Analysis of 2.5 Million Boston Taxi Trips
Boston’s Department of Transportation released a detailed dataset covering 2.5 million individual taxi trips in the city from May through November 2012. After some analysis, I found that the data is more than just a vast list of taxi pickup and drop-off coordinates: it tells the story of Boston. Which locations are “hotspots” for taxi activity? On average, what time are taxis most active? The dataset answers these questions and more.
Small Multiple Map by Month
The official taxi-trip record dataset contains data for over 2.5 million taxi trips from May through June 2015. Each individual trip record contains precise location coordinates for where the trip started and ended and timestamps for when the trip started and ended. At first, I extracted all of the data using QGIS to map the coordinates of every trip. However, once I plotted all of the points, I could not make any sense of the data because the culmination of all the data points was too dense to analyze. Consequently, after some research, I decided to use the heatmap tool available on QGIS to create a data visualization where I can see “hotspots.” Finally, I used Adobe Photoshop to create a small multiple of the data for each month, where the maps on the left represent the drop-offs and the maps on the right represent the pickups.
I decided to use this color scheme and create a small multiple map to display this data after reading Tufte’s ideas: I decided to choose yellow for the heatmap and dark grey for the background map to clearly differentiate each layer from each other, effectively allowing viewers to form relationships between the heatmap and the actual map of Boston. Similarly, I decided to create my data visualizations using small multiples because this technique allows viewers to enforce “comparisons of change,” as the information slices are positioned “within the eye span.”
After creating the small multiple map by month, I found that hotspots for taxi drop-offs from 5/12 to 11/12 were almost identical; similarly, I discovered that hotspots for taxi pickups within the same time frame were also almost indistinguishable. Thus, from the small multiple that I created, I concluded that taxi activity on average remains the same between each month, at least between May through November.
However, the data does not support circumstances where snow may play a factor in taxi drop-off and pickup locations, as I am missing data from December to April. Similarly, with data from only 2012 and none from other years, I cannot be sure that my small multiple can accurately model future taxi activities. Thus, in order to ensure that my findings are reliable, future research should include all months throughout a year and data from multiple years.
After comparing the drop-off maps to the pickup maps, I also found that hotspots were easier to identify on the pickups maps compared to the drop-off maps, as there were more areas on the pickup maps that were bright yellow. Although I found this offsetting at first, I came to the conclusion that taxi drop-offs should be more dispersed than taxi pick-ups because the destination of a trip could vary from the passenger’s house, the cemetery, to the Boston Public Library. However, a taxi pick-up would be more condensed because the pick-up location for taxis is not only dependent on the location of the passengers, but also the location of the taxis themselves. While drop-off locations are based on people’s destinations, pickups are dependent on both the passengers and taxis’ location, all of which forces taxi pickup locations to be more close to each other.
I also found that for both drop-off and pickup maps, the most popular hotspots include Hynes Convention Center, Wilbur Theater, Majestic Theater, and South Station. With all of the restaurants, bars, and entertainment surrounding the Hynes Convention Center, this result does make sense. To analyze the data in more detail, however, I decided to create a small multiple map based on time intervals.
Small Multiple Map by Time
The small multiple displays taxi pickup and drop-off locations based on different time intervals. Between midnight and 1am, I found that most people were getting dropped off around Stuart Street. Given that the area provides a lot of nightlife entertainment and restaurants – for example, the Bijou Nightclub, the Wang Theatre, and Genki Ya – this makes sense. Between 1-4am, taxi activity seems to have increased significantly; after further analysis, however, popular drop-off locations are still similar to ones before midnight with slight variances: bars, liquor stores, and hotels. As such, if a person were in need of a cab, I would recommend that person walk towards the nearest pub or bar as those locations are where taxi drivers are most active during that time.
Furthermore, I found that taxi activity was nearly nonexistent between 4-7am. This makes sense because nightlife usually ends around 4am, so the number of people needing transportation during that time interval should die down. Additionally, between 7-11am, the area with the most taxi activity is Summer Street outside of the Boston Convocation and Exhibition Center. There should be a lot of taxi activity there because the Boston Convention and Exhibition Center is located near the South Boston waterfront, Boston’s World Trade Center, and across the harbor from Logan International Airport. Around that area is also near the MBTA Silver Line, which has direct connections to South Boston and Logan Airport, making it a central transportation point. From 4pm till midnight, areas around Seaport Blvd also becomes extremely popular. With the number of restaurants around that street – for example, Del Frisco’s Legal Test Kitchen, Temazcal Tequila Cantina, and Legal Harborside – combined with the view of sea, the increase in taxi activity during that time frame is not surprising.
What is most surprising about the small multiple map, however, is that taxi activity seems to be most active during the night, but one would expect taxi activity to be most active during the day because that is the time period when people actually need to commute. One factor that could explain this phenomenon are companies such as Uber and Lyft. Assuming that Uber and Lyft drivers are more willing to work during the day, taxi activity may become diminished after 8pm because people are more willing to pay for those services rather than a normal cab. Assuming the same logic, people in need for transportation at night would be forced to take a cab because of the decrease supply in Uber and Lyft drivers, all of which creates more taxi activity at night. Furthermore, the number of intoxicated people could also contribute to the increase in taxi activity because people are more willing to take a cab as opposed to driving or walking home due to safety. Not surprisingly though, Logan Airport consistently has a lot of taxi activity regardless of the time, most likely due to erratic flight schedules and the fact that Ubers and Lyfts are banned around that area. To analyze the data in more detail, however, I decided to create a small multiple bar graph based on what day it is.
Small Multiple Bar Graph by Day
According to the data, demand for taxis drop after 8pm on most weekdays and 1am on weekends. Furthermore, taxi activity significantly drops between 3-5am for both weekend and weekdays, but gradually rises after that time frame. Interestingly, this contradicts our previous conclusion that taxi activity is low during the day compared to at night. This could be due to the way QGIS creates heatmaps: the heatmap simply shows how concentrated a location is, but it does not necessarily show the number of coordinates there are in total. For example, if every single Taxi coordinate were evenly distributed in Boston, even though there may be more taxi activity overall, the heatmap will not be able to detect that. Thus, our new data suggests that our previous conclusion is possibly wrong because the drop-off location is more randomized during the day compared to during the night. After some analysis, this does make sense because only bars, nightclubs, and certain other venues are open late at night, while passengers have the freedom during the day to visit other places such as the museum, the theaters, or the library. Thus, the taxi activity may look weaker during the day because the coordinates are more evenly distributed during that time frame.
Another interesting trend to note is the gradual increase in taxi activity by day after Sunday until Saturday night, where the taxi activity drops back to its “initial” value. This was surprising because I anticipated the taxi activity for each weekday to be similar: Why should taxi activity be greater on Tuesday than on Monday? With our current data, we can only make conjectures without any solid conclusion as to why this may happen, so future research is needed to analyze other possible factors. Also, the usual peak between 7-10am no longer exists during the weekend. This makes sense, because people no longer have an incentive to wake up early during the weekend.