Detecting anomalous spatial interaction patterns by maximizing urban population carrying capacity
Introduction
The basic manifestation of urbanization progress is the continuous growth of urban populations. According to the United Nations World Urbanization Prospects (United Nations, 2019), 68% of the world population is projected to be residing in urban areas by 2050. The rate of urbanization is even more pronounced in China. Over the past 40 years, China has experienced the largest and fastest urbanization in the world. In 2019, the urbanization rate in China reached 60.6% with a permanent urban population of nearly 850 million (National Bureau of Statistics of China, 2020). Urban agglomeration resulted in the gradual emergence of a series of social problems and rapid expansion of urban spaces. Therefore, improving urban population carrying capacity has attracted widespread attention from researchers and government officials (Shi, Shi, & Wang, 2019).
Existing researches have confirmed that the aggregated distribution of urban facilities has an agglomeration economics effect, which can significantly increase production efficiency and create more economic benefits. The influence of those areas with complete facilities can be radiated to surrounding areas and even the entire city. The powerful attractions of these areas usually aggregate crowd flow that exceed their population carrying capacities and result in overload-related anomalous interaction patterns. At the same time, in order to alleviate urban problems caused by sharp population growth, massive infrastructure projects have been developed in some suburb areas of a city. However, these newly-built areas may become “ghost cities” if there is not enough population to utilize the relatively complete facilities (Williams, Xu, Tan, Foster, & Chen, 2019). In this case, the lack of expected popularity crowd flow will produce underload-related anomalous interaction patterns in these areas.
The above-mentioned phenomena can be summarized as the spatial mismatch problem, meaning that the unreasonable distribution of urban infrastructures leads to that residents have to bear higher transportation costs to satisfy their multiple demands with the utilization of public facilities (Andersson, Haltiwanger, Kutzbach, Pollakowski, & Weinberg, 2014; Perle, Bauder, & Beckett, 2010). Nowadays, the spatial mismatch problem caused by the re-layout of urban spatial structure has gradually become the main factor of restricting the resident employment, traffic operation efficiency, economic development and so on (Sun & Lv, 2020; Wang et al., 2020). With the help of multi-source trip trajectory big data, the discovery of spatial interaction patterns within cities can provide latent knowledge for the spatial layout optimization of urban facilities (Wu et al., 2019); thus, it has been widely employed in the fields of complex networks, intelligent transportation, and urban geography (Hu, Wang, Wu, & Stanley, 2020; Zhang, Chong, Li, & Nie, 2020).
Spatial interaction is a general term for the movement of people, goods, or information over space (Fotheringham, 2001). In this study, spatial interaction is specifically defined as the human mobility across distinct urban sub-areas. As an important external manifestation of urban spatial mismatch, the process of spatial interaction can also drive the change of spatial distribution patterns of urban facilities (Timmermans, 2001). Consequently, spatial interaction related researches have received unprecedented attention in the era of big data (Shu et al., 2020; Song et al., 2019).Various studies have been conducted to theoretically analyze the spatial interaction patterns by considering dynamic evolution, spatiotemporal resolution, and mobility uncertainties (Espín Noboa, Lemmerich, Singer, & Strohmaier, 2016; Yin, Gao, Du, & Wang, 2016). For example, taxi service ranges were analyzed to guide the optimization of ride-sharing schemes (Zhang et al., 2017; Dong, Wang, Li, & Zhang, 2018). Saberi, Mahmassani, Brockmann, and Hosseini (2017) analyzed travel demand based on multi-layer complex network for detecting characteristic origin-destination areas of urban spatial interactions. Moreover, several measurement methods have been proposed to predict spatial interaction fluxes from the perspective of travel cost and urban structures (Sarkar et al., 2017; Shi, Zhou, Wang, & Wang, 2018). Although the spatial layout of urban functional areas is generally inconsistent with the governments' expectations (Liu, Wang, Xiao, & Gao, 2012; Zhong, Arisona, Huang, Batty, & Schmitt, 2014; Long & Thill, 2015), the characteristics of urban block functions and urban structure can be accurately deducted from spatial interaction patterns (Kong, Xia, Ma, Li, & Yang, 2019), to improve the efficiency of city operations (Kang & Qin, 2016).
Regarding various spatial interactions patterns, the detection of those anomalous ones is conducive to revealing the sources of spatial mismatches and further guide the adjustments of unreasonable urban spatial layouts (Liu et al., 2012). Excess commuting is a representative form of anomalous interaction patterns in cities, which is defined as the differences of the actual and the theoretical minimum commuting distances between workplaces and residential areas (Ma & Banister, 2006). Amounts of studies have been carried out to investigate the formation mechanism of excess commuting by taking multiple factors into consideration (Kanaroglou et al., 2015; Leith et al., 2016; Nick & Karolien, 2020). Additionally, some other studies aimed at evaluating the extent of actual commuting excess relative to the optimal status (Zhou & Murphy, 2019). In addition to excess commuting, some studies have been conducted to detect anomalous interaction patterns using spatial statistical approaches. Han, Liu, and Omiecinski (2013) detected anomalous trajectories of mobile objects on the road network by evaluating the direction and distance similarities with others. From the dynamic microscopic perspective, some researchers modeled the moving behavior on single or multiple objects mathematically to recognize anomalous inter-area interactions (Huang, 2015; Yuan, Liu, & Wei, 2017). Shi et al. (2018) designed an approach to identify anomalous patterns from traffic flow data by constructing dynamic neighborhoods (Shi, Deng, et al., 2018). Liu, Wu, et al. (2020) developed a network-constrained clustering method to statistically detect significant source or sink areas. Cai et al. (2020) mined the anomalous regions according to the spatial co-distribution pattern (Cai, Deng, Guo, Xie, & Shekhar, 2020; Cai, Xie, et al., 2020). And others discovered the convergence and divergence patterns of traffic flow over time (Fang, Yang, Xu, Shaw, & Yin, 2017; Huang, Wang, Zhang, Gao, & Schich, 2018). More recently, several novel methods for detecting arbitrarily shaped clusters of OD flows with different scales in flow space are proposed and have excellent capabilities in aggregation flow detection (Shu et al., 2020; Song et al., 2019).
In summary, the objectives of minimum travel time or distances in the classical excess commuting studies may produce inconsistent optimization results to the actual service ranges of urban facilities, especially in large cities. Additionally, other existing studies of anomalous interaction pattern mining mostly aim to detect the significant aggregation and sparsity of crowds or origin-destination flows by comparing with that in spatial adjacent areas (Tao & Thill, 2018; Jeong, Yin, & Wang, 2018). In other words, they are not suitable to detect those anomalous interaction patterns with significant deviations from the optimal status constrained by current urban facility allocations. This type of anomalous interaction pattern is a critical indicator for the spatial mismatch problem and can provide feasible guides for optimizing both the distribution of resident trips in space and the spatial layout of urban facilities. To overcome these problems, this study measures the influences of urban spatial layout on resident trips by quantifying the attraction of sub-areas, and aims to detect anomalous interaction patterns by successively answering the following questions:
- (1)
What is the optimal interaction flux between any two urban blocks?
- (2)
What are the characteristics of anomalous interaction patterns between the blocks?
- (3)
What are the differences between overload- and underload-related anomalous interaction patterns at different scales?
This study has the following three contributions:
- •
The proposed anomalous interaction pattern detection approach based on bipartite networks and taxi data provides a new perspective for exploring the possibility of optimizing urban population carrying capacities.
- •
A branch and bound (abbreviated as BnB) based augmenting path algorithm weighted by the attractiveness degree is proposed to obtain the optimal interaction fluxes between blocks.
- •
The interaction flux adjustment plan is designed by comparing and analyzing two types of anomalous interaction patterns, namely overload and underload interactions, from multiple perspectives, which can provide feasible suggestions for the spatial layout of urban facilities.
The remainder of this paper is structured as follows. Section 2 describes the research framework and the proposed method for anomalous interaction pattern detection based on bipartite networks. The study area and the data used in this research are elaborated in Section 3. Section 4 reports the results of the experiments conducted to analyze the relationship between two types of anomalous interaction patterns. Section 5 presents the conclusions and future research plans.
Section snippets
Overview of the research framework
By coupling the geospatial network with mathematical optimization, a bipartite network-based framework was developed to detect the anomalous interactions between urban subareas through global maximization of urban population carrying capacities. The proposed framework is mainly composed of four steps (Fig. 1). First, a fully connected bipartite network is constructed using traffic analysis zones (TAZs) as the network nodes, so as to quantitatively represent the spatial interactions between
Study area and data description
Wuhan City, located in Central China, is a typical city with a polycentric spatial structure. In this study, we selected the core built-up area of Wuhan as the study area in the experiments (Fig. 3). Moreover, in order to involve the fine-grained information of resident trips, we selected TAZs obtained the division of the study are by road network as the basic spatial unit.
Static data with fundamental geographic information, e.g., the road network structure, points of interest (POI)
Optimal interaction fluxes between TAZs
To obtain the optimal fluxes between TAZs, attractiveness intensities were quantified using the gravity-based attraction model defined in Section 2. Considering the different scales among population densities, POI densities, and road network distances involved in the model, the Z-score value was used to normalize the data used in this study.where and σ denote the mean and standard deviation of the target dataset. Moreover, to facilitate the subsequent analysis, the original Z values
Conclusions
Intra-urban anomalous interaction patterns that result in the overload or underload of the areal population play a key role in crowd diversion and urban facility reallocation. To accurately detect anomalous interaction patterns, this study first constructed a bipartite network of TAZs. This network was weighted by the attractiveness between TAZ pairs measured by using a gravity-based attraction model. The optimal interactions between TAZs were obtained by solving the bipartite network maximum
Declaration of Competing Interest
The authors declare that this research was conducted in the absence of any relationships that could be construed as a potential conflict of interest.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. 41730105, No. 42071452), the Natural Science Foundation of Hunan Province (No. 2020JJ4696), the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources (No. KF-2019-04-032), the Hunan key research and development project (No. 2018SK2052), the Fundamental Research Funds for the Central Universities of Central South University (No. 2018zzts198), and the Key
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