Virtual Scavenger Hunt – Collect Local Storefront Accessibility Data Using Crowdsourcing and AI

Storefront accessibility can substantially impact the way people who are blind or have low vision (BLV) travel in urban environments. Entrance localization is one of the biggest challenges to BLV people. Improperly designed staircases and obstructive store decorations can create considerable mobility challenges for BLV people, making it more difficult for them to navigate their community hence reducing their desire to travel. Existing digital map services don’t include such storefront accessibility information.

We’ve developed a web application, DoorFront, to collect large-scale accessibility data of NYC storefronts using crowdsourcing and AI (Machine Learning) approach. DoorFront enables volunteers to validate the accessibility information labeled by AI right in their homes. We’ll invite anyone who is interested in volunteering their time to helping the visually impaired participate in this Mapathon. We will offer incentives to 3 volunteers who have completed the most work.


  • Visit to begin!
  • The Mapathon starts at 3pm on 3/12 and ends at 3pm on 3/19. The three volunteers who have completed the most work will receive the following awards. Query image and Validation receive the same scores. Scores are based off of quality and accuracy.
  • The results will be announced on the app website (leaderboard) and the volunteers will also receive email notifications by 3/23.
  • Leaderboard shows the real-time scores, however our team will review the quality and accuracy of the work after the competition ends.
  • Make sure to sign up your account with accurate information.


  • 1st  place: $50 Amazon Gift Card
  • 2nd  place: $30 Amazon Gift Card
  • 3rd  place: $20 Amazon Gift Card

Hike and Map along the Staten Island Greenbelt

The Staten Island Greenbelt is missing trails and features on OpenStreetMap (OSM), a collaborative platform for collecting geographic data and displaying it; the “wiki” of maps. We will be using and sharing a wide range of paper and digital tools to collect data in-person while hiking from St. George’s new NYC Ferry terminal to Great Kills Beach. There will be an optional virtual event beforehand to talk about using OSM, other open data layers, and the trail, and the borough.

No OSM or hiking experience needed, but wear a pair of shoes with good grip. Vaccination and masks required! You may take off your mask when outside hiking.

Event will happen on Saturday, March 12, with Sunday, March 13 being the rain date.

Using Open Data for a Safer and Improved Cycling Network in NYC 🚲

Did you know New York City has the oldest bike lane in America, and one of the largest bike networks, plus bike share programs in North America? 🚲

The Covid-19 pandemic introduced hundreds of thousands of New Yorkers to this bicycle infrastructure for the first time. This session highlights student research from Cornell Tech that explores the intersection of open data and how the City can enhance multiple generations of infrastructure improvements with a focus on safety. Join us to learn how to use open data to understand the past and present, while building a safer future for all in New York City.

This hybrid session will be introduced by the City’s Chief Analytics Officer, Martha Norrick, and is organized by the Cornell Tech Urban Tech Hub.

** Important re: Registration **

To attend this session IN-PERSON, register for an “In-Person Attendee” ticket. Please be prepared to provide proof of vaccination and to wear a mask. See you on campus at the Verizon Executive Educaiton Center, 2 W Loop Rd, New York, NY 10044


To attend this session VIRTUALLY, register for a “virtual attendee” ticket.

Join us after the event!

All in-person attendees are invited to join us for a closing reception at Granny Annie’s Bar & Kitchen, 425 Main St, New York, NY 10044 (on Roosevelt Island).

We look forward to seeing you!

NYC Taxi & Limousine Commission Open Data: Overview and Data Hub Demo

The New York City Taxi & Limousine Commission (TLC) is the nation’s largest municipal taxi and for-hire vehicle regulator. The agency licenses over 100,000 vehicles and 175,000 drivers that make over one million trips per day. This session will provide attendees an overview of our extensive Open Data, the ways in which the industry utilizes the data, and a demonstration of the TLC Data Hub: an interactive portal that provides visualizations of our Open Data.

Presenters: Ted Metz, Policy Analyst and Nikita Voevodin, Senior Data Engineer

Beyond Static Open Data: A Citi Bike Time Series Case Study

Much open data is static. It often corresponds to either a snapshot in time or some historical summary. While static data is surely useful, looking at how data changes over time opens up new avenues for exploration. Looking backward, we can identify trends and garner insights. Looking forward, we can generate forecasts and try to predict the future. 

Citi Bike is the primary bikeshare in NYC, and they open up a lot of their data. They publish datasets about trips that riders have taken, and they have a real-time API that publishes the current information about all stations, such as the number of bikes and docks available. However, this data is largely static. If I want to answer questions like, “Will there be a dock available for me by the time I get to my destination station?”, I need to be able to forecast the number of docks at the destination station. And for that forecast, I need a time series of historical data about the number of docks at that station in order to build a forecasting model.

To answer such questions, I started pinging the Citi Bike API every 2 minutes back in 2016, and I have been collecting this data ever since. Data from August 2016 to December 2021 is publicly available on Kaggle. In this event, I will show how the data collection system works, and how I keep its operations cheap and worry-free. This data collection system can be reused for other open, real-time APIs. I’ll then show how we can analyze and visualize the data in order to learn about different Citi Bike stations in NYC. Finally, I’ll answer my original question by building a model to forecast the number of bikes available at a given station.

MTA BusTime: Big, Streaming Open Data 101

Some open data sets are sparse and infrequent. Real-time transit data is the opposite—copious and incessant. This workshop will dive into the details of dealing with big, streaming urban data with code examples that let you interact with the MTA New York City Transit BusTime API.

This virtual session is organized by the Cornell Tech Urban Tech Hub.

OpenStreetMap & Open Data: Mapping for Mobility in NYC (2 of 2)

This virtual workshop by TeachOSM is an introduction to open mapping using the OpenStreetMap platform. As a participant in this one-hour workshop, you will get an overview of how to add data to OpenStreetMap and then dive into adding bike racks & pedestrian safety features to improve mobility in NYC.

OpenStreetMap & Open Data: Mapping for Mobility in NYC (1 of 2)

This virtual workshop by TeachOSM is an introduction to open mapping using the OpenStreetMap platform. As a participant in this one-hour workshop, you will get an overview of how to add data to OpenStreetMap and then dive into adding bike racks & pedestrian safety features to improve mobility in NYC.

Bike Rack-a-thon (Mapping Bicycle Parking)

Join NYC Department of Transportation (NYC DOT), Manhattan Community Board 1, and BetaNYC in validating and adding bicycle parking points in Lower Manhattan!

🚲 🗺 🚲 🗺 🚲 🗺 🚲 🗺

This is a prototyping event! We will use two field mapping tools, FieldPapers and ArcGIS Survey123, to validate the location of NYC’s street furniture data. Ultimately, we want to validate NYC street furniture data on the open data portal. All data points updated by participants will be reflected in OpenStreetMap (OSM).


Together, we will work to identify a shared data literacy / civic engagement process that will validate NYC open data and contribute to OSM.


Join us to:

  • Learn about and try to find all the various bicycle parking in Manhattan Community Board 1.
  • Work in a small group to crowdsource information with digital or physical tools.
  • Contribute to open data validation and to OpenStreetMap!
  • Help us create a repeatable process for all of New York City.


This event will be in person, lunch and training will be provided.

Event will run from 1pm-5pm, rain or shine! Masks and comfortable shoes required!

Applications of MTA Data, Past and Future: Global Standards, Local Pathways

This talk will discuss how open data is used to build applications. Specifically, it will discuss how the use of MTA open data can be used to build iPhone apps for users who ride the subway or bus. Mike Sanderson has been an iPhone app developer building apps for consumers in different settings for 9 years. In 2013, when just starting, Mike won third in the “AppQuest” hackathon held by the MTA, and also released 3 independent subway apps in 2013 and 2014, using materials and open data from the MTA. Since then Mike has worked on iPhones for range of startups and large brands.

This talk will look specifically at how Open Data makes specific applications possible. It will extensively discuss the GTFS format–the standard General Transit Feed Specification– and how it has evolved over time. The talk will be in three parts. First, it will look at the MTA’s contest and Mike’s subway apps from 2013 and 2014, and find out what became of this first generation of subway app. What benefits were realized, and what didn’t pan out? Second, the structuring of data for transit will be discussed in “Systems Theory” approach, looking at the goals and interactions of transit agencies, tech platforms, app developers, users, and others. Third, the GTFS format itself will be looked at. This includes both GTFS “static” or “scheduled” and the “realtime”. A new way of thinking about transit questions is suggested, the concept of “horizon”. Finally, and still part of the GTFS discussion, a new way of visualizing and navigating stations is suggested, by thinking about navigation not as coordinates in space but as access pathways.