15.

DOHMH COVID-19 Antibody-by-Borough Health sars-cov-2, covid-19, coronavirus

This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by borough of residence. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-boro.csv

2,213 views

16.

DOHMH COVID Antibody-by-Age Health sars-cov-2; covid-19; coronavirus

This file contains person-level information on antibody testing: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by age group.

1,414 views

17.

Pregnancy-Associated Mortality Health maternal mortality, pregnancy-associated mortality

Maternal mortality is widely considered an indicator of overall population health and the status of women in the population. DOHMH uses multiple methods including death certificates, vital records linkage, medical examiner records, and hospital discharge data to identify all pregnancy-associated deaths (deaths that occur during pregnancy or within a year of the end of pregnancy) of New York state residents in NYC each year. DOHMH convenes the Maternal Mortality and Morbidity Review Committee (M3RC), a multidisciplinary and diverse group of 40 members that conducts an in-depth, expert review of each pregnancy-associated death of New York state residents occurring in NYC from both clinical and social determinants of health perspectives. The data in this table come from vital records and the M3RC review process. Data are not cross-classified on all variables: cause of death data are available by the relation to pregnancy (pregnancy-related, pregnancy-associated but not related, unable to determine), race/ethnicity and borough of residence data are each separately available for the total number of pregnancy-associated deaths and pregnancy-related deaths only.

3,484 views

18.

DOHMH Severe Maternal Morbidity Health maternal morbidity, pregnancy complications

Severe maternal morbidity (SMM) refers to life-threatening complications of labor and delivery that result in significant short- or long-term health consequences. SMM data are derived from linking NYC birth certificates for births occurring at NYC facilities with the mother's delivery hospitalization record from the New York Statewide Planning and Research Cooperative System. SMM is identified using an established algorithm developed by the Centers for Disease Control and Prevention that comprises 21 indicators that represent diagnoses of serious complications of pregnancy or delivery or procedures used to manage serious conditions. Each record represents the aggregated number and rate of SMM events in the population group specified for the year specified.

1,196 views

19.

DOHMH School Cafeteria inspections Health public, private, parochial, school, cafeteria, ...

The dataset contains current inspection data for cafeterias permitted in public, private, and parochial schools in NYC. All school cafeterias are required to be in compliance with NYS and NYC Food Safety Regulations, found in New York City Health Code Article 81. School cafeteria inspections are conducted at least annually to ensure compliance with food safety regulations. This dataset includes information obtained as part of the permitting process and data collected during inspections. This data includes inspection results for active school cafeterias for the last three years. Data for cafeterias that have ceased operations and any violations cited during the inspection that were dismissed during adjudication are excluded from this dataset

1,503 views

20.

Mosquito control events in NYC – Adult Mosquito Truck Spraying and Aerial Larviciding Events Health mosquito, pesticide application, ...

This dataset shows the mosquito spraing events in NYC.

For the historical data, please visit this page.

564 views

21.

Modified Zip Code Tabulation Areas (MODZCTA) Map Health

A shapefile for mapping data by Modified Zip Code Tabulation Areas (MODZCTA) in NYC, based on the 2010 Census ZCTA shapefile. MODZCTA are being used by the NYC Department of Health & Mental Hygiene (DOHMH) for mapping COVID-19 Data.

4,075 views

22.

Modified Zip Code Tabulation Areas (MODZCTA) Health modzcta, mzcta, covid, covid-19, coronavirus, ...

A shapefile for mapping data by Modified Zip Code Tabulation Areas (MODZCTA) in NYC, based on the 2010 Census ZCTA shapefile. MODZCTA are being used by the NYC Department of Health & Mental Hygiene (DOHMH) for mapping COVID-19 Data.

16,154 views

23.

COVID-19 Daily Counts of Cases, Hospitalizations, and Deaths Health sars-cov-2, coronavirus, covid-19

Daily count of NYC residents who tested positive for SARS-CoV-2, who were hospitalized with COVID-19, and deaths among COVID-19 patients.

Note that this dataset currently pulls from https://raw.githubusercontent.com/nychealth/coronavirus-data/master/trends/data-by-day.csv on a daily basis.

102,246 views

24.

COVID-19 Outcomes by Testing Cohorts: Cases, Hospitalizations, and Deaths Health sars-cov-2, covid-19, coronavirus

The dataset shows outcomes (confirmed cases, hospitalizations, and deaths) for cohorts defined by each date of specimen collection (specimen_date).

For example, if a NYC resident tested positive for SARS-CoV-2 and was subsequently hospitalized, both events would show under the same specimen_date, indicating the date of specimen collection for the positive test and not the date of the hospitalization.

For a comparable dataset showing diagnosis dates for confirmed cases, admission dates for hospitalized patients, and death dates for decedents, see https://data.cityofnewyork.us/Health/COVID-19-Daily-Counts-of-Cases-Hospitalizations-an/rc75-m7u3

21,134 views

25.

Emergency Department Visits and Admissions for Influenza-like Illness and/or Pneumonia Health emergency, emergency department, ...

Total emergency department visits, and visits and admissions for influenza-like and/or pneumonia illness by modified ZIP code tabulation area of patient residence.

6,140 views

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