Currently, MTA's monthly data is presented to MTA board members in thick printed report books that are overwhelmingly dense with information and difficult to understand at a glance.  The data is presented in extremely technical and un-relatable terms without any form of hierarchy that could inform readers of relationships between terms.

Currently, MTA's monthly data is presented to MTA board members in thick printed report books that are overwhelmingly dense with information and difficult to understand at a glance.  The data is presented in extremely technical and un-relatable terms without any form of hierarchy that could inform readers of relationships between terms.

Some sketches we did of various visualization methods

Some sketches we did of various visualization methods

Two big pictures frequently asked are "How long do different delays take?" and "What are the top 10?" Given that static data snapshots cannot capture dynamic systems, we created an interactive bar graph that displays the 10 causes and allows viewers to toggle between years and see shifts over time.

Officials would be able to do this for other metrics, like number of trains delayed and frequency.

Officials would be able to do this for other metrics, like number of trains delayed and frequency.

For those who may want to delve deeper into the data of a specific cause, such as person holding doors, they would be able to click on the cause from the previous screens and get to this one.  This would allow you to see the progress/decline over time for a specific delay and metric.

For those who may want to delve deeper into the data of a specific cause, such as person holding doors, they would be able to click on the cause from the previous screens and get to this one.  This would allow you to see the progress/decline over time for a specific delay and metric.

To see which lines were impacted most by certain delay causes, we created this form of visualization that reveals not only its frequency of delay occurrence (or some other metric) across lines, but also allows for a comparison between delay causes.

To see which lines were impacted most by certain delay causes, we created this form of visualization that reveals not only its frequency of delay occurrence (or some other metric) across lines, but also allows for a comparison between delay causes.

As mentioned previously, it's very hard to rank definitively and generally which delays are worse or better.  For example, a delay caused by someone holding the doors is frequent but low in number of trains delayed, whereas a delay caused by an injured person is less frequent, but high in number of trains delayed.   So, we decided to design scorecards for each cause that can communicate their impact across all metrics at a glance.   An MTA board member would be able to compare different causes as well as metrics within causes.

As mentioned previously, it's very hard to rank definitively and generally which delays are worse or better.  For example, a delay caused by someone holding the doors is frequent but low in number of trains delayed, whereas a delay caused by an injured person is less frequent, but high in number of trains delayed.   So, we decided to design scorecards for each cause that can communicate their impact across all metrics at a glance.   An MTA board member would be able to compare different causes as well as metrics within causes.

For those who wish to see a bird's eye view of delays in the overall system, they would be able to click to this map, which reveals the relative concentration of late trains across systems as measured by color.  By revealing the system's current and emerging hotspots, officials will be informed, at a glance, what stations to focus on and what preventative measure they may need to take.

For those who wish to see a bird's eye view of delays in the overall system, they would be able to click to this map, which reveals the relative concentration of late trains across systems as measured by color.  By revealing the system's current and emerging hotspots, officials will be informed, at a glance, what stations to focus on and what preventative measure they may need to take.

Our end recommendations to the MTA, based off our experience working with the data.

Our end recommendations to the MTA, based off our experience working with the data.

Below is the full presentation:

MTA MONDAY A.M. DELAYS

DATA VISUALIZATION

These designs were developed in collaboration with teammates Sneha Pai and Matthew Brigante for a 4-week data visualization workshop, led by Rachel Abrams of Turnstone Consulting and sponsored by the Metropolitan Transportation Authority (MTA).  

As a team, we were tasked with developing visualization tools or methods with a focus on "Monday Morning delays" that would better allow MTA officials - many of whom may not have extensive involvement in MTA's operational workings - have a better understanding of the data provided to them.

Given that the MTA measures delays by a multitude of metrics, it's very hard to rank definitively which delays are better or worse.  Since results would differ given context and the board member, we avoided providing a definite ranking system and instead on providing a flexible one that allows him/her explore delays in a format of their choosing.  

Skills: Excel, Processing, Photoshop, Illustrator, Keynote

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