Background

We all know that we should be getting more steps in our daily lives. Guidance from the medical field suggests that people aim for 10,000 steps per day. There have been many, many studies that support this recommendation. Walking is a low-impact form of exercise that has many dose-dependent health benefits. In particular, increased walking has been repeatedly linked to lower risks of adverse cardiovascular events, lower cholesterol, and overall higher satisfaction in life[1].

For many years, my partner (Sarah) and I lived in the suburban expanse of Norman, Oklahoma, a college town just south of the vast sprawl of Oklahoma City. On April 10, 2022 we moved to New York City. New Yorkers are well known to be walkers, and a quick Google search indicates that New Yorkers walk up to 3x as much as the average American.

For this project I wanted to analyze and visualize my own walking habits spanning a number of years including time when we lived in Oklahoma and following our move to New York City.

Data Sources

Let’s be honest… we are a phone addicted culture! For this project this is quite a good thing, as I have carried an iPhone with me essentially everywhere I have gone for the better part of a decade. Following the Apple instructions, I was able to export the entire history of data from the Health app. Using the Apple Health Parser python utility from alxdrcirilo on GitHub I was able to parse this enormous amount of data into a usable format (comma-separated value) quickly.

Later in the project design I became interested in exploring patterns in my walking as they relate to temperature/season. I utilized the NOAA Climate Data Online platform to retrieve daily records of average temperature, maximum temperature, and precipitation amounts covering all dates from 1/1/2021 to 3/23/2025. For the dates from 1/1/2021 to 4/9/2022, I used the USW00013967 (Oklahoma City Will Rogers Airport) station and for days from 4/10/2022 onward I USW00014732 (LaGuardia Airport) station. While temperature can vary by a small amount over the geographical area of a city, I believe that these two stations provide “accurate enough” data for this project.

Data Cleanup

Early on in this project I learned that the Apple HealthKit data structure stores steps in variable duration “walks”. Essentially, if the phone remains still for more than a few seconds then the next time the phone starts moving HealthKit starts a new “walk”. This results in highly variable time windows that may be as short as a few seconds or as long as half an hour. A few rows of raw data are shown below.

To deal with this I rolled up all the records for each day to arrive at a total number of steps for each calendar day. In doing so, I did lose much of the sub-day detail in the data. I hope to revisit this project in the future to add visualizations of how my walking trends on the hourly level, but for now I have chosen to simply focus on daily trends.

Data from NOAA was in a very simple structure with each row having fields for the date, average temperature, maximum temperature, and precipitation in inches. This data covered each day of the desired time range and had no missing data for any day. Thus, it did not require any efforts to cleanup. Data from NOAA was merged with the daily step totals using the date as the key between the two sources. A few rows of the final data source structure are below.

General Trends

Charting my Walking Habits

Walking Into The Future

Research Question

On January 5, 2025 the Central Business District Tolling Program (CBDTP), also known as Congestion Pricing or Congestion Relief, went into effect. This program charges drivers for most vehicular traffic south of 60th Street via the use of license plate scanners installed throughout midtown and lower Manhattan. This program has received a large amount of political and social opposition, both within New York as well as neighboring states whose commuters travel into Manhattan for work and pleasure. The primary stated goal of this program is to reduce traffic in midtown and lower Manhattan by incentivizing commuters to make use of the abundant public transit options in the form of ferries, busses, commuter trains, and the subway.

My primary question is: Does the data collected by the New York City 311 complaint database provide any evidence that this program is having the intended effect of reducing the amount of traffic in the congestion relief zone?

Dataset and Selected Complaints

The New York City Open Data Portal has a freely accessible 311 Complaint Dataset that covers approximately 43 million complaints since 2010. Members of the public may lodge a complaint with the 311 service via phone, mobile device, or internet. The 311 Complaint Dataset is updated nightly and each row in the dataset represents one complaint. These complaints are assigned to appropriate departments or agencies of the city and are thoroughly documented. I began by reviewing the Complaint Type and Descriptor fields to build a list of complaint data which could serve as a proxy for measuring the amount of traffic in the Congestion Relief Zone. The combination of search criteria used for this project are:

 

Field	Selected Values
Created Date	>=January 1, 2022 12:00 AM * <= February 28, 2025 11:45 PM
Complaint Type	Illegal Parking Noise – Commercial Noise – Vehicle Traffic
Descriptor	Blocked Bike Lane Blocked Crosswalk Blocked Hydrant Blocked Sidewalk Commercial Overnight Parking Double Parked Blocking Traffic Double Parked Blocking Vehicle Overnight Commercial Storage Parking Permit Improper Use Posted Parking Sign Violation Car/Truck Horn Car/Truck Music Engine Idling Congestion/Gridlock Drag Racing
Incident Zip	10001 10002 10003 10004 10005 10006 10007 10009 10010 10011 10012 10013 10014 10016 10017 10018 10019 10022 10036 10038 10280 10282

Note on Created Date: During the analysis it was determined that January and February 2022 had significantly reduced values as a result of the Omicron wave of the COVID-19 pandemic and were filtered out of the data prior to visualization.

Visualizations

Distribution of Complaint Types

Complaints by Type & Descriptor

The first thing I did was to compare the number of complaints for January and February of each year (2023-2025) to get a baseline for what to expect from the data. Some categories, such as Blocked Bike Lane, have received far fewer complaints, while Parking Permit Improper Use and Posted Parking Sign Violation show increased complaints this year.

It is curious that Posted Parking Sign Violation and Parking Permit Improper Use have increased in the number of reports. I suspect there are many factors which affect a persons likelihood of lodging a complaint and that these complaints are not the ideal proxies for the question at hand, but I still believe them to be important to the discussion.

Complaints by Month

Charting the number of complaints each month since January 2023 shows that the start of 2025 has the lowest number of complaints related to vehicles in the Congestion Relief Zone. This gap grows to an impressive reduction in complaints when the Parking Sign and Parking Permit categories are removed. Either way, this is a positive signal that Congestion Control is having an impact on the number of complaints that are being opened!

Mapping Complaints

From these maps it is easy to see that the number of complaints in January and February 2025 are dramatically lower than the numbers for previous years.

Conclusion

The launch of the Congestion Control program in Manhattan in early 2025 has had a clear impact on the number of complaints related to vehicles that originate in the affected zip codes. Complaints are down across nearly all categories in comparison to 2024.

The future of this program is uncertain, with the current presidential administration applying pressure from the federal government to end this program. If the opposition succeeds we can all look forward to a more crowded, louder, and less convenient experience in the streets of Manhattan.

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