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The success of compact development depends in part on accurately gauging its public demand and understanding residents' preferences towards it. Drawing upon a stated-preference survey in the Wasatch Front region in Utah, this paper estimates preferences for compact, walkable and transit-friendly neighbourhoods through the application of a discrete choice experiment. Results derived from a latent class analysis reveal significant heterogeneity in residential location preferences. Overall, strong preferences for compact development are more likely to occur among families with fewer school-age children, low-income and renter-occupied households, and those who appreciate social heterogeneity and have less desire for privacy. These tastes are also associated with personal preferences for walking and biking and supportive opinions toward environmental protection and urban growth boundary policies. By comparing respondents' preferences to their actual residential and travel choices in two contrasting subregions, we further address the complex relationships between environment, preferences, residential locations and travel.

This paper investigates regional inequality and development in China's Guangdong province by employing the multi-scale and multi-mechanism framework. Departing from previous studies in which the relationship between spatial dependence and regional inequality tends to be self-evident, we apply a spatial filtering method that eliminates spatial dependence of the data and quantifies the extent to which spatial effects have contributed to regional inequality at multiple scales. The results suggest that over 90% of the divide between the core of the Pearl River Delta (PRD) and the periphery areas of Guangdong province can be explained by the effect of strengthening spatial autocorrelation. By incorporating spatial filters in space-time models, we further reveal the space-time and core-periphery heterogeneities of development mechanisms. Our study confirms that the integration of a multi-scale and multi-mechanism framework and rigorous spatial analysis methods, such as spatial filtering and space-time modeling, helps better understand the spatial and temporal complexity of regional development in China.

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