Measuring the spatiotemporal variation and evolution of transport network of China＇s megaregions

Megaregion has become a prominent feature of modern China. Reflecting upon China＇s recent path of transport infrastructure construction, this research examines the spatiotemporal characteristics of transport network development and its accessibility impacts in China＇s ten megaregions from 1982 to 2010. Using historical transport network data and multiple national censuses （1982, 1990, 2000 and 2010）, we computed two levels of indicators of megaregional transport network： megaregion level and county level, and analyzed the intra-megaregion and inter-megaregion disparities of transport network of the ten megaregions of China. Transport networks at the megaregion level are measured by three indicators： 1） transport network density; 2） infrastructure endowment per capita; and 3） size of transport network＇s standard ellipse. Two accessibility indicators for measuring transportation network at the county level are calculated： weighted average travel time and potential accessibility. The research results show the following： 1） Road and rail network densities witnessed the greatest growth during the 2000-2010 period, and growth was more significant for railway network. 2） By 2010, average road endowments per capita in inland megaregions became higher than in coastal megaregions, while average rail endowments per capita in coastal megaregions became higher than in inland megaregions. 3） The sizes and directions of the standard deviational ellipses of road and rail network changed continuously during the study period. However the changes of road network ellipses were relatively small, while the changes of railway network ellipses were more significant. 4） Megaregions have all benefited significantly from transportation infrastructure improvement in the past few decades in terms of WATT and potential accessibility, but the three giant megaregions benefited most.

Multiple scenario simulation and optimization of an urban green infrastructure network based on complex network theory:a case study in Harbin City,China

Background:Urban green infrastructure(GI)networks play a significant role in ensuring regional ecological security;however,they are highly vulnerable to the influence of urban development,and the optimization of GI networks with better connectivity and resilience under different development scenarios has become a practical problem that urgently needs to be solved.Taking Harbin,a megacity in Northeast China,as the case study,we set five simulation scenarios by adjusting the economic growth rate and extracted the GI network in multiple scenarios by integrating the minimal cumulative resistance model and the gravity model.The low‑degree‑first(LDF)strategy of complex network theory was introduced to optimize the GI network,and the optimization effect was verified by robustness analysis.Results:The results showed that in the 5%economic growth scenario,the GI network structure was more complex,and the connectivity of the network was better,while in the other scenarios,the network structure gradually degraded with economic growth.After optimization by the LDF strategy,the average degree of the GI network in multiple scenarios increased from 2.368,2.651,2.189,1.972,and 1.847 to 2.783,3.125,2.643,2.414,and 2.322,respectively,and the GI network structure connectivity and resilience were significantly enhanced in all scenarios.Conclusions:Economic growth did not necessarily lead to degradation of the GI network;there was still room for economic development in the study area,but it was limited under existing GI conditions,and the LDF strategy was an effective method to optimize the GI network.The research results provide a new perspective for the study of GI network protection with urban economic growth and serve as a methodological reference for urban GI network optimization.

Distributed cache replacement method for geospatial data using spatiotemporal localitybased sequence

Specific features of tile access patterns can be applied in a cache replacement strategy to a limited distributed high-speed cache for the cloud-based networked geographic information services(NGISs),aiming to adapt to changes in the access distribution of hotspots.By taking advantage of the spatiotemporal locality,the sequential features in tile access patterns,and the cache reading performance in the burst mode,this article proposes a tile sequence replacement method,which involves structuring a Least Recently Used(LRU)stack into three portions for the different functions in cache replacement and deriving an expression for the temporal locality and popularity of the relevant tile to facilitate the replacement process.Based on the spatial characteristics of both the tiles and the cache burst mode with regard to reading data,the proposed method generates multiple tile sequences to reflect spatiotemporal locality in tile access patterns.Then,we measure the caching value by a technique based on a weighted-based method.This technique draws on the recent access popularity and low caching costs of tile sequences,with the aim of balancing the temporal and spatial localities in tile access.It ranks tile sequences in a replacement queue to adapt to the changes in accessed hotspots while reducing the replacement frequency.Experimental results show that the proposed method effectively improves the hit rate and utilization rate for a limited distributed cache while achieving satisfactory response performance and high throughput for users in an NGIS.Therefore,it can be adapted to handle numerous data access requests in NGISs in a cloud-based environment.