Urban green spaces play a critical role in public health and human wellbeing for urban residents. Due to the uneven spatial distribution of urban green spaces in most of cities, the issue of the disparity between supply and demand has aroused public concern. In a case of Shenzhen, a modified Gaussian-based two-step floating catchment area (2SFCA) method is adopted to evaluate the disparity between park provision and the demanders in terms of accessibility at hierarchical levels under four types of distance (e.g., Euclidean distance, walking distance, bicycling distance, and driving distance), which is well aligned with hierarchical systems in urban green spaces in urban planning practice. By contrast and correlation analysis, among the four types of distance, the statistical correlations are relatively high between Euclidean distance and the other three. Nonetheless, the pattern of spatial accessibility under different type of travel distance is apparently variant. Accessibility calculated by Euclidean distance is overestimated relative to that of the other three, while the pattern of walking distance and bicycling distance is similar to each other. The choice of type of distance is worthy of caution when evaluating spatial accessibility by 2SFCA method. Results show that the accessibility to parks at all hierarchical levels is high particularly, particularly at the natural level. However, the disparity between the supply and demand is significant. The percentage of communities that have high population density but low park accessibility is over 40% (equivalent to approximately 55% of the population). The finding may provide implications on access to urban greens paces for urban planners and authorities to develop effective planning strategies.

With the development of the economy and the acceleration of urbanization, traffic congestion has become a worldwide problem. Advances in mobile Internet and sensor technologies have increased real-time data sharing, providing a new opportunity for urban route planning. However, due to the difficulty of handling complex global information, making correct decisions in large-scale and complex traffic environments is a problem that urgently needs to be solved. In this paper, a multiple route planning model (multi-route dynamic programming (DP) model) is proposed to solve the urban route planning problem with traffic flow information. In particular, we adopt the DP algorithm in this model, design a reward function suitable for urban path planning problems, and generate multiple routes based on the Q values. In addition, we design different scenarios using real-world road networks to test our model. Through the experiments, we demonstrate that our model has the potential to yield optimal results under large-scale scenarios with high efficiency. The advantages of integrating the distance contribution index (DCI) in the reward function are also elaborated. Moreover, our model can provide alternative routes to divert traffic from the optimal route, thus mitigating the congestion drift problem.

As the era of big data approaches, big data has attracted increasing amounts of attention from researchers. Various types of studies have been conducted and these studies have focused particularly on the management, organization, and correlation of data and calculations using data. Most studies involving big data address applications in scientific, commercial, and ecological fields. However, the application of big data to government management is also needed. This paper examines the application of multi-source government data to urban construction and supervision in Tianjin, China. The analytic hierarchy process and a new approach called the correlation degree algorithm are introduced to calculate the degree of correlation between different approval items in one construction project and between different construction projects. The results show that more than 75% of the construction projects and their approval items are highly correlated. The results of this study suggest that most of the examined construction projects are well supervised, have relatively high probabilities of satisfying the relevant legal requirements, and observe their initial planning schemes.

Longevity is a near-universal human aspiration that can affect moral progress and economic development at the social level. In rapidly developing China, questions about the geographical distribution and environmental factors of longevity phenomenon need to be answered more clearly. This study calculated the longevity index (LI), longevity index for females (LIF) and longevity index for males (LIM) based on the percentage of the long-lived population among the total number of elderly people to investigate regional and gender characteristics at the county level in China. A new multi-scale geographically weighted regression (MGWR) model and four possible geographical environmental factors were applied to explore environmental effects. The results indicate that the LIs of 2838 counties ranged from 1.3% to 16.3%, and the distribution showed obvious regional and gender differences. In general, the LI was high in the East and low in the West, and the LIF was higher than the LIM in 2614 counties (92.1%). The MGWR model performed well explaining that geographical environmental factors, including topographic features, vegetation conditions, human social activity and air pollution factors have a variable influence on longevity at different spatial scales and in different regions. These findings enrich our understanding of the spatial distribution, gender differences and geographical environmental effects on longevity in China, which provides an important reference for people interested in the variations in the associations between different geographical factors.