GeoJournal DOI 10.1007/s10708-008-9182-4
A trust and reputation model for filtering and classifying knowledge about urban growth Mohamed Bishr Æ Lefteris Mantelas
Springer Science+Business Media B.V. 2008
Abstract In this paper we present a trust and reputation model to classify and filter collaboratively contributed geographic information. We hypothesize that users contribute information in a collaborative system akin to Web 2.0 collaborative applications. We build on previous work where trust is proposed as a proxy for information quality and propose a spatial trust model to filter and extract high quality information about urban growth behaviors contributed by users. The motivating scenario involves residents of recently urbanized areas taking into account their interactions with their surroundings. The main contribution of this paper is a formal trust and reputation model that takes into account the spatial context of users and their contributions. Keywords Social networks Trust Reputation Urban growth Residential choice
M. Bishr (&) Institute for Geoinformatics, University of Muenster, 48151 Muenster, Germany e-mail:
[email protected] L. Mantelas Regional Analysis Division, Institute of Applied and Computational Mathematics, Foundation for Research and Technology-Hellas, 71110 Heraclion Crete, Greece e-mail:
[email protected]
Introduction This paper proposes a trust and reputation model for Collaboratively Contributed Geographic Information (CCGI). The term CCGI is closely related to Volunteered Geographic Information (VGI), and it refers to individual observations by the users in the model presented in this paper. The central problem tackled in this paper is the collection, filtering and classification of CCGI, using contributed information about urban growth as a potential scenario. The term modeling refers to projecting a phenomenon or a process to a simplified sub-space of the real world, thus resulting in a rigid, less dimensional analog of the phenomenon or the process. Models are simplified versions of objects, conditions or processes (Ness and Low 2000). In terms of efficiency and comprehensibility, modeling involves choosing the smallest set of objects and the smallest set of the simplest relations that reproduce the patterns of change in a way that is accurate and consistent with the evolution of the real phenomenon. When presenting our trust and reputation model, it is tempting to try to capture all aspects of the problem of determining CCGI quality in one model. Such a comprehensive model is unattainable at the outset, so we adopt a layered approach and present the first layer in this paper. Our layered approach offers an initial model that can be expanded by inclusion of additional user behaviors, to incrementally increase the complexity of the model. For example, we
123
GeoJournal
exclude a temporal dimension from our current model, while acknowledging its importance in a more comprehensive model. In Bishr and Kuhn (2007) we presented a vision of CCGI, termed ‘geospatial information bottom-up’, that builds on the lessons learned from Web 2.0 (also sometimes called the Social web). Web 2.0 (Oreilly 2005) is centered on the idea of harnessing the collective intelligence of information communities. Web users are organized in virtual communities centered on commonalities of interest. These information communities collaborate and produce large amounts of information, including Geospatial Information (GI). Bishr and Kuhn (2007) also identified many of challenges posed by collaborative approaches to GI collection. Among those challenges is information quality. In large collaborative environments, there are not yet quality measures to filter and classify the information contributed by individuals, many of whom are not GI experts. The model we develop here rests upon a certain understanding of expertise. We assume that the contributors of CCGI are ordinary users with no specific training on GI. Our approach to developing ways of evaluating the quality of the GI they contribute borrows methods that have proven effective on the web. Specifically, we propose using trust and reputation as a proxy for GI quality, while extending the concept with the idea of spatio-temporal trust and reputation. Information communities and trust Using trust and reputation to evaluate information quality implies the existence of a community within which trust and reputation are established. In our model, we subscribe to a certain notion of community, that of an information community. Modeling is an inherent procedure in our lives since people use their own personalized empirical model of the world in order to make decisions. These models are built on people’s experiences and each single experience they have tests the adequacy and consistency of their models of the real world, expanding or altering their knowledge base. In that sense the collective of these personalized views represents a very good picture of reality, where each person’s view constitutes a small piece of a bigger picture that we term here the ‘‘collective empirical model’’ of the world in which a certain group of people lives. This group of people
123
can be referred to as an information community. This information community possesses information that is geospatial and temporal in nature. We are motivated here by the notion of geospatial information communities, originally proposed by the Open Geospatial Consortium (OGC) a decade ago. Information communities are defined here as ‘‘a community of geodata producers and users who share a common set of feature definitions and ontology of real world phenomena’’ (Bishr et al. 1999, p. 58). Having asserted that harnessing information communities is the basis of our trust and reputation model, we must point out that trust from a sociological point of view is a prerequisite for the existence of community. Functioning societies rely strongly on trust between the individuals (Sztompka 1999; Uslaner 2002; Seligman 1997). On the one hand, in real world communities citizens rely on subjective measures of trust. On the other hand, in online communities objective measures of trust have been used as users rate each other with trust values pertaining to how much one user trusts another in a certain context since trust is a highly contextualized phenomenon (Zaihrayeu et al. 2005a, b; Golbeck 2005). Many definitions of trust exist, and we adopt a simple definition, viewing trust as a bet that an individual makes about the future contingent actions of others (Sztompka 1999). Since our model depends on some form of social networks, four properties of trust can be identified, namely, transitivity, composability, personalization and asymmetry (Golbeck 2005). Transitivity is about the propagation of trust through chains of people from one person to the other, although trust is not mathematically perfectly transitive (Golbeck 2005). Composability is about situations where different actors rate the same person or item. From these ratings, we can then compose the value and establish an absolute rating of that person or item. Personalization asserts that trust is relative, and dependant on personal perspective. Given our adopted definition of trust, a good outcome for one person could be a bad outcome for another. Asymmetry pertains to the fact that trust might not be equal in both directions. That is, Person A might trust Person B independently of how Person B trusts Person A. Our computational model exhibits all of these properties of trust through the formalism presented.
GeoJournal
One important point to address is our use of the term ‘‘spatial’’ trust model. The question this raises is why do we assert a relationship between the spatial location of an information contributor and trust? Trust and reputation in our model occur over social networks. Studies have shown a direct relation between the evolution of social networks and geographic space (Metcalf and Paich 2005). Studies of how similarity between people breeds connection, also known as homophily (McPherson et al. 2001), showed that geographical proximity is a basic source of homophily. Zipf (1949) justifies this relationship as a matter of effort, noting that it takes less effort to build and maintain relations when the subjects are in a close geographical proximity. Also some research has used geographic distance as an indicator of network density (Buskens 2002). Although technologies have apparently loosened the effect of geography by lowering the effort involved (Kaufer and Carley 1993), communities still show strong geographic patterns (Verbrugge 1983). Also, technologies have allowed people to make homophilic relations through other dimensions (Wellman et al. 1996). Our spatial trust and reputation model combines trust and the spatial dimension to filter CCGI, to select the best candidate information to form a ‘‘collective empirical model’’ of the universe of discourse for a certain information community. In this paper we view the residents of urban areas as one such information community.
patterns of human behavior is a major challenge in building efficient, more realistic urban growth models (Cheng and Masser 2003). Existing Knowledge Base Extraction (KBE) methods that are based on the analysis of aggregate data also fail to do so. They cannot express the overall dynamic of a social system by mapping its partial dynamics. They lose the social, temporal and decision-making heterogeneity that exists in people’s behavior patterns. Furthermore, even if we could reproduce the exact patterns that occurred during the past, we can only apply them in a model under the assumption that these patterns will not change. We may capture the tendencies, but not necessarily the potentials. While Berry (2004) focuses on the way households make decisions on an aggregate level, we need to investigate the internal decision making process of individual households. The problem then is to build a collaborative KBE system, which leverages the collective intelligence of the information community to collect up-to-date dynamic information about urban growth in a continuous, evolving manner. In such a system, the question we need to answer is ‘‘How and why should we trust a certain individual piece of CCGI provided by a certain user?’’ The provided information in our case is local knowledge from residents about recently urbanized areas. This information can assist modelers in understanding the micro-level dynamics of urban growth. Our approach builds on a spatio-temporal trust model to filter through the potentially large flow of CCGI provided by the users, to extract the highest quality information.
Problem description
Problem approach
Understanding urban growth in terms of actors’ behavior requires a collection of certain types of data from residents living in areas of study. This collected information reflects the residents’ view of the area and the forces that are shaping urban growth through their residential choice. These information entities constitute the CCGI in question for this paper. As Agarwal et al. (2000) points out, models incorporating higher levels of human-decision-making are more centrally located with respect to spatial and temporal scales. That is, they fall within the middle ground of those scales, probably due to the lack of data availability at more extreme scales. Extracting information that captures micro-level
Cecchini and Rizzi (2001) suggest an urban analysis perspective to understand urban growth: ‘‘walk in the streets and interact with the inhabitants, and discover that those who move across the city do so for different reasons, each person for more than one reason’’. We suggest the utility of walking directly to the source of the urban growth phenomenon: urban residents themselves. Let the decision-makers (residents) model their own behavior and point out the important parameters and the way these parameters affect them, as well as explain the ‘‘reasons for’’ and ‘‘the reasons not to’’ take particular actions. If we extract this kind of knowledge from people, then we can map the very specific reasons that lead them to
123
GeoJournal
particular behaviors that affect urban growth processes. There is an implicit assumption here that the residents have a collective sense of the urban growth phenomenon, an assumption that is supported by the notion of information communities (Bishr et al. 1999) as discussed earlier. The methodologies of earlier approaches failed to capture micro-level details about people’s behaviors. A number of the previously presented approaches incorporate interview and questionnaire features. Luo and Sen (2004) and Otoo et al. (2006) made field surveys using questionnaires, formal and informal interviews, and focus group discussions to explore and understand the nature of the spatial growth processes. These approaches though, took into account certain groups of businessmen, peasants and local officials, and included a limited number of interviews and discussions processes while aiming more at the economic level of analysis. We propose a wider scale of application that relies on the foundations of Web 2.0 and overcomes the limitation of the Otto’s and Luo’s approaches. By building web applications utilizing CCGI gathered from the residents of growing urban areas, we can gain insights about the micro-level patterns of human behaviors and use this information to deliver better services. Initially, we need to focus on the institutional framework for the information required and define a set of specifications concerning the type and form of the CCGI we collect. Barredo et al. (2003) identifies five fundamental aspects of urban growth that should be included in the institutional framework: • • • • •
environmental characteristics, local-scale neighborhood characteristics, spatial characteristics, urban and regional planning policies and factors related to individual preferences, level of economic development, socio-economic and political systems.
CCGI about urban growth should touch on these elements to ensure coverage of the phenomenon within the reported information by the observers. Motivating scenario In this section we present a scenario that demonstrates the model presented in this paper. The
123
example is based on an urbanization scenario of areas that are showing rapid urban growth over relatively short periods of time. Urban modelers study an area that has been rapidly urbanized within the last decade in order to understand the micro-level dynamics behind urban growth. The modelers need to find a theoretical framework that fits the actual facts and sufficient data to implement and calibrate it. Instead, they may view the community of residents within this urban growth area as an information community in which each individual has their own model of the world and their experience with urban growth. The collective view of this community forms a collective empirical model of their world. Our scenario would include a system for collecting several elements of users’ experiences of urban growth as CCGI. Users would be provided with web interfaces and handheld devices with which they could log information pertaining to the criteria mentioned earlier, such as: • • • • •
marks for locations of urban sprawl around urban boundaries buildings that have recently been built parameters of recently built buildings (floors, heights, etc.) estimated income averaging for certain locations in the urban areas a variety of neighborhood characteristics (traffic jams, public transport hubs, etc.)
Residents could collect and mark this information and possibly any other parameters deemed necessary by the urban growth modelers using the map-assisted web forms or handheld devices. The forms and devices would require the residents to provide other specific information to help the system calculate trust ratings for filtering later. This information would include the following elements: • •
the location of the residents at the time where they noticed the information they contributed as CCGI if a particular CCGI was previously reported by some other resident, then the resident attempting to report it again would have to give the previous reporting a rating. The rating pertains to the quality of the previous reporting as an accurate representation of reality. Giving a rating would not preclude the user from re-reporting the
GeoJournal
•
information again on their own if the user has better information the system could count how many times the same CCGI entity was reported by different users.
With the information provided to this system, our trust and reputation model could be applied to compute trust ratings for the contributed CCGI from all the users of the system. The urban growth modelers could then study the micro-level factors behind the urban growth process using the extracted information. In the following section we illustrate how our trust and reputation model functions to achieve this required rating and filtration of CCGI.
Spatio-temporal trust and reputation model Extensive research has been done on trust in web based social networks (WBSN) for a variety of applications, including email filtering and web based recommender systems (Golbeck 2005). In this research, trust calculations have proved superior in providing a measure of the quality of movie reviews and emails, when compared to both the average rating and ratings generated by traditional collaborative filtering algorithms. Some web researchers are also interested in using social notions of trust to identify how answers provided on the web can be trusted. The Inference Web (McGuinness et al. 2004) aims to take vague query answers and make them more transparent by providing explanations. Zaihrayeu et al. (2005a, b) provide an extension of the inference web that is termed IWTrust. IWTrust uses quantified measures of trust in answers returned by search queries on the web. It utilizes trust values in social networks among users, and trust values between the users and the information to compute these trust ratings. Trust in WBSNs measures the value of information produced by a group of users to others users who consume this information (Ziegler and Lausen 2004; Golbeck 2005; Zaihrayeu et al. 2005a, b). Users who are trusted by others (aka. trustees) are the ones who, from the perspective of the trustors, provide more useful information. Generally in CCGI environments there is a lack of traditional quality criteria such as lineage, accuracy, consistency and completeness. We propose using trust as a proxy for GI quality. We view quality as a subjective measure to some extent,
but argue that if some trust rated geospatial information is useful and relevant to larger group of users it can be assumed to have satisfactory quality in a more objective sense (Bishr and Kuhn 2007). In Bishr (2007) we introduced a spatio-temporal trust model for social networks. The model presented here is different from its predecessor in that it takes into account ratings of information contributions and not just the number of times an information entity is reported. It also uses a log function for distance rather than raw distance. These two modifications improve the ratings of the model. First, by incorporating ratings of the information entities made by its consumers, the model has an explicit account of trust ratings that was not available in the earlier approach. This explicit account is important since we assume that the objective quality of an information entity is directly proportional to the trustworthiness of the information entity, as judged by information consumers. In the earlier model trust was not explicit. Second, the use of raw distance in the earlier model was problematic because large distances (e.g. a contributor living in a suburb at the outskirts of a city) resulted in trust values that were disproportionally small compared to people living shorter distances from the center. To smooth the trust function we resorted to a simple log function that yielded more uniform results across sufficiently large distance variations. It should be noted that our model in this stage does not account for the temporal dimension of some phenomena. Some features change continuously, such as traffic information or pollution, and so some locations would be associated with temporal variables in the model. As noted earlier, we adopt a layered approach to expanding model complexity and do not include temporal variables at this stage. We acknowledge the importance of integrating such variables and leave that for future work. When a resident marks a certain piece of CCGI the process result can be viewed as a bipartite graph (Fig. 1). The resulting bipartite graph of people (actors) and contributions is a two mood non-dyadic affiliation network (Fig. 1). Actors are possibly affiliated to many contributions and contributions are possibly contributed by many actors. Hence, our affiliation network consists of a set of actors and a set of contributed information entities. We observe two sets of nodes representing the two moods of the network shown below.
123
GeoJournal
N: is the set of all actors (people) who are contributing information to the network. Those actors can be viewed N ¼ n1 ; n2 ; . . .; ng in terms of the subset of CCGI to which they contributed. M: Is the set of all CCGI that are contributed by actors n, similar to the members of the set N, CCGI can also be viewed in terms of the subset of M ¼ fm1 ; m2 ; . . .; mh g actors who contributed these CCGI. In Fig. 1 a link exists between actors/person n2 for example and CCGIm3 when the person has marked, rated, and/or reported the information entity. Also between actors N we take into account a social network representing the relationships between people in a study area. Such relations include but not limited to, kinship, neighborhood and friendship. Our model has a hybrid network structure that represents an affiliation network between people and CCGI entities, as well as a social network between people involved in the system. The problem is now to calculate the trustworthiness of a given information entity such as m3 based on the reputation of the users who contributed this entity.
The first step is to calculate a trust rating based on the number of people contributing CCGI and weighted by two factors: • •
a rating given by actors N to CCGI entities M, this rating is denoted rn,m and is on a scale of 1–10. the distance between an actor and the CCGI they contributed, such a distance measure is an indication of the network density (as defined in Wasserman and Faust 1994). Network density has an inverse relation with distance, while (Nohria and Eccles 1992; Buskens 2002) suggested a positive relation between network density and trust. This positive relation is an artifact of the speed of transmission of information in networks and strength of relations between actors, which in turn fosters trust.
The proposed first component of the trust rating is based on the nodal degree d(mh) (Wasserman and Faust 1994). The first component of the model is then denoted 0 d(mh) such that: 0
dðmh Þ ¼
Xk
rðn;mÞi logðci Þ
i¼1
where ci [ 1
ð1Þ
where 0 d(mh) is an adjusted nodal degree for a certain CCGI such as m3 taking into account the two factors mentioned above and r(n,m)i is the ith rating from n to m and Ci is the distance between n and m at the time that n reports the CCGI entity m. we opted for a log10 function to smoothen the sharp variations in the equation implied by using raw distance. The second two equations are straightforward. Equation 2 represents a trust calculation at the social network level between the actors. N P
tng ¼
Fig. 1 An affiliation network of agents/users (white nodes) and the CCGI they contribute (black nodes) with social network links weighted by distance and user ratings. Lines connecting N nodes are a separate agent-agent social network, making this model a hybrid affiliation-one mode social network
123
ðtni ng Þðkni ng Þ
ni 2adjðng Þ;i¼1
N
ð2Þ
where tng is the trust rating of an actor n based on the trust ratings given to him/her by the actors with whom he/she is connected in the network, tni ;ng is the rating from actor i to actor g and kni ;ng is a rating that represents the strength of social ties between the actors. This rating is personal and should be determined based on user opinions and analysis of the nature of relations such as kinship or friendship for example. Based on Eq.2 the final trust rating of the information entity m is given by Eq. 3
GeoJournal
tmh
k X tng rðn;mÞi ¼ logðci Þ i¼1;g¼1
Conclusions where ci [ 1
ð3Þ
Equation 3 introduces the final trust rating of a certain CCGI contributed by a resident of an urban growth area. The metric presented is sensitive to geographic distance, and the nature of social relations, trust ratings and distance. By filtering information using this metric we can extract relevant CCGI related to the area of study and that are of adequate quality, since the model is premised upon the notion that the quality of the information is strongly determined by its spatial/ social context. One question that remains unanswered is how the model will behave in instances where users submit CCGI about a location where they made a direct observation, but they are not presently located. For example, a user might report information about the street where s/he lives while s/he is on a vacation elsewhere. Our model assumes the location of the observer at the moment he is making the observation and does not deal with such exceptional situations. However, our model could be easily adjusted by changing the definition of location, without altering the formal model itself. This can be done by assuming the location used in the model to be the observer’s usual location, where s/he generally has knowledge, instead of the location while actually making the observation. The Eqs. 1–3 represent the core of our formal model. They depend on the existence of a network model as in Fig. 1 to give trust ratings for CCGI tokens. By taking into account the location of observers when they make observations, ratings by the system users, and bi-partite graph properties (count of contributions) we can compute trust ratings for the CCGI observations. In a system where such a model is implemented, the trust-rated tokens can be filtered out and served to information consumers as reliable information. Traditional models of information authority are generally lacking in VGI environments and thus, alternative models are required. Our model builds a collaborative authority system that corresponds with the collaborative nature of VGI. Further research on other areas of VGI where this model could be of benefit is an ongoing effort.
Web 2.0 encompasses the key ideas of harnessing the collective intelligence of web communities, enabling interaction and collaboration between web users organized in communities centered on common goals and objectives. Users are no longer only content consumers, but are also content producers. As early in the web’s history as 1996 studies of the quality of user-provided information proved that large amounts of information were simply unreliable, outdated or plainly wrong (Janes and Rosenfeld 1996). The case becomes more evident in Web 2.0 applications, since contributors are a heterogeneous group of people with varying experiences and backgrounds, resulting in large amounts of information that is lacking traditional quality criteria. Traditional models of authority (libraries, publishers, editors, media outlets) that controlled the flow of information are being challenged. New models of authority need to emerge to control the flow of information in Web 2.0. Into this context, our paper contributes a community-based collaborative model of authority, one where the producers make information, and their peers and other information consumers judge the information quality. A valid metaphor here is the scientific peer review process, albeit on a much larger community scale and in a Web 2.0 collaborative manner. We propose using community based information authority to assure quality of information, mainly based on trust and reputation. Using trust and reputation as a measure of information relevance and quality is well-documented in web research, and our approach proposes a novel method to include spatial and temporal parameters in making trust judgments about geoinformation. Our trust and reputation model can be applied to filter and extract information about the residential choice process, to better understanding its effect on urban growth. One important element of our model must be assessed is its resilience to improper tampering or fraud. Some users might abuse the system and provide false or irrelevant ratings either on purpose or inadvertently. Such a problem is inherent in all trust and reputation systems. One answer is that the average of the volume of contributions will adjust itself to proper values overcoming improper usage of the system, and this view is supported by existing research (Surowiecki 2004). However, further
123
GeoJournal
research into this issue is essential to ensure model resilience to potential abuse by some users. Goodchild (2007) proposes the term VGI to describe a collection of applications and web phenomena that involves large-scale collaboration of heterogeneous users. Compared to geogspatial data developed and managed in more conventional ways, such as through spatial data infrastructures (SDIs), the phenomenon of VGI is unique in several ways. It has emerged from the bottom up and does not tend to rely upon top-down approaches to ensuring information quality or data sharing. Many traditional models of information authority that have been used with more conventional forms of spatial information are not present or useful with VGI. Identifying these and other gaps between VGI and traditional GIS is an important step in the emerging VGI research agenda. Identifying these gaps begins to highlight key research needs and directions, particularly with respect to the hardware, software, methodological, institutional framework, and data handling issues that will need to be addressed if we are to make use of volunteered information. The scenarios we have developed in this paper contribute to these efforts to identify key research directions in VGI, and respond specifically to the need for techniques that are suitable for working with these data, in light of their unique properties compared to other forms of spatial information. Based on the expectation that VGI will be tremendously diverse (because of the diverse nature of its contributors) and that the quality of contributed information will vary widely (because of the range of skills and experience of the contributors), we offer a model for information filtering and classification. Our model might be applied to extract the contributed information expected to be most reliable, from a more varied collection of information. Thinking beyond our specific technique described here, we would argue that the emerging research agenda must develop many such techniques for working with VGI. Specifically, the phenomenon of VGI centers upon generating and organizing information based upon its geographical properties, and further research must consider the implications of this for information archival and retrieval systems, information discovery, location-based services, and existing techniques for assessing information quality. Finally, in a broader societal sense, VGI research much also examine
123
questions of privacy, especially how these are to be handled technically, not just legally or institutionally. Our model brings up at least one issue with respect to privacy in the context of VGI. Our approach for assessing the quality and integrity of contributed information rest upon having some information about the contributors, in this case their residential location. This example illustrates that issues of privacy associated with VGI might emerge not only from the contributed information itself, but also from the models and methods that are developed and employed for working with this unique new source of geographic information. Acknowledgement The authors would like to thank the anonymous reviewers who have provided constructive feedback. We also would like to thank Sarah Elwood for her efforts in putting this publication in shape. This work is supported by COMPASS project (COastal Marine Perception Application for Scientific Scholarship), financed by e-research:e-Information program of JISC (Joint Information Systems Committee) in the United Kingdom.
References Agarwal, Ch., Green, G. M., Grove, J. M., Evans, T. P., & Schweik, Ch. M. (2000). A review and assessment of land-use change models: Dynamics of space, time, and human choice. In 4th International Conference on Integrating GIS and Environmental Modeling, Problems, Prospects and Research Needs. Banff, Alberta, Canada, 2–8 September 2000. Barredo, J., Kasanko, M., McCormick, N., & Lavalle, C. (2003). Modeling dynamic spatial processes: Simulation of urban future scenarios through cellular automata. Landscape and Urban Planning, 64, 145–160. Berry, B. (2004). A theoretical model for measuring the influence of accessibility in residential choice behaviour. In 44th European Congress of the European Regional Science Association, Porto, Portugal. Bishr, M. (2007). Weaving space into the web of trust: An asymmetric spatial trust model for social networks. CSSW. Leipzig, Germany: Bonner Ko¨llen Verlag. Bishr, M., & Kuhn, W. (2007). Geospatial information bottomup: A matter of trust and semantics. AGILE. Aalborg, Denmark: Springer. Bishr, Y. A., & Pundt, H., et al. (1999). Probing the concept of information communities-a first step toward semantic interoperability. In M. F. Goodchild (Ed.), Interoperating geographic information systems (pp. 55–70). Springer. Buskens, V. W. (2002). Social networks and trust. Dordrecht: Springer. Cecchini, A., & Rizzi, P. (2001). Is urban gaming simulation useful? Simulation Gaming, 32(4), 507. Cheng J., & Masser, I. (2003). Understanding urban growth system: Theories and methods. In 8th International
GeoJournal Conference on Computers in Urban Planning and Urban Management, Sendai City, Japan. Golbeck, J. A. (2005). Computing and applying trust in webbased social networks. Department of Computing, PhD thesis, University of Maryland. Goodchild, M. F. (2007). Citizens as sensors: The world of volunteered geography. GeoJournal, 69(4), 211–221. Huapu, L. (2002). Review of the urban growth over the past twenty years and prospects for the next 2 or 3 decades. Institute of Transportation Engineering, Tsinghua University. Janes, J. W., & Rosenfeld, L. B. (1996). Networked information retrieval and organization: Issues and questions. Journal of the American Society for Information Science, 47, 711–715. Kaufer, D., & Carley, K. (1993). Communication at a distance: The effect of print on socio-cultural organization and change. Hillsdale, NJ: Lawrence Erlbaum. Luo, X., & Sen, J. (2004). Cross-border urban growth: The case of Jiangyin economic development zone in Jingjiang. In 15th Biennial Conference on the Asian Studies Association of Australia, Canberra. McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27, 415–444. McGuinness, D. L., & da Silva, P. P., et al. (2004). IWBase: Provenance metadata infrastructure for explaining and trusting answers from the web. Springer. Metcalf, S., & Paich, M. (2005). Spatial dynamics of social network. Evolution, 51, 61801. Ness, G. D., & Low, M. M. (2000). Five cities: Modelling Asian urban population-environment (pp. 43–67). Oxford University Press: Dynamics. Nohria, N., & Eccles, R. G. (1992). Networks and organizations: structure, form, and action. Boston, MA: Harvard Business School Press. Oreilly. (2005). http://www.oreillynet.com/pub/a/oreilly/tim/ news/2005/09/30/what-is-web-20.html
Otoo, E. A., Whyatt, D. J., & Ite, U. E. (2006). Quantifying urban growth in Accra metropolitan area (Ama), Ghana and Exploring Causal Mechanisms, England Promoting Land Administration and Good Governance 5th FIG Regional Conference Accra, Ghana. Seligman, A. B. (1997). The problem of trust. Princeton University Press. Surowiecki, J. (2004). The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business, economies, societies, and nations. New York: Doubleday. Sztompka, P. (1999). Trust: A sociological theory. Cambridge: Cambridge University Press. Uslaner, E. M. (2002). The moral foundations of trust. Cambridge: Cambridge University Press. Verbrugge, L. (1983). A research note on adult friendship contact: A dyadic perspective. Social Forces, 62, 78–83. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press. Wellman, B., Salaff, J., Dimitrova, D., Garton, L., Gulia, M., & Haythronwaite, C. (1996). Computer networks as social networks: Collaborative work, telework and virtual community. Annual Review of Sociology, 22, 213–238. Zaihrayeu, I., da Silva, P. P., & McGuinness, D. L. (2005b). IWTrust: Improving user trust in answers from the web. Springer. Zaihrayeu, I., & da Silva, P. P., et al. (2005a). IWTrust: Improving user trust in answers from the web, Springer. Ziegler, C. N., & Lausen, G. (2004). Spreading activation models for trust propagation. The IEEE International Conference on e-Technology, e-Commerce, and e-Service. Taipei, Taiwan. Zipf, G. (1949). Human behavior and the principle of least effort. Menlo Park, CA: Addison Wesley.
123
