Measuring Dynamic Interaction in Movement Data
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The emergence of technologies capable of storing detailed records of object locations has presented scientists and researchers with a wealth of data on object movement. Yet analytical methods for investigating more advanced research questions from such detailed movement datasets remain limited in scope and sophistication. Recent advances in the study of movement data has focused on characterizing types of dynamic interactions, such as single-file motion, while little progress has been made on quantifying the degree of such interactions. In this article, we introduce a new method for measuring dynamic interactions (termed DI) between pairs of moving objects. Simulated movement datasets are used to compare DI with an existing correlation statistic. Two applied examples, team sports and wildlife, are used to further demonstrate the value of the DI approach. The DI method is advantageous in that it measures interaction in both movement direction (termed azimuth) and displacement. Also, the DI approach can be applied at local, interval, episodal, and global levels of analysis. However the DI method is limited to situations where movements of two objects are recorded at simultaneous points in time. In conclusion, DI quantifies the level of dynamic interaction between two moving objects, allowing for more thorough investigation of processes affecting interactive moving objects.
Long , J A & Nelson , T A 2013 , ' Measuring Dynamic Interaction in Movement Data ' Transactions in GIS , vol 17 , no. 1 , pp. 62-77 . DOI: 10.1111/j.1467-9671.2012.01353.x
Transactions in GIS
© 2012. Blackwell Publishing Ltd. This is the peer reviewed version of the following article: Measuring Dynamic Interaction in Movement Data Long, J. A. & Nelson, T. A. Feb 2013 In : Transactions in GIS. 17, 1, p. 62-77, which has been published in final form at http://dx.doi.org/10.1111/j.1467-9671.2012.01353.x. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
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