Visual exploration of big spatio-temporal urban data: A study of New York city taxi trips

Nivan Ferreira, Jorge Poco, Huy T. Vo, Juliana Freire, Claudio T. Silva

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Article number6634127
Pages (from-to)2149-2158
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Volume19
Issue number12
DOIs
StatePublished - 2013

Fingerprint

Human Activities
Reaction Time
Decision Making
Hot Temperature
Economics
Agglomeration
Visualization
Decision making
Engineers
Sensors
Datasets
Rendering (computer graphics)

Keywords

  • NYC taxis
  • Spatio-temporal queries
  • urban data
  • visual exploration

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Medicine(all)

Cite this

Visual exploration of big spatio-temporal urban data : A study of New York city taxi trips. / Ferreira, Nivan; Poco, Jorge; Vo, Huy T.; Freire, Juliana; Silva, Claudio T.

In: IEEE Transactions on Visualization and Computer Graphics, Vol. 19, No. 12, 6634127, 2013, p. 2149-2158.

Research output: Contribution to journalArticle

@article{7205c66d5575473c89ba99fc1ed6ef14,
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.",
keywords = "NYC taxis, Spatio-temporal queries, urban data, visual exploration",
author = "Nivan Ferreira and Jorge Poco and Vo, {Huy T.} and Juliana Freire and Silva, {Claudio T.}",
year = "2013",
doi = "10.1109/TVCG.2013.226",
language = "English (US)",
volume = "19",
pages = "2149--2158",
journal = "IEEE Transactions on Visualization and Computer Graphics",
issn = "1077-2626",
publisher = "IEEE Computer Society",
number = "12",

}

TY - JOUR

T1 - Visual exploration of big spatio-temporal urban data

T2 - A study of New York city taxi trips

AU - Ferreira, Nivan

AU - Poco, Jorge

AU - Vo, Huy T.

AU - Freire, Juliana

AU - Silva, Claudio T.

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - NYC taxis

KW - Spatio-temporal queries

KW - urban data

KW - visual exploration

UR - http://www.scopus.com/inward/record.url?scp=84886646086&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84886646086&partnerID=8YFLogxK

U2 - 10.1109/TVCG.2013.226

DO - 10.1109/TVCG.2013.226

M3 - Article

C2 - 24051781

AN - SCOPUS:84886646086

VL - 19

SP - 2149

EP - 2158

JO - IEEE Transactions on Visualization and Computer Graphics

JF - IEEE Transactions on Visualization and Computer Graphics

SN - 1077-2626

IS - 12

M1 - 6634127

ER -