The spatial structure of mobile communication networks

There has been a recent surge of interest in the relationship between the spatial and topological structure of communication networks with the availability of large scale anonymous datasets on the communication and mobility patterns of individuals. These datasets, captured as a by-product of modern communications technology, provide a detailed view of the daily interpersonal interactions of millions of people. Mobile phone call logs in particular offer an unparalleled source of information given their personal portable nature and ubiquity in modern society. The use of mobile phones has become so common that these datasets are no longer merely communication logs but close approximations of the network of interpersonal relationships that forms society. The analysis of these proxy networks has the potential to uncover knowledge about society at a scale never previously possible. Networks, and social networks in particular, have been the subject of investigation for more than a century with a rich corpus of theory and methods now available to researchers. Computational approaches to the study of networks are more recent but there are now a wide variety of structural analysis methods that have been developed and applied across many different disciplines and subject areas. The study of interactions across space has developed in parallel with theory, methods, models and a variety of applications. Recent studies of these proxy networks have tended to use computational approaches for analysing community structure and modelling spatial interacitions without much regard for the theory upon which they were built. The underlying assumption has been that all phenomena that can be represented as networks can be analysed with the same methods. In this thesis we demonstrate that this is not the case and identify a number of problems and misinterpretations that can arise when inappropriate methods or network representations are employed. Through a detailed theoretical and empirical analysis we identify appropriate combinations of network representation, spatial scale, and analysis methods for studying the spatial structure of communication networks. Using these findings we demonstrate the potential of such analysis when the appropriate methodology is employed

Examining the density and diversity of human activity in the built environment: The case of the pearl river delta, China

Rapid urbanization in China has been accompanied by spatial inefficiency in patterns of human activity, of which 'ghost towns' are the most visible result. In this study, we measure the density and diversity of human activity in the built environment and relate this to various explanatory factors. Using the Pearl River Delta (PRD) as an empirical case, our research demonstrates the distribution of human activity by multi-source data and then explores its dynamics within these areas. This empirical study is comprised of two parts. The first part explores location information regarding human activity in urbanized areas and shows density and diversity. Regression models are applied to explore how density and diversity are affected by urban scale, morphology and by a city's administrative level. Results indicate that: 1) cities with smaller populations are more likely to be faced with lower density and diversity, but they derive greater marginal benefits from improving land use efficiency; 2) the compactness of the layout of urban land, an index reflecting the plane shapes of the built environment, is highly correlated with density and diversity in built-up areas; and 3) the administrative importance of a city has a significant and positive impact on the density of human activity, but no obvious influence on its diversity

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