ABSTRACT Multi-day Global Positioning System (GPS) data are increasingly being used in research – including in the field of spatial epidemiology. We present several maps as ways to present multi-day GPS data. Data come from the NYC Low-Income Housing, Neighborhoods and Health Study ( n = 120).
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Participants wore a QStarz BT-Q1000XT GPS device for about a week (mean: 7.44, SD = 2.15). Our maps show various ways to visualize multi-day GPS data; these data are presented by overall GPS data, by weekday/weekend and by day of the week. We discuss implications for each of the maps.
Introduction Global Positioning System (GPS) data are the preferred method to map and understand people's spatial mobility. As such, multi-day GPS data are increasingly being used in research – including in the field of spatial epidemiology. Data on individual-level GPS have rapidly proliferated with the advent of GPS-enabled smart-phones, and the cost associated with obtaining and processing these data has come down considerably.
Substantively, GPS data are beginning to be used to overcome limitations of cross-sectional data analyzed using Geographic Information Systems (GIS) – including spatial misclassification ( Duncan, D. T., Kawachi, I., Subramanian, S., Aldstadt, J., Melly, S. J., & Williams, D. Examination of how neighborhood definition influences measurements of youths’ access to tobacco retailers: A methodological note on spatial misclassification. American Journal of Epidemiology, 179(3), 373– 381. Doi: 10.1093/aje/kwt251, ).
However, researchers and practitioners apply different methods for visualizing and mapping multi-day GPS data. Oftentimes, surprisingly researches in the field of spatial epidemiology do not visualize the GPS data collected and analyzed, because their goal is to evaluate relationships between GPS-derived activity spaces and health outcomes.
We note though that researches have utilized GIS-based geovisualization techniques to explore human activity-travel patterns ( Kwan, M. Feminist visualization: Re-envisioning GIS as a method in feminist geographic research. Annals of the Association of American Geographers, 92(4), 645– 661. Doi: 10.1111/1467-8306.00309,; Ren, F., & Kwan, M. Geovisualization of human hybrid activity-travel patterns. Transactions in GIS, 11(5), 721– 744.
Doi: 10.1111/j.1467-9671.2007.01069.x ), including special 3-D displays and algorithms tailored to variables in the urban environment confronting women to display space–time paths during the day ( Kwan, M. Feminist visualization: Re-envisioning GIS as a method in feminist geographic research. Annals of the Association of American Geographers, 92(4), 645– 661.
Doi: 10.1111/1467-8306.00309, ). GPS data are often presented in a map of GPS data, but visualization techniques are less often used to inform the reader of spatial patterns in the data. More often GPS data are simply quantified in a table – perhaps due to publication size limitations. However, these inherently spatial data can help researchers to identify unique phenomena when they are visually displayed. The visualization often allows for trends that were otherwise not apparent to appear which can then be tested for significance with various spatial statistics methodologies ( Pfeiffer, D., Robinson, T., Stevenson, M., Stevens, K.
B., Rogers, D. J., & Clements, A. Spatial analysis in epidemiology. New York, NY: Oxford University Press. In this study, we present several maps of ways to present multi-day GPS data to encourage researchers to map and visualize such data and explore these data visually in multiple ways. Conclusions In conclusion, there are many ways to visualize multi-day GPS data.
Here, we present GPS data in a variety of maps overlaid upon data from the city of New York's Open Data Initiative. In particular, in this study, for one participant, we visualized the aggregate total of GPS data as a density of points to illustrate not only GPS locations, but also duration spent in various locations, we also visualize data by day of the week, and data by weekday versus weekend.
While this current work represents a visualization and not formal data analysis, of interest are the differing mobility patterns between week and weekend days as represented in the and inset maps. Visualizing data in this way can help to formulate hypothesis about mobility patterns, and how individuals move through the built environment that may be of interest to public health researchers and practitioners. Of interest for instance was the weekday versus weekend inset, which perhaps can be explained as traveling further during the workday and closer by on weekends. We recognize that there are additional mapping options including mapping significant clusters of GPS points ( Vazquez-Prokopec, G. M., Stoddard, S.
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T., Paz-Soldan, V., Morrison, A. C., Elder, J. P., Kochel, T. J., Kitron, U. Usefulness of commercially available GPS data-loggers for tracking human movement and exposure to dengue virus. International Journal of Health Geographics, 8(1), 68.
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Retrieved from doi: 10.1186/1476-072X-8-68, ) as well as performing further analysis on rasterized data and displaying and identifying key locations visited as indicated by GPS data ( Xie, Z., & Yan, J. Kernel density estimation of traffic accidents in a network space. Computers, Environment and Urban Systems, 32(5), 396– 406. Doi: 10.1016/j.compenvurbsys.2008.05.001, ). In addition, there are time activity maps ( Seto, E.
Y., Knapp, F., Zhong, B., & Yang, C. The use of a vest equipped with a global positioning system to assess water-contact patterns associated with schistosomiasis. Geospatial Health, 1(2), 233– 241. Doi: 10.4081/gh.2007.271, ) and one could map all participants ( Seto, E. Y., Sousa-Figueiredo, J. C., Betson, M., Byalero, C., Kabatereine, N.
B., & Stothard, J. Patterns of intestinal schistosomiasis among mothers and young children from Lake Albert, Uganda: Water contact and social networks inferred from wearable global positioning system dataloggers. Geospatial Health, 7, 1– 13. Doi: 10.4081/gh.2012.99, ). Therefore, maps presented should align with the goals of the study or project.
We encourage researchers and practitioners to map and visualize GPS data in multiple ways. Software GPS participant data were downloaded using the Qstarz proprietary software and stored as.gpx files. The GPS data were then processed with several scripts built using the python programming language, and ArcGIS Models (Python Software Foundation. Python Language Reference, version 2.7. Available at: ) and ArcGIS version 10.2 (ESRI, Redlands, CA). GPS data were first converted from the open source.gpx file format into ESRI layers within a geodatabase, and then unique files were created for each participant and for various timestamps (day, week, weekday, weekend, and date).