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The continuous advancement of economic globalization and informatization, along with the rapid construction of transportation infrastructure, have made cities more closely connected and made urban networking a prominent trend. Network news is easily accessible and contains bundant geographical information. Studying urban networks from the view of toponym cooccurrences in the news is a brand -new perspective, and conclusions may help clarify the status of cities, deepen the understanding of the structure of urban networks. This paper conducts research from contact, node and network successively. Firstly it proposes a new method aiming at measuring relatedness of city pair based on toponym co-occurrences; then it uses the centrality of social network analysis to characterize urban influence and explores its spatial distribution; finally it studies the characteristics and structure of urban networks. Results show that the proposed measurement approach highlights the strength of the relatedness, and makes up for the shortcomings of Ochiia coefficient method such as ignoring multiple diverse toponyms in the news; coastal cities possess higher urban influence than inland ones; the backbone urban network presents an approximately diamond - shaped spatial structure, metropolises such as Beijing, Shanghai, Guangzhou, and Chongqing are the core nodes. [ABSTRACT FROM AUTHOR]

In this paper, we propose an individual income level inference method based on travel behavior of urban residents. Firstly, we define 13 residents' travel behavior features from 3 different aspects. These features are the mobility indicators based on trajectories, home-based travel characteristics, and activity chain patterns. Secondly, 80% of the daily travel diary data of residents in Guangzhou in 2013 is taken as the training sample and the remaining as the test data. Thirdly, the random forest method is trained and used to infer individual's income level. The experiment results show that our proposed method can obtain an overall accuracy of 80%. Among 13 travel behavior features, the home-based features (i.e. the mode of the travel distance from home during working time (9:00 AM to 18:00 PM)), the activity chain pattern, and travel distance and scope related features (i.e., the maximum distance between two successive activity points, and radius of gyration) have high feature importance. However, the features representing the spatial heterogeneity of the activity points have relatively low importance, such as the spatial diversity. [ABSTRACT FROM AUTHOR]