Exploring Human Mobility Dynamics: Causes and Implications with Geospatial Big Data Insights

The study of human mobility and migration has become increasingly significant over recent decades, primarily due to accelerated global movement patterns. This surge is attributed to conflicts, environmental changes, and intensified globalization, which drive both internal migration and cross-boundary relocations. Such movements are not merely about geographical shifts; they involve substantial socio-cultural exchanges that can lead to tensions within receiving communities. As migrations continue to reshape societies, understanding the implications of these dynamics has become crucial in the broader realms of urban planning, public policy, and societal cohesion.

The primary goal of this Research Topic is to explore the complexities of human mobility, exploring its drivers, consequences, and the analytical power embedded within mobility data. It aims to illuminate how mobility data can be harnessed to tackle key societal challenges, ranging from the strain on urban infrastructure to the threat of disease transmission. By showcasing recent methodological advances in geospatial data analysis and highlighting mobility's role in public health and environmental studies, this research seeks to provide a deeper understanding of migration patterns and facilitate informed policy decisions. It will also address inherent biases in data sources and discuss the significance of high-performance computing in extracting actionable insights.

The Research Topic is focused on understanding human mobility on various scales and its implications for societies worldwide. We invite contributions that cover a range of topics, such as:

• patterns of human movement at local, regional, and global scales

• challenges and opportunities associated with human mobility and migration

• differences in human movement dynamics between developing and developed countries

• demographic and socioeconomic determinants of cross-border migration

• the use of AI and geospatial big data to predict migration hotspots under future climate scenarios

• health vulnerabilities among migrant populations: a spatial analysis of access to care in host communities

• cultural integration and social friction: the social consequences of mass migration events

• innovative methods for extracting and analyzing large-scale human mobility datasets

• integration of urban planning with dynamic human movement patterns

• strategies for climate change adaptation informed by mobility data.

We encourage submissions in various formats, such as original research articles, review articles, and methodological papers.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Classification
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Classification
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Registered Report
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Human Mobility, Migration Patterns, Geospatial Data Analysis, Societal Impact, Big Data, Mobility Data, Public Policy

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.