Spatial road networks have been widely employed to model the structure and connectivity of cities. In such representation, the question of spatial scale of the entities in the network, i.e. what its nodes and edges actually embody in reality, is of particular importance so that redundant information can be identified and eliminated to provide an improved understanding of city structure. To address this, we investigate in this work the relationship between the spatial scale of the modelled network entities against the amount of useful information contained within it. We employ an entropy measure from complexity science and information theory to quantify the amount of information residing in each presentation of the network subject to the spatial scale and show that it peaks at some intermediate scale. The resulting network presentation would allow us to have direct intuition over the hierarchical structure of the urban organisation, which is otherwise not immediately available from the traditional simple road network presentation. We demonstrate our methodology on the Singapore road network and find the critical spatial scale to be 85 m, at which the network obtained corresponds very well to the planning boundaries used by the local urban planners, revealing the essential urban connectivity structure of the city. Furthermore, the complexity measure is also capable of informing the secondary transitions that correspond well to higher-level hierarchical structures associated with larger-scale urban planning boundaries in Singapore.
翻译:在这种代表性中,网络实体的空间规模问题,即网络节点和边缘在现实中实际体现的内容,特别重要,以便查明并消除多余的信息,从而增进对城市结构的了解。为了解决这一问题,我们在这项工作中调查模拟网络实体空间规模与网络内所含有用信息数量之间的关系。我们从复杂的科学和信息理论中采用了一个微小的测量尺度,以量化网络每个演示中所含受空间规模制约的信息数量,并表明网络在某种中间规模上达到顶峰。由此产生的网络演示将使我们能够对城市组织的等级结构有直接直觉,否则无法立即从传统的简单道路网络演示中获取这种结构。我们在新加坡公路网络上展示了我们的方法,并发现关键空间规模为85米,在这种空间规模上,网络获得的网络与当地城市规划人员所使用的规划界限非常吻合,揭示了城市的基本城市连通结构。此外,复杂性测量还能够向与较高级的层次结构相适应的新加坡二级过渡提供与较高级的层次结构相适应的信息。