Travel activities are embodied as people’s needs to be physically present at certain locations. The development of Information and Communication Technologies (ICTs, such as mobile phones) has introduced new data sources for modeling human activities. Based on the scattered spatiotemporal points provided in mobile phone datasets, it is feasible to study the patterns (e.g., the scale, shape, and regularity) of human activities. In this paper, we propose methods for analyzing the distribution of human activity space from both individual and urban perspectives based on mobile phone data. The Weibull distribution is utilized to model three predefined measurements of activity space (radius, shape index, and entropy). The correlation between demographic factors (age and gender) and the usage of urban space is also tested to reveal underlying patterns. The results of this research will enhance the understanding of human activities in different urban systems and demographic groups, as well as providing novel methods to expand the important and widely applicable area of geographic knowledge discovery in the age of instant access.

The rapid development of information and communication technologies (ICTs) has provided rich data sources for analyzing, modeling, and interpreting human mobility patterns. This paper contributes to this research area by developing the Spatio-temporal Edit Distance measure, an extended algorithm to determine the similarity between user trajectories based on call detailed records (CDRs). We improve the traditional Edit Distance algorithm by incorporating both spatial and temporal information into the cost functions. The extended algorithm can preserve both space and time information from string-formatted CDR data. The novel method is applied to a large data set from Northeast China in order to test its effectiveness. Three types of analyses are presented for scenarios with and without the effect of time: (1) Edit Distance with spatial information; (2) Edit Distance with time as a factor in the cost function; and (3) Edit Distance with time as a constraint in partitioning trajectories. The outcomes of this research contribute to both methodological and empirical perspectives. The extended algorithm performs well for measuring low-resolution tracking information in CDRs, as well as facilitating the interpretation of user mobility patterns in the age of instant access.

Recent advances in multimedia and mobile technologies have facilitated large volumes of travel photos to be created and shared online. Although previous studies have utilized geotagged photos to model travel patterns at individual locations, there is limited research on how these datasets can model international travel behavior and inter-country travel flows—a crucial indicator to quantify the interactions between countries in tourism economics. Realizing the necessity to investigate the potential of geotagged photos in tourism geography, this research investigates international travel patterns from two perspectives: 1) We apply a series of indicators (radius of gyration (ROG), number of countries visited, and entropy) to measure the descriptive characteristics of international travel in different countries; 2) By constructing a gravity model of trade, we investigate how distance decay influences the magnitude of international travel flow between geographic entities, and whether (or how much) the popularity of a given destination (defined as the percentage of tourist income in national gross domestic product (GDP)) affects travel choices in different countries. The results provide valuable input to various commercial applications such as individual travel planning and destination suggestions.

Understanding the dynamics of the individuals' daily mobility patterns is very important in a wide range of fields. However, lacking of tools to monitor the time-resolved location of individuals makes this research consume both tremendous time and money in the past. Nowadays, the rapidly developing ability to collect space-time activity (STA) data through new information technologies such as cellular phones, WiFi, and GPS methods is improving the quantity and quality of these data and reducing their cost. Also, “People-Based GIS” gives us a new perspective for analyzing and visualizing these data. In this paper, we introduce several preprocessing and spatiotemporal analysis methods for such new ICT data in individual human mobility patterns mining and urban analysis under the time geography framework. By using millions of raw mobile call records of a large city in China, we compute statistical characteristic of cell phone usage both at different time of a day and on different days of a week to derive aggregated mobility patterns for millions of mobile phone users and individual mobility patterns for different groups which are divided by some selected social factors, such as gender and age. The research methods and results also illustrate how to make 3D or 2D graphic representations of individual activities patterns and their evolution through space and time.