About this Research Topic
The contemporary world is intricately connected through networks that encompass diverse systems, ranging from transportation and communication to social interactions and ecological habitats. The evolution of such complex networks has a spatial component in the sense that nodes and edges are associated with geographical coordinates or that they emerge from objects moving in space over time. Spatial networks can be modelled and observed at different levels, ranging from micro-level contact and encounter networks between people to macro level socio-economic couplings between regions. Understanding the complexities of these networks is a crucial building block for many fields, such as efficient modelling of spreading diseases, urban planning, environmental sustainability, and policy-making.
Research on spatial networks has to face several challenges:
• Data management and privacy: Spatial network analysis often requires the integration of heterogeneous data sources. Furthermore, sensitive geospatial data induce challenges related to data openness and privacy.
• Dynamic Nature: Spatial networks can evolve over time, for example, due to changes in infrastructure, human behavior, and environmental factors. Capturing these temporal dynamics accurately and understanding how networks evolve is still an open issue.
We welcome original research related but not limited to the following topics:
• Data Collection and Management: Gathering and integration of spatial networks data from diverse sources and on multiple levels, including geographic information systems (GIS), satellite imagery, social media, transportation logs, and economical and demographic surveys.
• Modeling Techniques: Agent-based modeling, network simulation, and machine learning algorithms to create dynamic models that simulate network behaviors at different scales.
• Network Analysis Techniques: Graph theory metrics, spatial statistics, clustering algorithms, etc. to analyze network characteristics, identify vital nodes, and assess network resilience.
• Case Studies: Implement the developed models and analytical frameworks in case studies across varied domains to validate their efficacy and practical applicability.
Research on spatial networks has to face several challenges:
• Data management and privacy: Spatial network analysis often requires the integration of heterogeneous data sources. Furthermore, sensitive geospatial data induce challenges related to data openness and privacy.
• Dynamic Nature: Spatial networks can evolve over time, for example, due to changes in infrastructure, human behavior, and environmental factors. Capturing these temporal dynamics accurately and understanding how networks evolve is still an open issue.
We welcome original research related but not limited to the following topics:
• Data Collection and Management: Gathering and integration of spatial networks data from diverse sources and on multiple levels, including geographic information systems (GIS), satellite imagery, social media, transportation logs, and economical and demographic surveys.
• Modeling Techniques: Agent-based modeling, network simulation, and machine learning algorithms to create dynamic models that simulate network behaviors at different scales.
• Network Analysis Techniques: Graph theory metrics, spatial statistics, clustering algorithms, etc. to analyze network characteristics, identify vital nodes, and assess network resilience.
• Case Studies: Implement the developed models and analytical frameworks in case studies across varied domains to validate their efficacy and practical applicability.
Keywords: human mobility, geographic coupling, spatial networks, network analysis, complex networks
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.
