Using TaxiVis to compare taxi trips from Lower Manhattan to JFK and LGA airports in May 2011.
As increasing volumes of urban data are captured and become
available, new opportunities arise for data-driven analysis that can
lead to improvements in the lives of citizens through
evidence-based decision making and policies.
In this project, we focus on a particularly important urban data set:
taxi trips. Taxis are valuable sensors and information associated with taxi trips
can provide unprecedented insight into many different aspects of
city life, from economic activity and human behavior to mobility
patterns. But analyzing these data presents many challenges. The data are
complex, containing geographical and temporal components in addition
to multiple variables associated with each trip. Consequently, it is
hard to specify exploratory queries and to perform comparative
analyses (e.g., compare different regions over time). This problem is
compounded due to the size of the data---there are on average
500,000 taxi trips each day in NYC.
We propose a new model that allows users to visually query taxi trips.
Besides standard analytics queries, the model supports
origin-destination queries that enable the study of mobility across
the city. We show that this model is able to express a wide range of
spatio-temporal queries, and it is also flexible in that not only can
queries be composed but also different aggregations and visual
representations can be applied, allowing users to explore and compare
results. We have built a scalable system that implements this
model which supports interactive response times; makes use of an
adaptive level-of-detail rendering strategy to generate clutter-free
visualization for large results; and shows hidden details to the users
in a summary through the use of overlay heat maps. We present a series of case studies motivated by traffic engineers and
economists that show how our model and system enable domain experts
to perform tasks that were previously unattainable for them.