Progress in computational movement analysis – towards movement data science

Dodge, Somayeh; Gao, Song; Tomko, Martin; Weibel, Robert

doi:10.1080/13658816.2020.1784425

Cited by 25 publications

(13 citation statements)

References 23 publications

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“…In so doing, we hope to transcend the legacy of the data-centric view within GIScience that has limited CMA to aim, at best, for bridging the gap between low-level spatiotemporal data and the high-level movement patterns. Despite the importance of hypothesis generation [8,9] and mining causal relationships [10] in spatiotemporal (movement) modelling and explanation, a recent CMA journal special issue revealed little progress towards causal modelling and explanation (see [11]).…”

Section: Motivationmentioning

confidence: 99%

Beyond Objects in Space-Time: Towards a Movement Analysis Framework with ‘How’ and ‘Why’ Elements

Rahimi

Moore

Whigham

2021

IJGI

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Current spatiotemporal data has facilitated movement studies to shift objectives from descriptive models to explanations of the underlying causes of movement. From both a practical and theoretical standpoint, progress in developing approaches for these explanations should be founded on a conceptual model. This paper presents such a model in which three conceptual levels of abstraction are proposed to frame an agent-based representation of movement decision-making processes: ‘attribute,’ ‘actor,’ and ‘autonomous agent’. These in combination with three temporal, spatial, and spatiotemporal general forms of observations distinguish nine (3x3) representation typologies of movement data within the agent framework. Thirdly, there are three levels of cognitive reasoning: ‘association,’ ‘intervention,’ and ‘counterfactual’. This makes for 27 possible types of operation embedded in a conceptual cube with the level of abstraction, type of observation, and degree of cognitive reasoning forming the three axes. The conceptual model is an arena where movement queries and the statement of relevant objectives takes place. An example implementation of a tightly constrained spatiotemporal scenario to ground the agent-structure was summarised. The platform has been well-defined so as to accommodate different tools and techniques to drive causal inference in computational movement analysis as an immediate future step.

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Section: Motivationmentioning

confidence: 99%

Beyond Objects in Space-Time: Towards a Movement Analysis Framework with ‘How’ and ‘Why’ Elements

Rahimi

Moore

Whigham

2021

IJGI

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“…Movement patterns are also influenced to various degrees by people's lifestyles, feelings, internal, and external conditions across different individuals and depending on their personal and household circumstances (Dodge, 2021). While growing access to aggregate mobility indices has enabled movement analytics to model human behavioral responses to crisis situations such as the COVID-19 pandemic across local and global scales (Dodge et al, 2020), these data and our methods have yet to inform us about the differing impacts of a crisis on gendered movement behavior and accessibility.…”

Section: Spacetime Equitable Landscapesmentioning

confidence: 99%

Making Space in Geographical Analysis

Franklin¹,

Delmelle²,

Andris

et al. 2021

Preprint

Self Cite

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In this commentary we reflect on the potential and power of geographical analysis, as a set of methods, theoretical approaches, and perspectives, to increase our understanding of how space and place matter for women. We emphasize key aspects of the field, including accessibility, urban change, and spatial interaction and behavior, providing a high-level research agenda that indicates a variety of gaps and routes for future research that will not only lead to more equitable and aware solutions to local and global challenges, but also innovative and novel research methods, concepts, and data. We close with a set of gender, representation, and inclusion challenges to our discipline, researchers, and publication outlets.

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“…The world is geographically dynamic [1,2], and this dynamism has drawn increasing attention in recent years. This attention has focused on dynamic object extraction and analysis, dynamic mining methods, mining frameworks and tools [3][4][5][6] and especially on oceanic dynamics [3,[7][8][9][10]. Series of images taken by advanced Earth-observing technologies over long periods of time, combined with historical climate records, constitute the main source of continuous and consistent information about the marine environment [11,12] and offer new opportunities for monitoring oceanic dynamics and understanding their evolutionary patterns [10].…”

Section: Introductionmentioning

confidence: 99%

PoSDMS: A Mining System for Oceanic Dynamics with Time Series of Raster-Formatted Datasets

Xue

et al. 2022

Remote Sensing

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Many effective and advanced methods have been developed to explore oceanic dynamics using time series of raster-formatted datasets; however, they have generally been designed at a scale suitable for data observation and used independently of each other, despite the potential advantages of combining different modules into an integrated system at a scale suited for dynamic evolution. From raster-formatted datasets to marine knowledge, we developed and integrated several mining algorithms at a dynamic evolutionary scale and combined them into six modules: a module of raster-formatted dataset pretreatment; a module of process-oriented object extraction; a module of process-oriented representation and management (process-oriented graph database); a module of process-oriented clustering; a module of process-oriented association rule mining; and a module of process-oriented visualization. On the basis of such modules, we developed a process-oriented spatiotemporal dynamic mining system named PoSDMS (Process-oriented Spatiotemporal Dynamics Mining System). PoSDMS was designed to have the capacity to deal with at least six environments of marine anomalies with 40 years of raster-formatted datasets, including their extraction, representation, storage, clustering, association and visualization. The effectiveness of the integrated system was evaluated in a case study of sea surface temperature datasets during the period from January 1982 to December 2021 in global oceans. The main contribution of this study was the development of a mining system at a scale suited for dynamic evolution, providing an analyzing platform or tool to deal with time series of raster-formatted datasets to aid in obtaining marine knowledge.

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Progress in computational movement analysis – towards movement data science

Cited by 25 publications

References 23 publications

Beyond Objects in Space-Time: Towards a Movement Analysis Framework with ‘How’ and ‘Why’ Elements

Beyond Objects in Space-Time: Towards a Movement Analysis Framework with ‘How’ and ‘Why’ Elements

Making Space in Geographical Analysis

PoSDMS: A Mining System for Oceanic Dynamics with Time Series of Raster-Formatted Datasets

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