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Dr. Hua Shu
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences

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0 Data Mining
0 urban computing
0 Big geodata
0 sptial statisitcs
0 OD flow

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Short Biography

HUA SHU received the phD degree from the Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, in 2020. He is currently a post-doctoral researcher at the State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research. His research interests include spatial–temporal big data mining, mobile computing, and geographic information science.

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Journal article
Published: 29 January 2021 in Cities
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Job-housing flows are among the most important travel flows in a city. Therefore, it is essential to measure the extent to which the job-housing flow distribution covers the city space, which can be used as evidence for evaluating the land use status and transportation system. Existing studies have analyzed the spatial distribution of job-housing flows in terms of where and how the flows are aggregated in the city, but they have not revealed the extent to which the flow distribution fills the city space. In this study, we introduce a fractal dimension to measure the space-filling degree of the job-housing flows, which is defined based on the flow space, with the flow being the basic element. Because the flow fractal dimension is independent of the observation scale, we compared the box-counting dimensions of job-housing flows in Beijing and Shenzhen using mobile phone data. The results demonstrated that the fractal dimension was substantially higher in Beijing than in Shenzhen, indicating that the job and home distributions fill more space in Beijing, and the links between the job and home locations are more disordered and irregular in Beijing. These results are related to the more crammed urban land use and higher commuting demand from the suburbs to the city center.

ACS Style

Sihui Guo; Tao Pei; Shuyun Xie; Ci Song; Jie Chen; Yaxi Liu; Hua Shu; Xi Wang; Ling Yin. Fractal dimension of job-housing flows: A comparison between Beijing and Shenzhen. Cities 2021, 112, 103120 .

AMA Style

Sihui Guo, Tao Pei, Shuyun Xie, Ci Song, Jie Chen, Yaxi Liu, Hua Shu, Xi Wang, Ling Yin. Fractal dimension of job-housing flows: A comparison between Beijing and Shenzhen. Cities. 2021; 112 ():103120.

Chicago/Turabian Style

Sihui Guo; Tao Pei; Shuyun Xie; Ci Song; Jie Chen; Yaxi Liu; Hua Shu; Xi Wang; Ling Yin. 2021. "Fractal dimension of job-housing flows: A comparison between Beijing and Shenzhen." Cities 112, no. : 103120.

Journal article
Published: 26 October 2020 in ISPRS International Journal of Geo-Information
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Offline stores are seriously challenged by online shops. To attract more customers to compete with online shops, the patterns of customer flows and their influence factors are important knowledge. To address this issue, we collected indoor positioning data of 534,641 and 59,160 customers in two shopping malls (i.e., Dayuecheng (DYC) in Beijing and Longhu (LH) in Chongqing, China) for one week, respectively. The temporal patterns of the customer flows show that (1) total customer flows are high on weekends and low midweek and (2) peak hourly flow is related to mealtimes for LH and only on weekdays for DYC. The difference in temporal patterns between the two malls may be attributed to the difference in their locations. The customer flows to stores reveal that the customer flows to clothing, food and general stores are the highest; specifically, in DYC, the order is clothing, food and general, while in LH, it is food, clothing and general. To identify the factors influencing customer flow, we applied linear regression to the inflow density of stores (customers per square meter) of two major classes (clothing and food stores), with 10 locational and social factors as independent variables. The results indicate that flow density is significantly influenced by store location, visibility (except for food stores in DYC) and reputation. Besides, the difference between the two store classes is that clothing stores are influenced by more convenience factors, including distance to an elevator and distance to the floor center (only for LH). Overall, the two shopping malls demonstrate similar customer flow patterns and influencing factors with some obvious differences also attributed to their layout, functions and locations.

ACS Style

Tao Pei; Yaxi Liu; Hua Shu; Yang Ou; Meng Wang; Lianming Xu. What Influences Customer Flows in Shopping Malls: Perspective from Indoor Positioning Data. ISPRS International Journal of Geo-Information 2020, 9, 629 .

AMA Style

Tao Pei, Yaxi Liu, Hua Shu, Yang Ou, Meng Wang, Lianming Xu. What Influences Customer Flows in Shopping Malls: Perspective from Indoor Positioning Data. ISPRS International Journal of Geo-Information. 2020; 9 (11):629.

Chicago/Turabian Style

Tao Pei; Yaxi Liu; Hua Shu; Yang Ou; Meng Wang; Lianming Xu. 2020. "What Influences Customer Flows in Shopping Malls: Perspective from Indoor Positioning Data." ISPRS International Journal of Geo-Information 9, no. 11: 629.

Journal article
Published: 01 January 2020 in IEEE Access
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ACS Style

Ci Song; Tao Pei; Hua Shu. Identifying Flow Clusters Based on Density Domain Decomposition. IEEE Access 2020, 8, 5236 -5243.

AMA Style

Ci Song, Tao Pei, Hua Shu. Identifying Flow Clusters Based on Density Domain Decomposition. IEEE Access. 2020; 8 ():5236-5243.

Chicago/Turabian Style

Ci Song; Tao Pei; Hua Shu. 2020. "Identifying Flow Clusters Based on Density Domain Decomposition." IEEE Access 8, no. : 5236-5243.

Journal article
Published: 09 December 2019 in Sustainability
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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.

ACS Style

Wenlai Wang; Tao Pei; Jie Chen; Ci Song; Xi Wang; Hua Shu; Ting Ma; Yunyan Du. Population Distributions of Age Groups and Their Influencing Factors Based on Mobile Phone Location Data: A Case Study of Beijing, China. Sustainability 2019, 11, 7033 .

AMA Style

Wenlai Wang, Tao Pei, Jie Chen, Ci Song, Xi Wang, Hua Shu, Ting Ma, Yunyan Du. Population Distributions of Age Groups and Their Influencing Factors Based on Mobile Phone Location Data: A Case Study of Beijing, China. Sustainability. 2019; 11 (24):7033.

Chicago/Turabian Style

Wenlai Wang; Tao Pei; Jie Chen; Ci Song; Xi Wang; Hua Shu; Ting Ma; Yunyan Du. 2019. "Population Distributions of Age Groups and Their Influencing Factors Based on Mobile Phone Location Data: A Case Study of Beijing, China." Sustainability 11, no. 24: 7033.

Journal article
Published: 19 September 2019 in IEEE Access
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Interaction in large-scale outdoor space has been extensively studied due to its importance in understanding the human-land relationship. Models such as the gravity have been proven to be able to quantify the interaction between two places. However, interaction in indoor space still remains unclear even though humans spend over 70% of their time indoors. Few studies attempt to construct an indoor interaction model. In this study, we analyze the interaction between stores in shopping malls via customer flow to determine whether indoor mobility interaction follows the gravity law and what are its influencing factors. Based on indoor positioning data, two customer flow measures (connectivity flow, indicating the direct connection between stores, and association flow, indicating the association relationship between stores) and two distance measures (path distance, indicating the minimum travel cost, and store distance, indicating the mean travel cost) are defined to fit the traditional and extended gravity models (considering store floor and type). We find that 1) interaction between stores follows a power law distribution, indicating that only a small fraction of store pairs is closely related; 2) customer mobility is governed by the gravity law, where the distance decay exponent is 1-2 for the connectivity flow and 0-1 for the association flow; and 3) store floor and type are two important factors that affect the interaction between stores. These findings provide insights for modeling indoor interactions and can support indoor settings to optimize their layout, estimate the customer flow and promote sales.

ACS Style

Yaxi Liu; Tao Pei; Ci Song; Hua Shu; Sihui Guo; Xi Wang. Indoor Mobility Interaction Model: Insights into the Customer Flow in Shopping Malls. IEEE Access 2019, 7, 138353 -138363.

AMA Style

Yaxi Liu, Tao Pei, Ci Song, Hua Shu, Sihui Guo, Xi Wang. Indoor Mobility Interaction Model: Insights into the Customer Flow in Shopping Malls. IEEE Access. 2019; 7 (99):138353-138363.

Chicago/Turabian Style

Yaxi Liu; Tao Pei; Ci Song; Hua Shu; Sihui Guo; Xi Wang. 2019. "Indoor Mobility Interaction Model: Insights into the Customer Flow in Shopping Malls." IEEE Access 7, no. 99: 138353-138363.

Research articles
Published: 08 March 2019 in International Journal of Geographical Information Science
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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.

ACS Style

Hua Shu; Tao Pei; Ci Song; Ting Ma; Yunyan Du; Zide Fan; Sihui Guo. Quantifying the spatial heterogeneity of points. International Journal of Geographical Information Science 2019, 33, 1355 -1376.

AMA Style

Hua Shu, Tao Pei, Ci Song, Ting Ma, Yunyan Du, Zide Fan, Sihui Guo. Quantifying the spatial heterogeneity of points. International Journal of Geographical Information Science. 2019; 33 (7):1355-1376.

Chicago/Turabian Style

Hua Shu; Tao Pei; Ci Song; Ting Ma; Yunyan Du; Zide Fan; Sihui Guo. 2019. "Quantifying the spatial heterogeneity of points." International Journal of Geographical Information Science 33, no. 7: 1355-1376.

Journal article
Published: 05 October 2018 in Landscape and Urban Planning
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The rapid process of urbanization aggravates the imbalance between the supply and demand of urban public services. Urban parks are among the most important urban public services, and their use efficiency has been an important index for urban planning. Therefore, it is essential to estimate well their service area and influencing factors. Traditional survey data used to analyze the characteristics of urban park services are limited by small samples and high cost. Owing to thriving information communication technologies, vast amounts of human activity data have become available that enable understanding of human travel behavior. In this study, we analyzed a park service area, which is defined as the zone of influence of individual parks, in Beijing, and the factors that influence the service area. First, the service area was estimated using 1-SDE based on mobile phone signaling data. A multiple linear regression model was then used to analyze the influence of factors on the park service area. The results show that (1) external factors including population density, the number of commercial facilities, and traffic convenience have significant influences on the park service area; (2) employment places positively influence the park service area on the weekday; and (3) other factors such as park design and park reputation had inconsistent effects on the park service area, in either sign or significance, regarding the weekday and the weekend. The findings of this study will be of practical value when designing parks or undertaking city planning in the future.

ACS Style

Sihui Guo; Gege Yang; Tao Pei; Ting Ma; Ci Song; Hua Shu; Yunyan Du; Chenghu Zhou. Analysis of factors affecting urban park service area in Beijing: Perspectives from multi-source geographic data. Landscape and Urban Planning 2018, 181, 103 -117.

AMA Style

Sihui Guo, Gege Yang, Tao Pei, Ting Ma, Ci Song, Hua Shu, Yunyan Du, Chenghu Zhou. Analysis of factors affecting urban park service area in Beijing: Perspectives from multi-source geographic data. Landscape and Urban Planning. 2018; 181 ():103-117.

Chicago/Turabian Style

Sihui Guo; Gege Yang; Tao Pei; Ting Ma; Ci Song; Hua Shu; Yunyan Du; Chenghu Zhou. 2018. "Analysis of factors affecting urban park service area in Beijing: Perspectives from multi-source geographic data." Landscape and Urban Planning 181, no. : 103-117.

Journal article
Published: 10 September 2018 in International Journal of Geographical Information Science
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ACS Style

Ci Song; Tao Pei; Ting Ma; Yunyan Du; Hua Shu; Sihui Guo; Zide Fan. Detecting arbitrarily shaped clusters in origin-destination flows using ant colony optimization. International Journal of Geographical Information Science 2018, 33, 134 -154.

AMA Style

Ci Song, Tao Pei, Ting Ma, Yunyan Du, Hua Shu, Sihui Guo, Zide Fan. Detecting arbitrarily shaped clusters in origin-destination flows using ant colony optimization. International Journal of Geographical Information Science. 2018; 33 (1):134-154.

Chicago/Turabian Style

Ci Song; Tao Pei; Ting Ma; Yunyan Du; Hua Shu; Sihui Guo; Zide Fan. 2018. "Detecting arbitrarily shaped clusters in origin-destination flows using ant colony optimization." International Journal of Geographical Information Science 33, no. 1: 134-154.

Journal article
Published: 22 November 2016 in Sensors
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Queuing is common in urban public places. Automatically monitoring and predicting queuing time can not only help individuals to reduce their wait time and alleviate anxiety but also help managers to allocate resources more efficiently and enhance their ability to address emergencies. This paper proposes a novel method to estimate and predict queuing time in indoor environments based on WiFi positioning data. First, we use a series of parameters to identify the trajectories that can be used as representatives of queuing time. Next, we divide the day into equal time slices and estimate individuals’ average queuing time during specific time slices. Finally, we build a nonstandard autoregressive (NAR) model trained using the previous day’s WiFi estimation results and actual queuing time to predict the queuing time in the upcoming time slice. A case study comparing two other time series analysis models shows that the NAR model has better precision. Random topological errors caused by the drift phenomenon of WiFi positioning technology (locations determined by a WiFi positioning system may drift accidently) and systematic topological errors caused by the positioning system are the main factors that affect the estimation precision. Therefore, we optimize the deployment strategy during the positioning system deployment phase and propose a drift ratio parameter pertaining to the trajectory screening phase to alleviate the impact of topological errors and improve estimates. The WiFi positioning data from an eight-day case study conducted at the T3-C entrance of Beijing Capital International Airport show that the mean absolute estimation error is 147 s, which is approximately 26.92% of the actual queuing time. For predictions using the NAR model, the proportion is approximately 27.49%. The theoretical predictions and the empirical case study indicate that the NAR model is an effective method to estimate and predict queuing time in indoor public areas.

ACS Style

Hua Shu; Ci Song; Tao Pei; Lianming Xu; Yang Ou; Libin Zhang; Tao Li. Queuing Time Prediction Using WiFi Positioning Data in an Indoor Scenario. Sensors 2016, 16, 1958 .

AMA Style

Hua Shu, Ci Song, Tao Pei, Lianming Xu, Yang Ou, Libin Zhang, Tao Li. Queuing Time Prediction Using WiFi Positioning Data in an Indoor Scenario. Sensors. 2016; 16 (11):1958.

Chicago/Turabian Style

Hua Shu; Ci Song; Tao Pei; Lianming Xu; Yang Ou; Libin Zhang; Tao Li. 2016. "Queuing Time Prediction Using WiFi Positioning Data in an Indoor Scenario." Sensors 16, no. 11: 1958.

Conference paper
Published: 29 September 2016 in 2016 24th International Conference on Geoinformatics
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Intracity travel has increased significantly in second-tier cities in China, accompanied by a large number of passenger arrivals, departures, and transfers in airports, railway stations and so on. This increased travel demand has placed a strain on the surrounding traffic of such external traffic hubs. Also, the original designs of connecting lines may not be appropriate for new travel needs. Therefore, a study of passenger intracity OD (origin and destination) patterns can reveal the influence on inner-city traffic and provide a basis for urban transport planning. This research examines the rail and bus passenger OD spatial-temporal patterns of a typical second-tier city (Suzhou) in China by utilizing the Getis-Ord Gi* statistic and other statistical analyses of taxi GPS data. The results indicated the following two different functions of transport in Suzhou: tourism transport and transit transfer to external transportation. Most passengers were distributed in places of interest and commercial districts within the central town, and some passengers were distributed in other external traffic hubs. In addition, Suzhou Park Railway Station and Suzhou New District Railway Station, which are located farther away from the central town, better served passengers located in the neighborhood and facilitated the dispersal of non-tourist passengers. Furthermore, while there are some connections between several Suzhou external traffic hubs, there are no metros or shuttle buses between some related stations, such as Suzhou South Bus Passenger Station and Suzhou Railway Station. We suggest opening connection lines between these two stations.

ACS Style

Gege Yang; Hua Shu; Yuke Zhou. Research on intracity OD patterns of rail and Bus Passengers in a second-tier city: A case study of Suzhou, China. 2016 24th International Conference on Geoinformatics 2016, 1 -5.

AMA Style

Gege Yang, Hua Shu, Yuke Zhou. Research on intracity OD patterns of rail and Bus Passengers in a second-tier city: A case study of Suzhou, China. 2016 24th International Conference on Geoinformatics. 2016; ():1-5.

Chicago/Turabian Style

Gege Yang; Hua Shu; Yuke Zhou. 2016. "Research on intracity OD patterns of rail and Bus Passengers in a second-tier city: A case study of Suzhou, China." 2016 24th International Conference on Geoinformatics , no. : 1-5.

Journal article
Published: 01 September 2016 in ISPRS International Journal of Geo-Information
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Many patients prefer to use the best hospitals even if there are one or more other hospitals closer to their homes; this behavior is called “hospital bypass behavior”. Because this behavior can be problematic in urban areas, it is important that it be reduced. In this paper, the taxi GPS data of Beijing and Suzhou were used to measure hospital bypass behavior. The “bypass behavior index” (BBI) represents the bypass behavior for each hospital. The results indicated that the mean hospital bypass trip distance value ranges from 5.988 km to 9.754 km in Beijing and from 4.168 km to 10.283 km in Suzhou. In general, the bypass shares of both areas show a gradually increasing trend. The following hospitals exhibited significant patient bypass behavior: the 301 Hospital, Beijing Children’s Hospital, the Second Affiliated Hospital of Soochow University and the Suzhou Hospital of Traditional Chinese Medicine. The hospitals’ reputation, transport accessibility and spatial distribution were found to be the main factors affecting patient bypass behavior. Although the hospital bypass phenomena generally appeared to be more pronounced in Beijing, the bypass trip distances between hospitals were found to be more significant in Suzhou.

ACS Style

Gege Yang; Ci Song; Hua Shu; Jia Zhang; Tao Pei; Chenghu Zhou. Assessing Patient bypass Behavior Using Taxi Trip Origin–Destination (OD) Data. ISPRS International Journal of Geo-Information 2016, 5, 157 .

AMA Style

Gege Yang, Ci Song, Hua Shu, Jia Zhang, Tao Pei, Chenghu Zhou. Assessing Patient bypass Behavior Using Taxi Trip Origin–Destination (OD) Data. ISPRS International Journal of Geo-Information. 2016; 5 (9):157.

Chicago/Turabian Style

Gege Yang; Ci Song; Hua Shu; Jia Zhang; Tao Pei; Chenghu Zhou. 2016. "Assessing Patient bypass Behavior Using Taxi Trip Origin–Destination (OD) Data." ISPRS International Journal of Geo-Information 5, no. 9: 157.

Journal article
Published: 31 May 2016 in Progress in Geography
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ACS Style

华 舒; Shu Hua; 辞 宋; 韬 裴; Song Ci; Pei Tao. 室内定位数据分析与应用研究进展. Progress in Geography 2016, 35, 580 -588.

AMA Style

华 舒, Shu Hua, 辞 宋, 韬 裴, Song Ci, Pei Tao. 室内定位数据分析与应用研究进展. Progress in Geography. 2016; 35 (5):580-588.

Chicago/Turabian Style

华 舒; Shu Hua; 辞 宋; 韬 裴; Song Ci; Pei Tao. 2016. "室内定位数据分析与应用研究进展." Progress in Geography 35, no. 5: 580-588.