Discovering urban spatial-temporal structure from human activity patterns

Jiang, Shan; Ferreira, Joseph; González, Marta C.

doi:10.1145/2346496.2346512

Cited by 75 publications

(33 citation statements)

References 32 publications

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“…[1,6,16,30,24,25,26]. This question includes inferring the spatiotemporal activities that people engage in, and their travel (e.g., trip chaining, and road usage, etc.)…”

Section: Inferring Individual Activities and Travelmentioning

confidence: 99%

A review of urban computing for mobile phone traces

Jiang

Fiore

Yang

et al. 2013

Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing

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185

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In this work, we present three classes of methods to extract information from triangulated mobile phone signals, and describe applications with different goals in spatiotemporal analysis and urban modeling. Our first challenge is to relate extracted information from phone records (i.e., a set of time-stamped coordinates estimated from signal strengths) with destinations by each of the million anonymous users. By demonstrating a method that converts phone signals into small grid cell destinations, we present a framework that bridges triangulated mobile phone data with previously established findings obtained from data at more coarse-grained resolutions (such as at the cell tower or census tract levels). In particular, this method allows us to relate daily mobility networks, called motifs here, with trip chains extracted from travel diary surveys. Compared with existing travel demand models mainly relying on expensive and less-frequent travel survey data, this method represents an advantage for applying ubiquitous mobile phone data to urban and transportation modeling applications. Second, we present a method that takes advantage of the high spatial resolution of the triangulated phone data to infer trip purposes by examining semantic-enriched land uses surrounding destinations in individual's motifs. In the final section, we discuss a portable computational architecture that allows us to manage and analyze mobile phone data in geospatial databases, and to map mobile phone trips onto spatial networks such that further analysis about flows and network performances can be done. The combination of these three methods demonstrate the state-of-the-art algorithms that can be adapted to triangulated mobile phone data for the context of urban computing and modeling applications.

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“…[1,6,16,30,24,25,26]. This question includes inferring the spatiotemporal activities that people engage in, and their travel (e.g., trip chaining, and road usage, etc.)…”

Section: Inferring Individual Activities and Travelmentioning

confidence: 99%

A review of urban computing for mobile phone traces

Jiang

Fiore

Yang

et al. 2013

Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing

Self Cite

185

View full text Add to dashboard Cite

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“…The applications do not only include travel behavior and transportation modeling related research, such as mobility pattern discovery (Do & Gatica-Pereza, 2013;Schneider et al, 2013), transportation modelling and traffic analysis (Angelakis et al, 2013;Berlingerio et al, 2013;Calabrese et al, 2011), and urban planning (Becker et al, 2011;Jiang et al, 2012), but they also cover context-awareness services where user-centric assistance is provided based on users' specific location and activity context (García-Sánchez el al., 2013; Lee & Cho, 2013), and location tracking systems where knowledge of individuals' real-time locations and related routine activities is used in tools that provide support for industry, childcare, elderly health care and emergency rescue (Horng et al, 2011;Zhang et al, 2013;Zhou et al, 2014). Despite the multitude of possible applications, there are also challenges that are pertinent to the data, as acknowledged by some of the existing studies (e.g.…”

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confidence: 99%

Building a validation measure for activity-based transportation models based on mobile phone data

Liu

Janssens

Cui

et al. 2014

Expert Systems with Applications

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Activity-based micro-simulation transportation models typically predict 24-hour activitytravel sequences for each individual in a study area. These sequences serve as a key input for travel demand analysis and forecasting in the region. However, despite their importance, the lack of a reliable benchmark to evaluate the generated sequences has hampered further development and application of the models. With the wide deployment of mobile phone devices today, we explore the possibility of using the travel behavioral information derived from mobile phone data to build such a validation measure. Our investigation consists of three steps. First, the daily trajectory of locations, where a user performed activities, is constructed from the mobile phone records. To account for the discrepancy between the stops revealed by the call data and the real location traces that the user has made, the daily trajectories are then transformed into actual travel sequences. Finally, all the derived sequences are classified into typical activity-travel patterns which, in combination with their relative frequencies, define an activity-travel profile. The established profile characterizes the current activity-travel behavior in the study area, and can thus be used as a benchmark for the assessment of the activity-based transportation models. By comparing the activity-travel profiles derived from the call data with statistics that stem from traditional activity-travel surveys, the validation potential is demonstrated. In addition, a sensitivity analysis is carried out to assess how the results are affected by the different parameter settings defined in the profiling process.

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“…Location sharing has not only proven to benefit individuals but also society in general. In fact large datasets of people's locations have provided invaluable insights into the quality of urban services [18], [19] and sociodynamics of neighborhoods [31]. These urban insights can lead to improvements in current and public structures and ultimately improve the quality of the geographical area itself.…”

Section: Related Researchmentioning

confidence: 99%

I Know Where You Live

Liccardi

Abdul‐Rahman

Chen

2016

Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

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This research measures human performance in inferring the functional types (i.e., home, work, leisure and transport) of locations in geo-location data using different visual representations of the data (textual, static and animated visualizations) along with different amounts of data (1, 3 or 5 day(s)).We first collected real life geo-location data from tweets. We then asked the data owners to tag their location points, resulting in ground truth data. Using this dataset we conducted an empirical study involving 45 participants to analyze how accurately they could infer the functional location of the original data owners under different conditions, i.e., three data representations, three data densities and four location types.The study results indicate that while visual techniques perform better than textual ones, the functional locations of human activities can be inferred with a relatively high accuracy even using only textual representations and a low density of location points. Workplace was more easily inferred than home while transport was the functional location with the highest accuracy. Our results also showed that it was easier to infer functional locations from data exhibiting more stable and consistent mobility patterns, which are thus more vulnerable to privacy disclosures.We discuss the implications of our findings in the context of privacy preservation and provide guidelines to users and companies to help preserve and safeguard people's privacy.

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Discovering urban spatial-temporal structure from human activity patterns

Cited by 75 publications

References 32 publications

A review of urban computing for mobile phone traces

A review of urban computing for mobile phone traces

Building a validation measure for activity-based transportation models based on mobile phone data

I Know Where You Live

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