The NYC TLC Factbook is a public-facing PowerBI dashboard that showcases charts, maps, and key metrics on various data and policies, including trip counts, electric vehicles, driver pay, utilization rates, and driver demographics. The underlying data for the dashboard includes publicly available information, such as trip data, as well as more sensitive data that is kept confidential to protect driver privacy. The Data Analytics team, part of TLC’s Policy Division, invites members of the public to an interactive presentation that offers an in-depth look at the analysis behind the dashboard, as well as how it helps evaluate and monitor critical policies

DataKind started 2024 with an ambitious goal: to create an open-access tool populated with data at a hyperlocal level that would foster a deeper understanding of community needs and the complex factors influencing them. Working with collaborators representing many facets of health and wellbeing, DataKind launched the community health indicators software, a tool which enables social service providers, practitioners, policymakers and community stakeholders to access standardized data on their communities of impact and take meaningful action. This resource harmonizes and makes accessible 11 different public data sources of community data at three geographic levels (tract, zip, and county), with 49 unique data indicators drawing from a database of six million rows of data. Users have the option to create a free, secure individual or team account and upload and analyze additional datasets alongside the data included. Users can export analysis, including maps, from the system. This free and open software makes data insights accessible to any end-user regardless of data maturity.

This session will discuss the community-centered software design process, demonstrate the software with several use cases, and offer a rich, facilitated conversation with end-users of the software from social impact and governmental institutions. We will provide an introduction to question formulation, asset mapping, and data interpretation.

Attendees can submit their data questions here for use in the session or for future follow-up: https://forms.gle/TsWfe4Dses3jzVHm8

Join us for an exciting set of back-to-back presentations featuring Cornell Tech researchers and practitioners as they share their real-world applications of NYC Open Data. This session offers a unique opportunity to explore how open data is being leveraged to address urban challenges, improve decision-making, and drive innovation across a variety of fields. Participants will gain valuable insights from experts who are actively shaping the future of NYC through data-driven solutions.

This event is part of an Open Data Week series hosted by Cornell Tech— learn more below!

Presentations:
The ABCs of a PiTech Fellow: Artificial Intelligence (AI) & Natural Language Processing (NLP), Block Party & Community Boards
Breanna Green, PhD Candidate, Information Science, Cornell Tech

Data-driven analyses and optimization of 311 systems: Applications to NYC Parks
Zhi Liu, PhD Candidate, Operations Research and Information Engineering, Cornell Tech

Data analysis and modeling of public building construction in New York City
Sara Venkatraman, Senior Research Associate in Statistics, Weill Cornell Medical College

Democratizing Policy Counterfactuals with AI: A deep dive into Local Law 97, NYC’s groundbreaking law to cut carbon emissions in its largest buildings
Vianney Brandicourt, Urban Tech Fellow, Cornell Tech

Analyzing the effects of congestion pricing using Open Data
Nikhil Garg, Assistant Professor, Cornell Tech

Predictive Maintenance for NYC Subway Lines
Atmika Pai, MS Candidate, Information Systems, Jacobs Technion-Cornell
Bhoomika Mehta, MS Candidate, Information Systems, Jacobs Technion-Cornell

SenTree: Leveraging NYC Open Data to predict urban tree health
Rishabh Surendran, MBA Candidate, Johnson Cornell Tech
Shubham Arya, MBA Candidate, Johnson Cornell Tech

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This event is part of an Open Data Week series hosted by Cornell Tech on Wednesday, March 26 from 5-8pm. We’d love for you to join us for both sessions— though it is not required! We’ll kick off at 5:00 pm with a hands-on Discovering NYC Open Data class from the Open Data Ambassadors, where you’ll learn the fundamentals of NYC Open Data. Following that, at 6:30 pm, Cornell Tech researchers and experts will discuss their real-world applications of open data, during Open Data in Action: Driving Academic Research and Government Collaboration at Cornell Tech. Join us for a post-event happy hour next door at Anything at All!

There are many easy ways to get to Cornell Tech, located on Roosevelt Island. For maps and directions see here.

NYC School of Data is a community conference that demystifies the policies and practices around open data, technology, and service design. This year’s conference helps conclude NYC Open Data Week and features 30+ sessions organized by NYC’s civic technology, data, and design community! Our conversations and workshops will feed your mind and inspire you to improve your neighborhood.

To attend, you need to purchase tickets. The venue is accessible, and the content is all-ages friendly! If you have accessibility questions or needs, please email us at schoolofdata@beta.nyc.

Thank you to Reinvent Albany and Esri for helping to cover conference costs and making it possible to meet in 2025.

And If you can’t join us in person, tune into the main stage live stream provided by the Internet Society New York Chapter. Follow the conversation #nycsodata on Bluesky.

Purchase your tickets here.

In this virtual training, MTA’s Data & Analytics Team will walk participants through how to pull big datasets from the open data portal via the API using Python. Participants will briefly learn a bit about MTA’s Open Data Program before being invited to follow along while the team demos how to pull MTA’s larger datasets off of the Socrata platform. While the data is pulling, the team will show some interesting analysis you’ll be able to do, centered around the Congestion Relief Zone!