A Computational Movement Analysis Framework For Exploring Anonymity In Human Mobility Trajectories

Transcription

1 A Computational Movement Analysis Framework For Exploring Anonymity In Human Mobility Trajectories Jennifer A. Miller 2015 UT CID Report #1512 This UT CID research was supported in part by the following organizations: identity.utexas.edu

2 A COMPUTATIONAL MOVEMENT ANALYSIS FRAMEWORK FOR EXPLORING ANONYMITY IN HUMAN MOBILITY TRAJECTORIES Background Advancements in tracking technologies such as global positioning systems (GPS), radio frequency identification (RFID), cellular phone networks, and WiFi hotspots have resulted in significant increases in the availability of highly accurate data on moving objects, with unprecedented high spatial and temporal resolution. Within geographic information science (GIScience), computational movement analysis (CMA) has recently emerged as a subfield that focuses on the development and application of computational techniques for collecting, managing, and analyzing movement data in order to better understand the processes that are associated with them (Gudmundssen et al. 2012). As these technologies facilitate the collection of near-seamless (in some cases sub-second) movement tracks, the spatiotemporal footprint of an individual s movement can be explored using CMA techniques. These location data are often studied as trajectories, comprised of a series of timestamped sequential locations. Depending upon the collection method, the location information can be represented by precise latitude and longitude coordinates (e.g., GPS data from a smartphone or other device) or the unique catchment area of a single cellular tower (e.g. call detail records from cellular phones). These relatively low cost location data are used to explore human mobility patterns related to, for example, urban planning, transportation infrastructure, disaster planning/evacuation strategies, potential disease spread, and many other applications (Becker et al. 2013). The ability to study human mobility and issues related to interaction with the environment or other individuals, and the behaviors these interactions suggest has been greatly enhanced by technological advancements that facilitate the collection of high quality location data at unprecedented spatial and temporal resolutions. However, as often happens with technological advancements, the collection of these data has preceded extensive study on how and what they can (or should) be used for, as well as the privacy implications associated with distributing information on an individual s location. The research presented here explores issues related to privacy and identity associated with more recently available high resolution GPS location data. The analysis focuses on using methods from movement pattern analysis and spatial statistical methods to address the following issues: Can activity hot spots be identified from movement data and how can their spatiotemporal structure be explored?

3 How unique are anonymized movement trajectories? How is their uniqueness affected by spatial and temporal resolution? Can movement characteristics such as speed be used to uniquely characterize trajectories? Using movement pattern analysis to identify potential activity hot spots from GPS trajectory data: a case study using taxicab data in San Francisco. An important application using location data involves exploring the spatiotemporal pattern of activity they represent. Previous examples focused predominantly on call detail records (CDR) that were aggregated to their nearest cellular tower (see Gao 2015 for review). Spatial autocorrelation analysis was used to identify source areas with more outgoing calls and sink areas, where more incoming calls occurred. More recently, GPS data have been used to explore movement activity of taxis in Shanghai (Deng and Ji 2011), taxis in New York City (Qian et al. 2015), and cement trucks in Athens (Orellana et al. 2010). While there are certain predictable spatial patterns of taxicab location and movement related to city structure (e.g. greater activity in central business district) or time of day (e.g. towards and away from CBD in morning and evening, respectively), there are also stochastic elements associated with other factors that can often be related to ephemeral activities and passenger behaviors. I hypothesized that the spatiotemporal structure of the collective movement of the taxicabs could be used to infer points-of-interest (POI) or activity hot spots, and that some hot spots would emerge or disappear depending on the time of day. Research questions: How can movement pattern analysis and spatial statistics be used to identify collective points of interest from GPS location data? How can the spatiotemporal structure of these movement activities be explicitly analyzed and visualized? Data San Francisco Cab Dataset ( I used 40 cabs and extracted data for one weekday (Wednesday June 4, 2008) to examine how movement analysis and spatial statistics can be used to explore potential points of interest (POI). The temporal resolution was approximately 1 minute. GPS locations for each of the 40 cabs were partitioned into one of three temporal bins: morning (7-10 am, n = 4634), afternoon (4-7 pm, n = 6009), and evening (9pm-12 midnight, n = 6087). Methods Two different methods were used to explore hot spot activities: the first method involved aggregating the taxi locations to a 250 meter x 250 meter square (size was selected because it is greater than 1 city block but less than 2 blocks) for a subset of downtown San Francisco (peak activity). The number of taxi locations for each square and for each of the three time periods (morning, afternoon, evening) was counted and analyzed using global and local Moran s I.

4 Where x is the count of taxi cabs and wij is the spatial weights matrix used to represent what is near. I used both 1st and 2nd order (row-standardized) contiguity for spatial weights matrix here. Moran s I ranges from [-1] indicating extreme negative spatial autocorrelation to [+1], indicating extreme positive spatial autocorrelation, with values near 0 indicating no autocorrelation. Anselin (1995) introduced a local statistics that decomposed the global Moran s I to a local measure (LISA- local indicator of spatial autocorrelation) as: Where a value is calculated for each observation. A single statistic is no longer reported with LISA, but the values can be mapped and the spatial distribution of spatial autocorrelation can be explored. Figures 2-4 show the local spatial autocorrelation of the taxicabs for the morning (fig. 2), afternoon (fig. 3), and evening (fig. 4) time periods, along with the raw counts. There is a core of relatively high counts in the upper right of the study that is maintained for all time periods, but the magnitude of this core is different for each time period, ranging from a small cluster of high-high values in the morning (fig. 2b) to the largest cluster for the afternoon (fig. 3b). While the global Moran s I was positive and statistically significant for all time periods (using Monte Carlo permutations (n=499), indicating that the overall pattern was near values were similar to each other, there were outliers for each time period. There were 7 high-low cells in the morning- cells (fig. 2b) that had a high count surrounded by neighbors with low cells- which could indicate an isolated area of high activity. Additionally, a single statistically significant positive value for (global) Moran s I indicates overall positive spatial autocorrelation, but cannot differentiate between clusters of high values and clusters of low values. Mapping the local Ii values illustrates that, in addition to the core of high taxi activity, there is a core of low taxi activity in the bottom left for all time periods, as well as pockets of negative spatial autocorrelation (high-low and low-high).

5 The local statistic LISA can be further extended to measure cross-correlation between the value of a variable for a target cell compared to the lagged value of a different variable for its neighbors (in equation 2.0 above, the x variables on the right side of the equation would represent a different variable). Bivariate LISA statistics are particularly useful for studying the change in a variable across time periods (Anselin et al ). Figure 5a shows the bivariate LISA for morning counts as the target compared to the neighboring cells counts for the afternoon. A high-high and low-low cells would be interpreted as an area of high or low activity, respectively, across both time periods, while a low-high would identify a cell that had low activity in the morning compared to high activity among its neighbors in the afternoon. Conversely a high-low would indicate a cell that had high morning activity compared to the low afternoon activity of its neighbors. The low-high cells fringing the CBD show that the area of activity increases from morning to afternoon. Figure 6a shows the afternoon-evening pattern, where a hot spot emerges in the southeastern part of the study area near a major freeway (Bayshore). Figure 7a compares morning counts to evening, and this area is a large hot spot, confirming that it is an area of high activity in the morning and evening, but relatively low activity in the afternoon. A high-low cell here represents an area of high activity in the morning that is less active in the evening and low-high is cells for which activity is higher in the evening compared to morning. A more recent extension to bivariate LISA is the directional Moran scatter plot, which allows for better visualization of the dynamics between changing spatial patterns across time periods (Rey 2014). The directional LISA shows the movement of the statistics across two time periods, and therefore incorporates information from two different Moran scatterplots. For example, figure 5b shows the change in LISA statistic for each cell from morning to afternoon: each vector starts in its position for morning (from figure 2b) and ends in its position for afternoon (from figure 3b). The small arrows in the top left lowhigh quadrant represent cells that were low activity surrounded by high activity in both morning and afternoon. Figure 6b shows that there was much more variation in LISA statistics in afternoon and evening. The vector that is highlighted with a yellow star represents a cell that was a cold spot, or area of low activity in the afternoon, but became a hot spot in the evening. In addition to measuring the spatial pattern of aggregated counts to identify likely activity hotspots, a more novel method involves measuring the spatial autocorrelation of movement parameters, specifically speed. Orellana and Wachowicz (2011) used LISA to analyze pedestrian movement in order to uncover movement suspension (low-low clusters) they suggested would indicate points of interest or activity hotspots. After testing different nearest neighbor spatial weights matrices, one that considers only the 10 closest neighbors to be near was used to measure spatial autocorrelation for all points within each of the three time periods. As the variable of interest is now speed, a low-low could be used to suggest an area of interest or a hot spot, while high-high points would most likely be associated with freeways. Low-highs and high-lows would likely be difficult to disentangle from variable traffic patterns (e.g. stop and go traffic). Figure 8a shows the

6 pattern for the morning period (fig. 8b is zoomed in to downtown San Francisco). Figures 9a and 9b are for the afternoon period and figures 10a and 10b are evening. The spatial distribution of these suspended movement areas represented by low-lows varies with the time of day. In order to more easily visualize the differences in the spatial distribution of low-lows, kernel density of just the low-low points was calculated and a home range or area where most of them was occurred was extracted. While the afternoon and evening low-low clusters are in similar places, the morning low-low clusters extend farther downtown. Also, the afternoon and evening clusters at the bottom right represent the San Francisco Airport, where there was less slow speed among taxi cabs in the morning period. Examining the uniqueness of human mobility trajectories: a case study using smartphone data from Beijing Aggregating counts of GPS locations to a larger area approximates the spatial resolution available when these studies were done with cell phone towers and call counts were aggregated to the Voronoi polygon drawn around each cellular tower. However, there are important privacy issues associated with dealing with actual GPS locations that are often overlooked. Location data are often released after they have been anonymized which means that the trajectory has been stripped of any obvious identifying information such as name, address, phone number, etc. However, personal points of interest (home, work) can still be identified by mining trajectory data for movement patterns, and these points of interest are often associated with unique individuals. Additional locations may be resolved that could have negative implications (ex. repeated visits to a medical clinic may be a cause for concern for employers). Due to data availability, most of the previous work on unicity or measuring the uniqueness of movement traces or trajectories has been with much coarser scaled cell phone data. Surprisingly, even relatively coarse spatial resolution location data such as that associated with call detail records (CDR), where location is an area defined by its proximity to a specific cell phone tower, can be used to uniquely identify an individual. Locations of cell phone towers or antennae are based on population density and the area associated with each one varies considerably. In their study in a small European country, de Montjoye et al. (2013) found that the reception or catchment area for an antenna ranged from 0.15 km2 in urban areas to 15 km2 in rural areas. Zang and Bolot (2011) used anonymized CDR from 25 million individuals across the U.S. to determine the top N locations at which calls were recorded for each of three months. They found that when N = 2 (typically corresponding to work and home), they found that up to 35% of the individuals could be uniquely identified. When N = 3 (they suggested the 3rd location typically represented a school or shopping related location), 50% could be uniquely identified. In their seminal study, de Montjoye et al. (2013) used fifteen months of anonymized mobile phone data (CDR) for 1.5 million individuals in a western European country and found that

7 four randomly selected spatiotemporal points were sufficient to uniquely identify 95% of the individuals. Perhaps more troubling, they found that over 50% of individuals were uniquely identifiable from just two randomly selected locations (typically also corresponding to home and work). Song et al (2014) found similar results with a dataset of one week of mobility data for 1.14 million people (total 56 million records): with just two random points, 60% of the trajectories were unique. It is important to note that uniqueness does not equate to re-identifiability and the objectives of these studies were to examine how unique individual trajectories were, not to actually deanonymize them or re-attach an individual s information to a unique trajectory. However, the ability to determine uniqueness of trajectories is an important prerequisite for re-identification (which would involve correlation with an ancillary dataset) and therefore, represents a potential threat to individual privacy. The degree of uniqueness of trajectories can vary as a function of factors such as typical commuting patterns, transportation modes, and geographical region (which affects commuting patterns and transportation modes). There have been several methods proposed to quantify the anonymity of a database. The most commonly used method of k- anonymity was introduced by Sweeney (2002) as a measure to increase anonymity for non-spatial databases. When applied to spatial databases, it ensures that any set of records (locations) for an individual is at least the same as k-1 individuals. Generally, k = 2, ensuring that at least two trajectories are equivalent, but as k increases, so too does the anonymity. Extensions of k-anonymity include l-diversity and t-closeness (Li et al. 2007). These measures are generally used to manage trajectory datasets (i.e., data would be manipulated so that the level of anonymity reached the reported k level), but in order to quantify the actual level of anonymity of trajectory datasets, a rigorous analysis comparing random points from each trajectory to all other trajectories has to be conducted. With trajectory datasets now available at one second intervals, the volume of these data can result in computationally intensive analysis. Montjoye et al (2013) measured unicity as the percentage of 2500 random traces that were unique give p random points (p ranged from 2 to 5). Song et al (2014) defined uniqueness of trajectories as the percentage of all available trajectories that were uniquely associated with p random points, which they varied from 2 to 6. While anonymity (or lack thereof) has been studied with CDR data, as the previous examples show, it has not yet been addressed with finer spatiotemporal resolution available as GPS locations from, e.g., smartphones. These datasets could potentially be far more unique and therefore more difficult to anonymize. In this study, I do an extensive study of the unicity of GPS movement trajectories testing the effect of spatial resolution and temporal resolution. In addition to location, I also explore how effective movement parameters such as speed could be for uniquely identifying a trajectory. This is one of the first studies to measure unicity of trajectories composed of GPS locations.

8 I hypothesized that the unicity of trajectories will be greater for GPS locations than the coarser scaled CDR locations. I also hypothesized that the attenuating effects of coarsening spatial and temporal resolution will have less impact than they would with CDR locations. I expect that speed will also be effective for uniquely identifying a trajectory when several data points are used. Research questions: How can anonymity be quantified for different types of trajectories and how is it affected by spatial and temporal resolution? Can movement characteristics such as speed be used to uniquely characterize a trajectory in the absence of actual locations? Data Microsoft GeoLife Trajectories ( This is an extremely dense dataset, with temporal resolution of ~1-5 seconds and spatial resolution of ~5-10 meters. We used only one year of data (January December 2009) and used a spatial mask of Beijing (39.6 to 40.2 N latitude), (116 to E longitudes) to remove users who traveled outside of the city during this time period. This resulted in 71 users who had a total of 7,243 daily trajectories (number of locations visited within trajectories varied but the mean was 1600). Methods The basis of our unicity test involved extracting 500 sets of points of size n from each user and counting how many other trajectories they are found in. The percentage of 500 sets of points that matched only one trajectory was calculated and this was done for each of 71 users for four different point sizes (n = 2, 3, 4, and 5). Our measure of unicity, u, is the percentage of 500 random points of size n that are contained in only one trajectory averaged across all 71 users. A unicity value close to 100 indicates a highly unique trajectory that could theoretically be deanonymized, or re-connected with identifying user information more easily; a low unicity value suggests that the random set of points are contained in several different trajectories and therefore would make de-anonymizing trajectories far more challenging. The amount of information from each point was variedwe used just location (x and y), location and time (x,y, and t), and the absolute angle (the absolute angle for point i is measured between the x direction and the step built by relocations i and i + 1). The original latitude and longitude coordinates for these locations have a spatial precision of six decimal places (~0.1 m). In order to test how spatial and temporal resolution affected measurement of unicity, the geographic coordinates were coarsened first to four decimal places (~10m) and the temporal resolution was coarsened to 30 seconds, then further coarsened to three decimal places (100 m) and 60 seconds. Additionally, the precision of

9 the absolute angle measure was decreased from the original (five decimal places) to three decimal places. Figure 12 displays the trajectories for two different users for a single day. The zoomed in subset helps to illustrate the importance of location precision- there are several locations that would be the same for both users if the location precision was coarsened ten- or 100- fold. Figure 13 shows three different users daily trajectories in a space-time cube. The red and green users overlap in time, but not space, while the red and blue users overlap in space but not time. The use of all three pieces of coordinate information- x, y, and t- can be extremely important for uniquely associating a single trajectory. Results Table 1 shows the unicity values associated with size of each random point set. The mean was the average unicity across all 71 users, while the minimum and maximum show the variation in unicity among users. In general, the locations of points on a trajectory were highly unique. 90% of the random sets of just two points composed of only location (no timestamp) were associated with only one trajectory. Adding the timestamp increased the unicity of two points to 97%. When five points with location and timestamp were used, the unicity increased to almost 99%. Somewhat surprisingly, the angle of movement alone has fairly high unicity when the angle of three points are tested, the unicity is similar to the unicity of location for CDR as found in de Montjoye et al. (2013) and Song et al. (2014). Five angle values could uniquely identify a trajectory 73% of the time. Table 1: unicity results for location (x,y), location and time (x,y, t) and the absolute angle of a point. Means and ranges are reported for 500 sets of random points for each of 71 users. Unicity values for coarsened location, time, and absolute angle are shown in table 2. When just two points (no timestamp) are used at the coarser resolution (spatial precision reduced tenfold to ~10 m), only 68.5% of the time are the points associated with a unique trajectory. When the 30 seconds less precise timestamp is added, the unicity is similar to the original resolution, and with five points with location and timestamp, unicity increases to ~94%. The unicity of the absolute angle degrades substantially even using a set of five points results in less than 5% unicity.

10 Table 2: unicity results for location (x,y), location and time (x,y, t) and the absolute angle of a point. Spatial resolution has been coarsened to 4 decimal places; temporal resolution has been coarsened to 30 seconds; and absolute angle coarsened to three decimal places. Means and ranges are reported for 500 sets of random points for each of 71 users. Table 3 shows unicity values for the coarsest location coordinates: the spatial resolution of an x/y pair is now ~100 m and the temporal resolution was coarsened to one minute. The spatial resolution here is closer to the resolution of the antenna reception areas used in the de Montjoye et al. (2013) paper (where spatial resolution ranged from 115 m to 15 km), but the coarsened temporal resolution is still much more precise than the one used in the CDR studies. As a result, using location and time for just two points still results in a high unicity (mean 80.3%), while five points increases the mean unicity to almost 88%. Using just location (no timestamp), the unicity degrades to 32% for two points and 66% for five points. Table 3: unicity results for location (x,y), location and time (x,y, t). Spatial resolution has been coarsened to 3 decimal places; temporal resolution has been coarsened to 60 seconds. Means and ranges are reported for 500 sets of random points for each of 71 users. The mean unicity values for location and location + time, with different levels of coarsening are summarized in figure 14. With the much higher precision and spatial resolution of GPS data currently available, two x/y locations are sufficient to be uniquely associated with a

11 single trajectory 90% of the time, adding the timestamp matches a single trajectory 97% of the time. The three pieces of information- x, y, time- are so specific that increasing the number of points to match to five increases the unicity very little because it is already so high using just two points. The first level of coarsening for x,y,t (~10m spatial, 30 seconds temporal) has similar unicity to the original resolution for just x,y coordinates, and when four or five points are used, the coarsened x,y,t has slightly higher mean unicity. The most coarsened level for x,y,t (~100m, 60 seconds) still has a high unicity (80% for two points). The x,y coordinates (no timestamp) show the greatest increase in unicity when more points are used for matching. This suggests that there is a trade-off between location resolution and amount of information (location points) available. Discussion/Future Work While each of the issues addressed here focuses on a single dataset for the case study, I would expect the results to be generally applicable to other similar mobility datasets. The hotspot analysis illustrated that local spatial statistics can be used to identify hot spots of movement activity, and spatial statistics visualization tools are useful for exploring how these hot spots change through time. The spatial statistics used here all were extensions of Moran s I index, which requires a variable of interest that is measured on a ratio or interval scale, and locations of points do not meet this condition. Therefore, the points were aggregated to polygons and the counts were used as the variable of interest. In the second part of this study, speed (m/sec) was calculated for each point (based on the distance from and time since the previous location) and used as the variable of interest. Spatial autocorrelation of the speed associated with each point was classified into high-high (likely associated with highways), low-high, high-low, and lowlow, which were used here to indicate potential areas of interest. A better understanding of the spatiotemporal structure of human mobility could also increase the predictability of movement. For example, a pattern of high activity or relatively slow movement in certain locations at certain times of the day could be used to infer future movement at the same locations. In both of the above examples, location and relative speed were used as proxies for behavior, respectively. It is also important to note that these variables represented collective behavior, as all points for all 40 taxicabs were considered together. There are interesting future directions to go in with this research, particularly comparing the utility of associating relative speed with points-of-interest for different types of moving entities. Automobiles, and taxicabs in particular, move differently (and slow down for different reasons sometimes) compared to pedestrians and wildlife, and even regular vehicles. It would be interesting to also test how useful other movement parameters such as relative and absolute angle and step length would be for identifying points-of-interest. This is only applicable for entities that can move more freely across space and are less confined to street networks or sidewalks.

12 The unicity study has particularly important implications for privacy and the increasing availability of anonymized trajectory datasets. This is one of the first studies to explore unicity and anonymity with higher resolution GPS data and it should be troubling how unique a set of two location points can be. Decreasing the spatial and temporal resolution reduces the unicity, but five points with x,y coordinates at the coarsest resolution tested here were still uniquely associated with a single trajectory more than 60% of the time. Movement parameters such as speed, angle, and step length have not been tested as potential identifiers of trajectories, but the case study here focusing on absolute angle highlights their potential importance. Five absolute angle data points were uniquely associated with a single trajectory 72% of the time. This suggests that individual movement, irrespective of absolute geographic location, can be identifiable with a sufficient level of precision of angle measurements and data points. Future work should focus specifically on how movement parameters could be used singly or together to identify a trajectory. It is also important to note here that the focus of this study was not to re-attach user information to trajectories, it was just to examine how unique trajectories were based on different factors. The privacy issues associated with higher quality GPS location data should be addressed with the assumption that if a trajectory can be uniquely described with 2-5 GPS points, the trajectory could eventually be de-anonymized. References Anselin L (1995) Local Indicators of Spatial Association LISA. Geographical Analysis, 27(2), Anselin L, Sridharan S and Gholston S (2006) Using Exploratory Spatial Data Analysis to Leverage Social Indicator Databases: The Discovery of Interesting Patterns. Social Indicators Research, 82(2), Becker R, Cáceres R, Hanson K, et al. (2013) Human Mobility Characterization from Cellular Network Data. Commun. ACM, 56(1), de Montjoye Y-A, Hidalgo CA, Verleysen M, et al. (2013) Unique in the Crowd: The privacy bounds of human mobility. Scientific Reports, 3, Deng Z and Ji M (2011) Spatiotemporal structure of taxi services in Shanghai: Using exploratory spatial data analysis. In: IEEE, pp Gao S (2015) Spatio-Temporal Analytics for Exploring Human Mobility Patterns and Urban Dynamics in the Mobile Age. Spatial Cognition & Computation, 15(2), Gudmundsson J, Laube P and Wolle T (2012) Computational movement analysis. In: Kresse W and Danko DM (eds), Springer Handbook of Geographic Information, Springer Berlin Heidelberg, pp

13 Li N, Li T and Venkatasubramanian S (2007) t-closeness: Privacy Beyond k-anonymity and ldiversity. In: IEEE 23rd International Conference on Data Engineering, ICDE 2007, pp Orellana D and Wachowicz M (2011) Exploring Patterns of Movement Suspension in Pedestrian Mobility. Geographical Analysis, 43(3), Orellana DA, Wachowicz M, Knegt de HJ, et al. (2010) Uncovering patterns of suspension of movement. Piorkowski, M., Sarafijanovic-- -Djukic, N., and Grossglauser, M. CRAWDAD dataset epfl/mobility (v ), downloaded from doi: /c7j010, Feb Qian X, Zhan X and Ukkusuri SV (2015) Characterizing Urban Dynamics Using Large Scale Taxicab Data. Springer International Publishing. Rey SJ (2014) Spatial Dynamics and Space-Time Data Analysis. Springer Berlin Heidelberg. Sweeney L (2002) k-anonymity: A MODEL FOR PROTECTING PRIVACY. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05), Zang H and Bolot J (2011) Anonymization of Location Data Does Not Work: A Large-scale Measurement Study. In: Proceedings of the 17th Annual International Conference on Mobile Computing and Networking, MobiCom 11, New York, NY, USA: ACM, pp , Available from: (accessed 15 May 2015). Zheng, Y. Lizhu Zhang, Xing Xie, Wei-Ying Ma. Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of International conference on World Wild Web (WWW 2009), Madrid Spain. ACM Press: Zheng, Y., Quannan Li, Yukun Chen, Xing Xie, Wei-Ying Ma. Understanding Mobility Based on GPS Data. In Proceedings of ACM conference on Ubiquitous Computing (UbiComp 2008), Seoul, Korea. ACM Press: Zheng, Y. Xing Xie, Wei-Ying Ma, GeoLife: A Collaborative Social Networking Service among User, location and trajectory. Invited paper, in IEEE Data Engineering Bulletin. 33, 2, 2010, pp

14

15

16

17

18

19

20

21

22

23

24 Appendix:

25

26

27

28

29

30

31

32

33

34

35

36

37 2015 Proprietary, The University of Texas at Austin, All Rights Reserved. For more information on Center for Identity research, resources and information, visit identity.utexas.edu. identity.utexas.edu