As a huge threat to the public health, China's air pollution has attracted extensive attention and continues to grow in tandem with the economy. Although the real-time air quality report can be utilized to update our knowledge on air quality, questions about how pollutants evolve across time and how pollutants are spatially correlated still remain a puzzle. In view of this point, we adopt the PMFG network method to analyze the six pollutants' hourly data in 350 Chinese cities in an attempt to find out how these pollutants are correlated temporally and spatially. In terms of time dimension, the results indicate that, except for O$_3$, the pollutants have a common feature of the strong intraday patterns of which the daily variations are composed of two contraction periods and two expansion periods. Besides, all the time series of the six pollutants possess strong long-term correlations, and this temporal memory effect helps to explain why smoggy days are always followed by one after another. In terms of space dimension, the correlation structure shows that O$_3$ is characterized by the highest spatial connections. The PMFGs reveal the relationship between this spatial correlation and provincial administrative divisions by filtering the hierarchical structure in the correlation matrix and refining the cliques as the tinny spatial clusters. Finally, we check the stability of the correlation structure and conclude that, except for PM$_{10}$ and O$_3$, the other pollutants have an overall stable correlation, and all pollutants have a slight trend to become more divergent in space. These results not only enhance our understanding of the air pollutants' evolutionary process, but also shed lights on the application of complex network methods into geographic issues.
Classifying Urban Regions by Aggregated Pollutant Weather Correlation Strength: A Spatiotemporal Study
Maulana Abul Kalam Azad University of Technology, West Bengal（西孟加拉马乌拉纳·阿卜杜勒·卡兰·阿扎德理工大学）；S. N. Bose National Centre for Basic Sciences（S.N.玻色基础科学国家中心）；Indian Statistical Institute（印度统计研究所）
我们引入了一个模型，在这个模型中，城市人口的增长率与他们的“势力范围”的面积成正比，其中一个城市的影响力取决于它的人口（α次方）和距离城市（β次方），新的城市根据一定的随机规则出现。一个简单的非严格的渐近分析表明，当β> 2α $ the system exhibits "balanced growth" in which there are an increasing number of large cities, whose populations have the same order of magnitude, whereas for β< 2α$时，系统表现出“不平衡增长”，其中少数城市捕获了总人口的大部分。从概念上讲，该模型最好被视为组合的“中国餐馆过程”的空间模拟。
Twenty-Five Years of Air Quality in Bangladesh: Trends, Seasonality, and Spatial Pollution Regimes
North South University（北南大学）；University of Science and Technology（科学技术大学）；AIUB（伊斯兰阿齐兹大学）；Islamic University of Technology（伊斯兰技术大学）；University of Dhaka（达卡大学）
An Analytical Process of Spatial Autocorrelation Functions Based on Moran's Index
A number of spatial statistic measurements such as Moran's I and Geary's C can be used for spatial autocorrelation analysis. Spatial autocorrelation modeling proceeded from the 1-dimension autocorrelation of time series analysis, with time lag replaced by spatial weights so that the autocorrelation functions degenerated to autocorrelation coefficients. This paper develops 2-dimensional spatial autocorrelation functions based on the Moran index using the relative staircase function as a weight function to yield a spatial weight matrix with a displacement parameter. The displacement bears analog…
