Opening up Smart Cities: Citizen-Centric Challenges and Opportunities from GIScience
- a synthesis of citizen-centric challenges in the smart city context;
- a collection of relevant key contributions of and opportunities from GIScience to help address the identified challenges;
- a citizen-centric, technology-driven approach to address these challenges (the Open City Toolkit).
2. Related Work on Smart and Open Cities
2.1. Trends in Technologies, Architectures, and Infrastructures for Smart Cities
2.2. Beyond Technologies, Architectures, and Infrastructures
2.3. Open City Projects and Initiatives
2.4. Role of GIS and GIScience
3.1. Empowering Citizens
- Deep participation (C1): Recent works [9,57] have investigated citizen participation in various contexts, including smart cities, where people are often seen as data-collectors improving city services. Yet citizens are more than human sensors collecting data: deep participation is about raising awareness, building capacity, and strengthening communities . There is a need to work with the community and not just for, or on, the community , and this must be reflected in the overall strategy to envision a smart city. Furthermore, city councils must pay special attention to the design and execution of strategies to foster citizen participation at all levels.
- Data literate citizenry (C2): Smart cities are not only about ICT and infrastructures; smart cities are about smart citizens, who participate in their city’s daily governance, are concerned about enhancing the quality of life, and about protecting their environment. Data literacy should be a skill not just for scientists, but for all citizens. Cities can commit to open data, transparency and ICT as major enablers, but without the appropriate data literacy skills, co-creation and active participation with citizens is unlikely to occur. A key gap relates to how people can gain a sense of control. This necessitates fostering digital inclusion and data literacy skills to interpret and understand the processes and services that drive smart cities.
3.2. Analytical Methods and Tools
- Pairing quantitative and qualitative data (C3): Analysis methods that are able to integrate quantitative data and qualitative information through citizen science activities, social networks services, and crowdsourcing tools, will have a great impact on the future of our cities as more and more people live in urban areas . In some cases citizens-generated data takes the form of measurements or quantitative observations (e.g., noise and air pollution measurements). In others such observations are more qualitative or subjective (e.g., opinions, emotions, behaviors) but no less useful. There is a need to move beyond the traditional quantitative analysis of physical phenomena to include also new analytical methods to scrutinize qualitative perceptions of the same phenomena as they are perceived by those who live in and sense the city. A preliminary look into the rationales and challenges involved in the integration of quantitative and qualitative geographic data was provided in . In addition, the combination of datasets in the big data age needs to cope with a number of challenges listed in [20,61], for example efficient data representation, redundancy reduction and data compression, spurious correlations, and noise accumulation.
- Adoption of open standards (C4): standards are essential to ensure that underlying technology, systems, data, applications and services are able to interact seamlessly in a coherent manner. Not only does standardization refer here to service interfaces, communication protocols, and architectures but also to data. The adoption of open data standards can dramatically unlock the potential of all citizens to access and use open data. Many cities wrongly assume that making data available, say in pdf format, is enough to be tagged as an open data city. Unless one is a developer that can code a pdf crawler, all this open data remains useless to other citizens (see concrete examples in ). Even though many cities are leaders in open data, there still exist barriers (e.g., the lack of open standards) impeding the access and use of such data broadly by people. The point is that small changes towards open standards can eventually lead to big impacts like making city services more transparent, participative and trustable.
3.3. Citizen-Centric Services
- Personal services (C5): As human beings, we only use a very small part of the retina, called the fovea , to see the finer details of objects that we are looking at. The rest of the visual field, which is known as peripheral vision , plays a key role even though it does not allow us to distinguish such details. When we detect an object that requires our interest in the sides, we quickly put the fovea on it to identify the object properly. Without the ability to detect the presence of other objects that surround us through our peripheral vision, our vision would be severely limited to a small portion of the visual field.Turning back to the smart city context, a research gap is the lack of customized and focused services, i.e., personal services, that are capable of adapting to the peculiarities and needs of each individual citizen, and that help them in performing daily tasks, provide them with up-to-date information, or simply support them in finding their way through the ever-increasing data stream sources available in today’s cities by presenting the clearest picture possible of what all this data means. These personal services augment our “peripheral vision”, to put in the forefront the pieces of information that could be relevant and might require our immediate attention.When it comes to personal data and services, privacy is an important issue to tackle. Janssen et al.  mention the unclear trade-off between transparency and privacy values as one of the adoption barriers of open data (and consequently of all benefits associated with the use of open data to make the city smarter). Solove  discussed the concept of privacy in detail and pointed out that it covers many aspects. Particularly relevant to the current discussion are:
Technological progresses, the open data movement, and the trend of big data provide an environment where the risk of privacy harms related to the five aspects above-mentioned is increased. For example, Lyon  mentions that “big data practices are increasingly important to surveillance”, and that “in a big data context, the same data are increasingly used for different purposes”. Linked Data, which helps to describe the content and context of resources (see ), eases aggregation and identification. The open data movement requires increased accessibility. As a result, reducing the risk for privacy violation (e.g., by putting the citizen fully in control of the kind of information s/he would like to disclose) is, in the current context, a major challenge.Regarding GIScience, the field has focused on location privacy. As Dukham and Kulik  stress, “[o]ur precise location uniquely identifies us, more so than our names or even our genetic profile”. Challenges mentioned in  regarding location privacy include (i) understanding the techniques a hostile person might employ in order to invade another person’s privacy, and (ii) the development of truly spatiotemporal models of location privacy.
- surveillance: the watching, listening to, or recording of an individual’s activities;
- aggregation: the combination of various pieces of data about a person;
- identification: linking information to particular individuals;
- secondary use: the use of information for a purpose other than what it was initially collected for, without the data subject’s consent;
- increased accessibility: amplifying the accessibility of information.
- Persuasive interfaces (C6): City governments pursue novel ways to engage with citizens as to better support their needs and concerns, and to involve them in decisions that affect them. Among the existing methods for getting citizens engaged (e.g., public consultations, local meetings, etc.), the creation of persuasive interfaces is getting importance as user interfaces are seen by citizens as the only “visible interfaces” between city services and themselves. The field of persuasive interfaces is not new; it can be traced back to Tversky and Kahnemann’s pioneering work on the prospect theory about framing decisions and psychology of choice . City services need to go beyond traditional interfaces to pay attention to more user-centric interfaces that stimulate and encourage change. From the point of view of GIScience, the challenge lies not so much in design and psychology (which are important aspects), but in creating new types of user experiences that facilitate opportunistic interactions with citizens , and present information in such a way that citizens are persuaded to change their behavior and take actions accordingly. The stakes here are high, because citizens’ behavior plays an inescapable role against today’s most pressing environmental issues in cities .
4. Opportunities from GIScience
4.1. Empowering Citizens
- Deep participation (C1): participation at all levels and by all citizens has attracted relatively few attention in the smart cities literature. Public participation GIS (PPGIS) was perhaps one of the first attempts to put geospatial capabilities, tools and applications in the citizens’ hands to enhance effective participation and communication among experts and non-experts. Even though PPGIS literature applies to many application domains in cities, decision-making processes in urban planing have quite probably been the domain by excellence for collecting and exploiting local knowledge from citizens through geospatial collaborative tools . Geospatial visual methods, in varied forms, have been traditionally used to engage users and enable participation. Fechner and Kray  proposed an approach which relies on space and time as common integrators, and uses augmented interactive geo-visualizations to facilitate citizen engagement. They introduced three ideas, and exemplary tools, worth exploring in a smart city context: (i) synchronous distributed online collaborative mapping, (ii) the use of maps as spatial dialogue platforms, and (iii) the use of location-based services to highlight engagement opportunities both spatially and temporally.Improving deep participation in cities cannot be done without a deep understanding of the motivations of citizens to participate. Coleman et al.  provide a useful summary of contributors’ motivations (e.g., altruism, social reward, enhanced personal reputation, or mischief) to willingly produce geographic information. Since the very same contributors of geographic information are also actors (active or passive) in a smart city, deep participation strategies should take into account ’s synthesis about citizens’ motivations. Creating this type of win-win situations between city players is a critical success factor for smart cities, whereby city councils and organizations not just collect data and knowledge from citizens, but also give something back that is valued by citizens .Another example of this type of project is the Smart GraphHopper , which uses GraphHopper . in order to plan routes and subsequently compare them by evaluating different available sensor data, such as noise, air pollution and so on. NoiseTube  uses this initiative to gather data from citizens’ phones.Gamification is a current trend to overcome the limitations of PPGIS tools and applications, and to foster citizen participation and engagement. Martella et al.  have produced a gamification framework for Volunteered Geographic Information (VGI ) which has three main parameters: the user, the tasks of the user (data gathering, data validation or data integration), and the types of datasets manipulated by the user. Along the same lines,  discussed a combination of social gaming, geospatial mobile tools and data collection campaigns to increase the network of volunteers to capture urban morphology for climate modeling purposes.
- Data literate citizenry (C2): a data literacy strategy also requires simple and understandable presentations of existing datasets (e.g., in forms of visualizations or geo-visualizations). Fechner and Kray  argue that maps are one way of contextualizing and presenting primary data in an understandable and engaging way. As such, maps have a key role to play in the improvement of data literate citizenry. For example,  analyse measures describing the readability of maps themselves. Kraak  points out that maps have the ability to present, synthesize, analyze and explore the real world, and do this well because they visualize it in an abstract way, and only present a selection of the complexity of reality. Wakabayashi and Ishikawa  present the ability to organize, understand, and communicate with maps as one component of spatial thinking. As a result, insights from spatial thinking research can inform the design of better applications in a smart city context. For instance, the study documented in  concluded that people associate concrete spatial behavior in their daily lives (such as navigation and wayfinding in space, or sorting of furniture or packaging) with the act of thinking spatially. Uttal et al.  report that improving spatial thinking improves science, technology, engineering, and mathematics (STEM) achievements. This insight implies that part of making citizens smarter is the development of applications which help them improve their spatial thinking abilities.
4.2. Analytical Methods and Tools
- Pairing quantitative and qualitative data (C3): relevant to the smart city context is the use of cellular automata to model cities. Cellular automata appear on Goodchild’s list  of major GIScience achievements. Cellular automata help to model the environment as adjacent cells . Each cell has a state which refers to its attributes, and transitions between cell states are modeled using simple rules. A cellular model assumes only an action space (usually a grid), a set of initial conditions, and a set of behavior rules . In other words, they can be interpreted as generators of growth and decline. The wide use of the SLEUTH cellular automata model (e.g. [95,96] for reviews of cellular automata applications) provides evidence that cellular automata is a technique worth considering to predict and simulate urban growth in a smart city context.So far, GIScience’s approach towards the integration of quantitative and qualitative data has been the use of observation ontologies which take into account both (e.g., [97,98,99]). These works have the concept of observation at the core of their investigations, and are based on the premise that all we know about the world is based on observations . To make sense of observation data, GIScience has produced ODOE , the observation-driven framework for the engineering of geo-ontologies out of observation data. ODOE supports both human and technical sensors, and is therefore useful to consider when pairing quantitative (usually coming from technical sensors) and qualitative data (mostly produced by humans). Noteworthy also is the algebra for spatiotemporal data  which allows to derive objects and events from the three basic types of observations, namely time series, trajectories, and coverage. Stasch et al.  brought forth a theory which helps to enforce meaningful prediction and aggregation of observations. Kuhn  proposed eight ideas that many researchers found useful in their work on geographic information semantics:
Making sense of the wealth of available data in a smart city context can build upon these eight ideas. Finally, GIScience’s proposal of field as generic data type for big spatial data  is worth considering when dealing with issues of efficient data representation in a big data context.
- experiential realism: people conceptualize reality based on how they experience it through their bodies, sensing and acting in physical environments and in cultures;
- geographic information atoms: the simplest form of a geographic information is a tuple of location and attribute values;
- semantic reference systems: making the semantics of terms explicit and grounding them physically, so that transformations between them can be computed;
- semantic datum: useful to transform between different reference systems;
- similarity measurement: all semantics is context-dependent and can generally not be modeled objectively or even standardized;
- conceptual spaces: provide structures to solve conceptual problems through geometry;
- meaning as process: meaning comes from people using a word, rather than the words having a meaning on their own;
- constraining the process of meaning: tools can only be built to constrain the use and interpretation of terms, not specify their meaning. The latter presupposes a single meaning that one should strive towards defining, but as mentioned above, it is the people who mean something when they use terms in a specific context, not the terms which have a meaning per se. of these terms.
- Adoption of open standards (C4): In GIScience, standards of the Open Geospatial Consortium (OGC) . The Open Geospatial Consortium is an international not for profit organization which develops open standards for the global geospatial community. See  for further details.) are used in a wide variety of domains including environment, defense, health, agriculture, meteorology, sustainable development, and smart cities. Recent works [108,109] identify the importance of open location standards to any smart city project and propose a spatial information framework for urban systems and spatial decision-making processes based on the integration of OGC open standards and geospatial technology. The combination of open standards (and APIs) such as OGC CityGML (e.g., 3D spatial city visualization), IndoorGML (e.g., indoor/outdoor navigation/routing to map indoor spaces), Moving Features, and Augmented Reality Markup Language 2.0 (ARML 2.0), would ease the delivery of geospatial features, imagery, sensor observations and geo-referenced social media in a coherent way, and thereby support interoperable and cross-domain city services for urban spatial intelligence, spatial visualizations, and decision making purposes.Sensors are crucial for intelligent systems like smart cities [110,111] and are well covered by the OGC Sensor Web Enablement (SWE) . The OGC SWE standards suite specifies interfaces and metadata encodings to enable real-time integration of heterogeneous sensor networks . In this way, most types of sensors can be discovered, accessed and reused for creating web-accessible sensor applications and services (see examples in [114,115]). For example, Mitton et al.  combined cloud-based services to process SWE-encoded sensing data in smart cities.When using mobile devices as ubiquitous sensing tools, OGC SWE protocols for data exchange between mobile devices introduce considerable overhead and performance penalties . In addition, as SWE standards can be used for creating complex, time-consuming applications, such applications are often limited for resource-constrained devices . As a result, and due to the need for compatibility with mainstream technology (e.g., IoT), the OGC has recently delivered the OGC SensorThings API  as a candidate standard. The OGC SensorThings API can be considered as a lightweight OGC SWE profile, that follows a REST-like style, and is particularly well suited for developing IoT-based sensing applications to interconnect resource-limited IoT devices. SEnviro , a low-cost, Arduino-based IoT device that monitors atmospheric variables demonstrated that IoT protocols and the OGC SensorThings API can work together for real-life smart cities applications.
4.3. Citizen-Centric Services
- Personal services (C5) may be regarded as the new generation of location-based services (LBS). The ability to know the location, both in out- and indoor environments, in real-time paves the way for smart city-specific advances in areas such as location-context systems, real-time tracking and routing, location-based advertising, and so on. Duckham  identified seven key principles of research into location privacy: (i) geographic space presents constraints to movement, (ii) humans are not random, (iii) large user-contributed datasets are biased, (iv) continuous and snapshot queries are different, (v) location privacy attacks are as important as location privacy protection, (vi) decentralization does not always improve location privacy, and (vii) location accuracy, and location precision are not synonyms (although both can be used to hide information about a person’s location). These principles were identified from location privacy research over recent years. Given that location (or the spatial dimension) is a very important component of smart cities (see, e.g., [35,36] for arguments in favor of such a view), privacy research in a smart city context can use these seven principles, as both starting points and guiding insights.
- Regarding user-centric, more persuasive interfaces (C6), GIScience offers a theory of spatialization of user interfaces. In pioneering work, Kuhn  pointed out that “[s]pace is fundamental to perception and cognition because it provides a common ground for our senses as well as for our actions”, and discussed the need for spatial expertise in the field of human-computer interaction. He argued that designers need to be informed about human spatial cognition and properties of spaces in order to design more successful spatialized interfaces. His work introduced two key concepts for the design of intuitive user-interfaces: spatial metaphors and image-schemas. Both concepts are useful to understand how people think about space. A formalization of metaphors and image-schemas in the context of user interfaces was proposed in . Recent work in GIScience  has looked at gestural interaction with spatiotemporal (linked) open data. In particular, gestures were considered helpful in engaging people with the visualization of complex data . In summary, incorporating spatial elements and insights may help to provide more effective and intuitive interaction with (personal) smart city services.
5. Towards the Realization of Opportunities from GIScience
5.1. The Open City Toolkit
The Open City Toolkit (OCT) is a collection of tools, processes, specifications and guidelines to empower citizens to participate in and shape the future of their cities, and to deliver services based on open data that are useful for citizens, businesses and governing bodies alike.
- A set of tools to improve transparency: to enable citizens to inspect what data is gathered and how it is used, and to visualize key indicators so that all stakeholders can understand them. Transparency relates to the visibility and inferability of the information (see ), while participation relates to the involvement of citizens in city operations. This work assumes that greater transparency will have a positive impact on citizen participation. The set of tools then relates to the challenges of deep participation (C1) and data literate citizenry (C2);
- A curated set of examples of open source apps, open data and services: apps and services that are useful to cities/citizens, and relate to the challenges of pairing quantitative and qualitative data (C3), as well as the development of personal services (C5) and persuasive interfaces (C6);
- An abstract architecture: describes how apps, processes, services and data can be integrated in order to realize a smart open city. This abstract architecture is to be built upon open standards (C4);
- A “glue” to connect resources, apps and services to realize an open city: involves a set of APIs and specifications to link components and tap into data. This facilitates further development based on existing resources and artifacts, thereby opening up the smart city’s "living" ecosystem.
- Guidelines on how to realize an open city: interactive guidelines describing insights about how to facilitate transparency, collaboration, participation using methods from GIScience. The guidelines will also document insights as to how to support the transition to a smart and open city.
5.2. Future Research Directions at the Intersection between Smart Cities and GIScience
Conflicts of Interest
- IBM Smarter Cities. Available online: http://www.ibm.com/smarterplanet/us/en/smarter_cities/overview/ (accessed on 11 February 2016).
- Microsoft CityNext. Available online: http://www.microsoft.com/en-us/citynext/ (accessed on 11 February 2016).
- D’Aquin, M.; Adamou, A.; Daga, E.; Liu, S.; Thomas, K.; Motta, E. Dealing with diversity in a smart-city datahub. In Proceedings of the Fifth Workshop on Semantics for Smarter Cities, Riva del Garda, Italy, 19 October 2014; pp. 68–82.
- CitySDK. Available online: http://www.citysdk.eu/ (accessed on 11 February 2016).
- Yin, C.T.; Xiong, Z.; Chen, H.; Wang, J.; Cooper, D.; David, B. A literature survey on smart cities. Sci. China Inf. Sci. 2015, 58, 1–18. [Google Scholar] [CrossRef]
- Nam, T.; Pardo, T.A. Conceptualizing smart city with dimensions of technology, people, and institutions. In Proceedings of the 12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times, College Park, MD, USA, 12–15 June 2011; pp. 282–291.
- González, A.; Villazón-Terrazas, B.; Gómez, J.M. A linked data lifecycle for smart cities in Spain. In Proceedings of the Fifth Workshop on Semantics for Smarter Cities, Riva del Garda, Italy, 19 October 2014; pp. 9–14.
- Ojo, A.; Curry, E.; Zeleti, F.A. A tale of open data innovations in five smart cities. In Proceedings of the 48th Hawaii International Conference on System Sciences, Kauai, HI, USA, 5–8 January 2015; pp. 2326–2335.
- Janssen, M.; Charalabidis, Y.; Zuiderwijk, A. Benefits, adoption barriers and myths of open data and open government. Inf. Syst. Manag. 2012, 29, 258–268. [Google Scholar] [CrossRef]
- Masip-Bruin, X.; Ren, G.J.; Serral-Gracia, R.; Yannuzzi, M. Unlocking the value of open data with a process-based information platform. In Proceedings of 15th IEEE Conference on Business Informatics, Vienna, Austria, 15–18 July 2013; pp. 331–337.
- Department for Business, Innovation and Skills. Smart Cities, Background Paper. Available online: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/246019/bis-13-1209-smart-cities-background-paper-digital.pdf (accessed on 13 October 2015).
- Boulos, M.N.; Tsouros, A.D.; Holopainen, A. Social, innovative and smart cities are happy and resilient: Insights from the WHO EURO 2014 International Healthy Cities Conference. Int. J. Health Geogr. 2015, 14. [Google Scholar] [CrossRef]
- Batty, M. The New Science of Cities; MIT Press: Cambridge, MA, USA, 2013. [Google Scholar]
- Celino, I.; Kotoulas, S. Smart Cities. IEEE Internet Comput. 2013, 17, 8–11. [Google Scholar] [CrossRef]
- Da Silva, W.M.; Alvaro, A.; Tomas, G.; Afonso, R.; Dias, K.; Garcia, V. Smart cities software architectures: A survey. In Proceedings of the 28th Annual ACM Symposium on Applied Computing, Coimbra, Portugal, 18–22 March 2013; pp. 1722–1727.
- Boulos, M.N.K.; Al-Shorbaji, N.M. On the Internet of Things, smart cities and the WHO Healthy Cities. Int. J. Health Geogr. 2014, 13. [Google Scholar] [CrossRef]
- Zanella, A.; Bui, N.; Castellani, A.; Vangelista, L.; Zorzi, M. Internet of things for smart cities. IEEE IoT J. 2014, 1, 22–32. [Google Scholar] [CrossRef]
- Vlacheas, P.; Giaffreda, R.; Stavroulaki, V.; Kelaidonis, D.; Foteinos, V.; Poulios, G.; Demestichas, P.; Somov, A.; Biswas, A.R.; Moessner, K. Enabling smart cities through a cognitive management framework for the internet of things. IEEE Commun. Mag. 2013, 51, 102–111. [Google Scholar] [CrossRef]
- Atzori, L.; Iera, A.; Morabito, G.; Nitti, M. The Social Internet of Things (SIoT)—When social networks meet the Internet of Things: Concept, architecture and network characterization. Comput. Netw. 2012, 56, 3594–3608. [Google Scholar] [CrossRef]
- Chen, M.; Mao, S.; Liu, Y. Big data: A survey. Mob. Netw. Appl. 2014, 19, 171–209. [Google Scholar] [CrossRef]
- Branchi, P.; Fernández-Valdivielso, C.; Matias, I. Analysis matrix for smart cities. Futur. Internet 2014, 6, 61–75. [Google Scholar] [CrossRef]
- Kehoe, M.; Cosgrove, M.; Gennaro, S.D.; Harrison, C.; Harthoorn, W.; Hogan, J.; Meegan, J.; Nesbitt, P.; Peters, C. Smarter Cities Series: A Foundation for Understanding IBM Smarter Cities. Available online: http://www.redbooks.ibm.com/redpapers/pdfs/redp4733.pdf (accessed on 1 October 2015).
- Menychtas, A.; Kranas, P.; Donovang-Kuhlisch, M.; Heindrichs-Krusch, M.; Coote, R.W.; Schade, U.; Osman, K.A.; Kallipolitis, L.; Graaf, S.V.D.; Brebels, W.; et al. D2.3 Online Service Delivery Baseline and Technical Requirements Report. European Platform for Intelligent Cities. No: 270895. Available online: http://www.epic-cities.eu/sites/default/files/documents/D2.3B%20Online%20Service%20Delivery%20Baseline%20and%20technical%20requirements%20report.pdf (accessed on 1 October 2015).
- Menychtas, A.; Kranas, P.; Donovang-Kuhlisch, M.; Schade, U.; Vasilescu, C.; Rana, P.; Brebels, W.; Kallipolitis, L.; Gastel, M.V. D2.1 Project Vision. European Platform for Intelligent Cities. No: 270895. Available online: http://epic-cities.eu/sites/default/files/documents/D2.1%20Project%20Vision.pdf (accessed on 1 October 2015).
- Donovang-Kuhlisch, M.; Heindrichs-Krusch, M.; Stoll, W.; Osman, K.; Kallipolitis, L.; Kranas, P.; Menychtas, A. D2.1 Project Vision. European Platform for Intelligent Cities. No: 270895. Available online: http://www.epic-cities.eu/sites/default/files/documents/D3.1B%20Technical%20Integration%20Architecture%20Plan.pdf (accessed on 1 October 2015).
- Angelidou, M. Smart city policies: A spatial approach. Cities 2014, 41, S3–S11. [Google Scholar] [CrossRef]
- Zaman, A.U.; Lehmann, S. Challenges and opportunities in transforming a city into a “zero waste city”. Challenges 2011, 2, 73–93. [Google Scholar] [CrossRef]
- McGrath, B.; Pickett, S. The metacity: A conceptual framework for integrating ecology and urban design. Challenges 2011, 2, 55–72. [Google Scholar] [CrossRef]
- Open Cities Consortium. Project Open Cities. Available online: http://www.opencities.net/content/project (accessed on 1 October 2015).
- Forum Virium Helsinki. What is CitySDK. Available online: http://www.citysdk.eu/about-the-project/what-is-citysdk/ (accessed on 1 October 2015).
- Open311 Organisation. A Collaborative Model and Open Standard for Civic Issue Tracking. Available online: http://www.open311.org/learn/ (accessed on 1 October 2015).
- Connected Smart Cities Network. Open and Agile Smart Cities. Available online: http://connectedsmartcities.eu/ (accessed on 1 October 2015).
- Usländer, T.; Berre, A.J.; Granell, C.; Havlik, D.; Lorenzo, J.; Sabeur, Z.; Modafferi, S. The future internet enablement of the environment information space. In Environmental Software Systems. Fostering Information Sharing; Springer: Neusiedl am See, Austria, 2013; pp. 109–120. [Google Scholar]
- Brauer, B.; Eisel, M.; Kolbe, L.M. The state of the art in smart city research—A literature analysis on green IS solutions to foster environmental sustainability. In Proceedings of the Pacific Asia Conference on Information Systems, Singapore, Singapore, 5–9 July 2015.
- Daniel, S.; Doran, M. GeoSmartCity: Geomatics contribution to the Smart City. In Proceedings of the 14th Annual International Conference on Digital Government Research, Quebec city, QC, Canada, 17–20 June 2013; pp. 65–71.
- Roche, S. Geographic Information Science I: Why does a smart city need to be spatially enabled? Prog. Hum. Geogr. 2014, 38, 703–711. [Google Scholar] [CrossRef]
- Steinitz, C. A Framework for Geodesign: Changing Geography by Design; ESRI Press: Redlands, CA, USA, 2012. [Google Scholar]
- Goodchild, M.F. Twenty years of progress: GIScience in 2010. J. Spat. Inf. Sci. 2010, 1, 3–20. [Google Scholar] [CrossRef]
- Rodger, L. Smart Cities: Technology Challenges. Available online: http://urbanopus.net/smart-cities-technology-challenges/ (accessed on 26 October 2015).
- Lee, M.; Almirall, E.; Wareham, J. Open data and civic apps: First-generation failures, second-generation improvements. Commun. ACM 2015, 59, 82–89. [Google Scholar] [CrossRef]
- Kucera, J.; Chlapek, D. Benefits and risks of open government data. J. Syst. Integr. 2014, 5, 30–41. [Google Scholar]
- Martin, S.; Foulonneau, M.; Turki, S.; Ihadjadene, M. Open data: Barriers, risks and opportunities. In Proceedings of the 13th European Conference on eGovernment, Como, Italy, 13–14 June 2013.
- Zuiderwijk, A.; Janssen, M. The negative effects of open government data-investigating the dark side of open data. In Proceedings of the 15th Annual International Conference on Digital Government Research, Aguascalientes, Mexico, 18–21 June 2014; pp. 147–152.
- Martin, S.; Foulonneau, M.; Turki, S.; Ihadjadene, M. Risk analysis to overcome barriers to open data. Electron. J. E-Gov. 2013, 11, 348–359. [Google Scholar]
- Roberts, T. The Problem with Open Data. Available online: http://www.computerweekly.com/opinion/The-problem-with-Open-Data (accessed on 5 January 2016).
- Accelerating Smart City Deployments: Challenges, Competitions and Collaborations from Smart Cities Around the Globe and Across the Region. Available online: http://www.smartcitiesweek.com/Pages/Detail/17254 (accessed on 26 October 2015).
- Schaffers, H.; Komninos, N.; Pallot, M.; Trousse, B.; Nilsson, M.; Oliveira, A. Smart cities and the future internet: Towards cooperation frameworks for open innovation. In Future Internet Assembly; Domingue, J., Galis, A., Gavras, A., Zahariadis, T., Lambert, D., Cleary, F., Daras, P., Krco, S., Müller, H., Li, M.S., et al., Eds.; Springer: Berlin, Germany, 2011; pp. 431–446. [Google Scholar]
- Caragliu, A.; Del Bo, C.; Nijkamp, P. Smart Cities in Europe. J. Urban Technol. 2011, 18, 65–82. [Google Scholar] [CrossRef]
- Hollands, R. Will the real smart city please stand up? Intelligent, progressive or entrepreneurial? City 2008, 12, 303–320. [Google Scholar] [CrossRef]
- Naphade, M.; Banavar, G.; Harrison, C.; Paraszczak, J.; Morris, R. Smarter cities and their innovation challenges. IEEE Comput. 2011, 44, 32–39. [Google Scholar] [CrossRef]
- Granell, C.; Havlik, D.; Schade, S.; Sabeur, Z.; Delaney, C.; Pielorz, J.; Usländer, T.; Mazzetti, P.; Schleidt, K.; Kobernus, M.; et al. Future Internet technologies for environmental applications. Environ. Model. Softw. 2016, 78, 1–15. [Google Scholar] [CrossRef]
- Batty, M.; Axhausen, K.W.; Giannotti, F.; Pozdnoukhov, A.; Bazzani, A.; Wachowicz, M.; Ouzounis, G.; Portugali, Y. Smart cities of the future. Eur. Phys. J. Spec. Top. 2012, 214, 481–518. [Google Scholar] [CrossRef]
- Van den Bergh, J.; Viaene, S. Key challenges for the smart city: Turning ambition into reality. In Proceedings of the 48th International Conference on System Sciences, Grand Hyatt, HI, USA, 5–8 January 2015.
- Glasmeier, A.; Christopherson, S. Thinking about smart cities. Camb. J. Reg. Econ. Soc. 2015, 8, 3–12. [Google Scholar] [CrossRef]
- The Biggest Challenges to Develop a Successful Smart City Project. Available online: http://www.smart-circle.org/smartcity/blog/biggest-challenges-develop-successful-smart-city-project/ (accessed on 29 October 2015).
- Kogan, N. Exploratory Research on Success Factors and Challenges of Smart City Projects. Master’s Thesis, Department of Business Administration, Graduate School, Kyung Hee University, Seoul, Korea, 2014. [Google Scholar]
- Lathrop, D.; Ruma, L. Open Government: Collaboration, Transparency, and Participation in Practice; O’Reilly Media, Inc.: Sebastopol, CA, USA, 2010. [Google Scholar]
- Townsend, A.M. Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia; WW Norton & Company: New York, NY, USA, 2013. [Google Scholar]
- Craglia, M.; Granell, C. Citizen Science and Smart Cities; Number EUR 26652 EN; Publications Office of the European Union: Luxembourg, 2014. [Google Scholar]
- Degbelo, A.; De Felice, G.; Lee, J.H. Integration of quantitative and qualitative geographic data for disaster response: Research challenges. In Proceedings of the GI_Forum 2013, Salzburg, Austria, 2–5 July 2013.
- Fan, J.; Han, F.; Liu, H. Challenges of big data analysis. Natl. Sci. Rev. 2014, 1, 293–314. [Google Scholar] [CrossRef] [PubMed]
- How We Found the Worst Place to Park in New York City — Using Big Data. Available online: https://www.ted.com/talks/ben_wellington_how_we_found_the_worst_place_to_park_in_new_york_city_using_big_data (accessed on 26 October 2015).
- Fovea Centralis. Available online: https://en.wikipedia.org/wiki/Fovea_centralis (accessed on 11 February 2016).
- Peripheral Vision. Available online: https://en.wikipedia.org/wiki/Peripheral_vision (accessed on 11 February 2016).
- Solove, D. A taxonomy of privacy. Law Rev. 2006, 154, 477–564. [Google Scholar] [CrossRef]
- Lyon, D. Surveillance, Snowden, and big data: Capacities, consequences, critique. Big Data Soc. 2014, 1, 1–13. [Google Scholar] [CrossRef]
- Scheider, S.; Degbelo, A.; Kuhn, W.; Przibytzin, H. Content and context description—How linked spatio-temporal data enables novel information services for libraries. GIS Sci. 2014, 4, 138–149. [Google Scholar]
- Duckham, M.; Kulik, L. Location privacy and location-aware computing. In Dynamic and Mobile GIS: Investigating Changes in Space and Time; Billen, R., Joao, E., Forrest, D., Eds.; CRC Press: Melbourne, Australia, 2006; pp. 35–51. [Google Scholar]
- Tversky, A.; Kahneman, D. Advances in prospect theory: Cumulative representation of uncertainty. J. Risk Uncertain. 1992, 5, 297–323. [Google Scholar] [CrossRef]
- Hespanhol, L.; Tomitsch, M.; McArthur, I.; Fredericks, J.; Schroeter, R.; Foth, M. Situated interfaces for engaging citizens on the go. Interactions 2016, 23, 40–45. [Google Scholar] [CrossRef]
- Umpfenbach, K. Influences on Consumer Behavior-Policy Implications Beyond Nudging. Available online: http://ec.europa.eu/environment/enveco/economics_policy/pdf/Behaviour%20Policy%20Brief.pdf (accessed on 11 January 2016).
- Vinod Kumar, T.M. Geographic Information System for Smart Cities; Copal Publishing Group: New Delhi, India, 2014. [Google Scholar]
- Gruen, A. SMART Cities: The need for spatial intelligence. Geo Spat. Inf. Sci. 2013, 16, 3–6. [Google Scholar] [CrossRef]
- Domingo, A.; Bellalta, B.; Palacin, M.; Oliver, M.; Almirall, E. Public open sensor data: Revolutionizing smart cities. IEEE Technol. Soc. Mag. 2013, 32, 50–56. [Google Scholar] [CrossRef]
- Schade, S.; Granell, C.; Perego, A. Coupling public sector information and public-funded research data in Europe: A vision of an open data ecosystem. In Information and Communication Technologies in Public Administration: Innovations from Developed Countries; Reddick, C., Anthopoulos, L., Eds.; Boca Raton, FL, USA, 2015; pp. 275–298. [Google Scholar]
- Cabinet Office, MH Government. Open Data White Paper-Unleashing the Potential. Available online: https://data.gov.uk/sites/default/files/Open_data_White_Paper.pdf (accessed on 11 January 2016).
- Ratti, C.; Townsend, A. Harnessing residents’ electronic devices will yield truly smart cities. Sci. Am. 2011, 305, 42–48. [Google Scholar] [CrossRef] [PubMed]
- Fioretti, M. Open Data, Open Society: A Research Project about Openness of Public Data in EU Local Administration. Available online: http://www.dime-eu.org/files/active/0/ODOS_report_1.pdf (accessed on 1 October 2015).
- Department for Business, Innovation and Skills. Smart Cities International Case Studies: Global Innovators. Available online: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/249397/bis-13-1216-global-innovators-international-smart-cities.pdf (accessed on 29 October 2015).
- Bugs, G.; Granell, C.; Fonts, O.; Huerta, J.; Painho, M. An assessment of public participation GIS and Web 2.0 technologies in urban planning practice in Canela, Brazil. Cities 2010, 27, 172–181. [Google Scholar] [CrossRef]
- Fechner, T.; Kray, C. Georeferenced open data and augmented interactive geo-visualizations as catalysts for citizen engagement. eJ. eDemocr. Open Gov. 2014, 6, 14–35. [Google Scholar]
- Coleman, D.J.; Georgiadou, Y.; Labonte, J. Volunteered geographic information: The nature and motivation of produsers. Int. J. Spat. Data Infrastruct. Res. 2009, 4, 332–358. [Google Scholar]
- Smart GraphHopper. Available online: https://github.com/DIVERSIFY-project/SMART-GH (accessed on 11 February 2016).
- GraphHopper. Available online: https://github.com/graphhopper/graphhopper/ (accessed on 11 February 2016).
- Maisonneuve, N.; Stevens, M.; Niessen, M.E.; Steels, L. NoiseTube: Measuring and mapping noise pollution with mobile phones. In Information Technologies in Environmental Engineering; Springer: Berlin, Germany, 2009; pp. 215–228. [Google Scholar]
- Martella, R.; Kray, C.; Clementini, E. A gamification framework for volunteered geographic information. In Proceedings of the International AGILE 2015 Conference—Geographic Information Science as an Enabler of Smarter Cities and Communities, Lisbon, Portugal, 9–12 June 2015; pp. 73–89.
- Goodchild, M.F. Citizens as sensors: The world of volunteered geography. GeoJournal 2007, 69, 211–221. [Google Scholar] [CrossRef]
- See, L.; Fritz, S.; Perger, C.; Schill, C.; McCallum, I.; Schepaschenko, D.; Duerauer, M.; Sturn, T.; Karner, M.; Kraxner, F.; et al. Harnessing the power of volunteers, the internet and Google Earth to collect and validate global spatial information using Geo-Wiki. Technol. Forecast. Soc. Chang. 2015, 98, 324–335. [Google Scholar] [CrossRef][Green Version]
- Harrie, L.; Stigmar, H.; Djordjevic, M. Analytical estimation of map readability. ISPRS Int. J. Geo Inf. 2015, 4, 418–446. [Google Scholar] [CrossRef]
- Kraak, M. Why maps matter in GIScience. Cartogr. J. 2006, 43, 82–89. [Google Scholar] [CrossRef]
- Wakabayashi, Y.; Ishikawa, T. Spatial thinking in geographic information science: A review of past studies and prospects for the future. Proced. Soc. Behav. Sci. 2011, 21, 304–313. [Google Scholar] [CrossRef]
- Uttal, D.H.; Miller, D.I.; Newcombe, N.S. Exploring and enhancing spatial thinking links to achievement in science, technology, engineering, and mathematics? Curr. Dir. Psychol. Sci. 2013, 22, 367–373. [Google Scholar] [CrossRef]
- Batty, M. Cellular automata and urban form: A primer. J. Am. Plan. Assoc. 1997, 63, 266–274. [Google Scholar] [CrossRef]
- Clarke, K.; Gaydos, L. Loose-coupling a cellular automaton model and GIS: Long-term urban growth prediction for San Francisco and Washington/Baltimore. Int. J. Geogr. Inf. Sci. 1998, 12, 699–714. [Google Scholar] [CrossRef] [PubMed]
- Clarke, K.; Gazulis, N.; Dietzel, C.; Goldstein, N. A decade of SLEUTHing: Lessons learned from applications of a cellular automaton land use change model. In Classics from IJGIS— Twenty Years of the International Journal of Geographical Information Science and Systems; Fisher, P.F., Ed.; CRC Press: City, Country, 2007; pp. 413–427. [Google Scholar]
- Chaudhuri, G.; Clarke, K. The SLEUTH land use change model: A review. Int. J. Environ. Resour. Res. 2013, 1, 88–105. [Google Scholar]
- Kuhn, W. A functional ontology of observation and measurement. In Proceedings of the Third International Conference on GeoSpatial Semantics, Mexico City, Mexico, 3–4 December 2009; pp. 26–43.
- Degbelo, A. Modelling the spatial and temporal resolution of a sensor observation. In Proceedings of the GI_Forum 2013—Creating the GISociety, Salzburg, Austria, 2–5 July 2013; pp. 71–80.
- Degbelo, A. Spatial and temporal resolution of sensor observations; IOS Press: Amsterdam, Netherlands, 2015. [Google Scholar]
- Frank, A. Ontology for spatio-temporal databases. In Spatio-Temporal Databases: The CHOROCHRONOS Approach; Sellis, T., Koubarakis, M., Frank, A.U., Grumbach, S., Güting, R.H., Jensen, C.S., Lorentzos, N., Manolopoulos, Y., Nardelli, E., Pernici, B., et al., Eds.; Springer-Verlag: Berlin, Germany, 2003; pp. 9–77. [Google Scholar]
- Janowicz, K. Observation-driven geo-ontology engineering. Trans. GIS 2012, 16, 351–374. [Google Scholar] [CrossRef]
- Ferreira, K.R.; Câmara, G.; Monteiro, A.M.V. An algebra for spatiotemporal data: From observations to events. Trans. GIS 18, 253–269. [CrossRef]
- Stasch, C.; Scheider, S.; Pebesma, E.; Kuhn, W. Meaningful spatial prediction and aggregation. Environ. Model. Softw. 2014, 51, 149–165. [Google Scholar] [CrossRef]
- Kuhn, W. Cognitive and linguistic ideas in geographic information semantics. In Cognitive and Linguistic Aspects of Geographic Space; Raubal, M., Mark, D.M., Frank, A.U., Eds.; Springer-Verlag: Berlin, Germany, 2013; pp. 159–174. [Google Scholar]
- Camara, G.; Egenhofer, M.; Ferreira, K.; Andrade, P.; Queiroz, G.; Sanchez, A.; Jones, J.; Vinhas, L. Fields as a generic data type for big spatial data. In Proceedigns fo the 8th International Conference on Geographic Information Science, Vienna, Austria, 24–26 September 2014; pp. 159–172.
- OGC. Available online: http://www.opengeospatial.org/ (accessed on 11 February 2016).
- Reed, C.; Buehler, K.; McKee, L. OGC consensus: How successful standards are made. ISPRS Int. J. Geo Inf. 2015, 4, 1693–1706. [Google Scholar] [CrossRef]
- Percivall, G. OGC Smart Cities Spatial Information Framework. Available online: http://www.opengeospatial.org/pressroom/pressreleases/2181 (accessed on 11 January 2016).
- Li, W.; Li, L.; Goodchild, M.; Anselin, L. A geospatial cyberinfrastructure for urban economic analysis and spatial decision-making. ISPRS Int. J. Geo Inf. 2013, 2, 413–431. [Google Scholar] [CrossRef]
- Dameri, R.P.; Rosenthal-Sabroux, C. Smart City: How to Create Public and Economic Value with High Technology in Urban Space; Springer: Cham, Switzerland, 2014. [Google Scholar]
- Hancke, G.; Silva, B.; Hancke, G., Jr. The role of advanced sensing in smart cities. Sensors 2012, 13, 393–425. [Google Scholar] [CrossRef] [PubMed]
- Sensor Web Enablement. Available online: http://www.opengeospatial.org/ogc/markets-technologies/swe (accessed on 11 February 2016).
- Bröring, A.; Echterhoff, J.; Jirka, S.; Simonis, I.; Everding, T.; Stasch, C.; Liang, S.; Lemmens, R. New generation sensor web enablement. Sensors 2011, 11, 2652–2699. [Google Scholar] [CrossRef] [PubMed]
- Devaraju, A.; Jirka, S.; Kunkel, R.; Sorg, J. Q-SOS—A sensor observation service for accessing quality descriptions of environmental data. ISPRS Int. J. Geo Inf. 2015, 4, 1346–1365. [Google Scholar] [CrossRef]
- Tamayo, A.; Huerta, J.; Granell, C.; Díaz, L.; Quirós, R. gvSOS: A new client for OGC Sensor Observation Service interface standard. Trans. GIS 2009, 13, 46–61. [Google Scholar] [CrossRef]
- Mitton, N.; Papavassiliou, S.; Puliafito, A.; Trivedi, K. Combining Cloud and sensors in a smart city environment. EURASIP J. Wirel. Commun. Netw. 2012. [Google Scholar] [CrossRef]
- Tamayo, A.; Granell, C.; Huerta, J. Using SWE standards for ubiquitous environmental sensing: A Performance analysis. Sensors 2012, 12, 12026–12051. [Google Scholar] [CrossRef][Green Version]
- Vermesan, O.; Friess, P. Internet of Things Applications-From Research and Innovation to Market Deployment; The River Publishers: Aalborg, Denmark, 2014. [Google Scholar]
- OGC SensorThing API. Available online: http://ogc-iot.github.io/ogc-iot-api (accessed on 3 November 2015).
- Trilles, S.; Lujan, A.; Belmonte, O.; Montoliu, R.; Torres-Sospedra, J.; Huerta, J. SEnviro: A sensorized platform proposal using open hardware and open standards. Sensors 2015, 15, 5555–5582. [Google Scholar] [CrossRef] [PubMed]
- Duckham, M. Moving forward: location privacy and location awareness. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS, San Jose, CA, USA, 2 November 2010; pp. 1–3.
- Kuhn, W. Handling data spatially: Spatializating user interfaces. In Proceedings of the 7th International Symposium on Spatial Data Handling, Delft, The Netherlands, 12–16 August 1996.
- Kuhn, W.; Frank, A. A formalization of metaphors and image-schemas in user interfaces. In Cognitive and linguistic aspects of geographic space; Frank, A.U., Mark, D.M., Eds.; Kluwer Academic Publishers: Dordrecht, The Netherlands, 1991; pp. 419–434. [Google Scholar]
- Bartoschek, T.; Pape, G.; Kray, C.; Jones, J.; Kauppinen, T. Gestural interaction with spatiotemporal linked open data. OSGeo J. 2014, 13, 60–67. [Google Scholar]
- Batty, M. Big data, smart cities and city planning. Dialogues Hum. Geogr. 2013, 3, 274–279. [Google Scholar] [CrossRef]
- Miller, H.; Goodchild, M. Data-driven geography. GeoJournal 2015, 80, 449–461. [Google Scholar] [CrossRef]
- Wang, F. Geographic Information Systems and Crime Analysis; Idea Group Inc: Hershey, PA, USA, 2005. [Google Scholar]
- Barba, C.; Mateos, M.; Soto, P.; Mezher, A.; Igartua, M. Smart city for VANETs using warning messages, traffic statistics and intelligent traffic lights. In Proceedings fo the IEEE Intelligent Vehicles Symposium, Alcala de Henares, Spain, 3–7 June 2012; pp. 902–907.
- GEO-C. Available online: http://geo-c.eu/ (accessed on 11 February 2016).
- Michener, G.; Bersch, K. Identifying transparency. Inf. Polity 2013, 18, 233–242. [Google Scholar]
- ODI. Available online: http://theodi.org/ (accessed on 11 February 2016).
- Commission of the European Communities the Council, the European Economic and Social Committee, and the Committee of the Regions. COM (2014) 442: Towards a Thriving Data-Driven Economy. Available online: http://eur-lex.europa.eu/procedure/EN/1042141 (accessed on 11 January 2016).
- Damiani, M. Location privacy models in mobile applications: Conceptual view and research directions. Geoinformatica 2014, 18, 819–842. [Google Scholar] [CrossRef]
- Deterding, S.; Dixon, D.; Khaled, R.; Nacke, L. From game design elements to gamefulness: Defining gamification. In Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments, Tampere, Finland, 28–30 September 2011; pp. 9–15.
|Research Challenges||Existing GIScience Contributions to Tackle the Challenges|
|Deep participation (C1)|
|Data literate citizenry (C2)|
|Research Challenges||Existing GIScience Contributions to Tackle the Challenges|
|Pairing quantitative and qualitative data (C3)|
|Adoption of open standards (C4)|
|Research Challenges||Existing GIScience Contributions to Tackle the Challenges|
|Personal services (C5)|
|Persuasive interfaces (C6)|
|Research Challenges||GEO-C upcoming Features Beyond the State-of-the-Art|
|Deep participation (C1)|
|Data literate citizenry (C2)|
|Pairing quantitative and qualitative data (C3)|
|Adoption of open standards (C4)|
|Personal services (C5)|
|Persuasive interfaces (C6)|
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Degbelo, A.; Granell, C.; Trilles, S.; Bhattacharya, D.; Casteleyn, S.; Kray, C. Opening up Smart Cities: Citizen-Centric Challenges and Opportunities from GIScience. ISPRS Int. J. Geo-Inf. 2016, 5, 16. https://doi.org/10.3390/ijgi5020016
Degbelo A, Granell C, Trilles S, Bhattacharya D, Casteleyn S, Kray C. Opening up Smart Cities: Citizen-Centric Challenges and Opportunities from GIScience. ISPRS International Journal of Geo-Information. 2016; 5(2):16. https://doi.org/10.3390/ijgi5020016Chicago/Turabian Style
Degbelo, Auriol, Carlos Granell, Sergio Trilles, Devanjan Bhattacharya, Sven Casteleyn, and Christian Kray. 2016. "Opening up Smart Cities: Citizen-Centric Challenges and Opportunities from GIScience" ISPRS International Journal of Geo-Information 5, no. 2: 16. https://doi.org/10.3390/ijgi5020016