Cell Towers as Urban Sensors: Understanding the Strengths and Limitations of Mobile Phone Location Data

Understanding urban dynamics and human mobility patterns not only benefits a wide range of real-world applications (e.g., business site selection, public transit planning), but also helps address many urgent issues caused by the rapid urbanization processes (e.g., population explosion, congestion, pollution). In the past few years, given the pervasive usage of mobile devices, call detail records collected by mobile network operators has been widely used in urban dynamics and human mobility studies. However, the derived knowledge might be strongly biased due to the uneven distribution of people’s phone communication activities in space and time. This dissertation research applies different analytical methods to better understand human activity and urban environment, as well as their interactions, mainly based on a new type of data source: actively tracked mobile phone location data. In particular, this dissertation research achieves three main research objectives. First, this research develops visualization and analysis approaches to uncover hidden urban dynamics patterns from actively tracked mobile phone location data. Second, this research designs quantitative methods to evaluate the representativeness issue of call detail record data. Third, this research develops an appropriate approach to evaluate the performance of different types of tracking data in urban dynamics research. The major contributions of this dissertation research include: 1) uncovering the dynamics of stay/move activities and distance decay effects, and the changing human mobility patterns based on several mobility indicators derived from actively tracked mobile phone location data; 2) taking the first step to evaluate the representativeness and effectiveness of call detail record and revealing its bias in human mobility research; and 3) extracting and comparing urban-level population movement patterns derived from three different types of tracking data as well as their pros and cons in urban population movement analysis

Assessing Health Vulnerability to Air Pollution in Seoul Using an Agent-Based Simulation

This study aims to investigate the exposure to air pollution in Seoul and the consequent health effects in Seoul South Korea, and suggest possible solutions using agent-based modelling (ABM). ABM is a useful technique that can simulate pollution generation and exposure, mobility patterns of unique individuals, and explore future scenarios. The first study compared Universal Kriging and Generalised Additive Models to spatially interpolate pollution station data over Seoul. A new method was discovered to enhance the accuracy of NO2 on roads. Next, ABM was used to evaluate potential health loss for a set of demographic groups after being cumulatively exposed to particulates (PM10), with a nominal heath impact threshold of 100µg/m3. Finally, a traffic simulation examined the coupled problem of non-exhaust emissions and behaviour and estimate exposure to PM10 for groups of drivers and pedestrians in central Seoul. Having tested the sensitivity to calibrated parameters, scenarios of traffic restriction and modification of pedestrian behaviour to avoid polluted areas was investigated. With less difference between interpolation methods, the result showed a remarkable contrast between roadside and background NO2 as well as a daily cycle, while PM10 had a small variance between hours but had greater seasonal oscillation. The first ABM study showed that disparities in health may arise as a result of differences in socioeconomic status, especially when the group was exposed over a long period, and road proximity caused additional health loss. In the traffic simulation study, extreme PM10 was found along roadways, but although drivers were exposed to extreme values, longer exposure for pedestrians led to higher health risks. Despite the absence of reliable data linking exposure to actual health effects, it is possible to make progress with ABM. In addition, pollution exposure can vary by commuting patterns and the urban development of one’s location. Scenarios can be advantageous for healthcare policy – to aid the most vulnerable groups and districts

Revealing social dimensions of urban mobility with big data: A timely dialogue

Considered a total social phenomenon, mobility is at the center of intricate social dynamics in cities and serves as a reading lens to understand the whole society. With the advent of big data, the potential for using mobility as a key social analyzer was unleashed in the past decade. The purpose of this research is to systematically review the evolution of big data's role in revealing social dimensions of urban mobility and discuss how they have contributed to various research domains from early 2010s to now. Six major research topics are detected from the selected online academic corpuses by conducting keywords-driven topic modeling techniques, reflecting diverse research interests in networked mobilities, human dynamics in spaces, event modeling, spatial underpinnings, travel behaviors and mobility patterns, and sociodemographic heterogeneity. The six topics reveal a comprehensive, research-interests, evolution pattern, and present current trends on using big data to uncover social dimensions of human mobility activities. Given these observations, we contend that big data has two contributions to revealing social dimensions of urban mobility: as an efficiency advancement and as an equity lens. Furthermore, the possible limitations and potential opportunities of big data applications in the existing scholarship are discussed. The review is intended to serve as a timely retrospective of societal-focused mobility studies, as well as a starting point for various stakeholders to collectively contribute to a desirable future in terms of mobility

Revealing social dimensions of urban mobility with big data: A timely dialogue

Considered a total social phenomenon, mobility is at the center of intricate social dynamics in cities and serves as a reading lens to understand the whole society. With the advent of big data, the potential for using mobility as a key social analyzer was unleashed in the past decade. The purpose of this research is to systematically review the evolution of big data's role in revealing social dimensions of urban mobility and discuss how they have contributed to various research domains from early 2010s to now. Six major research topics are detected from the selected online academic corpuses by conducting keywords-driven topic modeling techniques, reflecting diverse research interests in networked mobilities, human dynamics in spaces, event modeling, spatial underpinnings, travel behaviors and mobility patterns, and sociodemographic heterogeneity. The six topics reveal a comprehensive, research-interests, evolution pattern, and present current trends on using big data to uncover social dimensions of human mobility activities. Given these observations, we contend that big data has two contributions to revealing social dimensions of urban mobility: as an efficiency advancement and as an equity lens. Furthermore, the possible limitations and potential opportunities of big data applications in the existing scholarship are discussed. The review is intended to serve as a timely retrospective of societal-focused mobility studies, as well as a starting point for various stakeholders to collectively contribute to a desirable future in terms of mobility

Encounter and its configurational logic: Understanding spatiotemporal co-presence with road network and social media check-in data

Public space facilitates the social interaction between people. It is widely accepted that the connection between spaces creates the possibility of the mutual visibility between people. The relationship between spatial configuration and the spatiotemporal encounters, however, has rarely been investigated explicitly in empirical cases. The focus of this study is two folded: firstly, it examines the way to measure spatiotemporal encounters between different groups of people based on their mobility records; secondly, it investigates how the design of the built environment contributes to physical co-presence on spatial and temporal dimensions. Using ubiquitous individual social media check-in data in Central Shanghai, China, this study proposes a framework for quantifying physical face-to-face co-presence patterns between the defined local random walkers and the remote visitors across time in every street. In the introduced People-Space-Time (PST) model, social capital is conceptualised as an integration among social difference, spatial distance (metric and geometrical distance) and time distance. The reliability of the applied data and the effectiveness of the introduced methods are validated by the investigations of the scaling nature of the extracted mobility patterns and the correlation between the outputs and surveyed data. The produced spatiotemporal patterns of face-toface co-presence reveal that city centres and the large-scale urban complexes (e.g., transport hubs, shopping malls, stadiums, etc.) are ideal places for people to encounter. The results of the regression analyses demonstrate that spatial and functional centrality measures are significant variables for predicting spatiotemporal co-presence in streets, but in which the functional centrality structures maintain a higher standard of explanatory power than the spatial network. The temporal complexity of the co-presence is revealed by the temporally shifting performance of the integrated regression models across time. The findings in this study yield that it is the spatio-functional interaction influencing spatiotemporal variation of the physical encounter between people, and reclaim the necessity of adding fine-scale land-use patterns in the traditional configurational analysis for deeply understanding the social processes with urban big data in the contemporary digitalised cities

A Realistic Mobility Model for Wireless Networks of Scale-Free Node Connectivity

Recent studies discovered that many of social, natural and biological networks are characterised by scale-free power-law connectivity distribution. We envision that wireless networks are directly deployed over such real-world networks to facilitate communication among participating entities. This paper proposes Clustered Mobility Model (CMM), in which nodes do not move randomly but are attracted more to more populated areas. Unlike most of prior mobility models, CMM is shown to exhibit scale-free connectivity distribution. Extensive simulation study has been conducted to highlight the difference between Random WayPoint (RWP) and CMM by measuring network capacities at the physical, link and network layers

Mobility choices - an instrument for precise automatized travel behavior detection & analysis

Within the Mobility Choices (MC) project we have developed an app that allows users to record their travel behavior and encourages them to try out new means of transportation that may better fit their preferences. Tracks explicitly released by the users are anonymized and can be analyzed by authorized institutions. For recorded tracks, the freely available app automatically determines the segments with their transportation mode; analyzes the track according to the criteria environment, health, costs, and time; and indicates alternative connections that better fit the criteria, which can individually be configured by the user. In the second step, the users can edit their tracks and release them for further analysis by authorized institutions. The system is complemented by a Web-based analysis program that helps authorized institutions carry out specific evaluations of traffic flows based on the released tracks of the app users. The automatic transportation mode detection of the system reaches an accuracy of 97%. This requires only minimal corrections by the user, which can easily be done directly in the app before releasing a track. All this enables significantly more accurate surveys of transport behavior than the usual time-consuming manual (non-automated) approaches, based on questionnaires