About this video

• Video Title: MHC++ Fall 2025 Datathon Kickoff
• Channel: Maryam Ilyas
• Speakers: Alan Hillary, Mariam Ilyas, Lisa, Renuanum, Jake Middleman, Daniel Brown
• Duration: 1:51:51

Overview

This video serves as the kickoff for the MHC++ Fall 2025 Datathon, an event focused on using data to solve urban challenges. The speakers introduce the datathon's purpose, rules, and structure, highlight key participants and sponsors, and provide an overview of the event's timeline and expectations. The core of the datathon revolves around analyzing MTA (Metropolitan Transportation Authority) open data, specifically focusing on the Automated Camera Enforcement (ACE) program. The video also includes workshops and resources to help participants succeed.

Key takeaways

• Datathon Overview: The datathon aims to encourage students to analyze data and develop solutions for urban challenges, with this year's focus on MTA open data related to the ACE program.
• MTA Data Focus: The event encourages participants to explore MTA datasets, particularly those concerning bus speeds, violations, and the impact of the ACE program on bus route efficiency.
• Participant Engagement: The datathon fosters networking opportunities and enhances participants' marketability by providing real-world data analysis experience.
• Structure and Schedule: The video outlines the datathon's schedule, including project deadlines, presentation dates, workshops, and mini-events like trivia and gaming nights.
• Support Resources: Participants are encouraged to utilize workshops, mentors, and online communities (like Discord) for guidance and support throughout the datathon.

The datathon presents three main areas for participants to explore, which can be framed as business questions:

1. Bus Route Utilization and Speed Changes:

   • Question: Which bus routes are highly utilized by CUNY students, and how have bus speeds changed over time on routes that are enforced by the Automated Camera Enforcement (ACE) program?
   • Relevant Datasets:
     • MTA Bus Route Segment Speeds data (a featured dataset for this datathon).
     • Data on which routes have the ACE program implemented (mentioned as a supporting dataset).
     • Datasets that can help identify routes utilized by students (e.g., routes serving specific campuses, or general CUNY student data if available).

2. Exempt Vehicle Violations:

   • Question: For vehicles that are exempt from fines (due to business reasons), are there repeat offenders, and where are these exempt vehicles frequently in violation?
   • Relevant Datasets:
     • MTA ACE Violation data (this dataset contains information on violations and exemptions).
     • Vehicle ID field within the ACE data (to identify repeat offenders).
     • Data on vehicle types to distinguish exempt categories (e.g., commercial vehicles, emergency vehicles).

3. Central Business District and Congestion Pricing Impact:

   • Question: For automated camera-enforced routes that travel within or across Manhattan's Central Business District (CBD), how have violations changed alongside the implementation of congestion pricing?
   • Relevant Datasets:
     • MTA ACE Violation data.
     • Data on which routes travel through Manhattan's CBD.
     • Information or datasets related to the implementation dates and zones of congestion pricing.
     • Potentially, bus speed data for these specific routes to correlate with violation changes.

The speakers also mentioned that while the "feature datasets" are recommended starting points, participants can explore other datasets from the MTA open data portal and potentially the New York City open data portal. They emphasized using data that is accessible and relevant to the questions posed.

The main urban challenge being addressed by the datathon participants is improving the efficiency and reliability of New York City's public transportation system, specifically focusing on bus services. This is being tackled through the analysis of data related to the MTA's Automated Camera Enforcement (ACE) program, which aims to reduce violations in bus lanes and at bus stops to speed up buses and enhance overall service.

Unfortunately, I cannot directly accept or process zip files. My capabilities are limited to processing text-based information that you provide directly in our chat.

However, you can still share the datasets with me by:

1. Listing the filenames and providing a brief description of the data contained in each file. For example:

   • ace_violations.csv: Contains details of ACE violations, including vehicle type, location, date, and whether a ticket was issued.
   • bus_speeds.csv: Provides average bus speeds for different routes and times.
   • route_definitions.csv: Lists all MTA bus routes with their general descriptions and service areas.
   • congestion_pricing_zones.geojson: Outlines the geographical boundaries of the congestion pricing zone.

2. Pasting a snippet of the data if the files are small enough and you can easily copy-paste a representative sample.

3. Describing the columns/fields within each dataset if you can't provide snippets.

Once you provide the information in a text-based format, I can help you identify the most suitable datasets for each question.

Based on the video transcript, here's other key information:

Datathon Logistics & Rules:

• Project Due Date: Wednesday, September 24th.
• Final Presentations: September 26th.
• Shortlisting for Presentations: An "intent to submit" form will be sent separately.
• Submission Format: A GitHub page is required, along with a 1-2 minute video describing the project.
• Review Process: An initial review will shortlist the top 20 teams for presentation.
• Judging: Judges will evaluate the top three teams.
• Presentation Attendance: At least one team member must attend the final presentations.
• In-Person Requirements: For any in-person events, participants need to bring a valid ID (and CUNY ID for CUNY students).

Key Data & Tools Mentioned:

• MTA Open Data Program: Emphasizes transparency and provides machine-readable, reusable, and free data.
• Open Data Portal: data.ny.gov is the state's open data portal where MTA datasets are hosted.
• MTA Metrics Site: metrics.mta.info offers visualized data and insights, useful for exploring bus service metrics.
• MTA Blog: Features resource-oriented content and insights into the MTA data team's work.
• Socrata (SODA API): The underlying platform for the open data portal, allowing interaction with datasets through APIs and query languages.
• SoCool (Socrata Query Language): A language similar to SQL used for querying data via the Socrata API.
• Python: Can be used with SoCool for querying and analyzing data.
• Geospatial Data Tools: Kepler.gl is mentioned as a tool for creating compelling visualizations with geospatial data. Tableau is also suggested for creating visually appealing graphics.

Speakers and Their Roles:

• Alan Hillary: Host of the kickoff, discusses the datathon's purpose, structure, and importance.
• Mariam Ilyas: President of MHC++, provides information about the organization and datathon events.
• Lisa: Manages the MTA Open Data Program, explains the program's origins and the scope of MTA data available.
• Renuanum: Data Scientist at MTA Open Data, demonstrates how to query data using the portal and SoCool.
• Jake Middleman: Transportation Planner with the MTA Department of Buses, provides detailed background on the ACE program.
• Daniel Brown: Data Consultant and CUNY alum, offers guidance on frameworks for project structure, data storytelling, and tools for visualization.

Additional Opportunities:

• New York City Open Data Week: An annual public event in March that participants can contribute to.
• Case Studies: Smaller, 8-10 week engagements, with a planned one with the MTA next semester.
• MHC++ Organization: An inter-university computer science club focused on building a community for those passionate about computing and technology.

Datathon Mindset:

• Iterative Process: Data analysis is often iterative; be prepared to ask new questions as you explore.
• Scoping: Be mindful of deadlines and avoid getting lost in "rabbit holes." Focus on creating a concrete deliverable.
• Ethical Data Use: Do not manipulate data; ensure your analysis is honest and ethical.
• Domain Knowledge: Understand the context of the data (e.g., the ACE program) to interpret it correctly.
• Quantifiable Metrics: Focus on answering questions with numbers.
• "Fundamental Law of Time Estimates": Expect tasks to take three times longer than initially estimated.
• Have Fun: The datathon is intended to be a fun and engaging experience.

This datathon project is about analyzing data from the MTA's Automated Camera Enforcement (ACE) program to understand its impact on New York City's bus system. Participants will use MTA open data to explore questions related to bus speeds, violations, route efficiency, and the potential influence of factors like congestion pricing. The goal is to derive insights and present findings that can demonstrate the program's effectiveness or identify areas for improvement.

This is a great business question! It's insightful because it touches upon both the effectiveness and the fairness of the ACE program. Here's why your question matters and how it connects to the datathon's goals:

• Effectiveness: It directly addresses whether the ACE program is achieving its intended goals, such as improving bus speeds and service reliability. This is crucial for justifying the program's existence and resource allocation.
• Fairness/Equity: It introduces the important aspect of distribution. Are certain routes or communities benefiting more than others? Are there any unintended negative consequences for specific areas or user groups? This is vital for ensuring that city initiatives serve all residents equitably.
• Data-Driven Decision Making: By framing it as a question answerable with data, you align perfectly with the datathon's purpose. You're looking to use evidence to evaluate a public service program.
• Actionable Insights: Answering this question could lead to recommendations for optimizing the ACE program's deployment, addressing potential biases, or reallocating resources to areas that need more improvement.
• Complexity: It acknowledges that the impact of such a program might not be uniform, requiring a nuanced analysis across different routes and potentially demographic factors.

Your question is well-aligned with the overall theme of using data to improve urban mobility and services. It's specific enough to guide your data exploration while also being broad enough to uncover potentially significant findings.

This datathon project focuses on analyzing the impact of the MTA's Automated Camera Enforcement (ACE) program on NYC bus routes. Participants will use MTA open data to evaluate the program's effectiveness in improving bus speeds and service reliability, while also considering the fairness and equitable distribution of its benefits across different bus routes and neighborhoods. The goal is to derive data-backed insights that can inform future transportation strategies.