Do Physicists Have Geography Envy? And What Can Geographers Learn from It?

Abstract

Recent years have seen an increasing amount of work by physicists on topics outside their traditional research domain, including in geography. We explore the scope of this development, place it in a historical context dating back at least to statistical physics in the nineteenth century, and trace the origins of more recent developments to the roots of computational science after the Second World War. Our primary purpose is not historical, however. Instead, we are concerned with understanding what geographers can learn from the many recent contributions by physicists to understanding spatiotemporal systems. Drawing on examples of work in this tradition by physicists, we argue that two apparently different modes of investigation are common: model-driven and data-driven approaches. The former is associated with complexity science, while the latter is more commonly associated with the 'fourth paradigm', more recently known as 'big data'. Both modes share technical strengths, and more importantly a capacity for generalization, which is absent from much work in geography. We argue that although some of this research lacks an appreciation of previous geographical contributions, when assessed critically, it nevertheless brings useful new perspectives, new methods and new ideas to bear on topics central to geography, yet neglected in the discipline. We conclude with some suggestions for how geographers can build on these new approaches, both inside and outside the discipline.

Integrating social network data into GISystems

“…Instead, these models represent humans as interpersonal agents who shape and connect places by foraging and maintaining personal relationships. The approaches described here can also help marry physicsinclined analyses of complex geospatial networks (O'Sullivan and Manson 2015) and GISystems to improve forecasting, spatial decision support systems, alternative scenario modeling, environmental impact statements, transportation planning, and city design.…”

Can big data tame a “naughty” world?

“…Unguided, automated exploration of big datasets is perceived as a productive way to analyze big data and compare multiple variables across space and time (e.g., Death ; Krause et al ; Pagano et al ). However, results from automated techniques can be misleading if they are not interpreted within the context of existing knowledge frameworks and the limitations of the dataset (O'Sullivan and Manson ).…”

“…The exponential increase in our ability to acquire, store, transmit, and analyze data has led various commentators to suggest that a world of “big data” has arrived, a world in which research questions can be answered by data directly, without reference to theoretical frameworks (Miller and Goodchild ). By covering massive spatial scales and diverse scientific domains, big data and accompanying analytical techniques offer the possibility of identifying new patterns and predictors from the chaos and complexity of human and environmental processes (Death ; O'Sullivan and Manson ). Big data, it is claimed, has the potential to provide unprecedented insights into environmental systems and human behaviour and offer an improved basis for decision making in a new era of data‐informed policy (White et al ).…”

Spatiotemporal enabled Content-based Image Retrieval

“…At the level of methods, there are similar shortfalls in our ability to store, manipulate, and analyze spatial big data (Wang and Liu 2009). And at the level of theory, we face many unresolved challenges in representing social and biophysical entities and relationships that operate at multiple levels of organization, over space, and through time (O'Sullivan and Manson 2015). TerraPop address these deserts in deluge of big spatial data.…”