Yihong Yuan

Correlating mobile phone usage and travel behavior - A case study of Harbin, China

Abstract Information and communication technologies (ICTs), such as mobile phones and the Internet, are increasingly pervasive in modern society. These technologies provide new resources for spatio-temporal data mining and geographic knowledge discovery. Since the development of ICTs also impacts physical movement of individuals in societies, much of the existing research has focused on examining the correlation between ICT and human mobility. In this paper, we aim to provide a deeper understanding of how usage of mobile phones correlates with individual travel behavior by exploring the correlation between mobile phone call frequencies and three indicators of travel behavior: (1) radius, (2) eccentricity, and (3) entropy. The methodology is applied to a large dataset from Harbin city in China. The statistical analysis indicates a significant correlation between mobile phone usage and all of the three indicators. In addition, we examine and demonstrate how explanatory factors, such as age, gender, social temporal orders and characteristics of the built environment, impact the relationship between mobile phone usage and individual activity behavior.

Everyday space–time geographies: using mobile phone-based sensor data to monitor urban activity in Harbin, Paris, and Tallinn

This paper proposes a methodology for using mobile telephone-based sensor data for detecting spatial and temporal differences in everyday activities in cities. Mobile telephone-based sensor data has great applicability in developing urban monitoring tools and smart city solutions. The paper outlines methods for delineating indicator points of temporal events referenced as ‘midnight’, ‘morning start’, ‘midday’, and ‘duration of day’, which represent the mobile telephone usage of residents (what we call social time) rather than solar or standard time. Density maps by time quartiles were also utilized to test the versatility of this methodology and to analyze the spatial differences in cities. The methodology was tested with data from cities of Harbin (China), Paris (France), and Tallinn (Estonia). Results show that the developed methods have potential for measuring the distribution of temporal activities in cities and monitoring urban changes with georeferenced mobile phone data.

Measuring similarity of mobile phone user trajectories– a Spatio-temporal Edit Distance method

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.

Analyzing the distribution of human activity space from mobile phone usage: an individual and urban-oriented study

ABSTRACT 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.

Exploring inter-country connection in mass media: A case study of China

Abstract The development of theories and techniques for big data analytics offers tremendous possibility for investigating large-scale events and patterns that emerge over space and time. In this research, we utilize a unique open dataset “The Global Data on Events, Location and Tone” (GDELT) to model the image of China in mass media, specifically, how China has related to the rest of the world and how this connection has evolved upon time. The results of this research contribute to both the methodological and the empirical perspectives: We examined the effectiveness of the dynamic time warping (DTW) distances in measuring the differences between long-term mass media data. We identified four types of connection strength patterns between China and its top 15 related countries. With that, the distance decay effect in mass media is also examined and compared with social media and public transportation data. While using multiple datasets and focusing on mass media, this study generates valuable input regarding the interpretation of the diplomatic and regional correlation for the nation of China. It also provides methodological references for investigating international relations in other countries and regions in the big data era.

Exploring Urban Mobility from Taxi Trajectories: A Case Study of Nanjing, China.

Identifying urban mobility patterns is a crucial research topic in geographic information science, transportation planning, and behavior modeling. Understanding the dynamics of daily mobility patterns is essential for the management and planning of urban facilities and services. Previous studies have utilized taxi trajectories collected from the Global Positioning System (GPS) to model various types of urban patterns, such as identifying urban functional regions and hot spots. However, there is limited research on how the results of these studies can be used to inform real-world problems in urban planning. This research examines the development of sub-centers in Nanjing, China based on Taxi GPS trajectories. The results indicate a clear separation between the urban center and the sub-centers. In addition, we also clustered the time series of taxi pick-up locations to model dynamic urban movement and identify outlier patterns. The results demonstrate the importance of considering human mobility patterns in identifying urban functional regions, which provides valuable input for urban planners and policy makers.

Evaluating gender representativeness of location-based social media: a case study of Weibo

ABSTRACT Researchers have utilized location-based social media (LBSM) as potential resources to characterize daily mobility patterns and social perceptions of place. Similar to other types of big data, LBSM data also have differential data-quality issues such as accuracy, precision, temporal resolution, and sampling biases across various population groups. However, these issues have not been investigated sufficiently for LBSM users. This research aims to quantitatively examine the sampling biases of a Chinese microblogging site, Weibo, which is functionally similar to Twitter. The analysis focuses on investigating the bias in gender groups, and how this bias varies/autocorrelates in different provinces of China. The results indicate that in general, women are more likely to use Weibo in China. We also detected a strong regional pattern for Weibo gender ratios. The results provide valuable input in quantifying demographic biases in Weibo, and the methodology can be applied to other LBSM to analyse sample biases. This study also offers a data preprocessing strategy to identify potential research questions in sociology, regional science, and gender studies.