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Residential mobility has become an increasingly important issue in China, where urban families participate frenetically in the commodity housing market leading to soaring property prices. Applying place dependence approach and logistic regression models, this paper examines the association between residential mobility intentions and place dependence for employment and education in Beijing. The results reveal that people's intentions to move homes are driven by place dependence on the access to high-quality schools for their children, while the dependence for employment is rather weak. The findings indicate children's education has emerged as a new driving force that shapes urban space in contemporary China and therefore should be a focal point for planners and policy makers in urban resource distribution. This paper provides evidences to further our understanding of the high premium of properties that enable access to good schools. The needs for other spatial utilities and sentimental attachment to a place also encourage residential mobility intentions, which deserves further research. • Residential mobility intention was analyzed in Zhongguancun area in Beijing. • Influences of children's education and parents' employment were measured. • Children's education has emerged as a new driving force affecting residential choices. • Place dependence provides a useful lens to comprehend housing mobility in contemporary cities. [ABSTRACT FROM AUTHOR]

The notion of path dependence provides a useful perspective to understand the dynamics of industrial space. However, it is much developed on institutional and technological aspects. This paper proposes the idea of spatial path dependence, arguing that previous spatial distribution of economic activities and associated factors in a given industrial space shall affect current and future ones. Availing of big data technology, the spatial distribution is quantified, and spatial path dependence is examined by means of standard deviation ellipse and machine learning method for information service and its sub-sectors in Beijing during the periods of 2008 and 2013. The analysis shows an existence of spatial path dependence for those industries in the two periods. The dominant factors are screened out, which are differ in different sub-sectors and in different periods, but contribute to the same or similar spatial path. The findings call for the attention of the existing situation for industrial spatial planning, and new emerging "people-oriented" factors in influencing the spatial layout of information services industries. • Spatial path dependence is suggested. • Big data technology (BDT) offers new opportunities for industrial space analysis. • Uses case study of Beijing's information services industry development, 2008–2013. • Shows spatial path dependence in industry as whole, and among individual industry types. • Demonstrates value of BDT in identifying and examining spatial path dependence. [ABSTRACT FROM AUTHOR]

The relationship between the built environment and travel behavior has long been a hot research topic. However, most existing research focus on aggregate measures of the patterns of daily travel (e.g. vehicle miles travelled, total distance travelled), while the mechanisms and processes that lead to these outcomes are much less researched. We therefore propose an activity-based model that simulates people's detailed decision making in daily travel and the influences of the neighborhood-scale built environment throughout the process, using Beijing as the study case. More specifically, it deals with eight activity types, eleven most common activity plans, 652 traffic analysis zones, six time slots and four travel modes. The model is then applied to simulate how people's travel behavior would change in response to changes in the built environment. The simulation is implemented by changing the built environment conditions. The major advantage of the model lies in its ability to 'trace back' the influence of the built environment on aggregate travel outcomes to detailed travel aspects, such as activity frequency, travel distance for various purposes, mode choice, etc. The simulation results show that although compact design generally reduce VMT, the mechanisms of influence by different built environment variables vary, e.g. through a major impact on the mode choice or through distributed impacts on several travel aspects. • Propose an activity-based model that simulates the detailed influences of neighborhood-scale built environment on travel • The model can 'trace back' the influence of the built environment on aggregate travel outcomes to detailed travel aspects • Although compact design generally reduces car use, the mechanisms of influence by different built environment variables vary [ABSTRACT FROM AUTHOR]