Geographic Knowledge Discovery Process
2.6 Future Application Domains for a Privacy-Aware GKDD ProcessProcess
2.6.7 Marketing: The Shift Towards Movement-Aware Marketing
Currently marketing is mostly done based on customers’ profile, which, normally, is statically defined. Such profiles are based on characteristics like gender, age, income, family situation and purchase history. On the basis of the customer’s char-acteristics, strategies can be developed to determine a need for purchasing certain products amongst potential or existing customers. Traditionally, the success of mar-keting depends on what is called the marmar-keting mix of four P’s: product, price, promotion and placement. This rather traditional view on marketing has been crit-icised, since its main focus is on a company or marketer rather than the consumer.
Furthermore, the traditional marketing mix model does not serve the marketing of services very well. In the last decades, suggestions for change in the various ele-ments of the marketing mix have been articulated. For example, placement needs to be converted to convenience, and promotion to communication. More elaborate knowledge about potential customers also needs to be improved in marketing mix models.
Current computer and database technology enable the storage, processing and analysis of much more variables of human behaviour that are relevant to marketing.
An important development is that of geo-marketing. Geo-marketing implies the use of GIS to add location information into the marketing mix. Based on spatial analysis, additional knowledge and insight might be gained about, for example, the spatial distribution of income and demographic composition of districts.
The main metaphor at this tier is that of movement-as-personalisation. Using movement data, marketers and service organisations can better target their infor-mation and services to specific users depending on their activities, relations and locations. The ‘scary’ outlook of many people to be spammed by location-based advertisements, generated by relative dumb LBS, can be alleviated by providing more intelligent information based on movement behaviour.
2.6.7.1 Tier 0: The Reality Space
Recently, LBS have been added to the geo-marketing sector as a new marketing tool. Using LBS, marketers can pinpoint their marketing mix and enhance their
2 Next Generation of Mobile Applications 67 communication with potential customers based on their exact location and time.
Most obvious examples are push marketing based on SMS messages sent when a customer passes a shop he or she might be interested in (mobile advertising).
As a next step, LBS might develop further towards movement-based services (MBS). One of the differences with ‘traditional’ LBS is that it will take into account the history, behaviour and relation with other movements. LBS only provide the context from the users, and the environment (whoiswhereat timet). MBS have the potential to add to this, knowledge aboutwhathe/she did,howhe/she did it and withwhom.
2.6.7.2 Tier 1: The Positioning Space
The data used for LBS usually describe only the location in space, a caller id and a time stamp. For MBS, information about the followed tracks, the movement charac-teristics (speed, acceleration, periodicity in movements, etc.) need to be added and stored. This is a substantial shift in how to deal with the data. Currently LBS do not need the analysis and storage of locations per se. The majority of LBS are user or event based. The discrete event approach of LBS may only requires the data for the moment the data is requested. MBS typically require the maintenance and storage of data to be able to infer patterns out of it. One of the concerns at this stage is privacy.
As data are required and requesting of the movements of individual people preserv-ing privacy is an important requirement. The ubiquitous nature of the collection of movement data makes privacy an even more pressing concern. The main metaphors in this tier are related to construction of the public/private divide and freedom from intrusion [20]. The control about what and when data about movement activities should be private or public need to be clear to and perhaps in control of the person carrying a sensor. The right to be let alone is a basic right of humans. Especially in marketing-based application, the control of the right should be part of the decision making about what part of the reality space should be sampled and registered.
2.6.7.3 Tier 2: The Geographic Space
Little is known about the use of movement patterns for marketing purposes. There is barely research after the marketing-related behaviour and movement behaviour. In principle there are two models that make use of movement data: the first, the consent model is based on informing or assisting users with information or services based on authorisation given by them. This means that people decide what type of services or information they are interested in. On the basis of their movement behaviour, these services/information are provided to them when required or needed in an intelligent fashion. In the second model, the informed model, users receive targeted informa-tion based on their movement behaviour, locainforma-tion, time and the behaviour of others, i.e. the behavioural pattern they are part of. The challenge of the informed model is to couple information about movement behaviour with other sources of (behaviour)
information like shopping history and non-behaviour information. For both models, the information should, in principle, be able to infer the basic knowledge on the following:
• What someone is doing
• How someone is moving and with whom
• Who else is moving in a similar manner (coinciding patterns)
Therefore, the definition of privacy in such an application depends on which of the above location information the sensor carrier is unwilling to have analysed. Fur-ther, any collection of information about clusters of people around an individual is also information about those in the cluster. This may result in a privacy conflict, between those in the cluster, who do not want their participation in the cluster to be known, and the sensor carrier. Both, the collection and analysis of data, as well as the selection of privacy-preserving knowledge discovery methods depend on the spec-ification of privacy goals and resolution of privacy conflicts among stakeholders.
Privacy-aware services can be developed, which are currently hard to realise, such as presenting services of information based on the type of movement. If you are, for example, driving a car on a crowded highway you probably would like to have information presented differently than when walking around in a city. So movement-based behavioural information might also facilitate the means and methods by which information is presented to customers.
2.6.7.4 Tier 3: The Social Space
On the basis of tier 2, the inferential space tries to discover the causes and conse-quences of movement behaviour. The discovery of geographic knowledge related to privacy aware marketing is mainly targeted to finding groups that show similar behaviour and to determine if this behaviour is interesting, given a certain marketing goal. Examples of typical knowledge are the following:
• Discover the general patterns that explain the behaviour of certain groups of people given a marketing perspective. Using the characteristics of these groups, marketing can be targeted and personalised.
• Discover the dependencies between movement behaviour and the effects of personalised movement-aware marketing. Can movements of people be influ-enced by certain marketing actions or are the effects of marketing dependent of movement behaviour?
• Discover the type of information appreciated by people when they are moving at a certain time, modality and location.
Currently the above types of knowledge discovery cannot be carried out or only limited based on marketing research.
2 Next Generation of Mobile Applications 69
2.7 Conclusions
This chapter introduces a geographic knowledge discovery process, in which the primary goal of identifying, associating and understanding patterns is used to infer the location, identity and relationships among mobile entities, and their respective trajectories in a spatial environment. In this case, the different types of inferences play a different role according to what a domain expert wants to infer, i.e. the loca-tion, changes, properties, identity or relationship among the appropriate metaphors.
It is the metaphor, and only after it makes sense that an unknown set of patterns can be interpreted and understood by a domain expert. Basically, three modes of rea-soning are presented using a multi-tier ontological framework. They are deductive, inductive and abductive modes of reasoning.
In the deductive mode of reasoning, the geographic knowledge discovery process involves the search for common attributes among a set of mobile trajectories, and then the arrangement of these trajectories into classes, clusters or patterns according to a meaningful metaphor. The focus is on applying statistical approaches (probabil-ity distributions, hypothesis generation, model estimation and scoring) for exploring classes, clusters or patterns from a data set.
In the inductive mode of reasoning, the geographic knowledge discovery pro-cess is based on learning due to the reduction of uncertainty in knowledge. Several techniques have been developed, such as rule induction, neural networks, genetic algorithms, case-based learning and analytical learning (theorem proving). Many techniques partition the target data set into as many regions as there are classes by using a function, for example, a posterior probability or linear discriminate func-tions. These techniques provide a data fit, in the sense that the main goal is to generate derived knowledge describing the data, often called concept hierarchies.
In the abductive mode of reasoning, the importance of cognitive tacit knowledge needs to be considered. Will the information have the same meaning and weight (in terms of privacy) if the patterns are used in contexts other than it was meant to be? In this case, the value of the discovered knowledge is judged and the decision is taken on its role in making decisions for the application domains such as transport man-agement, spatial planning and geomarketing. It might turn out that final decisions made are not in line with patterns suggested by the knowledge discovery process.
The political, economic or social realities of the decision-making process are some-times prevalent above the rational knowledge inferred from a geographic knowledge discovery process. Questions like why do people choose certain modality of trans-portations at certain times of the day, and why are certain transportation modalities more present in areaathan in areabare some examples where new metaphors could explain the relations between certain movement behaviour and the characteristics of a geographic environment.
The inevitable challenge facing the research community at the moment is directed towards a more complete integration of these modes of reasoning and their associ-ation to movement metaphors within a geographic knowledge discovery process. It is in this context of attempting to build bridges between them that three application
domains are identified and explained using the proposed multi-tier ontological framework on transport management, spatial planning and marketing.
This chapter has also shown our first attempt at integrating privacy requirements into a multi-tier ontological framework of a geographic knowledge process. Defini-tions of privacy, kinds of possible privacy threats and the complexity of the different requirements for privacy required by stakeholders have been discussed within a geographic knowledge discovery process.
References
1. G.D. Abowd, C.G. Atkeson, J. Hong, S. Long, R. Kooper, and M. Pinkerton. Cyberguide: A mobile context-aware tour guide.Wireless Network, 3(5):421–433, 1997.
2. C. Aldridge. A theoretical foundation for geographic knowledge discovery in databases.
In Proceedings First International Conference on Geographic Information Science (GI-Science’00), 2000.
3. K.W. Axhausen and T. G¨arling. Activity-based approaches to travel analysis: Conceptual frameworks, models and research problems.Transport Reviews, 12:324–341, 1992.
4. D. Banister, S. Watson, and C. Wood. Sustainable cities, transport, energy, and urban form.
Environment and Planning B: Planning and Design, 24(1):125–143, 1997.
5. B. Brunk and B. Davis. Sdat enterprise: Application of geospatial network services for collaborative airspace analysis. InProceedings of the CADD/GIS Symposium, 2002.
6. Y. Cai, N. Cercone, and J. Han. Attribute-oriented induction in relational databases. In G. Piatesky-Shapiro and W. Frawley (eds.), In Proceedings of Knowledge Discovery in Databases, pp. 213–218, 1991.
7. H.V.D. Cammen and M.A.D. Lange. Ontwikkelingen in wetenschap en technologie: Stur-ingstheorieen en landelijke gebieden. InNationale Raad voor Landbouwkundig Onderzoek, 1998.
8. S. Card, J. Mackinlay, and B. Shneiderman. Information visualization. In Readings in Information Visualization, pp. 1–34. Morgan-Kaufmann, 1998.
9. G. Chen and D. Kotz. A survey of context-aware mobile computing research. Technical Report TR2000-381, Department of Computer Science, Dartmouth College, November 2000.
10. D. Cook, A. Buja, J. Cabrera, and H. Hurley. Grand tour and projection pursuit.Journal of Computational and Graphical Statistics, 2:225–250, 1995.
11. R. Crane. The influence of urban form on travel: An interpretive review.Journal of Planning Literature, 15(1):3–23, 2000.
12. P. Deadman. Modelling individual behaviour and group performance in an intelligent agent-based simulation of the tragedy of the commons. Journal of Environment Management, 56:159–172, 1999.
13. F. Kleefmann.Planning als zoekinstrument. VUGA, ’s Gravenhage, 1984.
14. U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. From data mining to knowledge discovery: An overview. InAdvances in Knowledge Discovery and Data Mining. AAAI/MIT Press, 1996.
15. A.U. Frank. Ontology for spatio-temporal databases, pp. 9–78, 2003.
16. M. Gahegan, M. Wachowicz, M. Harrower, and T. Rhyne. The integration of geographic visualization with knowledge discovery in databases and geocomputation.Cartography and Geographic Information Science (special issue on research challenges in geovisualization), 28(1):29–44, 2001.
17. S. Geertman and J. Stillwell.Planning Support Systems in Practice. Springer, 1996.
18. E. Gendlin. Crossing and dipping: Some terms for approaching the interface between natural understanding and logical formulation.Journal Minds and Machines, 5:547–560, 1995.
2 Next Generation of Mobile Applications 71 19. T.M. Georges, J. Harlan, L. Meyer, and R. Peer. Tracking hurricane claudette with us air force one over the horizon radar.Journal of Atmospheric and Oceanic Technology, 10:441–451, 1993.
20. S. G¨urses, B. Berendt, and T. Santen. Multilateral security requirements analysis for pre-serving privacy in ubiquitous environments. In Proceedings of Workshop on Ubiquitous Knowledge Discovery for Users (UKDU’06).
21. T. H¨agerstrand. What about people in regional science? Papers of the Regional Science Association, (24):7–21, 1970.
22. J. Han and Y. Fu. Discovery of multiple-level association rules from large databases. In Pro-ceedings of 21st International Conference on very Large Data Bases (VLDB’95), pp. 420–431, 1995.
23. J. Han, K. Koperski, and N. Stefanovic. GeoMiner: A system prototype for spatial data min-ing. InProceedings of 1997 ACM-SIGMOD International Conference Management of Data (SIGMOD’97), pp. 553–556, 1997.
24. M. Harvey and J. Han. Geographic data mining and knowledge discovery: An overview. In Geographic Data Mining and Knowledge Discovery, pp. 3–32. Taylor and Francis, 2001.
25. C. Hsu and C.A. Knoblock. Using inductive learning to generate rules for semantic query opti-mization. InAdvances in Knowledge Discovery and Data Mining, pp. 425–445. MIT Press, 1996.
26. A. Hunter, N. El-Sheimy, and G. Stengouse. Close and grizzly gps/camera collar captures bear doings.GPS World, February Issue: 24–31, 2005.
27. J. Huttenlocher, L. Hedges, and S. Duncan. Categories and particulars: Prototype effects in estimating spatial location.Psychological Review, 98:352–376, 1991.
28. M. Johnson. Metaphorical reasoning.Southern Journal of Philosophy, 21(3):371–389, 1983.
29. G. Lakoff.Women, Fire, and Dangerous Things. University of Chicago Press, 1987.
30. A. Ligtenberg, M. Wachowicz, A.K. Bregt, A.J.M. Beulens, and D.L. Kettenis. A design and application of a multi-agent system for simulation of multi-actor spatial planning.Journal of Environment Management, 72:43–55, 2004.
31. A. MacEachren, M. Wachowicz, R. Edsall, D. Haug, and R. Masters. Constructing knowledge from multivariate spatiotemporal data: Integrating geographic visualization (gvis) with knowl-edge discovery in databases (kdd).International Journal of Geographic Information Science, 13(4):311–334, 1999.
32. T.P. McNamara, J.K. Hardy, and S.C. Hirtle. Subjective hierarchies in spatial memory.Journal of Experimental Psychology: Learning, Memory, and Cognition, 15:211–227, 1989.
33. G. Navratil and A. Frank. Data quality for spatial planning – An ontological view. In Proceedings on Competence Center of Urban and Regional Planning (CORP’06), (Geomulti-media’05), 2006.
34. I. Nonaka and H. Takeuchi. The Knowledge Creating Company. Oxford University Press, 1995.
35. R. Oppermann and M. Specht. A context-sensitive nomadic exhibition guide. In Proceed-ing of Second International Symposium on Handheld and Ubiquitous ComputProceed-ing (HUC’00), pp. 127–142, 2000.
36. D. Peuquet.Representations of Space and Time. The Guilford Press, 2002.
37. D. Pfoser and C.S. Jensen. Querying the trajectories of on-line mobile objects. InProc. 2nd ACM Intl Workshop on Data Engineering for Wireless and Mobile Access, pp. 66–73, 2001.
38. B. Poore and N. Chrisman. Order from noise: Toward a social theory of geographic information.Annals of the Association of American Geographers, 96(3):508–523.
39. R.M. Pulselli, F.M. Pulselli, C. Ratti, and E. Tizzi. Dissipative structures for understand-ing cities: Resource flows and mobility patterns. InProceedings of the First International Conference on Built Environment Complexity (BECON’05), pp. 271–279, 2005.
40. J.R. Quinlan. Learning logical definitions from relations.Machine Learning, 5(3):239–266, 1990.
41. N. Ramakrishnan and A.Y. Grama. Data mining-guest editors’ introduction: From serendipity to science.Computer, 32(8):34–37, 1999.
42. C. Ratti. Space syntax: Some inconsistencies.Environmental and Planning B: Planning and Design, 31:487–499, 2004.
43. I. Reginster and G. Edwards. The concept and implementation of perceptual regions as hier-archical spatial units for evaluating environmental sensitivity.Journal of Urban and Regional Information Systems Association, 13(1):5–16, 2001.
44. J. Richmond. Simplicity and complexity in design for transportation systems and urban forms.
Journal of Planning Education and Research, 17:220–230, 1998.
45. C. Schulzki-Haddouti. U.S. unternehmen ver¨offentlicht sensiblen daten aus seefrachtver-tr¨agen.http://www.heise.de/newsticker/meldung/76598, 2006.
46. J. Searle.The Construction of Social Reality. Free Press, 1995.
47. S. Shekhar, B. Hamidzadeh, A. Kohli, and M. Coyle. Learning transformation rules for seman-tic query optimization: A data-driven approach. InSpecial Issue on Learning and Discovery in Knowledge-Based Databases, Vol. 5(6), pp. 950–964. Institute of Electrical and Electronics Engineers, Washington, 1993.
48. M. Siegel. Automatic rule derivation for semantic query optimiser. In Proceedings of the International Conference on Databases and Expert Systems (DEXA’88), pp. 371–385, 1988.
49. C. Smyth. Mining mobile trajectories. InGeographic Data Mining and Knowledge Discovery, pp. 337–361. Taylor and Francis, 2001.
50. N. Spence and M. Frost. Work travel responses to changing workplaces and changing res-idences. InCities in Competition: Productive and Sustainable Cities for the 21st Century, pp. 359–381. Longman Australia Pty Ltd., 1995.
51. D. Stead and S. Marshall. The relationships between urban form and travel patterns: An inter-national review and evaluation.European Journal of Transport and Infrastructure Research, 1(2):113–141, 2001.
52. E. Tiezzi.Beauty and Science. WIT Press, 2004.
53. P. Troy. Let’s look at that again.Urban Policy and Research, 10(1):41–49, 1992.
54. A. van der Valk. The dutch planning experience.Landscape and Urban Planning, 58:201–210, 2001.
55. R. Vreeker, H.D. Groot, and E. Verhoef. Urban multifunctional land use: Theoretical and empirical insights on economies of scale, scope and diversity.Built Environment, 20(4):289–
307, 2004.
56. M. Wachowicz.Object-Oriented Design for Temporal GIS. Taylor and Francis, 1999.
57. M. Wachowicz. Geoinsight: An approach for developing a knowledge construction process based on the integration of gvis and kdd methods. InGeographic Data Mining and Knowledge Discovery, pp. 239–259. Taylor and Francis, 2001.
58. C. Wallace. Classification by minimum-message-length encoding. In Proceedings of the International Conference on Advances in Computing and Information, pp. 72–81. Springer, 1990.
59. W. Wang, J. Yang, and R. Muntz. Stinga: Statistical information grid approach to spatial data mining. In Proceedings of 23th International Conference on very Large Data Bases (VLDB’97), pp. 186–196, 1997.
60. J. Wisserhof. Landelijk gebied in onderzoek: Ontwikkeling en toepassing van een interdisci-plinair conceptueel kader. InKU Nijmegen, 1996.
61. C.V. Woerkum. Communicatie en interactieve beleidsvorming. InSam-som, Alphen aan den Rijn, 2000.