Editorial: GIScience for human dynamics research in a changing world

Shaw, Shih‐Lung; Sui, Daniel Z.

doi:10.1111/tgis.12474

Cited by 16 publications

(5 citation statements)

References 35 publications

(45 reference statements)

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“…On the other hand, for the construction of mobility networks, most studies focused on topologies of nodes in a 'network' or 'feature' space, for example, by directly connecting the origin and destination of a trip [52,53]. Such topological connections ignore the constraints of geographic Euclidean spaces.…”

Section: Mobility Network and Network Community Detectionmentioning

confidence: 99%

Revealing Dynamic Spatial Structures of Urban Mobility Networks and the Underlying Evolutionary Patterns

Liu

Chen

Yuan

et al. 2022

IJGI

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Urban space exhibits rich and diverse organizational structures, which is difficult to characterize and interpret. Modelling urban spatial structures in the context of mobility and revealing their underlying patterns in dynamic networks are key to understanding urban spatial structures and how urban systems work. Most existing methods overlook its temporal dimension and oversimplify its spatial heterogeneity, and it is challenging to address these complex properties using one single method. Therefore, we propose a framework based on temporal networks for modeling dynamic urban mobility structures. First, we cast aggregated traffic flows into a compact and informative temporal network for structure representation. Then, we explore spatial cluster substructures and temporal evolution patterns to acquire evolution regularities. Last, the capability of the proposed framework is examined by an empirical analysis based on taxi mobility networks. The experiment results enable to quantitatively depict urban space dynamics and effectively detect spatiotemporal heterogeneity in mobility networks.

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Section: Mobility Network and Network Community Detectionmentioning

confidence: 99%

Revealing Dynamic Spatial Structures of Urban Mobility Networks and the Underlying Evolutionary Patterns

Liu

Chen

Yuan

et al. 2022

IJGI

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“…On the other hand, among studies with clear concerns for geographical characteristics, few studies model places based on human or social features (Blaschke et al., 2018). Shaw and Sui (2018) pointed out that the past decades have seen shifts “from physical space to virtual space, from historical to real‐time, and from human to context” in the field of research on human dynamics. Goodchild (2015) argued that new theory and technology are needed to acquire, represent, store, and share rich information about places where people live, learn, and reason.…”

Section: Related Workmentioning

confidence: 99%

A GIS representation framework for location‐based social media activities

Wei

Yao

2022

Transactions in GIS

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The past couple of decades have witnessed tremendous growth of location‐based social media activities (LBSMA) data in virtual spaces, including virtual geographic environments. Such data become innovative resources for the analysis of human activities. Meanwhile, a shift of human interactions from geographical spaces to virtual spaces has been observed. Although this is an exciting research opportunity, it also imposes significant challenges on GIScience, as current GIS representation models are no longer sufficient to handle the increased sophistication of human activities data. This research formalizes an ontology for LBSMA data and a conceptual framework for representing such data in GIS. The framework contributes to GIScience as it enables interconnections of human activities in both the physical and virtual worlds to be represented, organized, retrieved, analyzed, and visualized. The proposed GIS representation model integrates a social dimension into the existing spatial–temporal representation models and allows data analysis in the spatial–temporal–social (STS) dimensions. The research tested this conceptual framework with a prototype and a case study using Facebook data. The prototype and the case study prove that the proposed framework can significantly enhance GIS capabilities for data organization, retrieval, and analysis of LBSMA data in STS dimensions.

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“…Various studies based on LBSN datasets to observe human check-in behavior under domains like privacy [73,76,77], gender differences [78], geographic spaces [56], urban emotions [79], activity location choice, lifestyle patterns [6,[80][81][82], and operations and production management [83] have been conducted. Li and Chen [63] studied location sharing by the users in the real world, and presented data analysis results over user profiles, update activities, mobility characteristics, social graphs, and attribute correlations.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

2019

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In a location-based social network, users socialize with each other by sharing their current location in the form of “check-in,” which allows users to reveal the current places they visit as part of their social interaction. Understanding this human check-in phenomenon in space and time on location based social network (LBSN) datasets, which is also called “check-in behavior,” can archive the day-to-day activity patterns, usage behaviors toward social media, and presents spatiotemporal evidence of users’ daily routines. It also provides a wide range of opportunities to observe (i.e., mobility, urban activities, defining city boundary, and community problems in a city). In representing human check-in behavior, these LBSN datasets do not reflect the real-world events due to certain statistical biases (i.e., gender prejudice, a low frequency in sampling, and location type prejudice). However, LBSN data is primarily considered a supplement to traditional data sources (i.e., survey, census) and can be used to observe human check-in behavior within a city. Different interpretations are used elusively for the term “check-in behavior,” which makes it difficult to identify studies on human check-in behavior based on LBSN using the Weibo dataset. The primary objective of this research is to explore human check-in behavior by male and female users in Guangzhou, China toward using Chinese microblog Sina Weibo (referred to as “Weibo”), which is missing in the existing literature. Kernel density estimation (KDE) is utilized to explore the spatiotemporal distribution geographically and weighted regression (GWR) method was applied to observe the relationship between check-in and districts with a focus on gender during weekdays and weekend. Lastly, the standard deviational ellipse (SDE) analysis is used to systematically analyze the orientation, direction, spatiotemporal expansion trends and the differences in check-in distribution in Guangzhou, China. The results of this study show that LBSN is a reliable source of data to observe human check-in behavior in space and time within a specified geographic area. Furthermore, it shows that female users are more likely to use social media as compared to male users. The human check-in behavior patterns for social media network usage by gender seems to be slightly different during weekdays and weekend.

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Editorial: GIScience for human dynamics research in a changing world

Cited by 16 publications

References 35 publications

Revealing Dynamic Spatial Structures of Urban Mobility Networks and the Underlying Evolutionary Patterns

Revealing Dynamic Spatial Structures of Urban Mobility Networks and the Underlying Evolutionary Patterns

A GIS representation framework for location‐based social media activities

Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

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