Discussed is the merit of inductive learning as a tool with which to discover geographic knowledge in data-rich environments, and particularly as an analysis tool in spatial decision-making theory. The capability and applicability of Quinlan’s C4.5 decision tree induction algorithm to the class of problems involving the choice among discrete travel destinations within an urban area are analyzed. The C4.5 algorithm and its relation to other decision tree induction algorithms and to spatial behavioral modeling are described, and its implementation on spatial behavior data from the Minneapolis-St. Paul metropolitan area is illustrated.
Get full access to this article
View all access options for this article.
References
1.
KoperskiK., AdhikaryJ., and HanJ.. Knowledge Discovery in Spatial Databases: Progress and Challenges. Technical Report 96-08. University of British Columbia, Vancouver, Canada, 1996.
2.
EsterM., KriegelH.-P., and SanderJ.. Spatial Data Mining: A Database Approach. In Advances in Spatial Databases (SchollM. and VoisardA., eds.), Lecture Notes in Computer Science, Springer-Verlag, Berlin, 1997, pp. 47–66.
3.
MichalskiR. S., BrastkoI., and KubatM.. Machine Learning and Data Mining. Methods and Applications. John Wiley and Sons, Chichester, U.K., 1998.
4.
FreemanT. A., and SkapuraD. M.. Neural Networks: Algorithms, Applications and Programming Techniques. Addison-Wesley, Reading, Mass., 1991.
5.
KolodnerJ.Case-Based Reasoning. Morgan Kaufmann Publishers, San Mateo, Calif., 1993.
6.
GoldbergD. E.Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, Mass., 1989.
7.
QuinlanJ. R.Induction of Decision Trees. Machine Learning, Vol. 1, 1986, pp. 81–106.
8.
Metropolitan Council. Home Interview Survey. Methodology and Results. Publication 550-92-061. Metropolitan Council, St. Paul, Minn., 1992.
9.
TimmermansH. J. P., and GolledgeR. G.. Applications of Behavioural Research on Spatial Problems II: Preference and Choice. Progress in Human Geography, Vol. 14, No. 3, 1990, pp. 311–354.
10.
ReckerW. W., and GolobT.. A Noncompensatory Model of Transportation Behavior Based on a Sequential Consideration of Attributes. Economic Geography, Vol. 57, 1979, pp. 373–383.
11.
TimmermansH. J. P.Noncompensatory Decision Rules and Consumer Spatial Choice Behavior: A Test of Predictive Ability. Professional Geographer, Vol. 35, 1983, pp. 449–485.
12.
JohnsonE., and MeyerR.. Compensatory Choice Models of Noncompensatory Processes: The Effect of Varying Context. Journal of Consumer Research, Vol. 11, 1984, pp. 528–541.
13.
BettmanJ. R.An Information Processing Theory of Consumer Choice. Addison-Wesley, Reading, Mass., 1979.
14.
SvensonO.Process Descriptions of Decision Making. Organizational Behavior and Human Performance, Vol. 23, 1979, pp. 86–112.
15.
TimmermansH. J. P.Decision Models for Predicting Preferences Among Multiattribute Choice Alternatives. In Recent Developments in Spatial Data Analysis: Methodology, Measurement, Models (Bahren-bergG., FischerM. M., and NijkampP., eds.), Gower, Aldershot, U.K., 1984, pp. 337–354.
16.
GärlingT., KwanM.-P., and GolledgeR. G.. Computational-Process Modelling of Household Activity Scheduling. Transportation Research, Vol. 28B, No. 5, 1994, pp. 355–364.
17.
KwanM.-P., and GolledgeR. G.. Computational Process Modelling of Disaggregate Travel Behaviour. In Recent Developments in Spatial Analysis (FischerM. M. and GetisA., eds.), Springer-Verlag, Berlin, 1997, pp. 171–185.
HowardJ. A., and SethJ. N.. The Theory of Buying Behavior. John Wiley and Sons, Inc., New York, 1969.
21.
ManskiC. F.The Structure of Random Utility Models. Theory and Decision, Vol. 8, 1977, pp. 229–254.
22.
FotheringhamA. S.Consumer Store Choice and Choice Set Definition. Marketing Science, Vol. 7, 1988, pp. 299–310.
23.
CromptonJ.Structure of Vacation Destination Choice Sets. Annals of Tourism Research, Vol. 19, 1992, pp. 420–434.
24.
ThillJ.-C.Choice Set Formation for Destination Choice Modelling. Progress in Human Geography, Vol. 16, 1992, pp. 361–382.
25.
ThillJ.-C., and HorowitzJ. L.. Travel-Time Constraints on Destination Choice Sets. Geographical Analysis, Vol. 29, 1997, pp. 108–123.
26.
NewellA., and SimonH. A.. Human Problem Solving. Prentice-Hall, Englewood Cliffs, N.J., 1972.
27.
DavisR., and KingJ.. An Overview of Production Systems. In Machine Intelligence 8 (ElcockE. W. and MichieD., eds.), John Wiley and Sons, New York, 1976, pp. 330–332.
28.
SmithT. R., and LundbergC. G.. Psychological Foundations of Individual Choice Behaviour and a New Class of Decision Making Models. In Recent Developments in Spatial Data Analysis (BahrenbergG., FischerM. M., and NijkampP., eds.), Gower, Aldershot, U.K., 1984.
29.
QuinlanJ. R.Decision Trees and Decision Making. IEEE Transactions on Systems, Man, and Cybernetics, Vol. 20, No. 2, 1990, pp. 339–346.
30.
QuinlanJ. R.C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, Calif., 1993.
31.
TimmermansH. J. P., and Van der HeijdenR.. Uncovering Spatial Decision-Making Processes: A Decision Nets Approach Applied to Recreational Choice Behaviour. Tijdschrift voor Economische en Sociale Geografie, Vol. 78, 1987, pp. 297–304.
32.
OskampA.LocSim: A Probabilistic Model of Choice Heuristics. Netherlands Journal of Housing and the Built Environment, Vol. 9, No. 3, 1994, pp. 285–309.
33.
WitloxF.Qualitative Housing Choice Modelling: Decision Plan Nets Versus Decision Tables. Netherlands Journal of Housing and the Built Environment, Vol. 10, No. 3, 1995, pp. 209–237.
34.
ClarkW. A. V., and SmithT. R.. Production System Models of Residential Search Behavior: A Comparison of Behavior in Computer-Simulated and Real-World Environments. Environment and Planning A, Vol. 17, 1985, pp. 555–568.
35.
SmithT. R., ClarkW. A. V., and CottonJ. W.. Deriving and Testing Production System Models of Sequential Decision-Making Behavior. Geographical Analysis, Vol. 16, No. 3, 1984, pp. 191–222.
36.
SmithT. R., PellegrinoJ. W., and GolledgeR. G.. Computational Process Modeling of Spatial Cognition and Behavior. Geographical Analysis, Vol. 14, No. 4, 1982, pp. 305–325.
37.
TverskyA.Elimination by Aspects: A Theory of Choice. Psychological Review, Vol. 79, 1972, pp. 281–299.
38.
TverskyA., and SattathS.. Preference Trees. Psychological Review, Vol. 86, 1979, pp. 542–573.
39.
MooreW. L., LehmannD. R., and PessemierE. A.. Hierarchical Representations of Market Structures and Choice Processes Through Preference Trees. Journal of Business Research, Vol. 14, 1986, pp. 371–386.
40.
ArentzeT. A., BorgersA. W. J., and TimmermansH. J. P.. The Integration of Expert Knowledge in Decision Support Systems for Facility Location Planning. Computers, Environment, and Urban Systems, Vol. 19, No. 4, 1995, pp. 227–247.
41.
GreeneD. P., and SmithS. F.. A Genetic System for Learning Models of Consumer Choice. In Genetic Algorithms and Their Applications: Proc., Second International Conference on Genetic Algorithms (GrefenstetteJ. J., ed.), Lawrence Erlbaum Associates, Mahwah, N.J., 1987, pp. 217–223.
42.
CurrimI. S., MeyerR. J., and LeN. T.. Disaggregate Tree-Structured Modeling of Consumer Choice Data. Journal of Marketing Research, Vol. 25, 1988, pp. 253–265.
43.
RaoV. R., and SabavalaD. J.. Inference of Hierarchical Choice Processes from Panel Data. Journal of Consumer Research, Vol. 8, June1981, pp. 85–96.
44.
BatsellR. R., and PolkingJ. C.. A New Class of Market Share Models. Marketing Science, Vol. 4, Summer1985, pp. 177–198.
45.
MeyerR. J., and EagleT. C.. Context-Induced Parameter Instability in a Disaggregate-Stochastic Model of Store Choice. Journal of Marketing Research, Vol. 19, February, 1982, pp. 62–71.
HyafilL., and RivestR. L.. Constructing Optimal Binary Decision Trees in NP-Complete. Information Processing Letters, Vol. 5, 1976, pp. 15–17.
48.
MichalskiR. S., and KaufmanK. A.. Data Mining and Knowledge Discovery: A Review of Issues and a Multistrategy Approach. In Machine Learning and Data Mining. Methods and Applications (MichalskiR. S., BrastkoI., and KubatM., eds.), John Wiley, Chichester, U.K., 1998, pp. 71–112.
49.
HuntE. B., MarinJ., and StoneP. J.. Experiments in Induction. Academic Press, New York, 1966.
50.
MorganJ. N., and SonquistJ. A.. Problems in the Analysis of Survey Data, and a Proposal. Journal of the American Statistical Association, Vol. 58, September1963, pp. 415–434.
51.
BreimanL., FriedmanJ., OlshenR., and StoneC.. Classification and Regression Trees. Wadsworth International, Belmont, U.K., 1984.
52.
PerreaultW. D., and BarksdaleH. C.. A Model-Free Approach to Analysis of Complex Contingency Data in Marketing Research. Journal of Marketing Research, Vol. 18, November1980, pp. 503–515.
53.
QuinlanJ. R.Discovering Rules by Induction from Large Collection of Examples. In Expert Systems in the Micro Electronic Age (MichieD., ed.), Edinburgh University Press, Edinburgh, 1979.
54.
KimH., and KoehlerG. J.. Theory and Practice of Decision Trees. Omega, Vol. 23, No. 6, 1995, pp. 637–652.
55.
MingersJ.An Empirical Comparison of Pruning Methods for Decision-Tree Induction. Machine Learning, Vol. 4, No. 2, 1989, pp. 227–243.
56.
QuinlanJ. R.Simplifying Decision Trees. International Journal of Man-Machine Studies, Vol. 27, 1987, pp. 221–234.
57.
EspositoF., MalerbaD., and SemeraroG.. A Comparative Analysis of Methods for Pruning Decision Trees. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 5, 1997, pp. 476–491.
58.
ThillJ.-C., and HorowitzJ. L.. Modeling Non-Work Destination Choices with Choice Sets Defined by Travel-Time Constraints. In Recent Developments in Spatial Analysis-Spatial Statistics, Behavioural Modelling and Neurocomputing (GetisA. and FischerM. M., eds.), Springer-Verlag, Nuremberg, Germany, 1997, pp. 186–208.
59.
ThillJ.-C., and WheelerA.. Knowledge Discovery and Induction of Decision Trees in Spatial Decision Problems. In Spatial Science: New Frontiers in Theory and Methodology (ReggianiA., ed.), Springer-Verlag, Nuremberg, Germany (in press).
60.
ThillJ.-C.Spatial Multicriteria Decision Making and Analysis. A Geographic Information Sciences Approach. Ashgate, Brookfield, U.K., 1999.
