USA人口与发展的空间网络连通性;对疾病传播的影响
Spatial network connectivity of population and development in the USA; Implications for disease transmission
摘要：
齐普夫定律指出，城市人口的等级规模分布遵循指数为-1的幂律。普遍幂律的主张是有争议的，因为线性和斜率似乎随时间和国家而变化。我们比较人口普查和夜灯亮度作为代理人口密度和强度的发展，在毗邻的美国。将人口密度和发展强度作为连续量处理，可以根据空间连通性水平定义空间网络。由此产生的空间网络组件（连接节点的子集）的分布随连接程度而变化，但保持一致的缩放范围广泛的网络大小。在大陆尺度上，从人口密度和夜间灯光亮度获得的空间网络秩大小分布是很好的拟合幂律与指数接近-1的密度和亮度阈值的范围很广。然而，最大的组成部分（10，000 - 100，000 sq.km）远远大于单个城市，代表城市、郊区和城郊发展的空间连续聚集。将美国SARS-CoV-2确诊病例的县级数量投影到人口与发展的空间网络上，可以将流行病的时空演变量化为不同连通性网络内的传播。结果显示，在2020年3月出现流动性减少之前，确诊病例从少数网络组成部分的缓慢增加突然转变为快速的地理分散到更多组成部分。
英文摘要：
Zipfs Law states that rank-size distributions of city populations follow a power law with an exponent of -1. The assertion of a universal power law is controversial because the linearity and slope appear to vary over time and among countries. We compare census enumerations and night light luminance as proxies for population density and intensity of development in the contiguous United States. Treating population density and development intensity as continuous quantities allows for the definition of spatial networks based on the level of spatial connectivity. The resulting distributions of spatial network components (subsets of connected nodes) vary with degree of connectivity, but maintain consistent scaling over a wide range of network sizes. At continental scales, spatial network rank-size distributions obtained from both population density and night light brightness are well-fit by power laws with exponents near -1 for a wide range of density and luminance thresholds. However, the largest components (10,000 - 100,000 sq.km) are far larger than individual cities and represent spatially contiguous agglomerations of urban, suburban and periurban development. Projecting county-level numbers of confirmed cases of SARS-CoV-2 for the US onto spatial networks of population and development allows the spatiotemporal evolution of the epidemic to be quantified as propagation within networks of varying connectivity.. The results show an abrupt transition from slow increases in confirmed cases in a small number of network components to rapid geographic dispersion to a larger number of components before mobility reductions occurred in March 2020.
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