Feb 10, 2014 - Open GC calls for open data, open-source software, open standards and ... 16.4.2 Problems and Challenges of Ubicomp and Spatial Big Data . ..... This trend is best reflected in Google's mantra that 'Google maps = Google in.
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Ubiquitous Computing, Spatial Big Data and Open GeoComputation Daniel Sui
Contents Abstract........................................................................................................................................... 375 16.1 Introduction........................................................................................................................... 376 16.2 Ubicomp and the Emergence of Ambient Intelligence: From Cardiac Pacemakers to a Smart Planet............................................................................................... 376 16.3 Ubicomp and GeoComputation: From the Mirror Worlds to Everyware in the Metaverse...........................................................................................................................380 16.3.1 Location/Position Sensing Technologies in Ubicomp............................................... 381 16.3.2 Coupling Ubicomp with GC: Ambient Spatial Intelligence and Decentralised Spatial Computing..................................................................................................... 382 16.4 Problems and Prospects: Towards Open GeoComputation................................................... 383 16.4.1 Ubicomp and Spatial Big Data.................................................................................. 385 16.4.2 Problems and Challenges of Ubicomp and Spatial Big Data.................................... 386 16.4.3 Prospects: Towards Open GeoComputation.............................................................. 388 16.5 Summary and Conclusions.................................................................................................... 389 Acknowledgement.......................................................................................................................... 390 References....................................................................................................................................... 390 The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it. Mark Weiser (1991, p. 94)
Abstract This chapter examines the implications of ubiquitous computing (ubicomp) for GeoComputation (GC). As computing power has gradually embedded itself into the environment of our daily lives, we have witnessed the emergence of ubicomp and the concomitant growth of ambient intelligence during the past two decades. Ubicomp has contributed a new sentient environment in which the virtual and physical world, digital bits and atoms, people and objects can be linked and tracked. Ubicomp has also led to the spatial big data deluge, characterised by three Vs – volume, variety and velocity. The next phase of GC development should be conducted in the context of ubicomp and spatial big data. There are multiple challenges along computational, theoretical, social, political, legal and environmental fronts. To better address these challenges, an open GC paradigm is proposed in this chapter. Open GC calls for open data, open-source software, open standards and open collaboration. 375
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16.1 Introduction Although GeoComputation (GC) has been part of the scientific lexicon for over 16 years, its precise meaning has yet to be settled as reflected by the great variety of definitions adopted by practitioners since the early days of GC all the way through to the contributors of this latest book (Gahegan, 1999; Ehlen et al., 2000; Fischer and Leung, 2010). Indeed, GC has meant different things to different people. Defining GC is, in many interesting ways, just like defining geography; it has become increasingly difficult and challenging due to the influence of centrifugal driving forces behind its development. Perhaps Longley’s (1998) more liberal definition – GC is what GC practitioners do – captures the dynamics and diversity of the field better than any of the restrictive definitions so far (Couclelis, 1998; Openshaw and Abrahart, 2000). Recent developments in spatial computing further complicate the task of precisely defining GC (Yang et al., 2011, 2012; Agouris et al., 2012). This chapter takes a more inclusive definition of GC: any form of computing that is motivated/ inspired by geographical/spatial concepts/theories or deals with geographical/spatial aspects of reality. The primary goal here is to discuss the field of GC in the broader context of ubiquitous computing (ubicomp) and the emergence of spatial big data. Ubicomp, also known as pervasive computing, refers to a new mode of computing in which information processing is embedded into everyday objects, activities and environments. In doing so, I aim to open a discussion on the implications of ubicomp for the next phase of GC development. In a broader sense, GC is intimately linked to ubicomp – both literally and physically, simply because in Latin, ubi literally means everywhere, which already implies geo or spatial. Unlike Couclelis (1998), this chapter does not make an explicit distinction between computing and computation, thus using the two words interchangeably. The rest of this chapter is organised as follows. After a brief introduction, Section 16.2 gives an overview of the recent developments in ubicomp, followed by an introduction of location technologies in ubicomp and how to design ubicomp to facilitate GC in Section 16.3. Section 16.4 is devoted to problems and prospects as provided by ubicomp for GC in the context of the big data deluge. Inspired by the spirit of emergent open science, this section also discusses how an open GC approach may better help practitioners deal with the challenging issues posed by the big data deluge. The last section contains a summary and conclusions.
16.2 Ubicomp and the Emergence of Ambient Intelligence: From Cardiac Pacemakers to a Smart Planet Since Stan Openshaw, who is generally recognised as being the father of GC (Openshaw, 2013), first raised the banner of GC in the mid-1990s, the fields of both geography (especially in terms of the location/positioning technologies) and computing have undergone dramatic changes. As far as computing is concerned, perhaps one of the most profound changes is that information processing capacity has been increasingly embedded in the environment around us (Mostéfaoui et al., 2008). Indeed, the tools we invented to study the world have increasingly become an integral part of the world (Sui and Morrill, 2004). In the early days of the twenty-first century, fewer than a quarter of the computer chips produced by Intel were destined for desktops. Instead, more and more computer chips are being embedded in the environment as sensors or other consumer products and household items (Kitchin and Dodge, 2011). Furthermore, computing devices are not only a repository of data but also communicate and process information. No longer confined to mainframes, server farms or desktops – typically housed in climate-controlled, weather-proof data centres – computing has become more mobile, embedded, distributed and disassembled (Figure 16.1) (Pierre, 2010). Along with the sensor web (sensor network designed for environmental monitoring) in the environment, ubicomp has become an integral part of an electronic skin wrapping around the Earth (Figure 16.2). More and more objects we interact with on a daily basis are now equipped with computing processing power, while many others are activated by passing processors carried by people or mounted on vehicles in increasingly sentient cities (Shepard, 2011). As Crang and Graham (2007) observed,
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Figure 16.1 Ubicomp and the emerging sentient environment in daily life. (Courtesy of Fraunhofer Verbund Mikroelectronic, Berlin, Germany.)
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Figure 16.2 Ubicomp and sensor webs: An electronic skin of planet Earth. (From Tao, V., The smart sensor web: A revolutionary leap in earth observation, GeoWorld Magazine, September Issue, figure first published in GeoWorld Magazine, September 2003, http://www.geoplace.com, 2003.)
‘our environment is not a passive backdrop but an active agent in organizing our daily lives. The spaces around us are now being continually created and re-created in informational and communication processes’ (p. 789). We are increasingly living in sentient cities ‘where we not only think about cities but cities think of us’ (p. 790). Indeed, in the ubicomp age, computers should not simply be regarded as external to the problem/solution; instead, they must be viewed as an active agent in shaping who we are and what we do. Ubicomp, also known as pervasive computing (Genco and Sorce, 2010), is typically invisible but accessible everywhere within a particular locale. Ubicomp represents the third wave of computing (Figure 16.3) (Weiser, 1993). The first wave of computing started with the invention of ENIAC (Electronic Numerical Integrator and Computer) in 1946 and ended with the appearance of the first personal computer (PC) in 1983. This period was dominated by mainframe computers, with one computer serving multiple users. The second wave of computing, from 1983 to present, has been
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Figure 16.3 Fundamental differences among the three paradigms of computing.
characterised by PCs with one person and one computer in an uneasy symbiosis, staring at each other across the desktop without really inhabiting each other’s worlds. The third wave of computing, Weiser (1993) argues, will be dominated by ubicomp (Figure 16.4). Unlike the previous two waves, ubicomp will operate in a mode where many computers serve one or many individuals, regardless of where the person is located in the world (Farman, 2012). Instead of interacting across the desktop, computers will be embedded into human bodies, furniture, walls, buildings, surrounding environments, neighbourhoods and cities, thus forming a nearly seamless digital habitat (Kuniavsky, 2010). Ubicomp is heralding a new age that is full of smart mobs and sentient objects via the Internet of Things (Rheingold, 2002; Hersent et al., 2012). Indeed, the world predicted in David Brin’s (1990) 18 Mainframe (one computer, many people)
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science fiction novel Earth, in which everybody is connected with everybody and everything else, has arrived much sooner than anticipated. Although the computer as we know it has not completely vanished as Weiser (1991) predicted, recent advances in ubicomp have accelerated the pace towards the disappearance of computers, as more and more embedded computers are found inside our bodies (such as cardiac pacemakers) as well as in our surroundings (e.g. ranging from homes, offices and shops, to streets, neighbourhoods and cities). With ubicomp so widespread throughout the health-care industry (Orwat et al., 2008), computer chips of various kinds are increasingly implanted into human bodies, leading Clark (2003) to argue that we are all now naturally born cyborgs. Furthermore, with the accelerated development of worldwide sensor networks (such as IBM’s smart planet project and HP/Cisco’s CeNSE project) and the Internet of Things (Tuters and Varnelis, 2006), ubicomp is changing human–computer interaction in many fundamental ways. The goal of developing ubicomp is ‘to put computing back in its place, to reposition it into the environmental background, to concentrate on human-to-human interfaces and less on human-to-computer ones’ (Weiser et al., 1999: 694). Weiser (1991) envisioned three basic forms for ubiquitous system devices: tabs (wearable centimetre-sized devices), pads (handheld decimetre-sized devices) and boards (metre-sized interactive display devices). These three forms proposed by Weiser are characterised by having a planar shape and incorporating visual output displays. However, recent developments in smart devices have expanded this range into a much more diverse and potentially more useful array of ubicomp devices. Three additional forms for ubiquitous systems have been reported (wikipedia.com): (1) dust (miniaturised devices without visual output displays, for example, microelectromechanical systems [MEMS], ranging from nanometres through micrometres to millimetres, such as the so-called smart dust); (2) skin (fabrics based on light-emitting and conductive polymers – organic computer devices – can be formed into more flexible non-planar display surfaces and products such as clothes and curtains) and (3) clay (ensembles of MEMS can be formed into arbitrary 3D shapes such as artefacts resembling many different kinds of physical object, forming tangible interfaces) (Poslad, 2009). These embedded computers, though invisible to users, are fast approaching the power and complexity of desktop PCs (National Research Council, 2001, 2003). According to an estimate by Bill Gates (2003), a typical middle-class American has already interacted with about 150 embedded systems every day at the outset of the twenty-first century, most of the time without knowing it. As ubicomp reaches maturity, these embedded computers – which use up to 90% of the microprocessors produced today – will inevitably perform more PC-like functions. More importantly, advances in wireless networks will make these embedded computers communicate seamlessly with their traditional PC counterparts. According to the Semiconductor Industry Association (http://www.sia-online.org/pre_statistics.cfm), the world microchip industry is currently producing approximately one billion transistors per person per year. Computing is becoming ubiquitous, at least in an increasing number of places in the developed world. Ubicomp was made possible by the convergence of new advances in distributed and mobile computer systems, wireless communication devices and new visualisation technologies (Krumm, 2009; Yang et al., 2012). With the disappearance of computers, we have witnessed the emergence of a new kind of AI (ambient intelligence) in recent years. AI refers to electronic environments that are sensitive and responsive to the presence of people (Friedewald et al., 2005). Its aim is to improve the standard of living by creating the desired environment and functionality via intelligent, personalised and interconnected systems and services. In an ideal ambient intelligent environment, the user is surrounded by a multitude of interconnected, invisibly embedded computer systems. AI is capable of recognising users, adapting to their preferences and offering natural means of interaction. The prototype project Ambient Agoras (http://www.ambient-agoras.org) has already demonstrated that the computer as a device will disappear, although its functionality will persist in a ubiquitous fashion. The Ambient Agoras environment was designed to transform places into social marketplaces of ideas and information (agoras) and provide situated services, place-relevant information and a feeling of the place (genius loci). It is possible to add new layers of information-based
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services to each place or memory that is accessible to users. All these functions are achieved by integrating information into architecture via smart artefacts and expanding reality by providing better affordances (a quality of an object or an environment that allows an individual to perform an action) and information processing to existing places and objects. The emergence of AI will represent significant progress towards the development of the information appliances that Norman (1998) envisioned. In summary, ubicomp is rapidly contributing to an emerging AI that has the following characteristics: (1) embedded (many networked devices are integrated into the environment), (2) context aware (these devices can recognise users and their situational context), (3) adaptive/ personalised (they can be tailored to individual needs) and (4) anticipatory (they can anticipate users’ desires without conscious mediation). We need to bear in mind that no technologies exist or develop in a vacuum. Like all powerful technologies, ubicomp’s development has been driven by a series of socioeconomic, political and even personal factors (Crang and Graham, 2007), including but not limited to the corporate sector’s relentless pursuit of friction-free capitalism, governments’ determination to increase security and surveillance in the context of the war on terror and our existential pursuit of meaning through more affective computing in the broader fields of art and culture (Dourish and Bell, 2011; Ekman and Fuller, 2012).
16.3 Ubicomp and GeoComputation: From the Mirror Worlds to Everyware in the Metaverse GC currently faces a computing environment that has drastically changed since it was first proposed as a field of inquiry nearly two decades ago. Instead of the traditional distinction of hardware and software, we have witnessed the emergence of everyware (Greenfield, 2006) as ubicomp replaces the traditional mainframe and desktop computers to become the dominant paradigm for computing. The future scenario of everyware (sometimes used interchangeably as ubicomp) – when people and objects are connected via distributed computing and unconstrained by geographical contexts – has arrived faster than expected. Concomitant with the growth of ubicomp/everyware, we are also rapidly entering a new age of metaverse – a hybrid world in which the virtual world based upon digital bits is increasingly linked to the atom-based physical world (http://www.metaverseroadmap.org). First coined by Neal Stephenson’s (1992) science fiction novel Snow Crash, metaverse refers to a fictional virtual world where humans, as avatars, interact with each other and software agents, in a 3D space that uses the metaphor of the real world. The rapidly evolving metaverse is a result of several converging technologies. According to the metaverse road map report, the browser for engaging this metaverse will be based upon a 3D web that brings together the following four technologies: • Mirror worlds – digital representations of the atom-based physical world, such as Google Earth, Microsoft Virtual Earth, NASA World Wind, ESRI ArcGlobe, USGS National Map and the massive georeferenced geographic information system (GIS) databases developed during the past 50 years • Virtual worlds – digital representations of imagined worlds, such as Second Life, World of Warcraft, computer games, various cellular automata models and agent-based models • Lifelogging – the digital capture of information about people and objects in the real or digital worlds, such as Twitter, blogs, Flickr, YouTube and social networking sites such Facebook or MySpace • Augmented reality – sensory overlays of digital information on the real and virtual worlds using a heads-up display (HUD) or other mobile/wearable devices such as cell phones or sensors via participatory sensing Nowadays, when I think about GIS in general and GC in particular, I cannot separate either of them from the emerging metaverse. Viewed from a metaverse perspective, our discussions of GC within
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the geospatial community have focused almost exclusively on the components of mirror worlds. In this ubicomp age, GC should be re-conceptualised to deal with everyware in the emerging metaverse. One of the defining characteristics of ubicomp is its context awareness, which generally refers to the capabilities of either mobile or embedded systems to sense their physical environment and adapt their behaviour accordingly. In ubicomp, context includes three essential elements: (1) where you are (location), (2) who you are with (identity) and (3) what resources are nearby (potential). Context is a broad term that includes nearby people, devices, lighting, noise level, network availability and even the social situation, for example, whether you are with your family or a friend from school or a colleague from work. Location is only part of the contextual information, but location alone does not necessarily capture things of interest that are mobile or changing. As far as GC is concerned, location is the starting point from which further spatial analysis and modelling can be conducted. In the age of ubicomp, location has assumed more important roles in shaping everything we do in the metaverse (Gordon and de Souza e Silva, 2011). This section first reviews the current existing location sensing technologies, followed by a discussion on how to design ubicomp for GC.
16.3.1 Location/Position Sensing Technologies in Ubicomp High-quality locational information is the foundation upon which GC is built. During the past two decades, multiple technological advances have been made in alternative location sensing technology, including techniques for indoor navigation, near field communication (NFC), MEMS, audio beacons, Wi-Fi, radio-frequency identification (RFID) and Bluetooth (Choi et al., 2008; ABI Research, 2012). Indeed, these alternative location sensing technologies have become embedded in more and more devices and consumer products, making computing not only more ubiquitous but also more location and context aware. As a result, we have witnessed the growth of ubiquitous geographic information in recent years (Kim and Jang, 2012), which is increasingly available and searchable on the web (Gordon and de Souza e Silva, 2011). Regardless of the specific techniques deployed to determine the locational information, three major methods/principles are used when attempting to determine a given location in ubicomp (Hightower and Borriello, 2001): • Triangulation can be done via lateration, which uses multiple distance measurements between known points, or via angulation, which measures angle or bearing relative to points with known separation. Satellite-based global positioning systems (GPSs) are based upon the principle of triangulation to determine location. GPS works well outdoors but usually does not work inside buildings. • Proximity measures nearness to a known set of points. RFID, Wi-Fi and audio beacons all rely on proximity measures. Most indoor location sensing techniques are based on the proximity method. • Scene analysis examines a view from a particular vantage point. Widely regarded as the most cost-effective means of tracking full-body motions in the world today, MotionStar is a location sensing technology based on the principle of scene analysis (http://www.vrealities.com/motionstar.html). These different location sensing techniques vary in accuracy, cost and area coverage, ranging from workspace and site-wide systems to regional, global and even interplanetary systems. In reality, hybrid positioning systems (e.g. Navizon, Xtify, PlaceEngine, Skyhook, Devicescape, openBmap) – using a combination of more than one location sensing method – are often used to locate and track people and objects. As geotagging becomes more common for information available online and IP addresses can be easily tied to their physical locations, we can determine where people and things are located more easily than at any time in human history. The close coupling of ubicomp with GC has contributed to an ambient spatially intelligent environment.
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16.3.2 Coupling Ubicomp with GC: Ambient Spatial Intelligence and Decentralised Spatial Computing The emergence of everyware in the metaverse is rapidly creating a new sentient environment with growing capabilities of ambient spatial intelligence (AmSI) (ambientspatial.net). As stated earlier, it is no longer a luxury to discuss GC in the context of ubicomp. It has become a necessity to design GC in the AmSI environment, where GC is closely coupled with the ubicomp environment. Some of the general principles have been laid out in the decentralised spatial computing (DeSC) framework of Duckham (2013). The goal of DeSC is to (1) respond efficiently to queries about events, (2) support better understanding of those events in real time and (3) improve human decision-making based on information about spatial events. According to Satoh (2005), ubicomp environments have several unique requirements as follows: • Mobility: Not only entities, for example, physical objects and people, but also computing devices can be moved from location to location. The location model in ubicomp is required to be able to represent mobile computing devices and spaces as well as mobile entities. Furthermore, it needs to be able to model mobile spaces, for example, cars, which may contain entities and computing devices. • Heterogeneity: A ubicomp environment consists of heterogeneous computing devices, for example, embedded computers, handheld/wearable computers, sensor networks of various kinds and public terminals. Location-based and personalised services must be executed using computing devices whose capabilities can satisfy the requirements of the services (Gartner and Ortag, 2011). GC is thus required to maintain the capabilities of computing devices as well as their locations. • Availability: Ubicomp devices may have limited memories and processors, so they cannot support all the services that they need to provide. Software must be able to be deployed at computing devices on demand. GC should be able to manage the (re)location of serviceprovider software. • Absence of centralised databases: Since ubicomp devices are organised in an ad hoc and peer-to-peer manner, they cannot always access database servers to maintain location models. The model should be available without database servers, enabling computing devices to be organised without centralised management servers. Satoh (2007) further developed and implemented a model for location-aware and user-aware services in ubicomp environments. This model can be dynamically organised and implemented like a tree based on geographical containment, such as user–room–floor–building, and each node in the tree can be constructed as an executable software component. The model is unique to existing approaches because it can be managed by multiple computers in an ad hoc manner and is also capable of providing a unified view of the locations of not only physical entities and spaces, including users and objects, but also computing devices and services. A prototype implementation of this model was developed on a Java-based mobile agent system. Figure 16.5 shows the overall design, which contains four components: (1) Virtual component (VC) is a digital representation of a physical entity or space in the physical world; (2) aura component is a virtual or semantic scope surrounding a physical entity or computing device; (3) proxy component bridges the world model and computing device and maintains the subtree of the model or executes services located in the VC; and (4) service component is a software module that defines application-specific services associated with physical entities or places. Huang et al. (2009) attempted to link ubicomp with Web 2.0/collective intelligence into mobile navigation services. They developed a mobile navigation system in a ubicomp environment to collect user-generated content explicitly and implicitly, such as ratings, comments, feedback, moving tracks and durations at decision points, and thus provide users with a new experience and smart wayfinding support (e.g. route recommendations based on collective intelligence). A smart environment
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Figure 16.5 Location-based pervasive computing environment. (From Satoh, I., Perv. Mobi. Comput., 3(2), 158, 2007.)
with a positioning module and a wireless communication module was set up to support users’ wayfinding, facilitate users’ interaction with an annotation of the smart environment and collect usergenerated content. In order to illustrate the benefits of introducing a smart environment and Web 2.0 into mobile navigation services, Huang et al. (2009) developed several collective intelligence–based route calculation algorithms to provide smart wayfinding support for users, such as the nicest route, the least complex route, the most popular route and the optimal route. Prototype systems reported by Huang et al. (2009) and Satoh (2007) represented significant steps towards performing meaningful GC in an indoor environment. Perhaps this may go down in history as ubicomp’s biggest contribution to GC as geospatial technologies have often been pejoratively labelled as a 15% technology, because most humans spend 85% of their time indoors, and until recently, we did not have efficient methods to track people in an indoor environment. As more and more computer chips and sensors are becoming integral parts of our homes, offices, shops, hospitals and neighbourhoods, GC will be better equipped with capabilities to model and track human and object movement in indoor as well as outdoor environments. The recent development of urban tomography (Krieger et al., 2010; Evans-Cowley, 2010) has enabled citizens to better document their lives spatiotemporally by capturing dense audiovisual records of urban phenomena, potentially linking both indoor and outdoor activities (Figure 16.6).
16.4 Problems and Prospects: Towards Open GeoComputation The explosive development of ubicomp during the past two decades has created unprecedented opportunities and daunting challenges for GC. This section reviews GC’s problems and prospects in the age of ubicomp.
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Figure 16.6 The urban tomography system (tomography.usc.edu). (From Krieger, M.H. et al., J. Urban Technol., 17(2), 21, 2010, Taylor & Francis Ltd., http://www.tandfonline.com/doi/abs/10.1080/10630732.2010.515087.)
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16.4.1 Ubicomp and Spatial Big Data Ubicomp has contributed to what is popularly known as the big data deluge. Because an increasing number of data carry spatial and temporal tags, GC must explore creative ways to deal with spatial big data (Shekhar et al., 2012). The spatial big data deluge is of particular concern primarily because of the three Vs: variety, volume and velocity (Janowicz, 2012). Big data is not only about the great variety and large volume but also the speed at which data are created and updated. The three Vs of spatial big data pose formidable technical challenges for GC in the coming decade. Until recently, the geospatial community has had a rather narrow definition of what is considered geographic data or information, often heavily influenced by the legacy of traditional cartography. But rapid advances in a plethora of technologies – GPS, smartphones, sensor networks, cloud computing, etc., especially all of the technologies loosely called Web 2.0 – have radically transformed how geographic data are collected, stored, disseminated, analysed, visualised and used (Chee and Franklin, 2010). This trend is best reflected in Google’s mantra that ‘Google maps = Google in maps’ (Ron, 2008). The insertion of an in between Google and maps perhaps signifies one of the most fundamental changes in the history of human mapping efforts. Nowadays, users can search though Google maps not only for traditional spatial/map information but also for almost any kind of digital information (such as Wikipedia entries, Flickr photos, YouTube videos and Facebook/ Twitter postings) as long as it is geotagged. Furthermore, in contrast to the traditional top-down authoritative process of geographic data production by government agencies, citizens have played an increasingly important role in producing geographic data of all kinds through a bottom-up crowdsourcing process. As a result, we now have a great variety of geocoded data growing on a daily basis from molecular to global scales covering almost everything we can think of on or near the Earth’s surface. Due to the ubiquity of information-sensing mobile devices, aerial sensory technologies (remote sensing), software logs, cameras, RFID readers, wireless sensor networks and other types of datagathering devices, 1–5 EB (1 EB = 1018 B) of data is created daily and 90% of the data in the world today were created within the past 2 years (MacIve, 2010). The amount of data humanity creates is doubling every 2 years; 2010 is the first year that we reached 1 ZB (1021 B), while in 2011 alone, the world generated approximately 1.8 ZB of data. The explosive growth of big data is rapidly transforming all aspects of governments, businesses, education and science. By 2020, the volume of the world’s data will increase by 50 times from today’s volume (Gantz and Reinsel, 2011). We will need 75 times more IT-related infrastructure in general and 10 times more servers to handle the new data. Metaphors of data storage have evolved from bank, to warehouse, to portal and now to cloud. Data storage cost has dropped dramatically during the past two decades. Between 2005 and 2011 alone, costs of storage dropped by 5/6. Not surprisingly, how to deal with the new reality of big data tops the agendas of governments, industry and multiple disciplines in the academy (IWGDD, 2009; CORDIS, 2010). Although it is a challenging task to estimate the precise volume of geospatial data out there, we can safely say geospatial data are becoming an important part of the big data torrent. Geospatial information in general and volunteered geographic information (VGI) in particular should be understood in the context of big data. Crowdsourcing, the Internet of Things and big data are rapidly converging in the domain of geospatial technologies (Ball, 2011). Of course, due to rapid technological advances, what is considered big or small is a moving target. In the McKinsey report (Manyika et al., 2011), personal location data have been singled out as one of the five primary big data streams. With approximately 600 billion transactions per day, various mobile devices are creating approximately 1 PB (1015 B) of data per year globally. Personal location data alone is a $100 billion business for service providers and $700 billion to end users (Manyika et al., 2011). The other four streams of big data identified by the McKinsey Global Institute – health care, public-sector administration, retail and manufacturing – also have a significant amount of data either geocoded or geotagged. So geospatial data are not only an important component of big data but are actually,
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to a large extent, big data themselves. For the geospatial community, big data presents not only bigger opportunities for the business community (Francica, 2011) but also new challenges for the scientific and scholarly communities to conduct ground-breaking studies related to people (at both individual and collective levels) and environment (from local to global scale) (Hayes, 2012). In fact, the geospatial community was tackling big data issues even before big data became a buzz word or trend (Miller, 2010). From very early on, geospatial technologies were at the forefront of big data challenges, primarily due to the large volumes of raster (remote-sensing imagery) and vector (detailed property surveys) data that need to be stored and managed. Back in 1997 when Microsoft Research initiated a pilot project to demonstrate database scalability, they used aerial imagery as the primary data (Ball, 2011). The Microsoft TerraServer developed then is still in use and functional today and sets the standard and protocol for today’s other remote-sensing image serving sites such as OpenTopography.org (LiDAR data). The three Vs in spatial big data have raised daunting technical challenges for GC. First, as early as 2007, our capacity to produce data had outpaced our abilities to store them (National Research Council, 2009). Although DNA-inspired data-encoding techniques are promising (Hotz, 2012), the lag time of practical implementation is still considerable. How to redesign our cyberinfrastructure to better deal with the situation is thus becoming a major challenge. Second, the quality of spatial big data is often problematic as they often have no sample scheme, no quality control, no metadata and no generalisability (Goodchild, 2012). Third, both analysis and synthesis of the great variety of data generated by ubicomp are currently difficult due to the lack of interoperability, common ontology or semantic compatibility (Sui, 2012).
16.4.2 Problems and Challenges of Ubicomp and Spatial Big Data In addition to these technical challenges, ubicomp and spatial big data have also raised a series of critical issues at the individual, social and environmental levels. While the potential applications and benefits of ubicomp are well documented, concerns over ubicomp’s long-term implications for privacy, the digital divide and sustainability also need to be addressed alongside the technical challenges outlined previously. At the individual level, ubicomp has intensified society’s concern over privacy as potentially troubling apps such as Girls Around Me (http://girlsaround.me) can be downloaded for free from iTunes, or condom use can be mapped using precise lat/long coordinates (http://wheredidyouwearit.com). Location-based services and social media have further exacerbated concerns over people’s locational privacy. Resolving issues related to locational privacy requires comprehensive approaches along legal, ethical and technical fronts (Sui, 2011). In particular, the development of trustworthy geospatial technologies in the context of ubicomp deserves attention. Generally, two major strategies have been developed and adopted: anonymity and obfuscation (Duckham and Kulik, 2006). Anonymity is often regarded as one of the privacy-sympathetic technologies (PST). Anonymity detaches or removes an individual’s locational information from electronic transactions. Anonymity is normally quite effective in protecting individual privacy. However, with recent advances in data-mining techniques, GC can integrate locational information with other data such as remotely sensed imagery; georeferenced social, economic and cadastral data; point-of-sale data; credit-card transactions; traffic monitoring and video surveillance imagery; and other geosensor network data, allowing identity to be inferred. Obfuscation techniques deliberately degrade locational information, using error and uncertainty to protect privacy, one of several privacy-enhancing technologies. They also include geographic masking for static data (Armstrong and Rushton, 1999). Duckham and Kulik (2006) extend the obfuscation approach to mobile objects. They also consider ways to counter potential threats from third parties who can refine their knowledge of a mobile object and compromise the obfuscation. Concomitant with the growth of this ever-expanding digital universe filled with big data, the world (people, manufactured objects and/or other things and environment) is increasingly being
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recorded, referenced and connected by vast digital networks. Almost paradoxically, as some parts of the world are flooded by big data and people are increasingly connected in a shrinking world, we must also be keenly aware that this world remains a deeply divided one – both physically and digitally. While a large majority of people in North America and Europe have access to the Internet (with Internet penetration rates at 78.3% and 58.3%, respectively, by the end of 2011), two-thirds of humanity does not have access to the rapidly expanding digital world; the world average Internet penetration rate is 30.2% with Asia (23.8%) and Africa (11.4%) trailing at the bottom. The geographical distribution of new digital data stored in 2010 reflects both the digital divide and uneven development levels across the globe, with the developed world or Global North (North America and Europe) having 10–70 times more data than the developing world or Global South (Africa, Latin America and Asia) (Manyika, 2011). Nearly a third of humanity (about 2 billion people) still lives on under $2 a day. We should also be mindful that sometimes simply having access to gadgets themselves is not enough. Many iPhone users in the developed world have enjoyed using one of multiple versions of restroom locators (e.g. have2p), but for a country like India, where there are more cell phones than toilets, simply having have2p installed on one’s iPhone would not help much in rural areas due to the severe lack of sanitary infrastructure. In the context of geographic information (and to some extent other types of data as well), the biggest irony remains that Murphy’s law is still at work – information is usually the least available where it is most needed. We have witnessed this paradox unfolding painfully in front of our eyes in the Darfur crisis in northern Sudan, the aftermath of the 2010 Haiti earthquake and the 2011 BP explosion in the Gulf of Mexico. Undoubtedly, how to deal with big data in a shrinking and stratified world remains a major challenge during the age of ubicomp and spatial big data. The strengths, weaknesses, opportunities and threats of VGI for improving the spatial data infrastructure are quite different in the two global contexts of North and South (Genovese and Roche, 2010). Furthermore, as Gilbert and Masucci (2011) show so clearly in their recent work on uneven information and communication geographies, we must move away from the traditional, linear conceptualisation of a digital divide, concerned primarily with physical access to computers and the Internet. Instead, we must consider the multiple divides within cyberspace (or digital apartheid) by taking into account the hybrid, scattered, ordered and individualised nature of cyberspaces (Graham, 2011). Indeed, multiple hidden social and political factors are at play in determining what is or is not available online (Engler and Hall, 2007). Internet censorship (Warf, 2011; MacKinnon, 2012), power laws (or the socalled 80/20 rule) (Shirky, 2006), homophile tendencies in human interactions (de Laat, 2010) and fears of colonial and imperial dominance (Bryan, 2010) are also important factors to consider for the complex patterns of digital divide and uneven practices of VGI at multiple scales on the global scene. Last, but not least, there are also concerns over the long-term environmental impacts of ubicomp. Will these smart devices individuals use and smart cities at the local and regional level automatically contribute to the formation of a smart planet at the global level? In other words, will ubicomp lead to more ubiquitous consumption, thus more material and energy consumption that will accelerate resource depletion and environmental devastation? Or will ubicomp further promote citizen science, better monitor environmental conditions and track products through their entire life cycle, thus helping in the effort to save our planet? Are we closer or further away from the goals of sustainability in the age of ubicomp? How can ubicomp be deployed to advance the cause for the environment? Will ubicomp sensors for animals, plants and physical elements result in new types of environmental rights? Furthermore, scientists and artists alike have been concerned about E-wastes generated by ubicomp each year and their long-term effects on the environment. Ubicomp’s impacts on human health, such as exposure to non-ionising radiation through ubicomp devices or psychological effects of implantable chips, have also been discussed in the literature. One promising avenue of research is ecological computing (part of nature-inspired computing), which is not only concerned with environmental impacts of computing technologies but also how to creatively use ecological principles to design and develop computing systems (Zhuge and Shi, 2004; Briscoe, 2012; Zhao and Brown, n.d.).
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16.4.3 Prospects: Towards Open GeoComputation To effectively address these multiple challenges, we need to rethink the way GC has been practised so far. In particular, we need to think about the ways ubicomp and spatial big data are changing how we do science. The broader scientific community’s push for a new paradigm under the general umbrella open science deserves our attention. As reflected in the presentations made during the recent Open Science Summit (opensciencesummit.com), exciting advances are being made every day in diverse scientific fields ranging from mathematics (the Polymath project), astronomy (Galaxy Zoo, Sloan Digital Sky Survey) and geology (the OneGeology project) to environmental science (Water Keeper, Global Community Monitoring), health and medicine (the HapMap project, CureTogether). Efforts devoted to open science are quickly fleshing out the details of emerging data-intensive inquiries, otherwise known as the fourth paradigm. The fourth paradigm was originally advocated by Jim Gray (2007) at IBM. According to Gray (2007), scientific discoveries until recently (the early days of the twenty-first century) have been driven by three dominant paradigms: the empirical (by describing natural phenomena), the theoretical (by using and testing models and general laws) and the computational (by simulating complex phenomena using fictional/artificial or small real-world data sets) approaches. Unlike these three paradigms, the fourth paradigm is data intensive, often dealing with data in peta- or even exabytes of different varieties (numbers, text, image and video) updated rapidly (in some cases, even real time). Despite diverse interpretations of the precise meaning of open science (e.g. open source, open data, open access, open notebook or networked science), we can safely claim that the emerging paradigm, in a nutshell, includes the following elements (Gezelter, 2009): (1) transparency in methods of data collection, observation and experiments; (2) public availability and reusability of scientific data to facilitate reproducibility (see Brunsdon, 2013); (3) public accessibility of scientific communication and publication; and (4) mass collaboration involving both experts and amateurs/ citizens using web-based tools. While ubicomp and big data are creating a new terra incognita, maybe even too big to know (Weinberger, 2012), Nielsen (2012) argues that these four basic principles of open science may serve to best guide new scientific discovery. Indeed, we need such signposts; as the stream of geospatial data rapidly merges with the big data deluge, and as open science is promoted as the Noah’s ark in which everyone is to survive the current information flood, it is natural that the next episode for GC is moving towards an open GC. In fact, I must say that the geospatial community had been working on big data in the spirit of open science long before it became the talk of the town. As a result, the geospatial community is well positioned to ride the current wave of open science because of our collective efforts in promoting data sharing, open-source software development and participatory sensing/mapping (citizens as sensors). For example, the Open Geospatial Consortium (opengeospatial.org) has been a pioneer in developing open standards to facilitate interoperability of geospatial data across platforms. Also notable, the FOSS4G (Free and Open Source Software for Geospatial) Conference has – since 2006 – been serving as the primary forum to promote the development of free and opensource software (foss4g.org). According to Steiniger and Hunter (2012), we now have a plethora of free and open software tools, ranging from web map servers for managing data and images (such as mapserver.org, geoserver.org), web GIS servers for data processing (52north.org, zooproject.org) and data storage software/spatial DBMS (postgis.refractions.net, mysql.com) to registry/catalogue and metadata software (geonetwork-opensource.org, wiki.deegree.org), desktop GIS clients for data updating and analysis (qgis.org, openjump.org) and web GIS development toolkits for browserbased clients (openlayers.org, openscales.org, mapbender.org). These free and open-source software tools have not only posed formidable challenges to the dominance and monopoly of commercially available software (Obe and Hsu, 2011), but they have also prompted commercial software vendors to open up their closed toolboxes to encourage users to develop and share their application modules (Stephens et al., 2012; see also Bivand, 2013).
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In terms of mass collaboration, we have seen many exciting new advances in the spirit of open science, following on the phenomenal success of OpenStreetMap (Sui et al., 2012) and a variety of projects in citizen science (Dickinson et al., 2012). The integration of social media, ubicomp and urban informatics has brought citizens together and engaged them for worthy causes (Foth et al., 2011). Particularly noteworthy is the area of emergency management and disaster relief. We now have Ushahidi, InRelief, Sahana and Crisis Commons playing crucial roles in various disaster relief efforts, all relying on VGI as a primary data source. These new developments, in turn, have further encouraged governments to be more open and transparent; more geocoded data are now available online (e.g. geo.data.gov) and new government-supported platforms are being developed to facilitate these developments (http://www.geoplatform.gov). Coupled with industry-initiated efforts for open ubicomp standards (Helal, 2010), these new developments will greatly facilitate interoperability among the rather heterogeneous ubicomp systems. Perhaps more important for us as individual researchers and scholars, websites such as OpenScholar (http://openscholar.harvard.edu), Wikiversity, Citizendium and Scholarpedia will further facilitate openness, sharing and collaboration among researchers and scholars, following the open science model. An interdisciplinary group of scholars have proposed several intriguing conceptual frameworks for us to understand the broader implications of ubicomp (Kitchin and Dodge, 2011; de Souza e Silva and Frith, 2012; Farman, 2012), which may potentially serve as a guiding frameworks for GC. Although it is quite breathtaking to witness these developments, there are still plenty of unresolved issues for an open GC paradigm. For businesses in the geospatial industry, there is the reality of competing against the free, which often requires business people to imagine a new business model with which to gain a slice of the increasing competitive market and ensure profitability (Bryce et al., 2011). For those of us in academia, there is the harsh reality that the current academic reward system is designed for the practice of closed science; new practices in the spirit of open science often contradict higher education’s push for commercialisation and a business bottom line and are frequently being discouraged or at least insufficiently rewarded. For government agencies, there is the struggle over how and where to draw the line in terms of openness and secrecy in the wake of WikiLeaks (now OpenLeaks). If the history of technological and scientific advances holds any useful lesson, it perhaps is this: ubicomp and open science, similar to all other well-intentioned human endeavours throughout history, will not be immune from their unintended consequences. Last, but certainly not least, these issues carry with them certain urgent questions for educators at all levels from K-12 to graduate school. What are the educational implications of these developments in ubicomp, spatial big data and open science? In what skill sets and habits of thinking should we train and educate our students in order to help them flourish both in the global labour market and as responsible citizens in the age of ubicomp and spatial big data?
16.5 Summary and Conclusions The goal of this chapter has been to examine the implications of ubicomp for GC. It is abundantly clear that computing has undergone a fundamental shift during the past two decades as computing power has gradually embedded itself into the environment of our daily lives. For the first time in human history, we have the capability of tracking the location of individuals and objects in real time. Ubicomp has led to a new sentient environment in which the virtual and physical world, digital bits and atoms, people and objects are linked. Ubicomp is also contributing to the spatial big data deluge. The volume, variety and velocity of spatial big data pose formidable challenges for GC. The next phase of GC development should be conducted in the context of ubicomp and spatial big data. There are multiple challenges along computational, theoretical, social, political, legal and environmental fronts. To better address these challenges, an open GC paradigm is proposed. Open GC calls for open data, open-source software, open standards and open collaboration. The open GC paradigm, as manifested in the diverse efforts of crowdsourcing
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geographic knowledge production (Sui et al., 2012) and various location-based services aimed at intelligent collective actions (Miller, 2012), is perhaps our best bet in the age of ubicomp and spatial big data that our enormous computing power will not be squandered (Strassmann, 1997).
Acknowledgement The author is grateful for the constructive comments on an earlier version of this chapter from the editors, two reviewers, Ningchuan Xiao and Shaun Fontanella. Research assistance by Bo Zhao and Samuel Kay is also gratefully acknowledged. Usual disclaimers apply.
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AUTHOR QUERIES [AQ1] Please confirm the inserted location in the source line of Figure 16.1. [AQ2] Please check if edit to sentence starting “Three additional forms...” is okay. [AQ3] Please provide complete details for all the URLs cited throughout the text and also update the same in the reference list. [AQ4] Please check if edit to sentence starting “In summary, ubicomp...” is okay. [AQ5] Manyika (2011) is cited in the text but not provided in the list. Please check. [AQ6] Please provide appropriate page range for “pp. x-x” in Bivand (2013), Brunsdon (2013) and Openshaw (2013). [AQ7] Please provide volume number and page range for Bryce et al. (2011) and Hotz (2012). [AQ8] Please provide publisher details for Choi et al. (2008), Huang et al. (2009), Satoh (2005), Shekhar et al. (2012). [AQ9] Please provide publisher location for Krumm (2009). [AQ10] Please provide accessed date for Tao (2003), Weiss (1996).
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