ABSTRACT
Collection of GPS data is becoming a standard experimental method for studies ranging from public health interventions to studying the browsing behavior of large non-human mammals. However, the millions of records collected in these studies do not lend themselves to traditional geographic analysis. Standardized feature sets likely to produce distinct classes or clusters may be a tool that is powerful in both end-use utility and model describability. In this paper we present a feature set drawn from three different mathematical heritages: the convex hull of activity space, the fractal dimension of the recorded GPS traces, and the entropy rate of individual paths. We analyze these features against three human mobility datasets. Taken together these features can distinguish datasets with known demographic or geographic differences, while equating datasets which have similar demography and geography.
- C Bradford Barber, David P Dobkin, and Hannu Huhdanpaa. 1996. The quickhull algorithm for convex hulls. ACM Transactions on Mathematical Software (TOMS) 22, 4 (1996), 469--483. Google ScholarDigital Library
- Ronald N Buliung and Pavlos S Kanaroglou. 2006. A GIS toolkit for exploring geographies of household activity/travel behavior. Journal of Transport Geography 14, 1 (2006), 35--51.Google ScholarCross Ref
- Basile Chaix, Yan Kestens, Camille Perchoux, Noëlla Karusisi, Juan Merlo, and Karima Labadi. 2012. An interactive mapping tool to assess individual mobility patterns in neighborhood studies. American journal of preventive medicine 43, 4 (2012), 440--450.Google Scholar
- John D Corbit and David J Garbary. 1995. Fractal dimension as a quantitative measure of complexity in plant development. Proc. R. Soc. Lond. B 262, 1363 (1995), 1--6.Google Scholar
- DJ Coughlin, JR Strickler, and B Sanderson. 1992. Swimming and search behaviour in clownfish, Amphiprion perideraion, larvae. Animal Behaviour 44 (1992), 427--440.Google ScholarCross Ref
- Nathan Eagle and Alex Sandy Pentland. 2006. Reality mining: sensing complex social systems. Personal and ubiquitous computing 10, 4 (2006), 255--268. Google ScholarDigital Library
- Source Economic Geography, Urban Spatial, Systems Jan, Frank E Horton, and David R Reynolds. 2016. Effects of Urban Spatial Structure on Individual Behavior Author (s): Frank E . Horton and David R . Reynolds Stable URL: http://www.jstor.org/stable/143224 Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, avai. 47, 1 (2016), 36--48.Google Scholar
- Reginald G Golledge. 1992. Place recognition and wayfinding: Making sense of space. Geoforum 23, 2 (1992), 199--214.Google ScholarCross Ref
- Mohammad Hashemian, Dylan Knowles, Jonathan Calver, Weicheng Qian, Michael C Bullock, Scott Bell, Regan L Mandryk, Nathaniel Osgood, and Kevin G Stanley. 2012. iEpi: an end to end solution for collecting, conditioning and utilizing epidemiologically relevant data. In Proceedings of the 2nd ACM international workshop on Pervasive Wireless Healthcare. ACM, 3--8. Google ScholarDigital Library
- Jana A Hirsch, Meghan Winters, Philippa Clarke, and Heather McKay. 2014. Generating GPS activity spaces that shed light upon the mobility habits of older adults: a descriptive analysis. International journal of health geographics 13, 1 (2014), 51.Google Scholar
- Ethica Data Services Inc. 2018. Ethica Data. Retrieved April 30, 2018 from https://www.ethicadata.com/Google Scholar
- Renaud Lopes and Nacim Betrouni. 2009. Fractal and multifractal analysis: a review. Medical image analysis 13, 4 (2009), 634--649.Google Scholar
- Pauline C Ng and Steven Henikoff. 2003. SIFT: Predicting amino acid changes that affect protein function. Nucleic acids research 31, 13 (2003), 3812--3814.Google Scholar
- Nathaniel D Osgood, Tuhin Paul, Kevin G Stanley, and Weicheng Qian. 2016. A theoretical basis for entropy-scaling effects in human mobility patterns. PloS one 11, 8 (2016), e0161630.Google ScholarCross Ref
- Zachary Patterson and Steven Farber. 2015. Potential Path Areas and Activity Spaces in Application: A Review. Transport Reviews 35, 6 (2015), 679--700.Google ScholarCross Ref
- Tuhin Paul. 2018. LZ entropy rate calculation. Retrieved June 02, 2018 from https://github.com/tuhinpaul/lz_entropy_rateGoogle Scholar
- Tuhin Paul et al. 2017. Modeling Human Mobility Entropy as a Function of Spatial and Temporal Quantizations. Ph.D. Dissertation.Google Scholar
- Weicheng Qian, Kevin G Stanley, and Nathaniel D Osgood. 2013. The impact of spatial resolution and representation on human mobility predictability. In International Symposium on Web and Wireless Geographical Information Systems. Springer, 25--40. Google ScholarDigital Library
- Gavin Smith, Romain Wieser, James Goulding, and Duncan Barrack. 2014. A refined limit on the predictability of human mobility. In Pervasive Computing and Communications (PerCom), 2014 IEEE International Conference on. IEEE, 88--94.Google ScholarCross Ref
- Chaoming Song, Zehui Qu, Nicholas Blumm, and Albert-László Barabási. 2010. Limits of predictability in human mobility. Science 327, 5968 (2010), 1018--1021.Google Scholar
- Michael AP Taylor, Jeremy E Woolley, and Rocco Zito. 2000. Integration of the global positioning system and geographical information systems for traffic congestion studies. Transportation Research Part C: Emerging Technologies 8, 1-6 (2000), 257--285.Google ScholarCross Ref
Index Terms
- A feature set for spatial behavior characterization
Recommendations
Differentiating Population Spatial Behavior Using Representative Features of Geospatial Mobility (ReFGeM)
Understanding how humans use and consume space by comparing stratified groups, either through observation or controlled study, is key to designing better spaces, cities, and policies. GPS data traces provide detailed movement patterns of individuals but ...
Optimizing Feature Set for Click-Through Rate Prediction
WWW '23: Proceedings of the ACM Web Conference 2023Click-through prediction (CTR) models transform features into latent vectors and enumerate possible feature interactions to improve performance based on the input feature set. Therefore, when selecting an optimal feature set, we should consider the ...
Convex hulls of spheres and convex hulls of disjoint convex polytopes
Given a set @S of spheres in E^d, with d>=3 and d odd, having a constant number of m distinct radii @r"1,@r"2,...,@r"m, we show that the worst-case combinatorial complexity of the convex hull of @S is @Q(@__ __"1"=<"i"<>"j"=<"mn"in"j^@__ __^d^2^@__ __), ...
Comments