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Location Based Services (LBS) provide a new perspective for spatiotemporally analyzing dynamic urban systems. Research has investigated urban dynamics using LBS. However, less attention has been paid to the analysis of urban structure (especially commuting pattern) using smart card data (SCD), which are widely available in most large cities in China, and even in the world. This paper combines bus SCD for a one-week period with a oneday household travel survey, as well as a parcel-level land use map to identify job–housing locations and commuting trip routes in Beijing. Two data forms are proposed, one for jobs–housing identification and the other for commuting trip route identification. The results of the identification are aggregated in the bus stop and traffic analysis zone (TAZ) scales, respectively. Particularly, commuting trips from three typical residential communities to six main business zones are mapped and compared to analyze commuting patterns in Beijing. The identified commuting trips are validated by comparison with those from the survey in terms of commuting time and distance, and the positive validation results prove the applicability of our approach. Our experiment, as a first step toward enriching LBS data using conventional survey and urban GIS data, can obtain solid identification results based on rules extracted from existing surveys or censuses. [ABSTRACT FROM AUTHOR]

This paper studies rural land value appreciation and the wealth redistribution effects of China's dual land system, taking Beijing as a case study. We propose a robust method to estimate the land value gap between the land granting price paid by the developer and the rural resident's compensation for the expropriated land. The study shows that the land value gap manifests dramatic variability. While, on average, expropriation charges amount to 15 per cent of the estimated market value of land, this share is under 5 per cent in about a quarter of all rural expropriation cases. The land value gap shows significant and positive correlation with market prices and the intended use for the land, and significant evidence of spatial dependence and heterogeneity. [ABSTRACT FROM AUTHOR]

Highlights: [•] Land use changed dramatically in post-reform Beijing from 1992 to 2008. [•] Cultivated land more than halved while two types of urban built land doubled. [•] Land price, cultivated land availability and location are significant dynamic factors. [•] Land reforms affected the spatial–temporal dynamics of land use changes. [•] Conformity of land price mechanism with reform goals depends on land use types. [Copyright &y& Elsevier]

Emerging on-line reservation services and special car services have greatly affected the development of the taxi industry. Surprisingly, taking a taxi is still a significant problem in many large cities. In this paper, we present an effective solution based on the Hidden Markov Model to predict the upcoming services of vacant taxis that appear at some fixed locations and at specific times. The model introduces a weighted confusion matrix and a modified Viterbi algorithm, combining the factors of time of day and traffic conditions. In our framework, the hotspot or hidden states extraction is implemented through kernel density estimation (KDE) and fuzzy partitioning of traffic zones is done via a Fuzzy C Means (FCM) algorithm. We implement the proposed model on a large-scale dataset of taxi trajectories in Beijing. In this use case, tests demonstrate the high accuracy of the modeling framework in predicting the upcoming services of vacant taxis. We further analyze the factors affecting the predictive accuracy via a prediction accuracy analysis and prediction location evaluation. The findings of this paper can provide intelligence for the improvement of taxi services, to increase the passenger capacity of taxis and also to improve the probability of passengers finding taxis. [ABSTRACT FROM AUTHOR]