Temperature and precipitation projections for NYC reported by the New York City Panel on Climate Change (NPCC).

The New York City Panel on Climate Change (NPCC) started in 2009 and was codified in Local Law 42 of 2012 with a mandate to provide an authoritative and actionable source of scientific information on future climate change and its potential impacts.

The Intergovernmental Panel on Climate Change (IPCC) is the United Nations body for assessing the science related to climate change.

Weather data of New York City from 2016 to Oct 25, 2022. This was downloaded from the Open-Meteo website.

aleksrutins/climate-nyc-historical dataset hosted on Hugging Face and contributed by the HF Datasets community

Bird-building collisions account for 365-988 million bird fatalities every year in the United States alone. Understanding conditions that heighten collision risk is critical to developing effective strategies for reducing this source of anthropogenic bird mortality. Meteorological factors and regional migration traffic may influence collision rates but also may be difficult to disentangle from other effects. We used 5 years of bird collision counts in New York City to examine the influence of nocturnal weather conditions and bird migration traffic rates on collisions with buildings during spring and fall. We found that seasonally unfavorable winds and conditions that impede visibility are important factors that influence rates of bird-building collisions during both seasons. Specifically, northerly and westerly winds and low visibility in the spring, and southerly and westerly winds and low cloud ceiling height in the fall are associated with higher collision risks. Generally, these weather variables associated most strongly with increased collisions when nocturnal bird migration traffic was high, with the exception of low visibility in spring, which was predicted to triple collision rates compared to high visibility, independent of bird migration traffic. Although legislation to turn off unnecessary nocturnal lighting for the entirety of the migration seasons may be an ultimate goal, a proximate goal invaluable for reducing collisions will be predicting which nights will be of highest risk and using this information to determine when mitigation efforts could be most effective. Methods This dataset includes five years of spring and fall data compiled from three sources: (1) bird collision data from NYC Audubon's Project Safe Flight, (2) bird migration traffic data processed from the KOKX radar station, and (3) historical weather data from LaGuardia Airport weather station. The bird collision data from NYC Audubon's Project Safe Flight was collected at a total of 27 buildings in NYC across 11 spring/fall monitoring seasons. Volunteer collision monitors walked a route consisting of 1 to 6 buildings once per day and, for each building, recorded their monitoring start and end times and the number of birds they found. Weather data are from the LaGuardia Airport weather station and provide local, proximate measurements of observed weather conditions at ground-level. Values included in this dataset are mean nocturnal (dusk-dawn) values of the following measurements: zonal (east-west) and meridional (north-south) wind components, cloud ceiling height, visibility distance, and air temperature. Due to a highly skewed distribution, we converted visibility distance into a categorical variable and considered visibility “low” when visibility distance was <10 km, “medium” when visibility distance was between 10 to 16 km, and “high” when visibility distance was >16 km. Bird migration traffic data was obtained and processed from the KOKX radar station. The migration traffic values included in this dataset are calculated nightly averages of migration traffic rate (# individuals/km/hr). We defined medium migration traffic as average migration traffic (x̄ = 9.46 x 102 individuals/km/hr in spring and 1.69 x 103 individuals/km/hr in fall), low migration traffic as x̄ - 1 SD (4 individuals/km/hr in spring and 9 individuals/km/hr in fall), and high migration traffic as x̄ + 1 SD (1.86 x 103 individuals/km/hr in spring and 3.42 x 103 individuals/km/hr in fall). For our study, we standardized cloud ceiling height, zonal wind component, meridional wind component, temperature, day of year, and average migration traffic variables to have a mean of 0 and a variance of 1 to aid model convergence. We did this using the scale() base R function, which uses the following equation: ((x – x̄)/SD), where x̄ is the mean and SD is the standard deviation.

Context

Hourly weather data for New York City. Extracted from online web sources. The following data set is cleaned for the purpose for NYC Taxi ETA calculation.

Content

We have features such as Date, Time, temperature (F), Dew Point (F), Humidity, Wind Speed (MPH), Condition.

Acknowledgements

The cleaned version is user owned. Used in past research for weather data analysis in Boston. Performed the similar calculation to extract the dataset.

Inspiration

The hourly dataset is cleaned with no missing values. Along with temperature the dataset also consists of features like Humidity and Condition such as snow, rain etc.

This dataset contains information about motor vehicle collisions in New York City, along with associated weather conditions. Each record includes details about the crash date, location, collision ID, crash time period, contributing factors, vehicle types, the number of injuries and deaths, street name, weather description, and temperature metrics.

In New York City (NYC), more than 200,000 motor vehicle collisions happen every year. This means about every 3 minute, a collision happens somewhere in NYC. To reduce collisions, there is a need to discover the key factors to improve. The use of NYC collision data and historical weather data to identify the worst location (total number of collisions) and worst weather condition (frequency of collision)

Context

Used for the NYC taxi sandbox https://www.kaggle.com/c/nyc-taxi-trip-duration

Content

Data for 1/1/16 - 7/1/16

9 columns:

1. Time/date stamp (d/m/y h:m)

2. temperature (degrees F)

3. windspeed (mph)

4. Relative Humidity (%)

5. Precipitation during last hour (inches)

6. Pressure (inches of mercury)

7. Description of Condition (string, eg "Overcast")

8. Total precipitation during the day

9. Total Snow during the day

10. current conditions include fog (boolean)

11. currently raining (boolean)

12. currently snowing (boolean)

Roughly 1 data point per hour

Acknowledgements

Data gathered from wunderground.com, pages like this one: https://www.wunderground.com/history/airport/KNYC/2016/1/1/DailyHistory.html?req_city=New+York&req_state=NY&req_statename=New+York&reqdb.zip=10001&reqdb.magic=7&reqdb.wmo=99999

Inspiration

Help improve a model for the taxi sandbox

Context

The NYC Citibike data on Kaggle was outdated. With a keen interest to do a toy project with the Citibike data, I put together the relevant files and hope that other people will find the data useful for their own interest.

Content

The data consists of - the yearly station level flow data (inflow, outflow and net outflow) from 2013 June to 2019 July - the station info data (details of each station) - historical weather data

Acknowledgements

Citibike data obtained from https://www.citibikenyc.com/system-data. Weather data extracted from https://www.ncdc.noaa.gov/cdo-web/datasets/GHCND/locations/CITY:US360019/detail

The NYC Parks outdoor pool season typically runs from late June to the Sunday after Labor Day. During the season, Parks' staff record data via a mobile app survey at the end of each pool session. The survey includes questions on attendance, staffing, meals, issues, weather conditions, and closures for that specific session.

NYC Parks operates two sessions at each pool every day of the pool season. First Session is from 11:00am - 3:00pm. Second Session is from 4:00pm - 7:00pm, with the requirement for Olympic / Intermediate pools to stay open for Extended Second Session from 7:00pm - 8:00pm when the City Heat Emergency Plan is activated.

For each pool season, every pool will have at least two survey submissions per day - one submission for the first session, and one submission for the second session. A pool will have a third submission if it stays open for an extended second session.

Data Dictionary: https://docs.google.com/spreadsheets/d/15lHSZF76W1cZnjwlWRSn7tzLh6EqVZVeZ2vwDFqXHMM/edit?usp=sharing

For reference, pool geography from Open Data can be found here: https://data.cityofnewyork.us/City-Government/Pools/3vjv-6tf5

It is expected that the highest temperature in Summer on average will be approximately *** degrees Fahrenheit hotter in New York City by 2050 compared to the year 2000. The Winter lowest temperature will be *** degrees hotter by 2050. The city of Chicago, Illinois expects an even higher increase of *** degrees Fahrenheit in Summer's highest temperature and an increase of *** degrees in Winter.

Extreme heat in the U.S. – additional information

Projected changes in global average temperature are associated with widespread changes in weather patterns. Scientific studies indicate that extreme weather events, such as heat waves, are likely to become more frequent or more intense within the next few years. These changes may lead to an increase in heat-related deaths in the United States. Outdoor temperatures can affect daily life in many ways. Extreme heat and the combination of high heat and humidity can pose a serious risk for human health. Exposure to extreme heat can lead to heat stroke and dehydration, as well as cardiovascular, respiratory and cerebrovascular disease. When the weather becomes excessively hot, it can be deadly. According to the National Weather Service, heat waves caused ** fatalities in the United States in 2015.

The average temperatures in the U.S. have risen significantly since 1895. Long-term changes in climate can directly or indirectly affect many aspects of a person’s life. For example, warmer days could increase air conditioning or water supply costs. One way to measure the influence of temperature change on energy demand is by using heating and cooling degree days. Cooling degree days measure the difference between outdoor temperature and a temperature that people generally find comfortable indoors. Cooling degree days have not increased significantly over the past decades. However, a slight increase is evident for this period. In 2014, there were around ***** cooling degree days in the U.S., compared to ***** in 2009. More cooling degree days indicate an increase in temperature, leading to a greater likeliness of using air conditioning.

In Newyork City, all taxi vehicles are managed by TLC (Taxi and Limousine Commission), here is a brief description about TLC:

The New York City Taxi and Limousine Commission (TLC), created in 1971, is the agency responsible for licensing and regulating New York City's Medallion (Yellow) taxi cabs, for-hire vehicles (community-based liveries, black cars and luxury limousines), commuter vans, and paratransit vehicles. The Commission's Board consists of nine members, eight of whom are unsalaried Commissioners. The salaried Chair/ Commissioner presides over regularly scheduled public commission meetings and is the head of the agency, which maintains a staff of approximately 600 TLC employees. Over 200,000 TLC licensees complete approximately 1,000,000 trips each day. To operate for hire, drivers must first undergo a background check, have a safe driving record, and complete 24 hours of driver training. TLC-licensed vehicles are inspected for safety and emissions at TLC's Woodside Inspection Facility.

Now NYC TLC has released its Trip Record data to public for research and study purposes. There are three main taxi types in NYC: Yellow taxis are traditionally hailed by signaling to a driver who is on duty and seeking a passenger (street hail), but now they may also be hailed using an e-hail app like Curb or Arro. Yellow taxis are the only vehicles permitted to respond to a street hail from a passenger in all five boroughs. Green taxis, also known as boro taxis and street-hail liveries, were introduced in August of 2013 to improve taxi service and availability in the boroughs. Green taxis may respond to street hails, but only in the areas indicated in green on the map (i.e. above W 110 St/E 96th St in Manhattan and in the boroughs). FHV data includes trip data from high-volume for-hire vehicle bases (bases for companies dispatching 10,000+ trip per day, meaning Uber, Lyft, Via, and Juno), community livery bases, luxury limousine bases, and black car bases. Uber as one of the biggest ride-hailing services providers, its trip records are collected in High Volume For-Hire Vehicle Trip Records as well.

Based on this dataset, there are some business goals we want to achieve to improve Uber's ride-hailing service: Exploratory data analysis, research data fhvhv_tripdata_2021 and figure out underlying trip patterns in 2021. Based on fhvhv_tripdata_2021 and weather data, build predict model to predict the peak footfall. Try explore Uber's user portrait in NYC (which orders are urgent and what kind of users should be given higher priorities?)

Some useful tips about this dataset: - The trip data of the for-hire vehicles named like fhvhv_tripdata_2021-0X.parquet - Columns' description of the trip data please refer to data_dictionary_trip_records_hvfhs.pdf. - taxi_zones folder contains the geospatial data of NYC taxi zones (geopandas would be helpful). - taxi_zone_lookup.csv stores taxi zones zip code and other relevant information. - nyc 2021-01-01 to 2021-12-31.csv record the weather data of year 2021,taxi+_zone_lookup.csv stored the zone information of all taxi, data file end with .parquet could be processed by pyarrow package and convert to Pandas DataFrame.

If you find this dataset helpful, please up-vote and more high-quality datasets will be published in future!❤️

The Flood Vulnerability Index (FVI) assesses the distribution of vulnerability to flooding across NYC in order to guide flood resilience policies and programs. Vulnerability contains three components: exposure to a hazard, susceptibility to harm from the exposure, and capacity to recover (Cutter et al., 2009). There are six hazard-specific FVIs, one for each of the six different flood hazard scenarios, which include current and two future storm surge scenarios and current and two future tidal flooding scenarios. Exposures vary for different types of flooding and different scenarios within each flood type.

Each FVI consists of two component sub-indices: an exposure index and an index that reflects susceptibility to harm and capacity to recover. The exposure index is different in each FVI in order to capture the different exposures to each of the flood hazard scenarios. The sub-index that reflects susceptibility to harm and capacity to recover -- the Flood Susceptibility to Harm and Recovery Index (FSHRI) -- is the same for each FVI. It aggregates 12 socio-economic indicators correlated with various types of hardships that people may suffer due to flooding and different dimensions of ability to recover.

For additional information, please visit this link.

Real-Time NYC Flood Depths

Half-Inch Resolution Recordings

By [source]

About this dataset

This dataset provides deep insights into New York City's flood water levels for efficient and cost-effective real-time flood detection. With a recording resolution of half an inch or less and readings collected no less than every five minutes, this dataset is great for exploring the depths and effects that floods can have on our cities. Users will find information related to the location of each sensor, how long the sensors were deployed, whether there were any errors detected in the data collection, what network each sensor was connected to, as well as other metadata all in one convenient place. Dive into data like never before and gain understanding surrounding flooding trends in New York City with this comprehensive dataset!

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How to use the dataset

This dataset provides real-time flood depths across 5 boroughs in New York City. In order to use it, you need to have an understanding of the data and its parameters.

The dataset contains columns such as time, app_name, bw_hz, deploy_type, error_flag, f_port and more. These columns provide information about the flood depth readings collected by sensors deployed in the various boroughs of NYC.

To begin using this dataset for analysis purposes it is recommended that you first familiarize yourself with all its features by exploring each column individually. This can be done by examining summary statistics such as median values for each column and plotting them on a graph for visual comparison between multiple variables within the same column. In order to gain deeper insights from this data set you should look into correlations between variables from different columns (or even within individual columns). You can do so by creating a heat map or constructing regression models (if appropriate). Additionally utilizing other spatial analysis techniques may also prove fruitful when analysing this dataset such as choropleths which depict interesting geographical trends among sample points collected via sensor readings across NYC's five boroughs at predetermined locations. Furthermore clustering algorithms are useful when dealing with categorical variables present throughout these datasets' numerous features which may often be overlooked during initial exploration stages into simplifying generalised situational parameters through understanding of primary outliers & influences dormant therein some phenomena otherwise left uninvestigated or undetected amongst a conglomerates population matrix previously described herewith herein?

Finally keep in mind that filtering & transforming aspects incorporated during raw data collection processes may regulate values towards particular thresholds dictated overall accuracy well being during process flow while relying heavily still upon not only machine learning derived assistance/input but human judgement & bias too! Therefore incorporating relevant strategies allowing keeping up with aforementioned constant maintenance workflows along side intellectual property protections attributed design department operations ending process below efficiently managing necessary integration routines conversely maintaining protocols / procedures having been specified requested organisation at large active thereupon...?

Research Ideas

• Accurately predicting sea level rise in certain areas of New York City, enabling better city planning and infrastructure development.
• Creating a real-time flood detection and alert system that warns citizens and emergency services of approaching floods based on live sensor readings.
• Better managing the water supply by understanding the natural changes in flood depths over time, and allowing for better control of over-utilization or under-utilization to promote conservation efforts or improve water availability when needed

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: FloodNet-data-export.csv | Column name | Description ...

This dataset identifies areas throughout the State that meet the final disadvantaged community definition as voted on by the Climate Justice Working Group on March 27th, 2023. It contains the 4,918 census tracts in New York State and identifies the 1,736 census tracts that make up the current Disadvantaged Communities (DAC) definition. The dataset also includes the 45 indicators, expressed as a percentile ranking, used to determine each census tracts’ Environmental Burden and Climate Change Risks, and Population Characteristics and Health Vulnerabilities. The source for the Census Tract data is the US Census Bureau, Geography Division, Year 2019. For more information, please visit https://www.census.gov/cgi-bin/geo/shapefiles/index.php. The chosen 45 indicators represent a variety of data sources, both private and public. Further details on the methodology and resources can be found at https://climate.ny.gov/DAC-Criteria in the Technical Documentation, Indicator Prioritization and Selection section.View Dataset on the Gateway