ated questions and speculations regarding where they may have been, where they might be going, with whom, and to do what). Second, it allows geodemographic analysis to be refined to withinactivity resolutions. This has already been put to use in the insurance industry, for example, to initiate pay-as-you-go vehicle coverage models, using GPS devices that report location information to insurance underwriters. Mobile phone providers have also experimented with business models based around location-based services and location-targeted advertising predicated on users’ locations within the cell-phone grid, and groups have already begun to experiment with targeting billboard and radio advertising to individual cars based on similar schemes. New GIS schemes based around space-time process models and events are well positioned to interact with these technologies. Nanosystems When spimelike devices are built at very small geographies, capable of sensing and even manipulating objects at exceptionally fine scales, they become useful for nanoengineering. In recent years, there has been a massive fueling of interest in nanoscale science and development of motors, actuators, and manipulators at nanoscales. With these developments have come a veritable land grab and gold rush for scientific inquiry at hitherto relatively underexplored scales: within the earth, within the body, within objects, within anything to be found between 1 and 100 nanometers. Geographers missed out on the last bonanza at fine scales and were mostly absent from teams tasked with mapping the genome. The cartography required to visually map the genome is trivial and the processes that govern genomic patterns are completely alien to most geographers’ skill sets, so their exclusion from these endeavors is understandable. The science and engineering surrounding nanotechnology differ from this situation, however, in that they are primarily concerned with spatiotemporal patterns and processes and the scaling of systems to new dimensions. These areas of inquiry are part of the geographer’s craft and fall firmly within the domain of geographic information technologies. Process models with spatial sensing and semantic intelligence could play a vital role in future nanoscale exploration and engineering. Illustration by Suzanne Davis, Esri therefore wallets), and the environment with such pervasiveness that they enable widespread activity and interaction tracking, particularly within a closed environment such as a supermarket. Coupled to something like a customer loyalty card, these systems allow for real-time feeds of who is interacting (or not) with (not just buying, but handling, or even browsing) what products, where, when, with what frequency, and in what sequences. The huge volumes of data generated by such systems provide fertile training grounds for process models. The increasing fusion of mobile telecommunication technologies with these systems opens up a new environment for coupling process models to mobile geodemographics. This is a novel development for two main reasons. First, it creates new avenues of inquiry and inference about people and transactions on the go (and associ- Computational Social Science Geographic process models also offer tremendous benefits in supporting research and inquiry in the social sciences, where a new set of methods and models has been emerging under the banner of computational social science. Computational social science, in essence, is concerned with the use of computation—not just computers—to facilitate the assessment of ideas and development of theories for social science systems that have proved to be relatively impenetrable to academic inquiry by traditional means. Usually, the social systems are complex and nonlinear and evolve through convoluted feedback mechanisms that render them difficult or impossible to analyze using standard qualitative or quantitative analysis. Computational social scientists have, alternatively, borrowed ideas from computational biology to develop a suite of tools that will allow them to construct synthetic social systems within a computer, in silico, that can be manipulated, adapted, accelerated, or cast on diverging evolutionary paths in ways that would never be possible in the real world. The success of these computational experiments relies on the ability of computational social science to generate realistic models of social processes, however, and much of the innovation in these fields has been contributed by geographers because of their skills in leveraging space and spatial thinking as a glue to bind diverse cross-disciplinary social science. Much of computational social science research involves simulation-building. To date, the artificial intelligence driving geography in these simulations has been rather simplistic, and development in process models offers a potential detour from this constraint. Moreover, computational social science models are often developed at the resolution of individual people and scaled to treat massive populations of connected “agents,” with careful attention paid to the social mechanisms that determine their connections. This often requires that large amounts of data be managed and manipulated across scales, and it is no surprise that most model developers turn to GIS for these tasks. Connections between agent-based models and GIS have been mostly formulated as loose couplings in the past, but recent developments have seen functionality from geographic information science built directly into agent software architectures, with the result that agents begin to resemble geographic processors themselves, with realistic spatial cognition and thinking. These developments are potentially of great value in social science, both in providing new tools for advanced model building and in infusing spatial thinking into social science generally. At the same time, developments in agent-based computing have the potential to feed back into classic GIS as architectures for reasoning about and processing human environment data. Prologue This is a wonderful time to be working with or developing geographic information technologies, at the cusp of some very exciting future developments that will bring GIS farther into the mainstream of information technology and will infuse geography and spatial thinking into a host of applications. Of course, some potential sobering futures for these developments should be mentioned. As process models are embedded in larger information, technical, or even sociotechnical systems, issues of accuracy, error, and error propagation in GIS become even more significant. Ethical issues surrounding the use of fine-grained positional data also become more complex when allied with process models that reason about the significance or context of that data. Moreover, the reliability of process models as appropriate representations of phenomena or systems must come under greater scrutiny. About the Author Dr. Paul M. Torrens is an associate professor in the School of Geographical Sciences at Arizona State University and director of its Geosimulation Research Laboratory. His work earned him a Faculty Early Career Development Award from the U.S. National Science Foundation in 2007, and he was awarded the Presidential Early Career Award for Scientists and Engineers by President George W. Bush in 2008. The Presidential Early Career Award is the highest honor that the U.S. government bestows on young scientists; Torrens is the first geographer to receive the award. More Information For more information, contact Dr. Paul M. Torrens, Arizona State University (e-mail: torrens@ geosimulation.com). AN