Sihui Guo

Chinese Academy of Surveying and Mapping

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i-10 index

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Tao Pei

Institute of Geographic Sciences and Natural Resources Research

Ci Song

Chinese Academy of Sciences

Hua Shu

Institute of Geographic Sciences and Natural Resources Research

Yaxi Liu

Institute of Geographic Sciences and Natural Resources Research

Chenghu Zhou

Chinese Academy of Sciences

Yunyan Du

Institute of Geographic Sciences and Natural Resources Research

Ting Ma

Harbin Institute of Technology

Jie Chen

Chiba University

Zide Fan

Chinese Academy of Sciences

Mingbo Wu

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

Nanjing University

China University of Mining and Technology

Zhejiang University

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

Peking University

Chinese Academy of Medical Sciences & Peking Union Medical College

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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)

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)

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)

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)

Revealing the heterogeneity of periodic visiting patterns across different ages and spaces using a flexible detection method: A case study of Beijing

Applied Geography (2024)

Zidong Fang Tao Pei Ci Song Xiaorui Yan Le Liu

10.1016/j.apgeog.2024.103315

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Citations (1)

Roles of locational factors in the rise and fall of restaurants: A case study of Beijing with POI data

Cities (2021)

Mingbo Wu Tao Pei Wenlai Wang Sihui Guo Ci Song

Capital (architecture)

10.1016/j.cities.2021.103185

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Citations (62)

Quantifying the Urban Visual Perception of Chinese Traditional-Style Building with Street View Images

Applied Sciences (2020)

Zhang Li-ying Tao Pei Wang Xi Mingbo Wu Ci Song

As a symbol of Chinese culture, Chinese traditional-style architecture defines the unique characteristics of Chinese cities. The visual qualities and spatial distribution of architecture represent the image of a city, which affects the psychological states of the residents and can induce positive or negative social outcomes. Hence, it is important to study the visual perception of Chinese traditional-style buildings in China. Previous works have been restricted by the lack of data sources and techniques, which were not quantitative and comprehensive. In this paper, we proposed a deep learning model for automatically predicting the presence of Chinese traditional-style buildings and developed two view indicators to quantify the pedestrians’ visual perceptions of buildings. Using this model, Chinese traditional-style buildings were automatically segmented in streetscape images within the Fifth Ring Road of Beijing and then the perception of Chinese traditional-style buildings was quantified with two view indictors. This model can also help to automatically predict the perception of Chinese traditional-style buildings for new urban regions in China, and more importantly, the two view indicators provide a new quantitative method for measuring the urban visual perception in street level, which is of great significance for the quantitative research of tourism route and urban planning.

10.3390/app10175963

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Citations (23)

Big geodata mining: Objective, connotations and research issues

Journal of Geographical Sciences (2020)

Tao Pei Ci Song Sihui Guo Hua Shu Yaxi Liu

10.1007/s11442-020-1726-7

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Citations (28)

I-index for quantifying an urban location's irreplaceability

Computers Environment and Urban Systems (2021)

Xi Wang Jie Chen Tao Pei Ci Song Yaxi Liu