AbstractThe relationship between built environment and behavior is a current focus in a number of disciplines, ranging from epidemiology and public health to transportation planning to ubiquitous computing. While the importance of understanding this relationship has been clearly identified, objective empirical measurement and modeling frameworks for operationalizing the study of these relationships are relatively few. A methodology for measuring movement, and activity, and location in real space-time has been developed and tested by an interdisciplinary team in Urban Design & Planning and Computer Science & Engineering. Preliminary data collection, standardization, and cleaning has been completed for a pilot study of 53 individuals over a one-week interval. The space-time measurement framework provides data that will be used to investigate patterns of movement as they relate to built environment features.

BEST MoveS: Built Environment Space-Time Movement Study

P. Hurvitz*, J. Lester†, A. Moudon*, G. Borriello§*University of Washington College of Built Environments: Urban Design & Planning

†University of Washington College of Engineering: Electrical Engineering§University of Washington College of Engineering: Computer Science & Engineering

1. SamplingA convenience sample of UW students, staff, and faculty was obtained by posting e-mail messages on departmental e-mail lists. The sample was fairly young, >60% male, well-educated, mostly white, with a bimodal income distribution; females were thinner than males (Figure 1).

Figure 1: Sample demographics

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AcknowledgmentsThis research was funded partially by the University of Washington Royalty Research Fund and the University of Washington Exploratory Center for Obesity Research

2. Data CollectionActivity and location were measured using a novel device, the Multi-Sensor Platform (MSP), which simultaneously measured accelerometry, audio, IR/visible light, high-frequency light, barometric pressure, humidity, temperature, and compass bearing (Lester, Choudhury et al. 2005). The MSP stored raw data on an internal SD card, and was worn on the belt (Figure 2). Self-reported activity diary entries were recorded hourly using the MyExperience tool (Froehlich, Chen et al. 2007) running on a Windows Mobile cell phone. Data were collected for a one-week period for 53 subjects over six months (see Figure 4 for data collection times for a single subject).

Figure 2: The MSP Figure 3: Cell phone and example ofhourly survey questions

Subject 03

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8.656.043.336.156.139.520.7

2007-11-302007-11-30 2007-12-012007-12-01 2007-12-02 2007-12-03

Figure 4: Data collection times for asingle subject

3. Initial Data ProcessingInitial data processing has occurred in Y distinct stages:1. Conversion and processing (performed by the CSE team), converts binary MSP files into separate data axes and

performs initial classification of 1 s interval data “moving (XY space) vs. stationary” and “up/down (Z space) vs. stationary” (Figure 5). Data were converted to comma-separated values ASCII files (Figure 6). Platform: MatLab on Ubuntu Linux

2. Lumping individual points into separate space-time bout vectors representing “trip,” “stop,” and “dwell” episodes. Platform: R on RedHat Linux/VMWare

3. Conversion of tabular representation of bouts to geometry-enabled open source SQL tables, export to ESRI-format shapefiles. Platform: PostgreSQL/PostGIS on RedHat Linux/VMWare

4. Manual editing of shapefile data to remove obviously erroneous coordinate locations. Platform: ArcGIS on Windows XP

Figure 5: MSP data axes

MSP movement datalongitude latitude unixtime updown moving-122.3130 47.67996 1209107889 d s-122.3135 47.68024 1209107890 d s-122.3135 47.68023 1209107891 d s-122.3135 47.68026 1209107892 d s-122.3135 47.68025 1209107893 d s-122.3135 47.68025 1209107894 d s

GPS datalongitude latitude altitude unixtime h_pos_err v_pos_err climb_rate svs hdop-122.3130 47.67996 84.40 1209107889 31.16 5.55 0.03 3 14.0-122.3135 47.68024 76.60 1209107890 13.72 4.73 -0.10 4 6.4-122.3135 47.68023 76.50 1209107891 12.25 4.67 -0.11 3 5.0-122.3135 47.68026 77.66 1209107892 11.29 4.65 -0.07 4 6.4-122.3135 47.68025 76.95 1209107893 10.83 4.65 -0.11 4 6.4-122.3135 47.68025 76.52 1209107894 10.95 4.66 -0.15 4 6.4

Figure 6: CSV data excerpts

4. Secondary Data ProcessingTo date, research activities have focused on data collection, processing, automation, cleaning, and conversion. Examples of processing include automated cleaning of raw GPS values for “dwell” locations (Figures 7 & 8) and extraction of individual “trips” from processed XYZ accelerometry/barometry data (Figure 9).

Figure 7: “Dwell” coordinates represented as raw GPS trace (multiple bouts)

Figure 8: “Dwell” occurrences extracted from raw GPS trace, unweighted mean centroids

Figure 9: “Trips” extracted from processedaccelerometry values

5. Next StepsDevelopment is currently underway to extract simple activity classes (e.g., walking, sitting, running, cycling, driving) from the MSP data. “Training” data will be used with Hidden Markov models with Decision Stumps to probabilistically classify second-by-second measures into activity types.After completion of data cleaning and activity processing, different types of trips, stops, and dwells will be associated with objective measures of the built environment obtained from GIS data sources. Several different spatial and temporal patterns and associations will be investigated, e.g.,

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• Total area of individual spatial realm of activity, land use, residential & employment density

• Land use differences of origins and destinations among trips of varying length, duration, and mode

• Temporal patterns (e.g., time of day, duration) of different travel modes

• Spatial realm sizes by different travel modes

• Activity type and composition & configuration of land use

• Amount of time spent in different activities and demographic characteristics & built environment patterns of home neighborhood

ReferencesFroehlich, J., M. Chen, et al. (2007). Myexperience: A system for in situ tracing and capturing of user feedback on mobile phones. MobiSys 2007, San Juan, Puerto Rico.Lester, J., T. Choudhury, et al. (2005). A hybrid discriminative/generative approach for modeling human activities. Nineteenth International Joint Conference on Artificial Intelligence (IJCAI).