Discovering Urban Spatial-Temporal Structure from Human Activity Patterns

Transcription

1 ACM SIGKDD International Workshop on Urban Computing (UrbComp 2012) Discovering Urban Spatial-Temporal Structure from Human Activity Patterns Shan Jiang, Joseph Ferreira, Jr., Marta C. Gonzalez, August 12, Beijing, China

2 1. Introduction Outline Motivation Data & Study Area 2. Urban spatial-temporal structure(sts) Measurement The Chicago Case 3. Clustering daily activity patterns and traces Activity Profile Clusters Trace Clusters 4. Urban spatial-temporal structure by region, activity pattern and type 5. Conclusions

3 1. Introduction Background Cities have evolved from monocentric to polycentric forms, due to the improvement of transportation systems the rise of consumer city (Glaeser et al. 2001) Swelling cities have become data repositories of human activities, due to the emergence of the use of Information and Communication Technologies (ICT) in everyday life ubiquitous urban sensors (e.g., GPS, mobile phone, online social media) Challenges Traditional measurement of urban structure measured by population and employment density, is static cannot capture the dynamics in space and time in cities Urban sensing data little information on social demographics/activity types of the users is known to researchers due to legal/privacy constraints

4 1. Introduction Our Approach We expand the traditional understanding of urban structure from spatial dimension to spatial-temporal dimensions We detect clusters of individuals by daily activity patterns, integrated with their usage of space and time, and show that daily routines can be highly predictable, with clear differences depending on the group. Data & Study Area Travel survey collected by the Metropolitan Planning Organization are representative of the population can inform us about who, what, when, where, why and how of travel for each person in a surveyed household In this study, we use the 2008 Chicago Travel Tracker survey data. a 1-day or 2-day survey, including a total of 10,552 households, 30,000+ individuals. We use Monday to Thursday as a representative weekday sample (23,527 distinct individuals)

5 Study Area & Data Chicago Temporal Activity Patterns: Weekday S. Jiang, J. Ferreira, M. Gonzalez (2012) 5

6 Chicago Temporal Activity Patterns: Weekday Ref: Jiang, S., J. Ferreira & M. González (2012) Clustering daily patterns of human activities in the city. Data Mining and Knowledge Discovery, 25,

7 2. Urban Spatial-Temporal Structure 2.1 Measurement and Estimation We define a spatial-temporal space S as: [1] Spatial-Temporal Activity Density [2] Time-cumulative Spatial Activity Density [3] Kernel Density Estimator [4]

8 2. Urban Spatial-Temporal Structure 2.2 Chicago Metro-Area Example Home Work School Shopping / errands Recreation/ entertainment

9 3. Clustering Activities & Traces 3.1 Activity Profile Clusters Other Cluster # 2 (1.3%) Overnight adventurers Civic Cluster # 8 (12.5%) Students Rec Cluster # 7 (3.1%) Afternoon workers Personal Shopping Cluster # 4 (5.5%) Afternoon adventurers Trans Cluster # 6 (33.2%) Stay-at-home Schl Cluster # 3 (13.0%) Morning adventurers Work Cluster # 5 (17.9%) Regular workers Home Time of Day (in Hour) 10 Time of Day (in Hour) Time of Day (in Hour) Cluster # 1 (13.5%) Early-bird workers Ref.: Jiang, S., J. Ferreira & M. González (2012) Clustering daily patterns of human activities in the city. Data Mining and Knowledge Discovery, 25,

10 3. Clustering Activities & Traces 3.2 Trace Clusters Cluster numbers (k) and the Dunn s Index (DI)

11 4. Urban spatial-temporal structure by region, activity profile and activity type

12 5. Conclusions To facilitate urban planners and scholars to understand the dynamics and complexity of polycentric cities, and how cities have been utilized by different types of individuals for different activity types in space and time We propose a concept of urban spatial-temporal structure(sts) Measurement: time-cumulative spatial activity density Estimation: kernel density estimator We analyzed the STS of a polycentric metropolitan area by clustering individuals by activity patterns and traces using k-means algorithm via PCA, and by estimating and visualizing the time-cumulative spatial densities of various activities by person types (of activity profiles and trace clusters) in one of the regions of Chicago This analysis presents the basis to capture collective activities at large scales and expand our perception of urban structure from the spatial dimension to spatial-temporal dimension.

13 Acknowledgements This research was funded in part by the MIT Department of Urban Studies and Planning, by the US Department of Transportation Region One University Transportation Center, and by the Singapore National Research Foundation (NRF) through the Singapore-MIT Alliance for Research and Technology (SMART) Center for Future Mobility (FM).

14 Questions? THANK YOU!