Title
Visual Exploration of Big Spatio-Temporal Urban Data: A Study of New York City Taxi Trips
Abstract
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 paper, 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.
Year
DOI
Venue
2013
10.1109/TVCG.2013.226
Visualization and Computer Graphics, IEEE Transactions
Keywords
Field
DocType
data visualisation,decision making,query processing,rendering (computer graphics),spatiotemporal phenomena,traffic information systems,New York City taxi trips,adaptive level-of-detail rendering strategy,big spatio-temporal urban data,clutter-free visualization,data-driven analysis,economic activity,evidence-based decision making,evidence-based decision policies,geographical components,human behavior,mobility patterns,origin-destination queries,spatiotemporal queries,standard analytics,taxi trip visually query,temporal components,traffic engineers,urban data set,visual exploration,visual representations,Analytical models,Cities and towns,Data models,Data visualization,Mathematical model,NYC taxis,Spatio-temporal queries,Time factors,Visual analytics,urban data,visual exploration
Data science,Computer vision,Data modeling,Data visualization,Visualization,Computer science,Taxis,Visual analytics,Artificial intelligence,TRIPS architecture,Analytics,Rendering (computer graphics)
Journal
Volume
Issue
ISSN
19
12
1077-2626
Citations 
PageRank 
References 
176
5.41
28
Authors
4
Search Limit
100176
Name
Order
Citations
PageRank
Nivan Ferreira130112.41
Jorge Poco232315.21
Huy T. Vo3103561.10
Juliana Freire43956270.89