Building Function Mapping Using Multisource Geospatial Big Data: A Case Study in Shenzhen, China

Building function labelling plays an important role in understanding human activities inside buildings. This study develops a method of function label classification using integrated features derived from remote sensing and crowdsensing data with an extreme gradient boosting tree (XGBoost). The classification framework is verified based on a dataset from Shenzhen, China. An extended label system for six building types (residential, commercial, office, industrial, public facilities, and others) was applied, and various social functions were considered. The overall classification accuracies were 88.15% (kappa index = 0.72) and 85.56% (kappa index = 0.69). The importance of features was evaluated using the occurrence frequency of features at decision nodes. In the six-category classification system, the basic building attributes (22.99%) and POIs (46.74%) contributed most to the classification process; moreover, the building footprint (7.40%) and distance to roads (11.76%) also made notable contributions. The result shows that it is feasible to extract building environments from POI labels and building footprint geometry with a dimensional reduction model using an autoencoder. Additionally, crowdsensing data (e.g., POI and distance to roads) will become increasingly important as classification tasks become more complicated and the importance of basic building attributes declines

The spatio-temporal relationship between land use and population distribution around new intercity railway stations: A case study on the Pearl River Delta region, China

Recently, transit-oriented development (TOD) projects have begun to prosper around new intercity railway (ICR) stations in China. An important question is whether the ICR-based TOD could perform as expected since the regional ICR is different from urban transit on which more TOD projects base. This article utilizes a remote sensing dataset, mobile-phone location-based big data as well as web map portal data and a Geographically and Temporally Weighted Regression (GTWR) model to explore the spatio-temporal relationship between land use and the population distribution in the ICR station area. It adopts the Pearl River Delta region in southern China as a study area, which undergoes rapid urbanization and is a pioneer to adopt TOD to promote the ICR project. The research finds the following. First, the combination of remote sensing, spatial big data, web map portal data and the GTWR model efficiently reveals the underlying spatio-temporal heterogeneities of the relationship between land use and population distribution in the ICR station area. Second, compared with the existing built-up area, the newly developed land after ICR construction has a weaker correlation with population distribution in the ICR station area. Third, the locations of the ICR station areas within the urban-rural system play a significant role in determining the relationship between land use and population distribution. For example, the association of working facilities with population distribution in suburban and town ICR station areas is significantly larger than that in urban and rural ICR station areas

Building Function Mapping Using Multisource Geospatial Big Data: A Case Study in Shenzhen, China

Building function labelling plays an important role in understanding human activities inside buildings. This study develops a method of function label classification using integrated features derived from remote sensing and crowdsensing data with an extreme gradient boosting tree (XGBoost). The classification framework is verified based on a dataset from Shenzhen, China. An extended label system for six building types (residential, commercial, office, industrial, public facilities, and others) was applied, and various social functions were considered. The overall classification accuracies were 88.15% (kappa index = 0.72) and 85.56% (kappa index = 0.69). The importance of features was evaluated using the occurrence frequency of features at decision nodes. In the six-category classification system, the basic building attributes (22.99%) and POIs (46.74%) contributed most to the classification process; moreover, the building footprint (7.40%) and distance to roads (11.76%) also made notable contributions. The result shows that it is feasible to extract building environments from POI labels and building footprint geometry with a dimensional reduction model using an autoencoder. Additionally, crowdsensing data (e.g., POI and distance to roads) will become increasingly important as classification tasks become more complicated and the importance of basic building attributes declines

Residents’ Green Purchasing Intentions in a Developing-Country Context: Integrating PLS-SEM and MGA Methods

This paper aims to examine the determinants of green purchasing intentions among different resident groups in a developing-country context. We first expand the theory of planned behaviour (TPB) and build a theoretical model based on green purchasing intention, including attitude, perceived behavioural control, subjective norms, environmental concern, habit, and socio-demographic characteristics (i.e., age, gender, residential area, and educational level). Following this, we collect 552 questionnaires from residents in Tianjin Municipality, China. We use partial least squares structural equation modelling (PLS-SEM) to analyse the green purchasing intention of the population sample and then employ a multi-group analysis (MGA) to explore the group differences in residents’ green purchasing intention. The results show that green purchasing intention is significantly and positively influenced by attitude, perceived behavioural control, subjective norms, and environmental concern but not by habit. The relationship chain of environmental concern→subjective norms→purchasing intention is the strongest. The results of the MGA show that for residential-area groups, the relationships between attitudes, perceived behavioural control, and habits and purchasing intention differ significantly between the downtown group and the outside-the-city group. For the educational-level groups, the relationship between environmental concern and subjective norms differs significantly between the high-education group and the low-education group. Finally, these findings contribute to the literature on the TPB model on green purchasing intention and provide some suggestions for the local government and green marketers

Impact of Housing and Community Conditions on Multidimensional Health among Middle- and Low-Income Groups in Hong Kong

With decades of urbanization, housing and community problems (e.g., poor ventilation and lack of open public spaces) have become important social determinants of health that require increasing attention worldwide. Knowledge regarding the link between health and these problems can provide crucial evidence for building healthy communities. However, this link has heretofore not been identified in Hong Kong, and few studies have compared the health impact of housing and community conditions across different income groups. To overcome this gap, we hypothesize that the health impact of housing and community problems may vary across income groups and across health dimensions. We tested these hypotheses using cross-sectional survey data from Hong Kong. Several health outcomes, e.g., chronic diseases and the SF-12 v. 2 mental component summary scores, were correlated with a few types of housing and community problems, while other outcomes, such as the DASS-21–Stress scores, were sensitive to a broader range of problems. The middle- and low-income group was more severely affected by poor built environments. These results can be used to identify significant problems in the local built environment, especially amongst the middle- and low-income group.Arts, Faculty ofNon UBCPsychology, Department ofReviewedFacult