The aim of this paper is to offer a contribution to the study of the relationship between the physical elements of a sidewalk and people's perception of it by applying the rough sets approach. In previous studies in this context statistical methods such as variance analysis and regression analysis are the most used techniques, and these suffer from the limitations of large sample sizes and rigorous statistical assumptions. However, the rough sets approach, as a nonparametric method of artificial intelligence, can handle this problem. In this study, twenty civil-engineering students and twenty noncivil-engineering students took part in an image preference survey assessing photographs of sidewalks. Then the rough sets approach was conducted by a four-step procedure to analyze the survey results. Finally, the output of the rough sets approach is used to express people's perception of good and bad features of sidewalk space. The results will hopefully help planners to make effective decisions about sidewalk features.
Get full access to this article
View all access options for this article.
References
1.
BaaijensSNijkampP, 1999, “Time pioneers and travel behaviour: An investigation into the viability of ‘slow motion’”Growth and Change30237–263
2.
DuntschIGedigaG, 1998, “Uncertainty measures of rough set prediction”Artificial Intelligence106109–137
3.
EgmondP VNijkampPVindigniG, 2003, “A comparative analysis of the performance of urban public transport systems in Europe”International Social Science Journal55(176) 235–247
4.
ErginAKaraesmenEMicallefAWilliamsA T, 2004, “A new methodology for evaluating coastal scenery: Fuzzy logic systems”Area36367–386
5.
FloridaR, 2002The Rise of the Creative Class: and How it's Transforming Work, Leisure, Community and Everyday Life (Basic Books, New York)
6.
FrankL DStoneBBachmanW, 2000, “Linking land use with household vehicle emissions in the Central Puger Sound: Methodological framework and findings”Transportation Research D5(3) 173–196
7.
FrankL DEngelkeP OSchmidT L, 2003Health and Community Design: The Impact of the Built Environment on Physical Activity (Island Press, Washington, DC)
8.
FukahoriKKubotaY, 2003, “The role of design elements on the cost-effectiveness of streetscape improvement”Landscape and Urban Planning63(2) 75–91
9.
GrecoSMatarazzoBSlowinskiR, 1998, “A new rough set approach to evaluation of bankruptcy risk”, in Operational Tools In The Management of Financial Risks Ed. ZopounidisC (Kluwer Academic, Dordrecht) pp 121–136
10.
HirokaneMFurutaHMorikawaYHamaguchiC, 2003, “Rough sets based extraction method of characteristics from bridge images”, in Proceedings of the First Workshop on Rough Sets and Kansei Engineering in Japan, Bulletin of International Rough Set Society7(1/2) 51–54
11.
JeongS, 2002, “Proposed policy guidelines for implementing pedestrian priority zone in Seoul”, Research Reports, Seoul Development Institute, Seoul (in Korean)
12.
LiS PWillB F, 2005, “A fuzzy logic system for visual evaluation”Environment and Planning B: Planing and Design32293–304
13.
LinT YCrconeN, 1997Rough Sets and Data Mining: Analysis for Imprecise Data (Kluwer Academic, Dordrecht)
14.
MeddaFNijkampP, 2003, “A combinatorial assessment for complex transport policy analysis”Integrated Assessment4(3) 214–222
15.
MüderrisoĝluHEroĝluEÖzkanŞAkK, 2006, “Visual perception of tree forms”Building and Environment41796–806
16.
NasarJ LHongX, 1999, “Visual preferences in urban signscapes”Environment and Behavior31671–691
17.
NijkampPRietveldPSpierdijkL, 2000, “A meta-analytic comparison of determinants of public transport use: Methodology and application”Environment and Planning B: Planning and Design27893–903
18.
PawlakZ, 1982, “Rough sets”International Journal of Computer and Information Sciences11341–356
19.
PawlakZ, 1991, “Rough set approach to knowledge-based decision support”European Journal of Operational Research9948–57
20.
PawlakZ, 1997Rough Sets: Theoretical Aspects of Reasoning about Data (Kluwer Academic, Dordrecht)
21.
PawlakZSkowronA, 2007, “Rough sets: Some extensions”Information Sciences17728–40
22.
PolkowskiLTsumotoSLinT Y, 2000Rough Set Methods and Applications: New Development in Knowledge Discovery in Information Systems (Physica, Heidelberg)
23.
PredkiBSlowinskiRStefanowskiJSusmagaRWilkS, “ROSE—software implementation of the rough set theory”, in Rough Sets and Current Trends in Computing 1998, LNAI 1412 Eds PolkowskiLSkowronA (Springer, Heidelberg) pp 605–608
24.
SaitoAMunemotoJMatsushitaD, 2005, “Study on inference of combination rules of form elements by sensibility evaluation employing rough sets theory—a case of Sanneizaka preservation district for groups of historic buildings”Journal of Architecture and Planning59485–91 (in Japanese)
25.
SchroederH WCannonW N, 1987, “Visual quality of residential streets: Both street and yard trees make a difference”Journal of Arboriculture13(10) 236–239
26.
SlowinskiRZopoundisC, 1995, “Application of the rough set approach to evaluation of bankruptcy risk”International Journal of Intelligence Systems in Accounting, Finance and Management427–41
27.
SlowinskiRGrecoSMatarazzoS, 2005, “Rough set based decision support”, in Search Methodologies: Introductory Tutorials in Optimization and Decision Support Eds BurkeEKendallG (Springer, Boston, MA) pp 475–527
28.
SriramR D, 2006, “Artificial intelligence in engineering: Personal reflections”Advanced Engineering Informatics203–5
29.
TodorovaAAsakawaSAikohT, 2004, “Preferences for and attitudes towards street flowers and trees in Sapporo, Japan”Landscape and Urban Planning69403–416
30.
WalczakBMasartD L, 1999, “Rough sets theory”Chemometrics and Intelligent Laboratory Systems471–16
31.
WalterA IScholzR W, 2007, “Critical success conditions of collaborative methods: A comparative evaluation of transport planning projects”Transportation34195–212
32.
WilliamsK, 2002, “Exploring resident preferences for street trees in Melbourne”Australia Journal of Arboriculture28(4) 161–169
33.
WuCYueYLiMAdjeiO, 2004, “The rough set theory and applications”Engineering Computations21488–511
