Agarwal , Rachit

Georgantas, Nikolaos

Issarny, Valérie

Agarwal, Rachit

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HAL Id: hal-01923856https://hal.inria.fr/hal-01923856Submitted on 15 Nov 2018HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.Impact of Spatial Scale on Mobility ParametersRachit Agarwal, Nikolaos Georgantas, Valérie IssarnyTo cite this version:Rachit Agarwal, Nikolaos Georgantas, Valérie Issarny. Impact of Spatial Scale on Mobility Parameters.NetSci 2018 - International Conference on Network Science, Jun 2018, Paris, France. Netsci 2018:Book of Abstracts (Posters - Social). ￿hal-01923856￿ÉQUIPE:MiMovehttps://mimove.inria.frInria-Paris2 Rue Simone Iff, Paris, 75589Research Supported by:Impact of Spatial Scale on Mobility ParametersRachit Agarwal+, Nikolaos Georgantas, Valerie Issarny{rachit.agarwal, nikolaos.georgantas, valerie.issarny}@inria.fr+	Corresponding	AuthorThe	authors	would	like	to	thank	Ambiciti - http://ambiciti.io - for	its	support	and	granting	us	access	to	the	specific	data	as	a	part	of	research	collaboration.• Huge body of work focusing on the understanding of human mobility characteristics [1] utilizing various types of data [2]• How spatial scale affect the human mobility parameters is still the question? • Positive lognormal distribution fits best (See Figure 2 and Table 1) both jump length and Rg• As expected spatial scale affects the distribution parameters• Other results: • Pause time also follow positive lognormal distribution but bias present due to nature of the dataset (crowdsourced dataset) used.• Distribution of max and min jump length from users also follow positive lognormal distribution with parameters µ=9.95 σ=2.75 and µ= 3.68 σ= 1.70 respectively at fine grain scale.The findings reported here will be leveraged for revisiting the identified parameters, based on further analysis using complementary datasets.[1] L. Alessandretti, P. Sapiezynski, S. Lehmann, and A. Baronchelli, “Multi-scale Spatio-Temporal Analysis of Human Mobility,” PLOS ONE, vol. 12, pp. 1–17, 02 2017.[2] D. Karamshuk, C. Boldrini, M. Conti, and A. Passarella, “Human Mobility Models for Opportunistic Networks,” IEEE Communications Magazine, vol. 49, pp. 157–165, December 2011.[3] V. Issarny, V. Mallet, K. Nguyen, P. Raverdy, F. Rebhi, and R. Ventura, “Do’s and Don’ts in Mobile Phone Sensing Middleware: Learning from a Large-Scale Experiment,” in Proceedings of the17th International Middleware Conference, (Trento), pp. 17:1–17:13, December 2016.[4] M. Gonzalez, C. Hidalgo, and A. L. Barabasi, “Understanding Individual Human Mobility Patterns,” Nature, vol. 453, pp. 779–782, June 2008. MOTIVATIONDATA COLLECTIONDATA PROCESSING• Perform data cleaning and labeling:• Clean by removing data points having location as (0,0) identified in private mode.• Associate each data point to arrondissements and cities in France towards spatial scales.• Generate paths at three scales using cleaned data. The cleaned dataset has 5629 user with 25,787,201 valid samples collected from 01/07/2015 to 30/09/2017• Identify PDF for Jump length and Radius of Gyration (Rg) [4].• Identify best fit using KS test• Identify KL divergence between distributions identified at different scales. RESULTSCONCLUSION/FUTURE WORKBIBLIOGRAPHYFIGURE 2: Log-Log plot of best fit distribution for jump-length and Rg at different spatial scales with different values of xmin. Toprow (Jump length): (a) fine grain samples, (b) samples mapped to arrondissements, (c) samples mapped to Ile-de-France cities.Bottom row (Rg): (d) fine grain samples, (e) samples mapped to arrondissements, (f) samples mapped to Ile-de-France cities.TABLE 1: Distribution fit with identified parameters.TABLE 2: KL divergence between identified distributions at different spatial scale.FIGURE 1: Ambiciti application (formerly called as SoundCity) monitors the environmental pollution the user is exposed to [3].xmin in meterspower law positive log-normal exponential truncated power lawα D µ σ D λ D α β in km DJumplengthfine grain 1.33201.164991.283490.320320.136336.311355.673132.345992.797910.049680.021190.383×10−40.366×10−40.654650.651561.000000000471.185496098943077600.185700.08300arrondissements 1.33201.103811.144470.499390.436749.9177939.9177951.576351.576410.0737760.0737701.550×10−51.551×10−50.233630.233791.000000000081.000000000818236300.523360.41185cities 101251.156511.258820.516250.436818.692078.692010.932150.932320.0563470.0563191.033×10−41.045×10−40.105790.106891.00000000111.0000000018881530.556260.40801Rgfine grain 1.33501.117771.172990.306890.275228.695919.591493.512292.619340.069450.050535.695×10−65.004×10−60.477780.435081.000000007331.00000000109243020040.240180.16950arrondissements 1.33501.101431.160460.469310.3863110.1440510.143731.577911.578440.0763560.0763471.568×10−51.571×10−50.163480.163761.000000006491.000000045088125570.512680.36802cities 109001.165491.586620.460700.253648.345098.462240.980440.826460.058470.022511.599×10−41.733×10−40.086370.047241.000000005451.0000000736849160.507140.19361fine grain arrondissements cities xmin in metersJump lengthfine grain 0 4.12646616206 8.14647628506 20arrondissements 7.7901952234 0 1.26852900653 20cities 1.23656465376 0.502427101463 0 125Rgfine grain 0 0.431600843735 4.30231619411 50arrondissements 0.210279300944 0 2.74652057699 50cities 0.79623349401 0.851536328754 0 900

Impact of Spatial Scale on Mobility Parameters

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Impact of Spatial Scale on Mobility Parameters

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