New developments in GPS and related satellite tracking technologies have facilitated the collection of highly accurate data on moving objects, far surpassing the ability to analyze them. Within geographic information science (GIScience), ‘movement pattern analysis’ (MPA) has developed as a subfield that addresses concepts and theories used to explore the spatio-temporal structure in data in order to perform meaningful analysis. Research in MPA extends previous work on time-geography that focused primarily on representation and semantics, therefore the methodological and analytical framework associated with MPA is new and still evolving. Most MPA applications address the movement of automobiles (e.g., traffic, fleet management), people (e.g., pedestrians, queues), or animals. Although there are obvious and fundamental differences among these three application types, there are also many important commonalities associated with the analysis of any movement data irrespective of the type of object.
Interactions, for which the basic unit of observation is a pair of locations for two individuals, can be considered a second order property of movement but their social and psychological explanations and implications are far less generalizable. Within MPA, human interactions have been studied far more extensively than any other type, and they are often based on detailed information such as ‘travel diaries’ from which activity spaces can be calculated and intersections of multiple activity spaces can be used to derive social interaction metrics (Farber et al. 2012). The nature of interactions between individuals of an animal population is a fundamental aspect of a species’ behavioral ecology and information on the frequency and duration of these interactions is vital to understanding mating and territorial behavior, resource use, and infectious disease epidemiology. However, the number of times an individual animal comes into contact with another is an extremely difficult parameter to estimate, and previous studies have focused on highly observable species living within protected areas or urban areas (ex. foxes in Bristol, UK: White & Harris 1994). For relatively rare or secretive species, or those occupying less-accessible habitats, complete and accurate information regarding rates of contact is generally scarce.
Preliminary research has shown that current interaction metrics produce quite variable results and do not facilitate meaningful interpretations of interaction rates in general (Miller 2012). The goal of this research is to develop a novel framework that can be used to analyze and interpret dynamic interactions between individuals. The case study used to explore these methods comprises GPS collar data from sixteen brown hyena individuals in Northern Botswana and interactions will be studied for eight intra-clan pairs (dyads) and eight inter-clan pairs of different composition (female-female, female-male). Five different techniques that have been used to quantify dynamic interactions based on GPS data of pairs of individuals will be used here, and they will be compared in the context of spatially explicit simulated data intended to represent biologically realistic null models for individual movement, and subsequently paired interactions. In addition, a new method will be introduced that quantifies change in movement trajectories for each individual in a dyad as a more informative indication of either positive or negative interaction (attraction or avoidance, respectively).
