Process Models and Next-Generation Geographic Information Technology Continued from cover The next generation of geographic information systems will be driven by process models. These are usually composed of algorithms and heuristics that will act on users’ requests for the GIS to perform some service for them, connect to digital networks to contextualize those requests, and interact seamlessly with other databases and processes to achieve users’ goals. Alternatively, process models may be used as a synthetic representation of system parts to build artificial phenomena “in silico” that can be subjected to experimentation and what-if scenario building in ways that are not possible “on the ground.” Geoprocessing has been featured with increasing priority in GIS for some time, and conventional GIS already relies on geoprocessing for spatial analysis and data manipulation. Process models represent an evolution from these existing technologies, catalyzed by artificial intelligence that takes traditional GIS operations into the world of dynamic, proactive computing on a semantic Web of interconnected data and intelligent software agents. Imagine, for example, building a representation of the earth’s boundary layer climate in GIS, but also being able to run dynamic weather patterns, storms, and hurricanes over that data, using climate models that sit in a supercomputing center on another continent. This article charts the development of process models in the geographic information sciences and discusses the technologies that have shaped them from the outside in. In addition, it explores their future potential in allying next-generation GIS to the semantic Web, virtual worlds, computer gaming, computational social science, business intelligence, cyberplaces, the emerging “Internet of Things,” and newly discovered nanospaces. Background Much of the innovation for process models in the geographic sciences has come from within the geographic information technology community. Geoprocessing featured prominently in the early origins of online GIS, where server-based GIS delegated much of the work that a desktop client would perform to the background, hidden from the user. Interest in geoprocessing has resurfaced recently, largely because of increased enthusiasm for online cartography and expanding interest in schemes for appropriating, parsing, and reconstituting diverse data sources from around the Web into novel mashups that lean on application programming interfaces—interfaces to centralized code bases—that have origins in search engine technology. Concurrently, many scholars in the geographic information science community have been developing innovative methods for fusing representations of space and time in GIS. This has seen the infusion of schemes from time geography into spatial database and data access structures to allow structured queries to be performed on data’s temporal, as well as spatial, attributes. Time geography has also been used in geovisualization, as a method for representing temporal attributes of datasets spatially, thereby allowing them to be subjected to standard spatial analysis. Much of this work has been based around a move toward creating cyberinfrastructure for cross-disciplinary research teams, and significant advances have been made in developing technologies to fuse GIS with real-time data from the diverse array of interconnected sensors and broadcast devices that now permeate inventory systems, long-term scientific observatories, transportation infrastructure, and even our personal communication systems. In parallel, work in spatial simulation has edged ever closer toward a tight coupling with GIS, particularly in high-resolution modeling and geocomputation using cellular and agent-based automata as computational vehicles for animating objects through complex adaptive systems. Automata are, essentially, empty data structures capable of processing information and exchanging it with other automata. Simulation builders often turn to GIS routines in search of algorithms for handling the information exchange between automata, and over time, a natural affinity between the two has begun to develop into a mutually influential research field often referred to as geosimulation. Much of the work in developing process models is finding its way into GIS from outside fields, however, and developments in information technology for the Web—and for handling geographic data on the Web—have been particularly influential. A massive growth in the volume and nature of The cloud of Wi-Fi signals that envelops central Salt Lake City, Utah, generated by approximately 1,700 access points. data in which we find our lives and work enveloped has catalyzed a transition from a previous model of the Web to a newer-generation phase. The Web remains fundamentally the same in its architecture, but the number of applications and devices that contribute to it has swelled appreciably, and with this shift, a phase change has taken place, instantiating what is now commonly referred to as Web 2.0. The previous iteration of Web development was centered on static, subscription-based content aggregated by dominant portals such as AltaVista, AOL, Excite, HotBot, Infoseek, Lycos, and Yahoo! By comparison, much of the current generation model for the Web is characterized by user-generated content (blogs, Twitter tweets, photographs, points of interest, even maps) and flexible transfers between diverse data sources. Moreover, these varied data streams interface seamlessly over new interoperable database and browser technologies and are often delivered in custom-controlled formats directly to browsers or handheld devices via channels such as Really Simple Syndication (RSS). This takes place dynamically, updating in near real time as the ecology of the Web ebbs and flows. Enveloping these developments has been a groundswell in the volume of geographic data fed to the Web. In many ways, Web 2.0 has been built on the back of the GeoWeb that has formed between growing volumes of location-enabled devices and data that either interface with the Web in standardized exchanges (uploading geotagged content to online data warehouses, for example) or rely on the Web for their functionality (as in the case of alternative positioning systems that triangulate their location based on wireless access points). The reduction in the cost of geographic positioning technologies led to the massive infusion of location-aware technology into cameras, continued on page 4