Detecting activity locations from raw GPS data: a novel kernel-based algorithm

International Journal of Health Geographics

Yan Kestens³  Basile Chaix¹  Benoit Thierry² 

[1] Université Pierre et Marie Curie-Paris6, Paris, UMR-S 707, France;Montreal University Hospital Research Center (CRCHUM), Pavillon Masson - 218 3850, St-Urbain, Montreal, H2W 1T7, Canada;Department of Social and Preventive Medicine, Université de Montréal, Montreal, Canada

关键词: Activity space;    MHealth;    Neighbourhood effects;    Kernel-based algorithm;    Activity location detection;    Global Positioning System (GPS);   

Others  :  810216
DOI  :  10.1186/1476-072X-12-14

 received in 2012-12-11, accepted in 2013-03-08,  发布年份 2013

【 摘 要 】

Background

Health studies and mHealth applications are increasingly resorting to tracking technologies such as Global Positioning Systems (GPS) to study the relation between mobility, exposures, and health. GPS tracking generates large sets of geographic data that need to be transformed to be useful for health research. This paper proposes a method to test the performance of activity place detection algorithms, and compares the performance of a novel kernel-based algorithm with a more traditional time-distance cluster detection method.

Methods

A set of 750 artificial GPS tracks containing three stops each were generated, with various levels of noise.. A total of 9,000 tracks were processed to measure the algorithms’ capacity to detect stop locations and estimate stop durations, with varying GPS noise and algorithm parameters.

Results

The proposed kernel-based algorithm outperformed the traditional algorithm on most criteria associated to activity place detection, and offered a stronger resilience to GPS noise, managing to detect up to 92.3% of actual stops, and estimating stop duration within 5% error margins at all tested noise levels.

Conclusions

Capacity to detect activity locations is an important feature in a context of increasing use of GPS devices in health and place research. While further testing with real-life tracks is recommended, testing algorithms’ performance with artificial track sets for which characteristics are controlled is useful. The proposed novel algorithm outperformed the traditional algorithm under these conditions.

【 授权许可】

   
2013 Thierry et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

【 参考文献 】

• [1]Hariharan R, Toyama K: Project Lachesis: Parsing and Modeling Location Histories. In Geographic Information Science. Volume 3234. Edited by Egenhofer MJ, Freksa C, Miller HJ. Heidelberg: Springer Berlin; 2004:106-124.
• [2]Ashbrook D, Starner T: Learning significant locations and predicting user movement with GPS. 2002, 101-108. [2002: IEEE]
• [3]Kang JH, Welbourne W, Stewart B, Borriello G: Extracting places from traces of locations. In Proceedings of the 2nd ACM international workshop on Wireless mobile applications and services on WLAN hotspots. Philadelphia, PA, USA: ACM; 2004:110-118.
• [4]Zheng Y, Zhang L, Xie X, Ma W-Y: Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of the 18th international conference on World wide web. Madrid, Spain: ACM; 2009:791-800.
• [5]Zhou C, Bhatnagar N, Shekhar S, Terveen L: Mining Personally Important Places from GPS Tracks. 526: 517; 2007:517-526. [Data Engineering Workshop, 2007 IEEE 23rd International Conference on: 17–20 April 2007 2007]
• [6]Cao X, Cong G, Jensen CS: Mining significant semantic locations from GPS data. Proc VLDB Endow 2010, 3(1–2):1009-1020.
• [7]Numri P: Identifying Meaningful Places. Helsinki: University of Helsinki; 2009.
• [8]Agamennoni G, Nieto J, Nebot E: Mining GPS data for extracting significant places. 2009, 855-862. [Robotics and Automation, 2009 ICRA '09 IEEE International Conference on: 12–17 May 2009 2009]
• [9]Biljecki F: Automatic segmentation and classification of movement trajectories for transportation modes. In: Workshop on Modelling Moving Objects. Informatics Institute, University of Amsterdam, Science Park; 2010.
• [10]Ashbrook D, Starner T: Using GPS to learn significant locations and predict movement across multiple users. Personal Ubiquitous Comput 2003, 7(5):275-286.
• [11]Xu R, Wunsch D II: Survey of clustering algorithms. Neural Networks IEEE Trans 2005, 16(3):645-678.
• [12]Liao L, Fox D, Kautz H: Extracting places and activities from gps traces using hierarchical conditional random fields. Int J Robot Res 2007, 26(1):119.
• [13]Kami N, Enomoto N, Baba T, Yoshikawa T: Algorithm for detecting significant locations from raw GPS data. In Proceedings of the 13th international conference on Discovery science. Canberra, Australia: Springer; 2010:221-235.
• [14]Silverman BW: Density Estimation for Statistics and Data Analysis. London: Chapman and Hall; 1986.
• [15]Levine N: Crime Mapping and the Crimestat Program. Geogr Analysis 2006, 38(1):41-56.
• [16]Gatrell ATB, Diggle P, Rowlingson B: Spatial point pattern analysis and its application in geographical epidemiology. Trans Inst Br Geogr 1996, 21:256-274.
• [17]James L, Matthews I, Nix B: Spatial contouring of risk: a tool for environmental epidemiology. Epidemiology 2004, 15(3):287-292.
• [18]Moreno B, Garcia-Alonso CR, Negrin Hernandez MA, Torres-Gonzalez F, Salvador-Carulla L: Spatial analysis to identify hotspots of prevalence of schizophrenia. Soc Psychiatry Psychiatr Epidemiol 2008, 43(10):782-791.
• [19]Kloog I, Haim A, Portnov BA: Using kernel density function as an urban analysis tool: Investigating the association between nightlight exposure and the incidence of breast cancer in Haifa, Israel. Comput Environ Urban Systems 2009, 33(1):55-63.
• [20]Carlos HA, Shi X, Sargent J, Tanski S, Berke EM: Density estimation and adaptive bandwidths: a primer for public health practitioners. Int J Health Geogr 2010, 9:39. BioMed Central Full Text
• [21]Almanza E, Jerrett M, Dunton G, Seto E, Ann Pentz M: A study of community design, greenness, and physical activity in children using satellite, GPS and accelerometer data. Health Place 2012, 18(1):46-54.
• [22]Worton BJ: Kernel Methods for Estimating the Utilization Distribution in Home-Range Studies. Ecology 1989, 70(1):164-168.
• [23]Kestens Y, Barnett T, Mathieu M-E, Thierry B, Lambert M: Monitoring and improving patients’ lifestyle behavior: Presentation of a spatio-behavioral application integrating GPS, accelerometer and heart rate data for a clinical lifestyle habits intervention. Washington DC: mHealth Summit; 2011.
• [24]Chaix B, Kestens Y, Bean K, Leal C, Karusisi N, Meghiref K, Burban J, Fon Sing M, Perchoux C, Thomas F: Cohort Profile: Residential and non-residential environments, individual activity spaces and cardiovascular risk factors and diseases--The RECORD Cohort Study. Int J Epidemiol 2011, 41(5):1283-1292.
• [25]Fuller D, Gauvin L, Kestens Y, Daniel M, Fournier M, Morency P, Drouin L: Use of a new public bicycle share program in Montreal. Canada Am J Prev Med 2011, 41(1):80-83.
• [26]Chaix B: Geographic Life Environments and Coronary Heart Disease: A Literature Review, Theoretical Contributions, Methodological Updates, and a Research Agenda. Annu Rev Public Health 2009, 30:81-105.
• [27]Cummins S: Commentary: Investigating neighbourhood effects on health–avoiding the ‘Local Trap’. Int J Epidemiol 2007, 36(2):355-357.
• [28]Kestens Y, Lebel A, Daniel M, Theriault M, Pampalon R: Using experienced activity spaces to measure foodscape exposure. Health Place 2010, 16(6):1094-1103.
• [29]Chaix B, Kestens Y, Perchoux C, Karusisi N, Merlo J, Labadi K: An interactive mapping tool to assess individual mobility patterns in neighborhood studies. Am J Prev Med 2012, 43(4):440-450.
• [30]Setton E, Marshall JD, Brauer M, Lundquist KR, Hystad P, Keller P, Cloutier-Fisher D: The impact of daily mobility on exposure to traffic-related air pollution and health effect estimates. J Expo Sci Environ Epidemiol 2010, 21(1):42-48.
• [31]Perchoux C, Chaix B, Cummins S, Kestens Y: Conceptualization and measurement of environmental exposure in epidemiology: Accounting for activity space related to daily mobility. Health PlaceIn Press(0)
• [32]Chaix B, Méline J, Duncan S, Merrien C, Karusisi N, Perchoux C, Lewin A, Labadi K, Kestens Y: GPS tracking in neighborhood and health studies: A step forward for environmental exposure assessment, a step backward for causal inference? Health Place 2013, 21:46-51.
• [33]Macintyre S, Ellaway A, Cummins S: Place effects on health: how can we conceptualise, operationalise and measure them? Soc Sci Med 2002, 55(1):125-139.
• [34]Cummins S, Curtis S, Diez-Roux AV, Macintyre S: Understanding and representing ‘place’ in health research: a relational approach. Soc Sci Med 2007, 65(9):1825-1838.
• [35]Hägerstrand T: What about people in regional science? Pap Reg Sci 1970, 24:7-24.
• [36]Yu H, Shaw J: Exploring potential human activities in physical and virtual spaces: A spatio-temporal GIS approach. Int J Geogr Inf Syst 2008, 22(4):409-430.
• [37]Rodriguez DA, Cho GH, Elder JP, Conway TL, Evenson KR, Ghosh-Dastidar B: Identifying Walking Trips From GPS and Accelerometer Data in Adolescent Females. J Phys Act Health 2011, 9(3):421-431.