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Human mobility describes physical patterns of movement of people within aspatial system. Many of these patterns, including daily commuting, are cyclicand quantifiable. These patterns capture physical phenomena tied to processesstudied in epidemiology, and other social, behavioral, and economic sciences.This paper advances human mobility research by proposing a statistical methodfor identifying locations that individual move to and through at a rateproportionally higher than other locations, using commuting data for thecountry of New Zealand as a case study. These locations are termed mobilityloci and they capture a global property of communities in which people commute.The method makes use of a directed-graph representation where verticescorrespond to locations and traffic between locations correspond to edgeweights. Following a normalization, the graph can be regarded as a Markov chainwhose stationary distribution can be calculated. The proposed permutationprocedure is then applied to determine which stationary distributions arelarger than what would be expected, given the structure of the directed graphand traffic between locations. The results of this method are evaluated,including a comparison to what is already known about commuting patterns in thearea as well as a comparison with similar features.

A Statistical Method for Identifying Areas of High Mobility Applied to Commuting Data for the Country of New Zealand