The problem of emergency facility location is a critical component in evacuation planning. The emergence of geographic information systems (GIS) has provided a useful operational platform to assist this issue. A previously overlooked facet is the consideration of a hierarchical structure in the placement of emergency shelters. Due to the fact that survivors' needs change over time during post-disaster evacuations, shelters have now been categorized on a temporal scale based on their functions at different evacuation phases. This article proposes a three-level hierarchical location model for optimizing the placement of earthquake shelters by taking into account this temporal variance. The article not only scrutinizes the modeling procedure but also implements the model in a planning area with many real-world details. Based on the optimization results derived from a GIS context, we have found that the quality of the earthquake response procedure is not only dependent on the placement strategy of shelters, but more importantly on the financial constraints imposed on the planning and construction of these shelters. A discussion has been proposed to balance the trade-off between budget planning and evacuation efficiency. As the first attempt to model the hierarchical configuration of emergency shelters with specific focus on evacuees' escalating sheltering demands, this article will be of great significance in helping policy makers consider both the spatial and financial aspects of the strategic placement of emergency shelters.

The housing market in Chinese metropolises have become inflated significantly over the last decade. In addition to an economic upturn and housing policies that have potentially fueled the real estate bubble, factors that have contributed to the spatial heterogeneity of housing prices can be dictated by the amenity value in the proximity of communities, such as accessibility to business centers and transportation hubs. In the past, scholars have employed the hedonic pricing model to quantify the amenity value in relation to structural, locational, and environmental variables. These studies, however, are limited by two methodological obstacles that are relatively difficult to overcome. The first pertains to difficulty of data collection in regions where geospatial datasets are strictly controlled and limited. The second refers to the spatial autocorrelation effect inherent in the hedonic analysis. Using Beijing, China as a case study, we addressed these two issues by (1) collecting residential housing and urban amenity data in terms of Points of Interest (POIs) through web-crawling on open access platforms; and (2) eliminating the spatial autocorrelation effect using the Eigenvector Spatial Filtering (ESF) method. The results showed that the effects of nearby amenities on housing prices are mixed. In other words, while proximity to certain amenities, such as convenient parking, was positively correlated with housing prices, other amenity variables, such as supermarkets, showed negative correlations. This mixed finding is further discussed in relation to community planning strategies in Beijing. This paper provides an example of employing open access datasets to analyze the determinants of housing prices. Results derived from the model can offer insights into the reasons for housing segmentation in Chinese cities, eventually helping to formulate effective urban planning strategies and equitable housing policies.

Decentralization development and changing urban form will increase the mobility and contribute to global CO₂ emissions, in particular for developing countries which are experiencing rapid economic growth and urban expansion. In this paper, an integrated analytical framework, which can quantify the impact of changing urban form on commuting CO₂ emissions, is presented. This framework simultaneously considers two emission dependent factors, commuting demand and modal share based on the concept of excess commuting and accessibility analysis, and ensures its applicability to other cities where the detailed individual travel data is not available. A case study of Beijing from 2000 to 2009 is used to illustrate this framework. The findings suggest that changing urban form in Beijing did have a significant impact on commuting CO₂ emission increase. Changing to a more decentralized urban form in Beijing had a larger impact on commuting distance and increased usage of cars, which resulted in a significant rise in CO₂ emissions. There is a larger space and an urgent need for commuting CO₂ emission reduction, in 2009 in Beijing, by planning and by strategic measures in order to promote sustainable transport.

Transit-Oriented Development (TOD) is an urban planning approach that encourages a modal shift from private to public transportation. This shift can generate additional benefits from a sustainability perspective. This study aims to assess the efficiency of TOD by applying the data envelopment analysis (DEA) method. The ridership of public transportation is considered as the direct output characteristic of TOD efficiency, and nine indicators of ridership are selected as inputs on the basis of the core concepts of TOD. These concepts include density, diversity, and design (3Ds). The Tokyu Den-en Toshi Line in Japan is presented as a typical case of TOD because this line includes TOD and non-TOD stations. Assessing and comparing the results of all railway stations reveal that almost all indicator values of non-TOD stations are higher than those of TOD stations. The results suggest that TOD planning and programs are inefficient in terms of ridership generation. This implication, however, may be attributed to the inadequacy of the selected indicators for TOD assessment. The results obtained after adding operation year as input indicators and removing transfer station show that TOD stations perform efficiently and in accordance with expectations. This response indicates that the inclusion of influential factors is necessary for equitable TOD assessment. Therefore, other influential factors must be considered when evaluating the efficiency of TOD-based stations with different inherent attributes. In addition, the design input with the largest impact on all inefficient units was identified, suggesting that management of bus service and railway system should be well enhanced.

Open public places, such as pedestrian streets, parks, and squares, are vulnerable when the pedestrians thronged into the sidewalks. The crowd count changes dynamically over time with various external factors, such as surroundings, weekends, and peak hours, so it is essential to predict the accurate and timely crowd count. To address this issue, this study introduces graph convolutional network (GCN), a network-based model, to predict the crowd flow in a walking street. Compared with other grid-based methods, the model is capable of directly processing road network graphs. Experiments show the GCN model and its extension STGCN consistently and significantly outperform other five baseline models, namely HA, ARIMA, SVM, CNN and LSTM, in terms of $RMSE$, $MAE$ and R². Considering the computation efficiency, the standard GCN model was selected to predict the crowd. The results showed that the model obtains superior performances with higher prediction precision on weekends and peak hours, of which R² are above 0.9, indicating the GCN model can capture the pedestrian features in the road network effectively, especially during the periods with massive crowds. The results will provide practical references for city managers to alleviate road congestion and help pedestrians make smarter planning and save travel time.