Although sharing city is by definition a “place-based” approach to understand sharing activities, and despite the fact that spatial proximity and configuration can affect the formation of sharing practices, neither the impacts of sharing activities on space nor the different spatial attributes, which in turn condition sharing activities and behaviors, have been adequately explicated. In this article, the sociospatial dimensions of sharing space are encapsulated through the following three vectors on different spatial scales—namely, urban sharing, sharing a living space, and shared social spaces—and described through the case examples of the dockless bikeshare program, sharing a domestic space, and the coworking space and hackerspace, respectively. These vectors are then framed as the contours of a general theory of sharing spaces.

Accurate and timely classification and monitoring of urban functional zones prove to be significant in rapidly developing cities, to better understand the real and varying urban functions of cities to support urban planning and management. Many efforts have been undertaken to identify urban functional zones using various classification approaches and multi-source geospatial datasets. The complexity of this category of classification poses tremendous challenges to these studies especially in terms of classification accuracy, but on the opposite, the rapid development of machine learning technologies provides us with new opportunities. In this study, a set of commonly used urban functional zones classification approaches, including Multinomial Logistic Regression, K-Nearest Neighbors, Decision Tree, Support Vector Machine (SVM), and Random Forest, are examined and compared with the newly developed eXtreme Gradient Boosting (XGBoost) model, using the case study of Yuzhong District, Chongqing, China. The investigation is based on multi-variate geospatial data, including night-time imagery, geotagged Weibo data, points of interest (POI) from Gaode, and Baidu Heat Map. This study is the first endeavor of implementing the XGBoost model in the field of urban functional zones classification. The results suggest that the XGBoost classification model performed the best and was able to achieve an accuracy of 88.05%, which is significantly higher than the other commonly used approaches. In addition, the integration of night-time imagery, geotagged Weibo data, POI from Gaode, and Baidu Heat Map has also demonstrated their values for the classification of urban functional zones in this case study.

Walkability has increasingly been recognized as an important factor for sustainable urban development that, however, has seldom been investigated in rapid urbanizing cities, especially in the Asian context. This paper assessed walkability in Futian District in the central area of Shenzhen, which has undergone very rapid urbanization within a short period of time. Walkability was assessed for 2013 and 2018 using a walkability index adapted from Walk Score. It was compared with housing prices of 215 randomly selected residential buildings, to further explore the relationships between walkability and residential estate value, provided as one practical application of the assessment of walkability in urban management. Our results show that Futian District has low walkability level, although walkability has been generally improved from 2013 to 2018. A high spatial variation of walkability level within this area was observed in both years. Overall, there was a negative relationship between walkability and housing prices (significant only in 2018), which is inconsistent with studies elsewhere. The results suggest that the housing prices in Futian District are more strongly influenced by other factors rather than by walkability. In addition, the ability of the walkability model to explicitly delineate spatial variation of walkability level makes it a powerful tool to be applied in urban planning and management. Results of this study also have practical applications, which can be used as a reference for residents’ home selection and enable them to make informed decisions in selecting walkable neighborhoods with acceptable prices.

Mixed methods research (MMR) is useful for addressing complex and multidisciplinary urban problems. This article demonstrates an integrated MMR approach with a novel two-phase exploratory sequential design while studying play, play space, and children’s (age 7-15 years) location preference for play in three residential areas in Dhaka. We used directly administered survey and interviews in the first phase to describe play and play space from children’s perspective. Informed by the first, we employed GIS-based spatial and statistical analysis in the second phase to study patterns of children’s location preference for play. Our article contributes to the methodological literature by combining MMR with urban spatial analysis in children’s play environment studies.

The transit-oriented development (TOD) model provides benefits to urban areas in terms of transportation, the economy, society, and the environment. Given the complexity and limitations arising from the various backgrounds, objectives, and other aspects of previous studies, empirical research on specific cases has often failed to fully analyze the influence of built environment indicators on TOD performance. This paper systematically reviews and analyzes related empirical studies conducted worldwide up until June 2023. The correlations between built environment indicators and their impacts on TOD performance indicators are visualized and measured. General patterns of the influences of built environment indicators on TOD performance are summarized using social network analysis (SNA), and the key indicators are identified via cluster analysis. Finally, by analyzing the key indicators such as diversity, density, design, and distance to transit stations, as well as their associated built environment indicators, the built environment characteristics that ensure TOD performance are thoroughly examined. These insights provide a better understanding of the influences of the built environment on TOD performance, thus offering guidance for the development and application of the TOD model.