Quantifying Distance Overestimation From Global Positioning System in Urban Spaces.

OBJECTIVES To investigate accuracy of distance measures computed from Global Positioning System (GPS) points in New York City. METHODS We performed structured walks along urban streets carrying Globalsat DG-100 GPS Data Logger devices in highest and lowest quartiles of building height and tree canopy cover. We used ArcGIS version 10.1 to select walks and compute the straight-line distance (Geographic Information System-measured) and sum of distances between consecutive GPS waypoints (GPS-measured) for each walk. RESULTS GPS distance overestimates were associated with building height (median overestimate = 97% for high vs 14% for low building height) and to a lesser extent tree canopy (43% for high vs 28% for low tree canopy). CONCLUSIONS Algorithms using distances between successive GPS points to infer speed or travel mode may misclassify trips differentially by context. Researchers studying urban spaces may prefer alternative mode identification techniques.

[1]  Philip S. Yu,et al.  Transportation mode detection using mobile phones and GIS information , 2011, GIS.

[2]  Kay W. Axhausen,et al.  Identifying trips and activities and their characteristics from GPS raw data without further information , 2008 .

[3]  Susan L Handy,et al.  How the built environment affects physical activity: views from urban planning. , 2002, American journal of preventive medicine.

[4]  Sean W. MacFaden,et al.  High-resolution tree canopy mapping for New York City using LIDAR and object-based image analysis , 2012 .

[5]  Scott Duncan,et al.  Using global positioning systems in health research: a practical approach to data collection and processing. , 2011, American journal of preventive medicine.

[6]  Zhen Liu,et al.  Performances of Different Global Positioning System Devices for Time-Location Tracking in Air Pollution Epidemiological Studies , 2010, Environmental health insights.

[7]  Catherine T. Lawson,et al.  A GPS/GIS method for travel mode detection in New York City , 2012, Comput. Environ. Urban Syst..

[8]  Chanam Lee,et al.  Assessment of wearable global positioning system units for physical activity research. , 2012, Journal of physical activity & health.

[9]  David Ogilvie,et al.  Use of global positioning systems to study physical activity and the environment: a systematic review. , 2011, American journal of preventive medicine.

[10]  Hans Kromhout,et al.  Performance of GPS-devices for environmental exposure assessment , 2013, Journal of Exposure Science and Environmental Epidemiology.

[11]  Nicole Edwards,et al.  Associations between park features and adolescent park use for physical activity , 2015, International Journal of Behavioral Nutrition and Physical Activity.

[12]  J. Irwin,et al.  The influence of the physical environment and sociodemographic characteristics on children's mode of travel to and from school. , 2009, American journal of public health.

[13]  Reid Ewing,et al.  Creating and validating GIS measures of urban design for health research. , 2009, Journal of environmental psychology.

[14]  Wei-Ying Ma,et al.  Understanding mobility based on GPS data , 2008, UbiComp.

[15]  P. Groves Shadow Matching: A New GNSS Positioning Technique for Urban Canyons , 2011, Journal of Navigation.

[16]  Muhammad Awais Use of acceleration data for transportation mode prediction , 2015 .

[17]  Scott Duncan,et al.  Dynamic Accuracy of GPS Receivers for Use in Health Research: A Novel Method to Assess GPS Accuracy in Real-World Settings , 2014, Front. Public Health.

[18]  Paul A. Zandbergen,et al.  Accuracy of iPhone Locations: A Comparison of Assisted GPS, WiFi and Cellular Positioning , 2009 .

[19]  M. Duncan,et al.  Portable global positioning system receivers: static validity and environmental conditions. , 2013, American journal of preventive medicine.