1. Seasonal Decomposition of Cell Phone Activity Series and Urban Dynamics Blerim Cici, Minas Gjoka, Athina Markopoulou, Carter T. Butts 1

2. Complex Urban Environments o “Urban Dynamics” –Social & Economic activities –Social Interaction o Existing Methods to understand cities: –Surveys Expensive Take time 2

3. Mobile Phone Activity & Urban Dynamics o Mobile phone activity –Human Mobility –Large Population –No Extra Cost o Aggregated CDR –Easier to Manage –Facilitates Data Sharing o What they tell about urban dynamics ? 3

4. Milano CDR Dataset o Big Data Challenge –Aggregate CDRs –Telecom Italia o City: – Milano –100x100 grid –Duration: 4 weeks o Activity in grid-square: –Total Calls and SMS 4 Video snapshot for heatmap activity, Milan - Nov.1 st 2013 Dataset “Telecommunications – SMS, Call, Internet – MI”

5. Related Work o CDRs: –Behavioral Traces o Previous work: –Requires cultural knowledge: Weekdays vs. Weekends e.g. [Soto et. al. HotPlanet, 2011] –Systematic component only (e.g. typical days) e.g. [Soto et. al. HotPlanet, 2011], [Toole et. al. UrbComp, 2012] o Our work: –Principled method to extract systematic components –Go beyond systematic components Work with data previously considered as noise 5

6. Original - SCS FFT High-amplitude Decomposition Overview Hypothesis: Regular Patterns Hypothesis: Irregular Patterns 6

7. Clustering with SCS (1) o Our Goal: –Segment city into distinct areas o Hierarchical clustering –Easily Interpretable Dendrogram –Generality o Distance function: –Pearson correlation 7

8. Clustering with SCS (2) 8 o Low-skewness segmentation: –Comparable sized clusters

9. SCS clusters vs. ground truth *Ground truth from Milano Public data: Residential, Universities, Businesses, Bus stops, green areas, etc (http://dati.comune.milano.it/) 9

10. SCS clusters compared to state-of-the-art CategoryEntropy [Soto et. al. HotPlanet, 2011] Entropy for hierarchical SCS clustering Universities0.970.96 Green (%)1.270.94 Businesses1.330.82 Population1.340.97 10

11. How to use RCS o Residual Communication Series (RCS) –(Original – SCS) 11 o Hypothesis: –RCS captures how squares affect each other

12. Maximal regions of mutual influence o DiGraph, G(V,E): –Nodes: grid-squares –Edges: Lagged correlation (lag = 1) We keep only the strongest (5σ) o Strongly connected components of G: –Areas subject to mutual social influence 12

13. Validation of RCS 2 o Square-to-Square traffic matrix [T] –Source: Mi-to-Mi data –How much various areas of the city talk to each other o Quadratic Assignment Procedure (QAP) –Testing for correlation with [T] against a null hypothesis QAP test resultsSCSRCS Correlation0.050.27 Min random-0.018-0.005 Mean random00 Max random0.0110.004 13

14. Conclusion and Future Work o RCS and SCS –Distinct Probes of Urban Dynamics –Obtained from the same underlying data. o Future Work: –Apply technique to more cities –Apply technique to geo-social activity data (e.g. Foursquare, Twitter) –Use current findings to activity prediction 14

15. Questions ? o More info: –“On the Decomposition of Cell Phone Activity Patterns and their Connection with Urban Ecology”, to appear in MobiHoc 2015. o Contact Info: –bcici@uci.edubcici@uci.edu Video snapshot for heatmap activity, Milan - Nov.1 st 2013 Dataset “Telecommunications – SMS, Call, Internet – MI” 15 http://tinyurl.com/cdr-decomposition