Human Dynamics Research Research Articles

Human Dynamics Research in GIScience: challenges and opportunities

AbstractThe Symposium on Human Dynamics Research, first organized at the 2015 AAG Meeting in Chicago, celebrated its 10th anniversary at the 2024 AAG Meeting in Honolulu, marking a decade of transformative advancements in the field. Over the past decade, the focus of human dynamics research has shifted from traditional spatial-temporal analyses to sophisticated modeling of human behavior in a hybrid physical-virtual world. This evolving field now examines the intricate interdependencies between physical and digital environments, addressing critical issues such as urban resilience, public health, social equity, and community sustainability. The symposium emphasized the growing importance of interdisciplinary collaboration, advanced data-driven analytical platforms, and innovative theoretical frameworks to better understand human interactions across these spaces. As human dynamics continue to shape global urban systems, these advancements are pivotal for future research and real-world problem-solving, offering novel insights into the interconnectedness of mobility, technology, and societal well-being in a rapidly changing world.

Contextualizing human dynamics: Understanding the semantics of movement trajectories with Wi-Fi data

The increasing popularity of Wi-Fi enabled smart devices, such as cell phones, poses unprecedented opportunities for modeling large-scale human movement trajectories. While these datasets enable the extended spatiotemporal scales and refined granularities for human dynamics research, the lack of semantic information hinders the understanding of the processes and rationales behind human movements. In this study, we employ Wi-Fi detectors to identify track points along travelers’ movement trajectories. Also, we propose a semantic space–time matrix (SSTM) method to derive the semantic information from the movement trajectories. The temporalities and activity types of track points are aggregated to understand the mobility patterns of different travelers. We have further applied the method to a small tourist community, the Shichahai scenic area of Beijing, China, with the Wi-Fi data collected from 463,874 individuals over two weeks. The derived semantic information has been further validated by a field survey, justifying the feasibility of Wi-Fi data for movement tracking and semantic analysis. The Wi-Fi data and the proposed analytical method can shed new insights into human dynamics research by enriching the context of individual travel.

Understanding verified users' posting behavior from the perspective of human dynamics: a case study of Sina micro-blog

PurposeThe purpose of this paper is to present a new human dynamics model to explain the process of verified users' (VUs) posting on Sina micro-blog.Design/methodology/approachA common human dynamics research method with three steps is applied. Firstly, a large-scale behavioral dataset is collected involving 495 VUs and five topics on Sina micro-blog. Second, five important indicators that reveal the characteristics of posting behavior are analyzed. Then, a quantitative model is constructed to describe the process of posting behavior, and its validity is verified by simulations.FindingsThree important characteristics of VUs' micro-blog posting behavior are observed: fat-tailed distribution, fluctuation and periodicity. These characteristics do not fit the assumption of interest-driven models proposed by previous literature. An optimized task-driven model is introduced to describe this complex phenomenon mathematically. The model is verified on empirical data, confirming that task-driven models can be optimized to explore information behavior on social media.Originality/valueBeing different from previous studies that mainly describe common users' posting behavior on social media by applying interest-driven models, this paper customizes an optimized task-driven model for VUs, who mainly treat social media as a platform for work and play a crucial role in information creation on social media.

Empirical analysis on human dynamics of sharing-bicycles’ user behavior

With the development of mobile communication and global positioning system navigation and positioning technology, analysis of user behavior on mobile Internet has become a hot topic in research area. Sign in sharing-bicycles’ app, find bicycle location, and selected has become a part of mobile Internet user’s daily life. Based on the data analysis of the spatial and temporal characteristics, find out sharing-bicycle’s user behavior obeys power-law distribution. In the time interval, user behavior of sharing-bicycle has strong intermittency and weak memory; the exponent of probability that K edge nodes is three by fitting the distance of sharing-bicycle’s data curve. It is verified that mobile Internet is long to scale-free networks. We conclude seven characteristics of user’s behavior of sharing-bicycle in mobile Internet application from experimental results. The analysis of sharing-bicycle’s behavior has become a complement and extension in human dynamics research field.

Understanding the New Human Dynamics in Smart Spaces and Places: Toward a Splatial Framework

The smart technologies led by advances in artificial intelligence, machine learning, and the emerging data science in recent years are transforming many facets of society in profound ways. One of these affected areas is the experience of human dynamics in general and human mobility in particular with the growing maturity of smart technologies. The goal of this article is to critically examine the concepts of space and place in geography in general and in geographic information science (GIScience) in particular so that intelligent geographic information systems incorporating concepts of smart space and smart place can be developed to support human dynamics research. We argue that the current discussions on smart technologies are conceptually constrained due to their confinement to absolute space and physical place. By engaging research on smart technologies with geography and GIScience, we seek to move beyond the crude, and often simplistic, conceptualizations of space and place by synthesizing the multiple dimensions of both space and place. By doing so, we can better understand human dynamics through a synergistic perspective of both space and place. The space–place (splatial) framework proposed in this article will enable us to creatively study the human dynamics in the age of smart technologies. Our approach will not only allow us to better understand human dynamics but also advance and enrich our theoretical and methodological frameworks for studying smart technologies and the profound social impacts from a geographic perspective. Challenges for the implementation of the proposed framework are discussed and directions for future research are highlighted. Key Words: GIScience, human dynamics, place, space, splatial framework.

Editorial: GIScience for human dynamics research in a changing world

Editorial: GIScience for human dynamics research in a changing world

Human activity under high pressure: A case study on fluctuation scaling of air traffic controller’s communication behaviors

Recent human dynamics research has unmasked astonishing statistical characteristics such as scaling behaviors in human daily activities. However, less is known about the general mechanism that governs the task-specific activities. In particular, whether scaling law exists in human activities under high pressure remains an open question. In air traffic management system, safety is the most important factor to be concerned by air traffic controllers who always work under high pressure, which provides a unique platform to study human activity. Here we extend fluctuation scaling method to study air traffic controller’s communication activity by investigating two empirical communication datasets. Taken the number of controlled flights as the size-like parameter, we show that the relationships between the average communication activity and its standard deviation in both datasets can be well described by Taylor’s power law, with scaling exponent α≈0.77±0.01 for the real operational data and α≈0.54±0.01 for the real-time training data. The difference between the exponents suggests that human dynamics under pressure is more likely dominated by the exogenous force. Our findings may lead to further understanding of human behavior.

ヒューマン・ダイナミクス研究の展望

ヒューマン・ダイナミクス研究の展望

Learning Communities — Understanding Information Flow in Human Networks

The Human Dynamics research group is developing methods to automatically map the flow of information within groups and communities using audio collected from wearable sensors such as mobile phones or PDAs. Computational models of group interaction dynamics are then derived from this data, allowing us to answer questions such as: Who influences whom? How much? How can we modify group interactions to promote better information diffusion? The goal is real-time learning and modification of information flow within organisations; we describe initial results and discuss concerns about user privacy.