4 COVER STORIES about their position in a network of stores and even the supply geography of individual packets on a shelf. Similarly, transactions may be tagged at the point of sale and associated uniquely to customers using loyalty, debit, and credit cards that also link customers to their neighbors at home and similar demographic groupings in other cities, using sophisticated geodemographic analyses. The influence between location-aware technology and sociology is also beginning to reverse. Other code spaces facilitate the emergence of “smart mobs” or “flash mobs,” social collectives organized and mediated by Internet and communications technologies: text messaging, instant messaging, and tweeting, for example, for the purposes of political organization; social networking; or, as is often the case, simple fun. The Internet of Things In technology circles, objects in a code space are referred to as “spimes,” artifacts that are “aware” of their position in space and time and their position relative to other things; spimes also maintain a history of this location data. The term spime has arisen in discussions about the emergence of an Internet of Things, a secondary Internet that parallels the World Wide Web of networked computers and human users. The Internet of Things is composed of (often computationally simple) devices that are usually interconnected using wireless communications technologies and may be selforganizing in formation. While limited individually, these mesh networks adopt a collective processing power that is often greater than the sum of its parts when their independent process models are networked as large “swarms” of devices. Moreover, swarm networks tend to be very resilient to disruption, and their collective computational and communication power often grows as new devices are added to the swarm. Networks of early-stage spimes (proto-spimes) of this kind have already been developed using, for example, microelectromechanical systems (MEMS), which may be engineered as tiny devices that are capable of sensing changes in electrical current, light, chemistry, water vapor, and so on, in their immediate surroundings. When networked together in massive volumes, they can be used as large-geography sensor grids for earthquakes, hurricanes, and security, for example. Sensor readings can be conveyed in short hops between devices over large spaces, back to a human observer or information system for analysis. MEMS often contain a conventional operating system and storage medium and can thus also perform limited processing on the data that they collect, deciding, for example, to take a photograph if particular conditions are triggered, and geotagging that photograph with a GPS or based on triangulation with a base station. Geodemographics and Related Business Intelligence The science and practice of geodemographics are concerned with analyzing people, groups, and populations based on tightly coupling who they are with where they live. The who in this small formula can provide information about potential debtors’, customers’, or voters’ likely economic profile, social status, or potential political affiliation on current issues, for example. The where part of the equation is tasked with identifying what part of a city, postal code, or neighborhood those people might reside in, for the purposes of allying them to their neighboring property markets, crime statistics, and retail landscapes, for example. Together, this allows populations and activities to be tagged with particular geodemographic labels or value platforms. These tags are used to guide a host of activities, from drawing polling samples to targeting mass mailing campaigns and siting roadside billboards. The dataware for geo- Process Models and Next-Generation Geographic Information Technology Continued from page 3 phones, running shoes, and cars; atop bicycle handlebars; and in clothing, pets, handheld gaming devices, and asset-tracking devices on the products that we buy in supermarkets. Devices all over the world began to sense and communicate their absolute and relative positions, allowing, first, the devices to be location tagged; second, those tags to become a significant medium for organizing, browsing, searching, and retrieving data; and, third, their relative geography to become the semantic context that ascribes to those objects (and their users) information. Indeed, for many online activities, maps and GIS have become the main portal to the Web. Semantic intelligence is driving the next evolution of the Web, characterized by the use of process models (usually referred to as software agents or Web services) as artificial intelligence that can reason about the meaning of data that courses through Internet and communication networks. A slew of ontological schemes—methods for classifying data and its relationships—provides the scaffolding that supports semantic reasoning online. Geography and location ontology is an important component of online semantics, allowing processes to not only know where something is in both network space and the tangible geography of the real world but also to reason about where it might have been, where it might go and why, whether that is usual or unusual behavior, what might travel with it, what might be left behind, what activities it might engage in along the way or when it reaches its destination, and what services might be suggested to facilitate these activities. Often, these may be location-based services that make use of the geographic position of a device, its user, or the local network of related devices, or they may make use of the network to deliver “action at a distance” to enrich a user’s local experience, by connecting the user to friends across the world, for example. Process models have also been developed in other information systems. Much of the potential for advancing geographic information technology stems from the ability of GIS to interface with other processes and related informatics through complementary process modeling schemes. The early precursors of this interoperability are already beginning to take shape through the fusion of GIS and building information models (BIMs). BIMs offer the ability of urban GIS to focus attention on a much finer resolution than ever, to the scale of buildings’ structural parts and their mechanical systems. GIS allows BIMs to consider the role of the building in a larger urban, social, geological, and ecosystem context. When process models are added to the mix, the complementary functionality expands even farther. Consider, for example, the uses of a GIS that represents the building footprints of an entire city but can also connect to building information models to calculate the energy load of independent structures for hundreds of potential weather scenarios, or BIMs that can interact with an earthquake simulation to test building infrastructural response to subsurface deformation in the bedrock underneath, using cartography to visualize cascading envelopes of projected impact for potential aftershocks. Virtual Worlds Many advocates of the semantic Web envision a massive dynamic system of digitally networked objects and people, continuously casting “data shadows” with enough resolution and fidelity to constitute a virtual representation of the tangible world. These virtual worlds are already being built, and many people and companies choose to immerse themselves in online virtual worlds and massively multiplayer online role-playing gaming (MMORPG) environments for socializing, conducting business, organizing remotely, collaborating on research projects, traveling vicariously, and so on. Here, process models are also driving advances in technology. Process models from computer gaming engines have been ported to virtual worlds, to populate them with automated digital assistants and synthetic people that behave and act realistically and can engage with users in the game world in much the same way that social interactions take place in the real world. Virtual worlds have been coupled with realistic, built and natural environment representations constructed using geometry familiar to GIS. The current generation of process models for MMORPG environments is relatively simple in its treatment of spatial behavior, but rapid advances are being made in infusing them with a range of behavioral geographies and spatial cognitive abilities that will enable more sophisticated spatial reasoning to be included in their routines. Gaming is just one application of process models in virtual worlds. The actions and interactions of synthetic avatars representing real-world people can be traced with perfect accuracy in virtual worlds because they are digital by their very nature, and often, that data may be associated with the data shadows that users cast from their real-world telecommunications and transactional activities in the tangible world. Virtual worlds are seen by many as terra novae for new forms of retailing, marketing, research, and online collaboration in which avatar representations of real people mix with process models that study them, mimic missing components of their synthetic physical or social environments, mine data, perform calculations, and reason about their actions and interactions. Code Space Aspects of the semantic Web may seep into the real world, from cyberspace to “meatspace.” In many ways, the distinction between the two has long ago blurred, and for many of us, our lives are already fully immersed in cyberplaces that couple computer bits and tangible bricks, and we find much of our activity steeped in flows of information that react to our actions and often shape what we do. Geographers have begun to document the emergence of what we might term a “code space,” a burgeoning software geography that identifies us and authenticates our credentials to access particular spaces at particular times and regulates the sets of permissions that determine what we might do, and with whom, while we are there. Commercial vehicle traffic for interstate commerce, commuter transit systems, and airports are obvious examples of code space in operation in our everyday lives. Mail systems transitioned fully to coded space a long time ago: for parcel delivery services, almost every object and activity can be identified and traced as it progresses through the system, from collection to delivery on our doorstep. Other code spaces are rapidly moving to the foreground: patients, doctors, and supplies are being handled in a similar fashion in hospitals. Goods in supermarkets and shopping malls are interconnected through intricate webs of bar codes, radio-frequency identification (RFID) tags, and inventory management systems that reason demographics traditionally relied on mashing up socioeconomic data collected by census bureaus and other groups with market research and pointof-sale data gathered by businesses or conglomerates. Traditionally, the science has been relatively imprecise and plagued with problems of ecological fallacy in relying on assignment of group-level attributes to individual-level behavior. Because of early reliance on data from census organizations, which aggregate returns to arbitrary geographic zones, the spatial components of geodemographics have also suffered from problems of modifiable areal units (i.e., there are an almost infinite number of ways to delineate a geographic cluster). Data is often collected for single snapshots in time and is subject to serious problems of data decay; households, for example, may frequently move beyond or between lifestyles or trends, without adequate means in the geodemographic classification system to capture that transition longitudinally. Process models could change geodemographics. When users browse the Web, their transactions and navigation patterns, the links they click, and even the amount of time that their mouse cursor hovers over a particular advertisement can be tracked and geocoded uniquely to their machine. Users’ computers can be referenced to an address in the Internet protocol scheme, which can be associated to a tangible place in the real world using reverse geocoding. Along retail high streets and in shopping malls, customers now routinely yield a plethora of personal information in return for consumer loyalty cards, for example, or share their ZIP Codes and phone numbers at the point of sale, in addition to passively sharing their names when using credit or debit cards. By simply associating an e-mail address to this data, it is relatively straightforward, in many cases, to cross-reference one’s activity in the tangible world with one’s data shadow in cyberspace. Developments in related retail intelligence, business analytics, inferential statistics, and geocomputing have increased the level of sophistication with which data can be processed, analyzed, and mined for information. This allows the rapid assessment of emerging trends and geodemographic categories. Process models are even coded into the software at cash registers in some instances. Much of this technology is allied to spimes and code spaces. Technologies based around RFID and RFID tagging, initially designed for automated stock taking in warehouses and stores, are now widely embedded in products, cards (and