This dataset contains the New York City Population By Community Districts.The community boards of the New York City government are the appointed advisory groups of the community districts of the five boroughs. There are currently 59 community districts, including twelve in Manhattan, twelve in the Bronx, eighteen in Brooklyn, fourteen in Queens, and three in Staten Island.

Comprehensive demographic dataset for Liberty Island, Manhattan, NY, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

The data represent web-scraping of hyperlinks from a selection of environmental stewardship organizations that were identified in the 2017 NYC Stewardship Mapping and Assessment Project (STEW-MAP) (USDA 2017). There are two data sets: 1) the original scrape containing all hyperlinks within the websites and associated attribute values (see "README" file); 2) a cleaned and reduced dataset formatted for network analysis. For dataset 1: Organizations were selected from from the 2017 NYC Stewardship Mapping and Assessment Project (STEW-MAP) (USDA 2017), a publicly available, spatial data set about environmental stewardship organizations working in New York City, USA (N = 719). To create a smaller and more manageable sample to analyze, all organizations that intersected (i.e., worked entirely within or overlapped) the NYC borough of Staten Island were selected for a geographically bounded sample. Only organizations with working websites and that the web scraper could access were retained for the study (n = 78). The websites were scraped between 09 and 17 June 2020 to a maximum search depth of ten using the snaWeb package (version 1.0.1, Stockton 2020) in the R computational language environment (R Core Team 2020). For dataset 2: The complete scrape results were cleaned, reduced, and formatted as a standard edge-array (node1, node2, edge attribute) for network analysis. See "READ ME" file for further details. References: R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. Version 4.0.3. Stockton, T. (2020). snaWeb Package: An R package for finding and building social networks for a website, version 1.0.1. USDA Forest Service. (2017). Stewardship Mapping and Assessment Project (STEW-MAP). New York City Data Set. Available online at https://www.nrs.fs.fed.us/STEW-MAP/data/. This dataset is associated with the following publication: Sayles, J., R. Furey, and M. Ten Brink. How deep to dig: effects of web-scraping search depth on hyperlink network analysis of environmental stewardship organizations. Applied Network Science. Springer Nature, New York, NY, 7: 36, (2022).

The dataset used in this project is a publicly available dataset containing Airbnb listing data for New York City. It provides comprehensive details about various aspects of Airbnb listings, such as neighborhood, room types, prices, availability, and host information.

Key Features of the Dataset Here are some of the main columns included in the dataset and what they represent:

id: A unique identifier for each listing. name: The name or title of the Airbnb listing. host_id: The unique ID of the host. host_name: The name of the host. neighbourhood_group: The borough where the listing is located (e.g., Manhattan, Brooklyn, Queens, Bronx, Staten Island). neighbourhood: The specific neighborhood within the borough. latitude and longitude: The geographic coordinates of the listing. room_type: The type of room being offered: Entire home/apt Private room Shared room price: The price per night to stay at the listing. minimum_nights: The minimum number of nights required for booking. number_of_reviews: The total number of reviews received by the listing. reviews_per_month: The average number of reviews the listing receives each month. availability_365: The number of days the listing is available for booking in a year. calculated_host_listings_count: The total number of listings managed by a host. Dataset Characteristics Timeframe: The dataset represents a snapshot of listings and reviews within a specific time period (usually the latest available at the time of collection). Geography: Includes all five boroughs of New York City: Manhattan Brooklyn Queens Bronx Staten Island Diversity: The dataset captures a diverse set of listings, from luxury apartments in Manhattan to budget-friendly shared rooms in the Bronx. Why This Dataset? This dataset is ideal for analysis because:

It allows us to explore trends in NYC's Airbnb market, such as pricing patterns, popular room types, and host activity. It offers valuable insights into neighborhood preferences and pricing strategies for hosts. It helps identify potential areas of improvement, such as boosting listings in underrepresented neighborhoods like Staten Island and Queens. Dataset Source This dataset is commonly hosted on platforms like Kaggle or Inside Airbnb, a project that compiles publicly available information on Airbnb listings. It is designed to provide transparency and insight into Airbnb activity across cities.

Context

This dataset is a record of every building or building unit (apartment, etc.) sold in the New York City property market over a 12-month period.

Content

This dataset contains the location, address, type, sale price, and sale date of building units sold. A reference on the trickier fields:

• BOROUGH: A digit code for the borough the property is located in; in order these are Manhattan (1), Bronx (2), Brooklyn (3), Queens (4), and Staten Island (5).
• BLOCK; LOT: The combination of borough, block, and lot forms a unique key for property in New York City. Commonly called a BBL.
• BUILDING CLASS AT PRESENT and BUILDING CLASS AT TIME OF SALE: The type of building at various points in time. See the glossary linked to below.

For further reference on individual fields see the Glossary of Terms. For the building classification codes see the Building Classifications Glossary.

Note that because this is a financial transaction dataset, there are some points that need to be kept in mind:

• Many sales occur with a nonsensically small dollar amount: $0 most commonly. These sales are actually transfers of deeds between parties: for example, parents transferring ownership to their home to a child after moving out for retirement.
• This dataset uses the financial definition of a building/building unit, for tax purposes. In case a single entity owns the building in question, a sale covers the value of the entire building. In case a building is owned piecemeal by its residents (a condominium), a sale refers to a single apartment (or group of apartments) owned by some individual.

Acknowledgements

This dataset is a concatenated and slightly cleaned-up version of the New York City Department of Finance's Rolling Sales dataset.

Inspiration

What can you discover about New York City real estate by looking at a year's worth of raw transaction records? Can you spot trends in the market, or build a model that predicts sale value in the future?

A dataset listing New York counties by population for 2024.

NYC Wi-Fi Hotspot Locations Wi-Fi Providers: CityBridge, LLC (Free Beta): LinkNYC 1 gigabyte (GB), Free Wi-Fi Internet Kiosks Spot On Networks (Free) NYC HOUSING AUTHORITY (NYCHA) Properties Fiberless (Free): Wi-Fi access on Governors Island Free - up to 5 Mbps for users as the part of Governors Island Trust Governors Island Connectivity Challenge AT&T (Free): Wi-Fi access is free for all users at all times. Partners: In several parks, the NYC partner organizations provide publicly accessible Wi-Fi. Visit these parks to learn more information about their Wi-Fi service and how to connect. Cable (Limited-Free): In NYC Parks provided by NYC DoITT Cable television franchisees. ALTICEUSA previously known as “Cablevision” and SPECTRUM previously known as “Time Warner Cable” (Limited Free) Connect for 3 free 10 minute sessions every 30 days or purchase a 99 cent day pass through midnight. Wi-Fi service is free at all times to Cablevision’s Optimum Online and Time Warner Cable broadband subscribers. Wi-Fi Provider: Chelsea Wi-Fi (Free) Wi-Fi access is free for all users at all times. Chelsea Improvement Company has partnered with Google to provide Wi-Fi a free wireless Internet zone, a broadband region bounded by West 19th Street, Gansevoort Street, Eighth Avenue, and the High Line Park. Wi-Fi Provider: Downtown Brooklyn Wi-Fi (Free) The Downtown Brooklyn Partnership - the New York City Economic Development Corporation to provide Wi-Fi to the area bordered by Schermerhorn Street, Cadman Plaza West, Flatbush Avenue, and Tillary Street, along with select public spaces in the NYCHA Ingersoll and Whitman Houses. Wi-Fi Provider: Manhattan Downtown Alliance Wi-Fi (Free) Lower Manhattan Several public spaces all along Water Street, Front Street and the East River Esplanade south of Fulton Street and in several other locations throughout Lower Manhattan. Wi-Fi Provider: Harlem Wi-Fi (Free) The network will extend 95 city blocks, from 110th to 138th Streets between Frederick Douglass Boulevard and Madison Avenue is the free outdoor public wireless network. Wi-Fi Provider: Transit Wireless (Free) Wi-Fi Services in the New York City subway system is available in certain underground stations. For more information visit http://www.transitwireless.com/stations/. Wi-Fi Provider: Public Pay Telephone Franchisees (Free) Using existing payphone infrastructure, the City of New York has teamed up with private partners to provide free Wi-Fi service at public payphone kiosks across the five boroughs at no cost to taxpayers. Wi-Fi Provider: New York Public Library Using Wireless Internet Access (Wi-Fi): All Library locations offer free wireless access (Wi-Fi) in public areas at all times the libraries are open. Connecting to the Library's Wireless Network •You must have a computer or other device equipped with an 802.11b-compatible wireless card. •Using your computer's network utilities, look for the wireless network named "NYPL." •The "NYPL" wireless network does not require a password to connect. Limitations and Disclaimers Regarding Wireless Access •The Library's wireless network is not secure. Information sent from or to your laptop can be captured by anyone else with a wireless device and the appropriate software, within three hundred feet. •Library staff is not able to provide technical assistance and no guarantee can be provided that you will be able to make a wireless connection. •The Library assumes no responsibility for the safety of equipment or for laptop configurations, security, or data files resulting from connection to the Library's network

NYC Taxi Trip Description Dataset

This dataset contains NYC taxi trip data from May 1-7, 2013, excluding trips to and from Staten Island. It includes 2,957 sequences with 362,374 events and 8 location types. The data can be downloaded from NYC Taxi Trips and is subject to the NYC Terms of Use. The detailed data preprocessing steps used to create this dataset can be found in the TPP-LLM paper and TPP-Embedding paper. If you find this dataset useful, we kindly invite you to cite the… See the full description on the dataset page: https://huggingface.co/datasets/tppllm/nyc-taxi-description.

This dataset lists executed evictions within the five boroughs for the years 2017-Present (data prior to January 1, 2017, is not available). The data fields may be sorted by 20 categories of information including Court Index Number, Docket Number, Eviction Address, Marshal First or Last Name, Borough, etc..

Eviction data is compiled from New York City Marshals. City Marshals are independent public officials appointed by the Mayor. Marshals can be contacted directly regarding evictions, and their contact information can be found at https://www1.nyc.gov/site/doi/offices/marshals-list.page.

Author's Note:

This dataset was originally coined: "Speed Limits in New York City". Since then, I have changed the name of the dataset to "Describing New York City Roads" to better reflect the contents of the dataset.

- Curtis

Context

New York City Speed Limits

The New York Department of Transportation Regulates the speed limits for its roads (Afterall, we can't be hitting 88 MPH on a regular day). This dataset describes the speed limits for particular road segments of New York City streets.

The New York City Centerline

Which streets are inherently faster? How will speed limits come into play? How will nearby bike lanes slow down vehicles (and ultimately taxis)? These are the kinds of questions that can only be answered with contextual data of the streets themselves.

Fortunately, most major cities provide a public Centerline file that describes the path of all railroads, ferry routes, and streets in the city. I've taken the New York City Centerline and packaged a dataset that tries to extract meaning out of all the road connections within the city.

Content

New York City Speed Limits

Every speed limit region is a straight line. (Which represents a segment of road). These lines are expressed by two pairs of coordinates.

lat1 - The first latitude coord

lon1 - The first longitude coord

lat2 - The second latitude coord

lat2 - The second longitude coord

street - The name of the street the speed limit is imposed on

speed - The speed limit of that road section

signed - Denotes if there is a physical sign on the street that displays the speed limit to cars.

region - The city region that the road resides in. There are 5 regions: (Bronx, Brooklyn, Manhattan, Queens, and Staten Island)

distance - The length of the speed limit road section (in Miles).

The New York City Centerline

street - The name of the street

post_type* - The extension for the street name.

st_width - The width of the street (in feet). There are varying widths for the size of a street so it was hard to derive a lane count/ street using this feature. As a rule of thumb, the average lane is around 12 feet wide.

bike_lane - Defines which segments are part of the bicycle network as defined by the NYC Department of Transportation. There are 11 classes:

• 1 = Class I

• 2 = Class II

• 3 = Class III

• 4 = Links

• 5 = Class I, II

• 6 = Class II, III

• 7 = Stairs

• 8 = Class I, III

• 9 = Class II, I

• 10 = Class III, I

• 11 = Class III, II

Bike class information: https://en.wikipedia.org/wiki/Cycling_in_New_York_City#Bikeway_types

bike_traf_dir** - Describes the direction of traffic: (FT = With, TF = Against, TW = Two-Way)

traf_dir** - Describes the direction of traffic: (FT = With, TF = Against, TW = Two-Way)

rw_type - The type of road. There are 6 types of roads: (1 = Street, 2 = Highway, 3 = Bridge, 4 = Tunnel, 9 = Ramp, 13 = U-Turn). Note: I parsed awkward path types such as "Ferry route" and "trail".

start_contour*** - Numeric value indicating the vertical position of the feature's "from" node relative to grade level.

end_contour*** - Numeric value indicating the vertical position of the feature's "to" node relative to grade level.

snow_pri - The Department of Sanitation (DSNY) snow removal priority designation.

• V = Non-DSNY

• C = Critical (These streets have top priority)

• S = Sector (These streets are second priority)

• H = Haulster (Small spreaders with plows attached for treating areas with limited accessibility - can hold two tons of salt)

region - The city region that the road resides in. There are 5 regions: (Bronx, Brooklyn, Manhattan, Queens, and Staten Island)

length - The length of the road (in Miles).

points - The coordinates that define the road. Each coordinate is separated by '|' and the lat and lon values per coordinate are separated by ';'. (Side note: Round road sections are plotted by points along the curve).

*For those who may not be aware, road names are based on a convention. "Avenue"s, "Boulevard"s, and "Road"s are different for distinct reasons. I left these fields in the dataset in case you wish to find any patterns that are pertinent to those types of roads. To learn more about road conventions, visit this link: http://calgaryherald.com/news/local-news/in-naming-streets-strict-rules-dictate-roads-rises-trails-and-more

**To explain how direction works I'll provide you with an image: http://imgur.com/a/UflwX. Think of every road on the centerline as a vector. It points from one location to another. It always points from the very first coordinate to the very last coordinate. Now pay attention to the direction of the road (circled). Note how it points in the same direction as the vector denoted by the centerline data. The "...

A dataset listing all subway and Staten Island Railway stations, with data aggregated by station complex. This dataset includes information on station names, their locations, Station IDs, Complex IDs, GTFS Stop IDs, the services that stop there, the type of structure the station is on or in, whether they are in Manhattan’s Central Business District (CBD), and their ADA-accessibility status.

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

This data set contains New York City Police Department provided domestic violence incident data for calendar years 2020, 2021 and 2022. In addition, ENDGBV obtained through Open Data the number of shooting incidents for calendar years 2020, 2021 and 2022. The data includes counts of the number of domestic violence incidents, shooting incidents and the number of expected domestic violence incidents and shooting incidents by: race (American Indian/Alaska Native, Asian/Pacific Islander, Black, and White) and sex (male, female) for New York City, each borough (Bronx, Brooklyn, Manhattan, Queens and Staten Island). It also provides the count and rate of domestic violence and shooting incidents by police precinct. The expected number of domestic violence incidents and shooting incidents were calculated by taking the total number of actual domestic violence and shooting incidents for a given geography (New York City, the Bronx, Brooklyn, Manhattan, Queens and Staten Island) and proportioning them by demographic breakdown of the geographic area.

Topographic indices calculated in support of Kelleher and McPhillips (in review). We calculated two topographic indices - absolute sink depth (m) and topographic wetness index (-) - using TauDEM (v. 5.3) software and the D-infinity flow routing algorithm.

Watersheds include those in Manhattan (CP1, CP2, M1, M2) and Staten Island (SI1, SI2). Naming convention and sites are shown in the associated manuscript. Note that processing for Baltimore is limited to the extent of each watershed that overlaps with the Baltimore city limits, though processing occurred for the entire watershed and was masked to this area.

Values were processed based on the LiDAR digital elevation models (DEM) for NYC, linked below in references (note: to make datasets comparable, the NYC DEM was coarsened to 0.91 m resolution). As presented in the associated manuscript, all topographic index values were extracted for all surfaces (e.g., bare soil, pavement, sidewalks, and vegetated areas) that excluded open water and building footprints (where topographic processing and DEM coverages are less reliable). Land cover datasets are linked below.

Naming convention for all files first specifies watershed name (NYC: cp1, cp2, m1, m2, si1, si2) followed by topographic index type (twi = topographic wetness index, sink = sink depth).

Descriptions for how each topographic index are calculated are specified in the associated manuscript. Generally, sink depths were calculated by differencing the filled and unfilled DEMs, and TWI was calculated from topographic slope and accumulated area, both processed within TauDEM (note: when negative slopes were calculated, these were replaced with very small values, e.g., 0.001).

MTA Subway Hourly Ridership: Beginning February 2022

MTA Subway Hourly Ridership Data

General Description

The Metropolitan Transportation Authority (MTA) is a public-benefit corporation responsible for public transportation in the state of New York serving 12 counties in southeastern New York, along with two counties in southwestern Connecticut under contract to the Connecticut Department of Transportation, carrying over 5 million passengers on an average weekday system-wide, and over 800,000 vehicles on its seven toll bridges and two tunnels per weekday. The MTA is the largest transportation agency in North America.

Subway service within New York City is operated by MTA New York City Transit. This dataset provides subway ridership, compiled at the hourly level, by station complex and fare payment method (OMNY or MetroCard). These ridership metrics are within 1% of ridership figures provided at public MTA board meetings. These numbers differ slightly since the data subsequently incorporates additional budget reconciliation. Data is released daily and is subject to revision. You can learn more about OMNY, the contactless fare payment system for public transportation in the New York region at omny.info.

This dataset was published during the first phase of the MTA’s commitment to increasing transparency. We continually examine all our published and publishable data with a view to both providing datasets that can be effectively utilized by our customers and the public at large, and to providing regular, automated updates to these datasets efficiently and sustainably. Consequently, this dataset may be restructured and/or combined with other similar datasets in the future.

Data Collection Methodology

This dataset captures rider entries made at subway turnstiles by using OMNY and MetroCard taps/swipes. MetroCard data is aggregated from on-premises MTA data servers, while OMNY data is provided by a third-party vendor named Cubic.

Before being released this data undergoes substantial data cleaning; transactions are deduplicated, and MetroCard swipes and OMNY taps collected by partner agencies (PATH, Westchester Bee-Line Bus, NICE Bus, Roosevelt Island Corporation) are removed from the dataset.

A very small number of MetroCard and OMNY transactions arrive late due to temporary hardware malfunctions. This late-arriving data is incorporated as it is made available, and thus historical data in this dataset is subject to slight adjustments.

Statistical and Analytic Issues

Data is released to the Open Data portal daily and is subject to revision for late-arriving data.

Context

As a participant to NYC Taxi Trip Duration, I'm providing additional data, to help extracting many new usefull features.

To do so I'm using a high performance routing engine designed to run on OpenStreetMap data.

Having the whole blind test data, I decided also to share a small amount concerning erroneous samples (less than 0.15%), so competitors can focus matching real world data and to not try to fit randomness.

Note: The steps files are big so I split them into two parts. Part 1, Part2

Content

Description of different tables used.

Train/Test augmented

id: Record id

distance: Route distance (m)

duration: OSRM trip duration (s)

motorway, trunk, primary, secondary, tertiary, unclassified, residential:

The proportion spent on different kind of roads (% of total distance)

nTrafficSignals: The number of traffic signals.

nCrossing: The number of pedestrian crossing.

nStop: The number of stop signs.

nIntersection: The number of intersections, if you are OSRM user, intersection have different meaning than the one used in OSRM.

*Intersection can be crossroad, but not a highway exit...

srcCounty, dstCounty: Pickup/Dropoff county.

NA: Not in NYC

1: Brooklyn

2: Queens

3: Staten Island

4: Manhattan

5: Bronx

Train/Test steps

For each trip we saved all the ways (route portion).

id: train/test id.

wayId: Way id, you can check the way using www.openstreetmap.org/way/*wayId*

portion: The proportion of the total distance

Polylines table

It contains encoded nodes (lon/lat coordinates), of the used ways.

wayId: The way identification.

polyline: Encoded polylines.

Train/Test bugs (0.15% of total data)

id: Same as original data.

bug: kind of the bug (0=none)

1. Trip duration higher than 1 day;

2. Drop off on the day after pickup, and trip duration higher than 6h;

3. Drop off time at 00:00:00 and vendor_id eq 2.

trip_duration: Taxi trip duration

Credits

1. Real-time routing with OpenStreetMap data (http://project-osrm.org/)
2. OSM (http://www.openstreetmap.org)
3. Banner (Photo by Nicolai Berntsen on Unsplash)

A dataset listing the richest zip codes in New York per the most current US Census data, including information on rank and average income.

The Motor Vehicle Collisions crash table contains details on the crash event. Each row represents a crash event. The Motor Vehicle Collisions data tables contain information from all police reported motor vehicle collisions in NYC. The police report (MV104-AN) is required to be filled out for collisions where someone is injured or killed, or where there is at least $1000 worth of damage (https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/documents/ny_overlay_mv-104an_rev05_2004.pdf). It should be noted that the data is preliminary and subject to change when the MV-104AN forms are amended based on revised crash details.For the most accurate, up to date statistics on traffic fatalities, please refer to the NYPD Motor Vehicle Collisions page (updated weekly) or Vision Zero View (updated monthly).

Due to success of the CompStat program, NYPD began to ask how to apply the CompStat principles to other problems. Other than homicides, the fatal incidents with which police have the most contact with the public are fatal traffic collisions. Therefore in April 1998, the Department implemented TrafficStat, which uses the CompStat model to work towards improving traffic safety. Police officers complete form MV-104AN for all vehicle collisions. The MV-104AN is a New York State form that has all of the details of a traffic collision. Before implementing Trafficstat, there was no uniform traffic safety data collection procedure for all of the NYPD precincts. Therefore, the Police Department implemented the Traffic Accident Management System (TAMS) in July 1999 in order to collect traffic data in a uniform method across the City. TAMS required the precincts manually enter a few selected MV-104AN fields to collect very basic intersection traffic crash statistics which included the number of accidents, injuries and fatalities. As the years progressed, there grew a need for additional traffic data so that more detailed analyses could be conducted. The Citywide traffic safety initiative, Vision Zero started in the year 2014. Vision Zero further emphasized the need for the collection of more traffic data in order to work towards the Vision Zero goal, which is to eliminate traffic fatalities. Therefore, the Department in March 2016 replaced the TAMS with the new Finest Online Records Management System (FORMS). FORMS enables the police officers to electronically, using a Department cellphone or computer, enter all of the MV-104AN data fields and stores all of the MV-104AN data fields in the Department’s crime data warehouse. Since all of the MV-104AN data fields are now stored for each traffic collision, detailed traffic safety analyses can be conducted as applicable.

The Mayor's Office to End Domestic and Gender-Based Violence (ENDGBV) develops policies and programs, provides training and prevention education, conducts research and evaluations, performs community outreach, and operates the New York City Family Justice Centers. The office collaborates with City agencies and community stakeholders to ensure access to inclusive services for survivors of domestic and gender-based violence (GBV) services. GBV can include intimate partner and family violence, elder abuse, sexual assault, stalking, and human trafficking. ENDGBV operates the New York City Family Justice Centers. These co‐located multidisciplinary domestic violence service centers provide vital social service, civil legal and criminal justice assistance for survivors of intimate partner violence and their children under one roof. The Brooklyn Family Justice Center opened in July 2005; the Queens Family Justice Center opened in July 2008; the Bronx Family Justice Center opened in April 2010; Manhattan Family Justice Center opened in December 2013 and Staten Island Family Justice Center opened in June 2015. OCDV also has a Policy and Training Institute that provides trainings on intimate partner violence to other City agencies. The New York City Healthy Relationship Academy, with is part of the Policy and Training Institute, provides peer lead workshops on healthy relationships and teen dating violence to individuals between the age of 13 and 24, their parents and staff of agencies that work with youth in that age range. The dataset is collected to produce an annual fact sheet on intimate partner violence in New York City. The fact sheet is produced annually by the end of February and is placed on the ENDGBV website. The criminal justice numbers (IPV Homicides, DIRs) are provided by the New York City Police Department; the NYC Domestic Violence Hotline call numbers are provided by Safe Horizon, which is contracted by the City to manage the hotline. The other data is provided by ENDGBV.

Context

Inspired by the Onyx Data - Data DNA Dataset Challenge - October 2021 - NYPD Arrest Data (Year to Date), I decided to go deeper and see how the insights compare.

Content

This data was acquired from NYC open data, it lists every arrest in NYC going back to 2006 through the end of the previous calendar year (2020).

Acknowledgements

This data is manually extracted every quarter and reviewed by the Office of Management Analysis and Planning before being posted on the NYPD website.

Inspiration

This data can be used by the public to explore the nature of police enforcement activity. See below a description of the columns: | Column Name | Column Description | | --- | --- | | ARREST_KEY | Randomly generated persistent ID for each arrest | | ARREST_DATE | Exact date of arrest for the reported event | | PD_CD | Three digit internal classification code (more granular than Key Code) | | PD_DESC | Description of internal classification corresponding with PD code (more granular than Offense Description) | | KY_CD | Three digit internal classification code (more general category than PD code) | | OFNS_DESC | Description of internal classification corresponding with KY code (more general category than PD description)| | LAW_CODE | Law code charges corresponding to the NYS Penal Law, VTL and other various local laws | | LAW_CAT_CD | Level of offense: felony, misdemeanor, violation | | ARREST_BORO | Borough of arrest. B(Bronx), S(Staten Island), K(Brooklyn), M(Manhattan), Q(Queens) | | ARREST_PRECINCT | Precinct where the arrest occurred | | JURISDICTION_CODE | Jurisdiction responsible for arrest. Jurisdiction codes 0(Patrol), 1(Transit) and 2(Housing) represent NYPD whilst codes 3 and more represent non NYPD jurisdictions | | AGE_GROUP | Perpetrator’s age within a category | | PERP_SEX | Perpetrator’s sex description | | PERP_RACE | Perpetrator’s race description | | X_COORD_CD | Midblock X-coordinate for New York State Plane Coordinate System, Long Island Zone, NAD 83, units feet (FIPS 3104) | | Y_COORD_CD | Midblock Y-coordinate for New York State Plane Coordinate System, Long Island Zone, NAD 83, units feet (FIPS 3104) | | Latitude | Latitude coordinate for Global Coordinate System, WGS 1984, decimal degrees (EPSG 4326) | | Longitude | Longitude coordinate for Global Coordinate System, WGS 1984, decimal degrees (EPSG 4326) |