Hua Shu

Institute of Geographic Sciences and Natural Resources Research

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Tao Pei

Institute of Geographic Sciences and Natural Resources Research

Ci Song

Chinese Academy of Sciences

Sihui Guo

Chinese Academy of Surveying and Mapping

Yaxi Liu

Institute of Geographic Sciences and Natural Resources Research

Jie Chen

Hunan University of Technology

Chenghu Zhou

Chinese Academy of Sciences

Ting Ma

Harbin Institute of Technology

Yunyan Du

Institute of Geographic Sciences and Natural Resources Research

Xi Wang

Shanxi University

Xiao Chen

Institute of Geographic Sciences and Natural Resources Research

Cooperative Institutions

Chinese Academy of Sciences

Institute of Geographic Sciences and Natural Resources Research

University of Chinese Academy of Sciences

State Key Laboratory of Resources and Environmental Information System

Zhejiang University

Nanjing University

Shanghai Jiao Tong University

Wuhan University

Chinese Academy of Medical Sciences & Peking Union Medical College

Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application

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L-function of geographical flows

International Journal of Geographical Information Science (2020)

Hua Shu Tao Pei Ci Song Xiao Chen Sihui Guo

Geographical flow (hereafter flow) can be modeled as an orderly connected point pair composed of an origin (O) and a destination (D). Aggregation is the most common form of spatial heterogeneity of flows, which we define as their deviation from complete spatial randomness (CSR), and the aggregation scale is an important indicator for its perception. Nevertheless, quantifying the aggregation scale of flows is still an unsolved problem. In this paper, we propose the L-function for flows as a solution, derive theoretical null models of the K-function and L-function in a flow space. We conduct simulation experiments to validate the L-function and its capability to detect aggregation scales. Finally, we apply the solution in a case study with taxi data in Beijing and identify nine aggregation scales of taxi OD flows, ranging from 170 m to 22.1 km. These scales correspond to three classes: less than 300 m, from 600 m to 700 m and more than 1500 m. The classes are related to the sizes of the urban facilities where the dominant flow clusters occur, indicating that the L-function in flow space can detect the aggregation scale of flows at the building scale, the block scale and the district scale.

10.1080/13658816.2020.1749277

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Citations (45)

Visualizing road connectivity under traffic influence: a CiteSpace-based method

Cartography and Geographic Information Science (2024)

L. Jiang Jingyu Jiang Xiaohan Liu Zidong Fang Tianyu Liu

The connectivity of roads under the influence of traffic is determined by the topological connections established by the road network and the transport connections formed by moving vehicles between two roads simultaneously. A thorough examination of this connectivity is crucial for comprehending traffic patterns and providing guidance to address transportation issues such as traffic congestion. However, existing methods ignore the intuitive representation and explanation of this connectivity, limiting the understanding of actual traffic patterns. This study aimed to intuitively visualize the connectivity of roads under the influence of traffic using CiteSpace, a bibliometric tool for analyzing the relationship between literature keywords. Here, the literature was replaced by trajectory, and keywords were replaced by roads. We then presented the connectivity of roads under the influence of traffic in a network, where nodes represented roads and edges represented the topological and transport connections between roads. A case study was conducted in Beijing, demonstrating the dynamic connectivity of roads under the influence of traffic in the visualized network. By comparing the visualized network with the topological road network, we identified three types of road connectivity under the influence of traffic: roads with topological connections but lacking transport connections, roads with transport connections but lacking topological connections, and roads with both topological and transport connections. This finding further revealed the traffic relationship between roads. Additionally, we simultaneously presented the multidimensional traffic characteristics in the visualized network, aiding in predicting traffic states.

10.1080/15230406.2024.2310884

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Citations (1)

Quantifying the spatial heterogeneity of points

International Journal of Geographical Information Science (2019)

Hua Shu Tao Pei Ci Song Ting Ma Yunyan Du

Variation in the spatial heterogeneity of points reflects the evolutionary process or mechanism of geographical events. The key to depicting this variation is quantifying spatial heterogeneity. In this paper, the spatial heterogeneity of a point pattern is defined as the degree of aggregation-type deviation from complete spatial randomness. In such a case, a goodness-of-fit-type statistic based on the distribution of nearest-neighbor distances called the level of heterogeneity (LH*) is regarded as a standard measurement, and a normalized version called the normalized level of heterogeneity (NLH*) is proposed for datasets with different point numbers and study region areas. Considering the complex integration calculation of LH* and NLH*, simulation experiments are implemented to test the capability of some classic nearest-neighbor statistics in quantifying spatial heterogeneity. The results showed that except for the standard LH* statistic, only Clark and Evans' statistic (A-w) and Byth and Ripley's statistic (H-xw) are robust. Statistics NLH*, (A-w) and (H-xw) are validated by quantifying the spatial heterogeneity of two-dimensional crime events, three-dimensional earthquake events and four-dimensional origin-destination (OD) events. The results indicate that these statistics all have a reasonable explanation in quantifying spatial heterogeneity for real-world geographical events of different types and with different dimensions. Compared with NLH*, Clark and Evans' (A-w) statistic and Byth and Ripley's (H-xw) statistic are recommended from the perspective of accessibility.

Statistic

Spatial heterogeneity

Scan statistic

Spatial Dependence

10.1080/13658816.2019.1577432

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Citations (11)

Population Distributions of Age Groups and Their Influencing Factors Based on Mobile Phone Location Data: A Case Study of Beijing, China

Sustainability (2019)

Wenlai Wang Tao Pei Jie Chen Ci Song Xi Wang

The fine-grained population distributions of different age groups are crucial for urban planning applications. With the development of information and communication technology (ICT), detailed population data retrieved from various big data sources, especially on a fine scale, have been extensively used for urban planning. However, studies estimating the detailed population distributions of different age groups are still lacking. This study constructs a framework to generate fine-grained population data for different age groups and explores the influence of various factors on the distributions of different age groups. The population is divided into the following four age groups: (1) early adulthood people: 18 ≤ age ≤ 24, (2) young people: 25 ≤ age ≤ 39, (3) middle-aged people: 40 ≤ age ≤ 59, and (4) elderly people: 60 ≤ age. The results indicate that education and accommodation factors have a major influence on the distributions of early adulthood and elderly people, respectively. Business, restaurant, and accommodation factors are the main factors influencing the population distributions of young and middle-aged people. The accommodation factor plays a major controlling role at night, and its explanatory power gradually decreases during the day, while the explanatory powers of the business and restaurant factors increase and become leading factors during the day. Specifically, the hospital factor has a greater effect on the distribution of elderly people. The entertainment factor has very little explanatory power for the population distributions of the different age groups.

Age groups

Explanatory power

Middle age

Mobile phone

10.3390/su11247033

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Citations (6)

Revealing spatiotemporal matching patterns between traffic flux and road resources using big geodata - A case study of Beijing

Cities (2022)

Xiaorui Yan Ci Song Tao Pei Xi Wang Mingbo Wu

10.1016/j.cities.2022.103754

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Citations (20)

Finding and Evaluating Community Structures in Spatial Networks

ISPRS International Journal of Geo-Information (2023)

You Wan Xicheng Tan Hua Shu

Community detection can reveal unknown spatial structures embedded in spatial networks. Current spatial community detection methods are mostly modularity-based. However, due to the lack of appropriate spatial networks serving as a benchmark, the accuracy and effectiveness of these methods have not been tested sufficiently so far. This study first introduced a spatial autoregressive and gravity model united method (SARGM) to simulate benchmark spatial networks with known regional distributions. Then, a novel spectral clustering-based spatial community detection method (SCSCD) was proposed to identify spatial communities from eight kinds of benchmark spatial networks. Comparative experiments on SCSCD and three other methods showed that SCSCD performed the best in accuracy and effectiveness. Moreover, the scale parameter and the community number setting of the SCSCD were investigated experimentally. Finally, a case study was applied to the SCSCD to demonstrate its ability to extract the internal community structure of a high-speed train network in China.

Benchmark (surveying)

Modularity

10.3390/ijgi12050187

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Citations (3)

Detecting arbitrarily shaped clusters in origin-destination flows using ant colony optimization

International Journal of Geographical Information Science (2018)

Ci Song Tao Pei Ting Ma Yunyan Du Hua Shu

An origin-destination (OD) flow can be defined as the movement of objects between two locations. These movements must be determined for a range of purposes, and strong interactions can be visually represented via clustering of OD flows. Identification of such clusters may be useful in urban planning, traffic planning and logistics management research. However, few methods can identify arbitrarily shaped flow clusters. Here, we present a spatial scan statistical approach based on ant colony optimization (ACO) for detecting arbitrarily shaped clusters of OD flows (AntScan_flow). In this study, an OD flow cluster is defined as a regional pair with significant log likelihood ratio (LLR), and the ACO is employed to detect the clusters with maximum LLRs in the search space. Simulation experiments based on AntScan_flow and SaTScan_flow show that AntScan_flow yields better performance based on accuracy but requires a large computational demand. Finally, a case study of the morning commuting flows of Beijing residents was conducted. The AntScan_flow results show that the regions associated with moderate- and long-distance commuting OD flow clusters are highly consistent with subway lines and highways in the city. Additionally, the regions of short-distance commuting OD flow clusters are more likely to exhibit 'residential-area to work-area' patterns.

Identification

10.1080/13658816.2018.1516287

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Citations (44)

Coupling human mobility and social relationships to predict individual socioeconomic status: A graph neural network approach

Transactions in GIS (2024)

Xiao Chen Tao Pei Ci Song Hua Shu S. Guo

Abstract Understanding individual's socioeconomic status (SES) can provide supporting information for designing political and economic policies. Acquiring large‐scale economic survey data is time‐consuming and laborious. The widespread mobile phone data, which can reflect human mobility and social network characteristics, has become a low‐cost data source for researchers to infer SES. However, previous studies often oversimplify human mobility features and social network features extracted from mobile phone data into general statistical features, resulting in discounting some important temporal and relational information. Therefore, we propose a comprehensive framework for individual SES prediction that effectively utilizes a combination of human mobility and social relationships. In this framework, Word2Vec module extracts human mobility features from mobile phone positioning data, and graph neural network (GNN) module GraphSAGE captures social network characteristics constructed from call detail records. We evaluated the effectiveness of our proposed approach by training the model with real‐world data in Beijing. According to the experimental results, our proposed hybrid approach outperformed the other methods evidently, demonstrating that human mobility and social links are complementary in the characterization of SES. Coupling human mobility and social links can further deepen our understanding of cities' economic geography.

Mobile phone

Social network (sociolinguistics)

10.1111/tgis.13189

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Citations (0)

Spatiotemporal Flow L-function: a new method for identifying spatiotemporal clusters in geographical flow data

International Journal of Geographical Information Science (2023)

Xiaorui Yan Tao Pei Hua Shu Ci Song Mingbo Wu

A geographical flow (hereafter flow) is defined as a movement between locations at two different times. A group of spatiotemporal flows can be viewed as a cluster if their origins and destinations are both spatiotemporally concentrated. Identifying spatiotemporal flow clusters may help reveal underlying spatiotemporal mobility trends or intensive relationships between regions. Despite recent advances in flow clustering methods, most only consider spatial attributes and ignore temporal information, and may fail to differentiate space-close but time-separated clusters. To this end, we derive global and local versions of the Spatiotemporal Flow L-function, extended from the classical L-function for points, and thereby construct a clustering method. First, the global version is utilized to check whether flow data contain clusters and estimate the spatial and temporal scales of the clusters. The local version is then employed to extract the clusters with the estimated scales. Experiments of simulated data demonstrate that our method outperforms three state-of-the-art methods in identifying spatiotemporal flow clusters with arbitrary shapes and different densities and reducing subjectivity in the parameter selection process. A case study with taxi data shows that our method reveals residents' spatiotemporal moving patterns, including rush-hour commuting and whole-daytime transferring among railway stations.

10.1080/13658816.2023.2204345

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Citations (9)

Fractal dimension of job-housing flows: A comparison between Beijing and Shenzhen

Cities (2021)

Sihui Guo Tao Pei Shuyun Xie Ci Song Jie Chen

10.1016/j.cities.2021.103120

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Citations (15)