Multi-perspective analysis of mobile phone call data records: A visual analytics approach

Analysis of human mobility is currently a hot research topic in data mining, geographic information science and visual analytics. While a wide variety of methods and tools are available, it is still hard to find recommendations for considering a data set systematically from multiple perspectives. To fill this gap, we demonstrate a workflow of a comprehensive analysis of a publicly available data set about mobile phone calls of a large population over a long time period. We pay special attention to the evaluation of data properties. We outline potential applications of the proposed methods

Routine pattern discovery and anomaly detection in individual travel behavior

Discovering patterns and detecting anomalies in individual travel behavior is a crucial problem in both research and practice. In this paper, we address this problem by building a probabilistic framework to model individual spatiotemporal travel behavior data (e.g., trip records and trajectory data). We develop a two-dimensional latent Dirichlet allocation (LDA) model to characterize the generative mechanism of spatiotemporal trip records of each traveler. This model introduces two separate factor matrices for the spatial dimension and the temporal dimension, respectively, and use a two-dimensional core structure at the individual level to effectively model the joint interactions and complex dependencies. This model can efficiently summarize travel behavior patterns on both spatial and temporal dimensions from very sparse trip sequences in an unsupervised way. In this way, complex travel behavior can be modeled as a mixture of representative and interpretable spatiotemporal patterns. By applying the trained model on future/unseen spatiotemporal records of a traveler, we can detect her behavior anomalies by scoring those observations using perplexity. We demonstrate the effectiveness of the proposed modeling framework on a real-world license plate recognition (LPR) data set. The results confirm the advantage of statistical learning methods in modeling sparse individual travel behavior data. This type of pattern discovery and anomaly detection applications can provide useful insights for traffic monitoring, law enforcement, and individual travel behavior profiling

Geo-Adaptive Deep Spatio-Temporal predictive modeling for human mobility

Deep learning approaches for spatio-temporal prediction problems such as crowd-flow prediction assumes data to be of fixed and regular shaped tensor and face challenges of handling irregular, sparse data tensor. This poses limitations in use-case scenarios such as predicting visit counts of individuals' for a given spatial area at a particular temporal resolution using raster/image format representation of the geographical region, since the movement patterns of an individual can be largely restricted and localized to a certain part of the raster. Additionally, current deep-learning approaches for solving such problem doesn't account for the geographical awareness of a region while modelling the spatio-temporal movement patterns of an individual. To address these limitations, there is a need to develop a novel strategy and modeling approach that can handle both sparse, irregular data while incorporating geo-awareness in the model. In this paper, we make use of quadtree as the data structure for representing the image and introduce a novel geo-aware enabled deep learning layer, GA-ConvLSTM that performs the convolution operation based on a novel geo-aware module based on quadtree data structure for incorporating spatial dependencies while maintaining the recurrent mechanism for accounting for temporal dependencies. We present this approach in the context of the problem of predicting spatial behaviors of an individual (e.g., frequent visits to specific locations) through deep-learning based predictive model, GADST-Predict. Experimental results on two GPS based trace data shows that the proposed method is effective in handling frequency visits over different use-cases with considerable high accuracy

HUMAN INTERACTIONS IN PHYSICAL AND VIRTUAL SPACES: A GIS-BASED TIME-GEOGRAPHIC EXPLORATORY APPROACH

Information and communication technologies (ICT) such as cell phone and the Internet have extended opportunities of human activities and interactions from physical spaces to virtual spaces. The relaxed spatio-temporal constraints on individual activities may affect human activity-travel patterns, social networks, and many other aspects of society. A challenge for research of human activities in the ICT age is to develop analytical environments that can help visualize and explore individual activities in virtual spaces and their mutual impacts with physical activities. This dissertation focuses on extending the time-geographic framework and developing a spatio-temporal exploratory environment in a space-time geographic information system (GIS) to facilitate research of human interactions in both physical and virtual spaces. In particular, this dissertation study addresses three research questions. First, it extends the time-geographic framework to assess the impacts of phone usage on potential face-to-face (F2F) meeting opportunities, as well as dynamic changes in potential F2F meeting opportunities over time. Secondly, this study extends the time-geographic framework to conceptualize and represent individual trajectories in an online social network space and to explore potential interaction opportunities among people in a virtual space. Thirdly, this study presents a spatio-temporal environment in a space-time GIS to facilitate exploration of the relationships between changes in physical proximity and changes in social closeness in a virtual space. The major contributions of this dissertation include: (1) advancing the time-geographic framework in its ability of exploring processes of virtual communication alerting physical activity opportunities; (2) extending some concepts of the classical time geography from a physical space to a virtual space for representing and exploring virtual interaction patterns; (3) developing a space-time GIS that is useful for exploring patterns of individual activities and interactions in both physical and virtual spaces, as well as the interactions between these two spaces