Detecting anomalous spatial interaction patterns by maximizing urban population carrying capacity

Rapid urbanization in China has prompted plenty of urban facilities to be constructed with the expectation of harmonizing with the rapid growth of urban population. However, regarding the spatial interactions produced by cross-area human mobility, the diversity and variability of residents' trip requirements inevitably cause the deviations of the real interaction patterns from the optimal status determined by the current allocation of urban facilities. To maximize the utility of urban facility allocation, we designed a bipartite network-based approach to explore anomalous spatial interaction patterns within cities. First, considering the potential area attractiveness, a weighted origin-destination bipartite network was constructed to structure the spatial interactions between traffic analysis zones. Then, a branch and bound (BnB) based augmenting path algorithm was proposed to optimize the distribution of spatial interactions, which can maximize the urban population carrying capabilities. Finally, anomalous interaction patterns causing both overload and underload were detected through comparisons between the actual and optimal spatial interaction distribution. The experimental results show that the two types of anomalous interaction patterns have significantly different spatial distribution characteristics. Through further analyzing the relationships between the two types of anomalous interaction patterns and urban evolution process, this study can also provide targeted decision supports for the accommodating of urban facility allocations to the distributions of resident trips in space.