The selection of any land-use plan requires judgement by the decisionmaker(s). The role of the analyst is to improve the information available to the decisionmaker(s). If the planning problem is represented by a mathematical model, then methods of generating alternative solutions by manipulating the model, that is, ideation methods, are desirable tools for the analyst. In this paper, a vector-space approach is used to develop and unify various ideation methods within a formal mathematical framework that provides a geometric interpretation of the ideation process. A realistic land-use planning example is used to illustrate the concepts, and hierarchical clustering methods are used to examine the differences between alternative land-use plans.
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References
1.
AnderbergM R, 1973Ouster Analysis for Applications (Academic Press, New York)
2.
AndrewsJ R, 1976Computer Implementation of Mathematical Programming Model for Minimizing Environmental Conflict, Energy Consumption, and Capital Expenditures in Urban Planning MSc thesis, Department of Electrical Engineering and Computer Science, University of Illinois, Chicago, IL
3.
BammiDBammiDPatonR, 1976, “Urban planning to minimize environmental impact”Environment and Planning A8245–259
4.
BMDP Statistical Software1981 (University of California Press, Berkeley, CA)
5.
BrillE DJr, 1979, “The use of optimization models in public sector planning”Management Science25413–422
6.
BrillE DJrChangS-YHopkinsL D, 1982, “Modeling to generate alternatives: The HSJ approach and an illustration using a problem land use planning”Management Science28221–235
7.
ChangS-YBrillE DJrHopkinsL D, 1979, “Preliminary evaluation of the HSJ method for generating alternatives: A land use planning example II report to the Research Board, University of Illinois at Urbana-Champaign, Urbana, IL Chang
8.
BrillS-Y E DJrHopkinsL D, 1982a, “Efficient random generation of feasible alternatives: A land use example”Journal of Regional Science22303–314
9.
ChangS-YBrillE DJrHopkinsL D, 1982b, “Use of mathematical models to generate alternative solutions to water resources planning problems”Water Resources Research1858–64
DaviesO L, (Ed.), 1963The Design and Analysis of Industrial Experiments (Oliver and Boyd, Edinburgh)
12.
DeiningerR A, (Ed.), 1973, “Systems analysis for environmental pollution control”in Models for Environmental Pollution Control (Ann Arbor Science, Ann Arbor, MI) pp 3–18
13.
DuranB SOdellP L, 1974Ouster Analysis: A Survey (Springer, Berlin)
14.
HeibergerR M, 1978, “Generation of random orthogonal matrices”Applied Statistics27199–206
15.
HopkinsL D, 1975Optimum-seeking Models for the Design of Suburban Land Use Plans PhD thesis, Department of City and Regional Planning, University of Pennsylvania, Philadelphia, PA
16.
KshirsagarS R, 1983Ideation and Evaluation in Environmental Planning PhD thesis, Department of Civil Engineering, University of Illinois at Urbana-Champaign, Urbana, IL
17.
LiebmanJ C, 1976, “Some simple-minded observations on the role of optimization in public systems decision making”Interfaces6102–108
18.
NowinskiJ L, 1981Application of Functional Analysis in Engineering (Plenum, New York)
19.
OsborneE E, 1965, “Smallest least-squares solutions of linear equations”SIAM Journal on Numerical Analysis, Series B2300–307
20.
RoyB, 1976, “From optimization to multicriteria decision aid: Three main operational attitudes” in Multiple Criteria Decision Making Eds ThiriezHZiontsS, (Springer, Berlin) pp 1–34
21.
SinhaK CAdamskJ TSchlagerK J, 1973, “Use of random search techniques to obtain optimal land use plan design”Highway Research Recordnumber 422, 53–65
22.
Southeastern Wisconsin Regional Planning Commission, 1973, “Land use plan design model, 3” final report NTIS PB-225 08510, National Technical Information Service, 5285 Port Royal Road, Springfield, VA 2261
