School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia.
Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia.
PLoS One. 2020 Dec 7;15(12):e0243390. doi: 10.1371/journal.pone.0243390. eCollection 2020.
Analyses of urban scaling laws assume that observations in different cities are independent of the existence of nearby cities. Here we introduce generative models and data-analysis methods that overcome this limitation by modelling explicitly the effect of interactions between individuals at different locations. Parameters that describe the scaling law and the spatial interactions are inferred from data simultaneously, allowing for rigorous (Bayesian) model comparison and overcoming the problem of defining the boundaries of urban regions. Results in five different datasets show that including spatial interactions typically leads to better models and a change in the exponent of the scaling law.
城市规模法则的分析假设不同城市的观测结果与附近城市的存在无关。在这里，我们引入了生成模型和数据分析方法，通过显式地建模不同位置个体之间的相互作用来克服这一限制。描述规模法则和空间相互作用的参数是从数据中同时推断出来的，这允许进行严格的（贝叶斯）模型比较，并克服了定义城市区域边界的问题。在五个不同数据集上的结果表明，包括空间相互作用通常会导致更好的模型和规模法则指数的变化。
