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Biology

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published: February 25th, 2013

DOI:

10.3791/50130

1School of Environmental and Life Sciences, Kean University, 2Department of Geography, University of Wisconsin-Madison

A suite of spatiotemporal processing methods are presented to analyze human trajectory data, such as that collected using a GPS device, for the purpose of modeling pedestrian space-time activities.

It is well recognized that human movement in the spatial and temporal dimensions has direct influence on disease transmission1-3. An infectious disease typically spreads via contact between infected and susceptible individuals in their overlapped activity spaces. Therefore, daily mobility-activity information can be used as an indicator to measure exposures to risk factors of infection. However, a major difficulty and thus the reason for paucity of studies of infectious disease transmission at the micro scale arise from the lack of detailed individual mobility data. Previously in transportation and tourism research detailed space-time activity data often relied on the time-space diary technique, which requires subjects to actively record their activities in time and space. This is highly demanding for the participants and collaboration from the participants greatly affects the quality of data4.

Modern technologies such as GPS and mobile communications have made possible the automatic collection of trajectory data. The data collected, however, is not ideal for modeling human space-time activities, limited by the accuracies of existing devices. There is also no readily available tool for efficient processing of the data for human behavior study. We present here a suite of methods and an integrated ArcGIS desktop-based visual interface for the pre-processing and spatiotemporal analyses of trajectory data. We provide examples of how such processing may be used to model human space-time activities, especially with error-rich pedestrian trajectory data, that could be useful in public health studies such as infectious disease transmission modeling.

The procedure presented includes pre-processing, trajectory segmentation, activity space characterization, density estimation and visualization, and a few other exploratory analysis methods. Pre-processing is the cleaning of noisy raw trajectory data. We introduce an interactive visual pre-processing interface as well as an automatic module. Trajectory segmentation5 involves the identification of indoor and outdoor parts from pre-processed space-time tracks. Again, both interactive visual segmentation and automatic segmentation are supported. Segmented space-time tracks are then analyzed to derive characteristics of one's activity space such as activity radius etc. Density estimation and visualization are used to examine large amount of trajectory data to model hot spots and interactions. We demonstrate both density surface mapping6 and density volume rendering7. We also include a couple of other exploratory data analyses (EDA) and visualizations tools, such as Google Earth animation support and connection analysis. The suite of analytical as well as visual methods presented in this paper may be applied to any trajectory data for space-time activity studies.

1. Getting Data

  1. Trajectory data can be collected with handheld GPS units, GPS-enabled smart phone tracking applications, as well as A-GPS (assisted GPS) devices such as the one employed in our study, a commercial child tracker device.
  2. Trajectory data is usually saved in terms of time-latitude-longitude records. A desired time interval should be set based on application needs. Often the most frequent interval is desired for space-time activity studies.
  3. Convert the data to com.......

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Trajectory data was collected by volunteering undergraduate students from Kean University (NJ, USA) in spring 2010. The purpose was to study activity patterns of students who caught influenza (diagnosed by doctor or self-diagnosed) in comparison to those who did not. In order to illustrate the methods and procedure presented in this paper we took the trajectories collected within the suburban campus area to generate representative results. Trajectories within the campus area are mostly pedestrian trajectories, wit.......

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We used add-in mechanism of ArcGIS to develop the interface. All the interactive operations were implemented using C++. All the automatic processing and analysis functions were developed using Python.

AGPS data, or GPS data collected by pedestrian presents unique challenge in preprocessing as the errors can be massive due to adjacency to buildings and frequent indoor stops. Moreover, the focus of preprocessing should not be data reduction as what is usually done for vehicle GPS trajector.......

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This work is funded by NIH grant 1R03AI090465.

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Name Company Catalog Number Comments
Name of the reagent Company Catalogue number Comments (optional)
WorldTracker GPRS Tracking The World
A personal computer for running the analysis
ArcGIS software ESRI
Trajectory Analyzer Extension

  1. Stoddard, S. T., Morrison, A. C., et al. The role of human movement in the transmission of vector-borne pathogens. PLoS Negl. Trop. Dis. 3 (7), e10 (2009).
  2. Morens, D. M., Folkers, G. K., et al. The challenge of emerging and re-emerging infectious diseases. Nature. 430, 242-249 (2004).
  3. Viboud, C., Bjornstad, O. N., et al. Synchrony, waves, and spatial hierarchies in the spread of influenza. Science. 312, 447-451 (2006).
  4. Shoval, N., Isaacson, M. The Application of tracking technologies to the study of pedestrian spatial behaviour. The Professional Geographer. 58 (2), 172-183 (2006).
  5. Yu, H. Spatio-temporal GIS design for exploring interactions of human activities. Cartography and Geographic Information Science. 33 (1), 3-19 (2006).
  6. Kwan, M. Interactive geovisualization of activity-travel patterns using three-dimensional geographical information systems: a methodological exploration with a large data set. Transportation Research Part C. 8, 185-203 (2000).
  7. Demšar, U., Virrantaus, K. Space-time density of trajectories: exploring spatio-temporal patterns in movement data. International Journal of Geographical Information Science. 24 (10), 1527-1542 (2010).
  8. Kraak, M., Koussoulakous, A., Fisher, P. . A visualization environment for the space-time cube. , 189-200 (2004).
  9. MacEachren, A. M., Polsky, C., et al. Visualizing spatial relationships among health, environmental, and demographic statistics: interface design issues. , 880-887 (1997).
  10. Levory, M. Display of surfaces from volume data. IEEE Computer Graphics and Application. 8 (5), 29-37 (1998).
  11. Drebin, R. A., Carpenter, L., et al. Volume Rendering. Computer Graphics. , (1998).
  12. Lee, W., Krumm, J., Zheng, Y., Zhou, X. Trajectory preprocessing. Computing with Spatial Trajectories. , 3-34 (2011).
  13. Han, B., Comaniciu, D., et al. Sequential kernel density approximation and its application to real-time visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence. , (2007).

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