Moving ahead with computational movement analysis

Abstract

EDITORIALMoving ahead with computational movement analysis "We would like to dedicate this special issue to the memory of Professor Rein Ahas (1966 -2018), a pioneer of computational movement analysis and mobility analytics, who sadly and very unexpectedly passed away shortly before this issue went to print."

Towards an integrated science of movement: converging research on animal movement ecology and human mobility science

“…New generation of multimodal sensors (e.g. smart watches, fitness trackers, GPS collars) equipped with accelerometers and gyroscopes provide auxiliary activity data of moving objects (Long et al 2018). Geosensor networks are wirelessly communicating, sensor-enabled, small computing devices distributed in geography and connected as a network to enable in-situ monitoring of dynamic properties such as location change and movement (Duckham 2012).…”

Progress in computational movement analysis – towards movement data science

“…The work presented in this special section responds to previously identified gaps in the CMA literature (Dodge et al 2016, Long et al 2018, Dodge 2019, Miller et al 2019 on computationally intensive movement data analytics and visualization (Graser et al, in this issue), representation of collective movement and interactions in groups of trajectories (Buchin et al, in this issue), sensor fusion and data integration to contextualize movement (Li et al and Ma et al, in this issue), as well as movement pattern analysis using crowdsourced data (Xin and MacEachren; Qiang and Xu, in this issue). These studies and the research presented in a preceding IJGIS special section on 'Big Spatiotemporal Data Analytics' (Yang et al 2020) highlight important achievements in data-driven approaches to modeling, representation and analytics of movement using 'big' mobility and crowdsourced data, forming one of the pillars of the 'data science framework for movement' (Dodge 2019) to advance the knowledge and understanding of movement processes and individuals' behavior.…”

“…In contrast to our previous special issues in the area of movement data analysis (Dodge et al 2016, Long et al 2018, which mainly used animal tracking data to demonstrate the applicability of CMA methods, the majority of papers in this issue focused on human mobility, with the exception of the work presented in Buchin et al This also reflects the increasing access to human mobility data and the availability of crowdsourced data at higher temporal resolution. However, further research is required to bridge the gap between CMA for human and animal movement towards an integrated science of movement (Miller et al 2019).…”

“…Today more than ever we recognize the significance of movement data and movement analytics in urban planning, crisis mitigation, public health (Wang and Taylor 2016, Li et al 2019, Kraemer et al 2020. While our community has played a major role in the progress of movement data analytics and advancing its methods and applications over the past two decades (Demšar et al 2015, Dodge et al 2016, Long et al 2018, Dodge 2019, Miller et al 2019, we still have to further advance developing methods that enable the large-scale characterization of mobility patterns and knowledge discovery in large mobility data (Scherrer et al 2018). With ever-increasing access to large repositories of raw trajectory data contributed voluntarily or often involuntarily through the widespread use of cellphones (Huang et al 2019), location-aware apps and mobile services, the technology industry has gained an unprecedented data advantage to develop new computational movement analysis methods.…”

A Novel Process-Oriented Graph Storage for Dynamic Geographic Phenomena

“…Advanced remote sensing and crowdsourcing survey technologies provide a foundation for obtaining these dynamic phenomena and make it possible to analyze and discover their spatiotemporal patterns and evolutions [2,3]. To effectively analyze and discover the spatiotemporal patterns of these dynamic phenomena, an appropriate spatiotemporal data model is needed to represent and organize them [4][5][6].…”