Development and application of a livable environment evaluation support system using Web GIS

Abstract. This research proposes a Web-Service that supports the interpre-tation of the local environment objectively through visualized spatialinformation, and evaluation of the value subjectively through interactiveinterfaces. This article focuses on the livable environment whose criteria havebecome diversified in recent years. In order to highlight such varieties ofvalues during evaluation, the proposed system, LEES (Livable EnvironmentEvaluation-support System) aids exploratory evaluating process by:1) clarifying ambiguous preferences of livability using a Fuzzy StructureModel, 2) analyzing areas using multi-criteria according to the individualpreference structure, and 3) visualizing distributions of the results forsupporting decision-making. In order to validate this system, sensitivities oftypical preference scenarios are analyzed. Additional uses of this Web GIS-based system are also discussed. An empirical study directly involvingcitizens is in progress to improve the social effectiveness of the system.

Key words: Livable Environment, Exploratory Evaluation, Multi-criteriaDecision Analysis, Fuzzy Structure Model, Web GIS

JEL classification: R21, C67, D80, C69, Z00

1 Introduction

1.1 Web GIS as a participatory DSS

In many areas of decision making and evaluating problems, GIS is thus farutilized to help with perception and understanding of spatially distributedphenomena. Location-Allocation Decision Support Systems (LADSS)(Densham 1991) is an example of such a system to solve spatial decisionproblems using GIS. These systems link heuristic optimization techniques formatching the supply of facilities such as schools, shopping centers, orhospitals to public demand. As is universally recognized in the literature,GIS supports complex spatial decisions through advanced visualization,

J Geograph Syst (2004) 6:175–195

DOI: 10.1007/s10109-004-0135-2

Development and application of a livable environmentevaluation support system using Web GIS

Ai Sakamoto1and Hiromichi Fukui

2

1 Graduate School of Media and Governance, Keio University, 5322 Endoh, Fujisawa,

Kanagawa 252-8520, Japan (e-mail: [email protected])2 Professor, Faculty of Policy Management, Keio University

interactive modeling, and analysis environments (Densham and Armstrong1994; Densham 1996).The benefits of GIS have now widely spread to the Internet. Web GIS

facilitates public access to geo-referenced statistical information. Thisevolution leads GIS away from data browsing, analyzing and managingfor individual decisions, and more towards group participating andcommunicating on social decision issues (Carver 1999). As Craig et al.(1999) describes the Public Participation GIS (PPGIS) principles asaccessibility, understandability, and accountability, Web GIS continues todraw attention as a public participation tool (Kingston et al. 2000;Al-Kodmany 2001; Sakamoto et al. 1998). Carver (1996) shows an exampleof a Web GIS-based social decision support system (DSS) by means of a siteselection problem for radioactive waste disposal. In their system, decisionmakers can select items of criteria, but the goal of each criterion (e.g.minimize, maximize, decrease, increase) is explicitly determined in advance.However, users are blind to the effects of weightings on the final evaluatedresult. This type of DSS is meant to be useful for decision problems withexplicit evaluating criteria and strategies, such as for example water qualityassessment (Halls 2003; Bernard et al. 2003).Recently, as the discussion of issues has become more complicated with

various aspects, it is now more crucial to explore decision strategy amongmulti-criteria. In terms of web-based input procedures by stakeholders, thefollowing typical decision rules are implemented by the interactive spatialexploration tool CommonGIS (2002): ideal point method (DECADE)(Jankowski et al. 2001) and Ordered Weighted Averaging (OWA) (Rinnerand Malczewski, 2002). CommonGIS originated from the Descartessoftware package (Andrienko and Andrienko 1999, 2001) developed by theFraunhofer Institute for Autonomous Intelligent Systems, Sankt Augustin,Germany. Angi (2002) presented a map-centered discourse combiningCommonGIS with Zeno, an open-source platform for e-discourses. Thuswhat is required more is the way in which technologies can be used toimprove the processes of human interaction for individual and collectiveunderstanding as well as problem-solving.

1.2 Diversity of livability

The livable environment evaluation problem is considered to be one of themost private issues of such a complicated system stated above. The JapaneseNational Land Agency (2000) reported that the requirements for livableenvironment vary according to life stages, accommodation types andinhabited area. Family composition factors based on family size or growthof children make the population in the 30s age group value dwelling spacemore in exchange for commute times or accessibility to goods andcommodities. The population in the 40s age group prefer a dwelling spacethat is accessible to public or health care facilities, and the 50–60 age group,free from raising children, have other values. The investigations of Watanabeand Asami (1999) focused on detailed household factors such as the role ofthe family, experiences of moving or home buying, resident-age, and so onobtained from personal questionnaire. The Tokyo Metropolitan Govern-ment Bureau of Homing (1998) summarizes regional tendencies drawn by a

176 Ai Sakamoto and Hiromichi Fukui

local poll of residents between the ages of 20–30 have complaints aboutaccessibility to facilities for child-care, culture, sports, and recreationcontrary to the full satisfaction for other utilities.In addition to such items as convenience and comfort to the quality of life,

additional factors on health and safety or risk are emphasized after asequence of hazards in Japan: the disastrous nuclear accident in Tokaimurain 1999, the volcanic eruption at Mount Usu in Hokkaido, the earthquake inWestern Tottori Prefecture, and flooding around Nagoya in 2000. Further-more, natural hazards and various environmental risks from toxic chemicalsand soil contamination have become the focus in Japan where there has beenlittle attention paid to environmental risks as compared with economicefficiencies until just a decade ago.Livability preferences changes with diversified individual values, life style,

social conditions, and historical backdrops. What preferences mean here isan order of something precious made by individuals with a certainconsistency. As economic goods, services, allocation of time, music or arts,preferences on livable environment lay the base for evaluation. Howeversometimes we have unconscious obedience to these preferences. In thissituation, exploratory spatial data analysis is expected to better characterizethe preference structure. The next section of this paper describes how theseevaluating processes are integrated and their methodology used. Theimplementation section shows the system configuration and interactivegraphical user-interfaces for implementing the proposed evaluation process.The effectiveness testing section tests various sensitivities of the proposedevaluating methods and introduces ongoing empirical studies. The paperconcludes with a summary and further direction for the potential services.

2 Methodology

In this section, the methodology for exploratory livable environmentevaluation that is implemented in the LEES is described. We first discusshow the proposed evaluating process contributes to an exploratory evalu-ation in total. The data requirement for livable evaluation is briefly discussedin the context with some samples. This followed by a description on howUtility Function and FSM (Fuzzy Structure Modeling) are applied in thisresearch.

2.1 Implementation of an exploratory evaluating process

We propose an exploratory evaluating process for livable environment byintegrating both Spatial Multi-criteria Analysis (SMCA) and FSM. Theincorporation of SMCA is essential, because the livable environment consistsof multiple surrounding criteria and groups of alternate places representedby local grid sets instead of sample values (mean, median, max, min, etc.).Malczewski (1999) provides a detailed definition of SMCA and describesseveral applications using GIS. Another critical methodology applied to thisevaluating process is FSM (Tazaki and Amagasa, 1979). FSM includeshierarchies of preferences, about livability into this problem, using a fuzzysubordination matrix, referred to as a priority matrix in this article, and

Development and application of a livable environment evaluation 177

fuzzy algebra. With FSM hierarchies of preferences are generated in theprocess of modeling, while AHP requires a unique hierarchy of preferencesbefore evaluating procedure. The differences are demonstrated using a simpleexample in Sect. 2.4.Fig. 1 shows the evaluating process according to the general framework of

environment evaluation, indicating the relation between crucial factors ofapplied methodologies and user inputs. The process is implemented in threestages to reflect environmental preferences:

1) Selection of a set of adopting profiles among prepared various environ-mental profiles (see Sect. 2.2);

2) Setting of original utility functions which are suitable for quantified valuesof each adopting profile (see Sect. 2.3);

3) Creating a priority matrix about all paired combinations of profilesselected at stage 1), which is used to identify an original fuzzy diagramshowing preference hierarchies and to conclude an aggregation rule (seeSect. 2.4).

The major part of this proposed method is the integration of FSM intoSMCA for aggregating processes to avoid other restrictive weightingmethods. A comparison with Pairwise Comparison and AHP is demon-strated in Sect. 2.4.

2.2 Environmental profiles

The primary indices shown in Fig. 1 indicate objective quantified environ-mental conditions such as concentration of population and business, trafficfacilities, land use control, earthquakes, pollution, landscape, and publicfacilities. In this paper, they are called environmental profiles and arerepresented as map layers. But citizens’ environmental preferences vary from

Fig. 1. Evaluation process and related factors of SMCA/FSM


time to time, locally and individually. Hence, all possible profile data shouldbe provided for diverse options as much as data constraints allow (e.g.openness, quality, and existence). Table 1 shows samples of candidateenvironmental profiles that can later be updated and appended.Some of the environmental profile data are originally calculated and

produced. One pollution index, the concentration of nitrogen oxide (Nox)derived from mobile emission sources, is estimated using data such as roadnetworks and traffic flows using a standard air diffusion model (Plume,Pasquill-Gifford model) for an average long-term situation (atmospherestability class: D; wind velocity: 3.6m/s; wind direction: constant). Thebenefits from a park are quantified differently between its scale, accessibility,and gross area in certain buffers. The effects of the irritation facility are moredifficult to scale because the definition and affected distance vary amongcitizens. In this case, we assume that waste disposal and supply facilities (e.g.garbage incinerators, water and sewerage treatment plants) and industrialsites (e.g. factories, warehouses, truck terminals) create negative effects forthe surrounding neighborhoods and are called Not In My Backyard(NIMBY) facilities. However, it is desirable to allow users to select theiroriginal irritation targets from various facility types. Alternatives for eachenvironmental profile are represented with 100 meter resolution grid cell datasets.

2.3 Utility function

Every user has their own ideas about the types of utility function (e.g.increasing/decreasing/constant, linear/non-linear). For example, an illustra-tion of scaling utility of accessibility to major rail lines, the user may preferan area close to the line because of convenience (decreasing utility), or theymay prefer distant area because of the bustle of the city (increasing utility), orthey may prefer a certain distance taking into account both reasons statedabove (parabolic, convex utility).

Table 1. Sample environmental profiles

Category Environmental profile data

Population Population (total), Population rate (young, producing, ageing),

Labors (primary, secondary, tertiary industries)

Business Establishment (total, primary, secondary, tertiary industries),

Employee (total, primary, secondary, tertiary industries)

Traffic facility Accessibility (to station, to major line)

Land use control Zoning, Floor-area ration, Building to land ratio, Limitation of

building height

Hazard Earthquake vulnerability index (building fall, fire, human,

evacuation), Earthquake vulnerability rank (total),

Pollution Air pollution by traffic (Nox concentration)

Landscape Green cover ratio

Utility Park (accessibility/scale, gross area/buffers), Irritation facility

(gross area/1000m buffer)

Daily facility Grocery (accessibility), Health Care (accessibility/type), Nursery

(accessibility/open time)


Accordingly, in this evaluating process, every assessing user is required toidentify an original utility function representing an individual value scale.For ease of identification, we propose a simplified approach based on theideas of Keeney and Raiffa (1976). A user selects the most suitable functiontype from a set of prototypes represented graphically (e.g. linear, non-linear,segment linear function, characteristic number). The participant thencorrects or adjusts variable parameters (e.g. end-points, curvature, a top ofa parabola) referring to statistics (e.g. mean, median, quantile, max, min,variance). This scaling function can be processed within a GIS environment.In order to be more specific, effects of the function can be distributed intoalternative space through geographic data.

2.4 Weighting by FSM

FSM is a modeling method developed from Interpretive Structure Modeling(ISM) (Sawaragi and Kawamura 1981) which uses subordinate degreetogether with fuzzy set theory as well as binary relations. The structure of asubordinate relation is uniquely determined depending on a threshold p anda structure parameter k. Subordinate degrees of linked factors are calculated,given all paired comparison data represented by the interval [0,1] subordinatematrix. Details on FSM theory and related proof on fuzzy algebra aredescribed in Tazaki and Amagasa (1979).A graphic presentation of this hierarchy helps to easily interpret a

contextual relation among the elements of the system. Such a hierarchicalstructure is classified into four level sets: a top level set LtðsÞ which is notsubordinate to anyone; an intermediate level set LiðsÞ which is subordinateand has sets subordinate to itself; a bottom level set LbðsÞ which has nothingsubordinate to itself; and an isolation level set LisðsÞ which is not subordinateto any sets and has nothing subordinate to itself. Fig. 2 illustrates an examplefuzzy diagram with two block sets and an isolation level set. The nodes andbranches of the fuzzy diagram portray the elements and subordinaterelations respectively, and the subscripts indicate the subordinate degree cijbetween element i and j. Hoshiya (1984) studied how the fuzzy structureparameter control membership functions in the FSM process. In this paper,

LtTop level set

LiIntermediate level set

Lis Isolation level set

Lb Bottom level set

Block sets Q1 Block sets Q2

S12 S14

S2 S7

S6 S1

S3 S15

S8S13

S4 S10

S11

S5 S9

0.6 0.5

0.5 0.6

0.7

0.59

0.5

0.5

0.6

0.7 0.7

0.6 0.7

0.6 0.5

0.072 0.060

0.051

0.0300.025 0.022 0.022

0.030

0.072

0.051

0.084

0.120250.120250.120250.12025

Si

Cij: Subordinate degree

Wi: Weighting value

Fig. 2. An example fuzzy diagrams and weighting values


is assigned to be constant for simplification and only threshold p is undercontrol.In the next aggregating stage, algorithms for calculating weights, generally

referred to as decision rules, are required according to subordinatehierarchies. This allocating weighting method, which plays a vital role onintegrating SMCA and FSM, is described in the following steps:

1) Score 100 is given to all sets belonging in both top level and isolation level,

wi ¼ 100:0 ðsl 2 LtðsÞjsl 2 LisðsÞÞ ð1Þ2) From the starting point of set Lt and Lis with score 100, other sets

belonging in both intermediate level and bottom level are calculated bymultiplying their upper level sets score wi" and subordinate degreecijð0 � cij � 1Þ from the top down. In this step, if calculating sets havemulti branches, the lowest score is adopted,

wi ¼ wi" � Cijðsi 2 LiðsÞjsi 2 LbðsÞÞ ð2Þ3) After allocating score wi to all sets si, total sum score

Pwi is calculated to

normalize score wi, except for both top level sets and isolation level sets,

Wi ¼ wi=X

wi ðsi 2 LbðsÞjsi 2 LiðsÞÞ ð3Þ

4) Regarding sets belonging in both top level and isolation level, differencevalue between sum of standardized weighting value

PWi and 1 is divided

by the number of sets belonging in top level nt and isolation level nis. Thisstep works for sum of weighting values to be 1.

W 0i ¼ 1�

XWi

� ��nt þ nisÞ ðsi 2 LtðsÞjsi 2 LisðsÞÞ ð4Þ

Using calculated weighting values, a set of scaled sub-index scores (see inFig. 1) are aggregated into a total evaluated score by the Simple AddingWeighting (SAW) method.Table 2 and Fig. 3 each show comparisons with the Pairwise Comparison

and Analytical hierarchy Process (AHP) weighting methods. The pairwisecomparison matrix is produced to keep the subordinate relation defined inFSM (upper-right half), and to keep reciprocal relations based on thepairwise comparison theory (lower-left half). A difference between PairwiseComparison and FSM is found in the consistency. In the PairwiseComparison method, the relative importance of evaluation criteria has tobe clearly decided with a reasonable level of consistency (Consistency Ratio;CR<0). The most different aspect regarding AHP is that an evaluatinghierarchy and its rating are decided before comparative judgment and thesynthesis of priorities (Saaty 1980). This is the reason why the hierarchydiagram of AHP in Fig. 3 differs from the other methods.

3 Implementation

In this section, the implementation of the evaluating methodology proposedin Sect. 2 is discussed. Initially, feasible system architectures and the mainfunctions are presented. This is followed by a description of the interactiveinterfaces that are an indispensable part for the exploratory process.


Table

2.Comparisonofweightingvalues

(Pairwisecomparisona/A

HP/FSM)

Environmental

Profiles

Subordinate

matrix

(left)

Priority

matrix

(right)

Weightingvalue

EA

FG

PC

TPair.C.

AHP

FSM

Earthquake

:E

0.001.00

0.721.60

0.621.28

0.913.18

0.892.84

0.812.06

0.601.22

0.2143

0.2750

0.2710

Airpollution

:A

0.280.62

0.001.00

0.923.39

0.781.88

9.645.17

0.913.18

0.220.53

0.2138

0.1125

0.1951

Irritationfacility

:F

0.140.78

0.040.29

0.001.00

0.761.78

0.350.73

0.751.73

0.110.35

0.1077

0.1125

0.1171

Green-cover

:G

0.300.31

0.240.53

0.330.56

0.001.00

0.872.59

0.460.92

0.160.44

0.0893

0.0625

0.0890

Park

:P

0.080.35

0.410.19

0.571.36

0.030.39

0.001.00

0.080.29

0.270.61

0.0681

0.0625

0.0774

Floor-arearatio

:C

0.200.48

0.290.31

0.420.58

0.581.08

0.583.39

0.001.00

0.460.92

0.1121

0.1875

0.0878

Trafficaccess

:T

0.460.82

0.581.88

0.722.84

0.862.29

1.001.64

1.001.08

0.001.00

0.1947

0.1875

0.1626

aConsistency

index

(CI):0.1163,Consistency

Ratio(C

R):0.0883<0.1


3.1 System architecture

The proposed system consists of a general, three-layered WebGIS structurefor mapping using CORBA (Fig. 4). At the second layer, within the ServiceSever, an application server (referred to FSM Evaluation Server in thisresearch) lies parallel to the GIS Service Server. It enables users to analyzeFSM models beyond potential of the current client server architecture usinga vast array of data. The user interface is implemented in Java andprovides the input and output system for visualization during evaluatingprocess and when the results are registered into another server forduplicating.The main functions blocks of this system are categorized into three

types: 1) evaluating support, 2) decision support, 3) collecting information.The ‘‘evaluating support’’ function block contains basic statistical calcu-lations for histogram display, scaling of each of the profiles within,modeling of criteria hierarchies, and aggregating with estimated weightingvalues. It also provides basic browsing operations (zoom, pan, overlaywith satellite or raster images, landmark/address searching, etc). Thesecond ‘‘decision support’’ block involves the calculation of differencebetween the evaluated scores and current land prices in addition to rankfor a selected spatial unit. Finally, the ‘‘information collection’’ functionaids in the collection of information about livable preferences andpersonal attributes which are obtained from evaluating log files andregistration information.

3.2 Interactive interfaces

Fig. 5 shows user the interaction via the graphical interfaces, allocatedalong a handling process for a typical use case. An important point duringthe implementation described above is for the functionality to beinteractively linked with map indices, statistics, fuzzy diagrams, and aranking list. This enables users to check the effects of each evaluatingprocess in exploratory thinking. The significant interfaces are describedbelow:

� Linkage between map and histogram to indicate where the statisticallyinterpreted values are located on map or otherwise, that is where thespatially interpreted values are mapped to a histogram (1, 1a);

Fig. 3. Hierarchy diagram: Pairwise Comparison, AHP, FSM


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Fig.4.System

architecture


(3b) Local ranking(3) Examining evaluated output(3a) Fuzzy graph

(2b) Subordinate slide-bar(2) Setting criteria

(1) Browsing environmental profiles (Main window)

(2a) Utility function

(1a) Histogram

Fig. 5. Graphical interfaces of exploratory evaluation using LEES


� Graphical demonstration referring to basic statistics for a useful referencein changing end-points or a top of a trajectory to aid in the decision ofutility functions (2, 2a, 1a);

� Reflected map of utility function effect (2, 1);� Slider bar linked with a priority matrix to aid in the input of subordinate

relations for the FSM (2, 2b);� Graphical fuzzy diagram illustrating complicated subordinate hierarchies

and basic statistics of total score (3a);� Visual display of weighting effects on the map (3);� Linkage between the map and local ranking (total score, divergence with

land prices) used for interpretation of which area is suitable for theevaluation and where that area is located on the map (3, 3b).

4 Effectiveness testing

The purpose of this system is to make characterize users’ livable preferencesthrough exploratory thinking. In this section, the sensitivity analysis of theproposed method is discussed. This is followed by three evaluation casesassuming typical livable preferences, and then finally our ongoing experi-mental study is presented.

4.1 Sensitivity analysis

4.1.1 Methods

Minshull (1975) and Macgill (1989) mention that the purpose of sensitivityanalysis is to test the model for output over a range of legitimate uncertaintyand to create different but equally valid scenarios by changing the parametricinputs of the model. In this case, we examine a proposed method that has agood potential for highlighting differences between individual livablepreferences. To determine a sensitivity scale of utility functions andweightings, we investigated scaled indices and total score indices, respec-tively, with maps and line charts of statistically categorized area-ratios. A setof preference scenarios used especially for weighting sensitivities weredeveloped and details are investigated regarding: 1) the distribution ofscore maps, 2) areas of high-score consistent among different scenarios,3) threshold p in FSM.Preference scenarios are explored using four facets segmented by both

macro level and micro level axis (Fig. 6), following the steps of John et al.(1995). In this case, a macro level axis determines the relative importance ofeach attribute in terms of risk or benefit. A micro level segment indicatesnatural/social attributes in the context of how they are first triggered andcreated. At the macro level, Macro I treats both risk-related and benefit-related attributes equally, Macro II emphasizes only risk-related attributes,and Macro III is the converse of Macro II. At the micro level, Risk a regardsnatural and social attributes as having the same priority, Risk b and Benefit bgive priority to attributes generated naturally, and Risk c and Benefit aadversity prevail. Table 3 shows 18 scenarios of livable preferences created


from combinations of scenario types at both the macro and micro levels. Thestudy area is comprised of the 23 central wards of metropolitan Tokyo.The adopted environmental profiles are defined as follows:

� (Hazard) Earthquake vulnerability of catastrophic building structuralfailure <E>,

� (Pollution) Air pollution caused by traffic <A>,� (Utilities) Accessibility to irritation facility <F>, Accessibility to large

parks <P>,� (Landscape) Green cover ratio <G>,� (Land Use Control) Floor area ratio <C>,� (Traffic Facility) Accessibility to major rail lines <T>.

Fig. 6. Scenario levels and facets for livable preferences

Table 3. Scenario types and priority combinations of risk/benefit and natural/social

MICRO

LEVEL

MACRO

LEVELMacro IP

Risk =0.5P

Benefit =0.5

Macro IIPRisk =0.75

PBenefit =0.25

Macro IIIPRisk =0.25

PBenefit =0.75

Risk a

(E=A=F)

Benefit a

(G=P < C=T)

Scenario 1 Scenario 7 Scenario 13

Risk a

(E=A=F)

Benefit b

(G=P > C=T)


Risk b

(E>A=F)

Benefit a

(G=P < C=T)


Risk b

(E>A=F)

Benefit b

(G=P > C=T)


Risk c

(E<A=F)

Benefit a

(G=P < C=T)


Risk c

(E<A=F)

Benefit b

(G=P > C=T)



4.1.2 Results

Reflecting a sensitivity of utility functions, we found that distributions ofscaled score and total score depend on both function type and distribution ofthe primary index. In this sub-section, sensitivities of weighting aresummarized.

(1) Effects of weighting values to map distributionFig. 7 shows that a different scenario level and type leads to a differenttendency in score map distribution. The following points are remarkable:

� There is much more sensitivity at the macro level (Risk/Benefit) than atmicro level (Natural/Social);

� Among macro level priority settings, scenario types valuing benefit tend tohave high score class distributions;

� Scenario 11 is independent in the set noted as Macro II.

(2) Livable diversityContrary to above analysis, we focused next on the area that only has a smallrange of effects in evaluated output among different scenarios. These areasare expected to have a good potential for simultaneously satisfying severaltypes of livable preferences. Based on such an idea, an index of livablediversity LDg is defined, indicating the number of applicable criteria definedby scenarios at grid g:

scenarioSDg ¼ scenarioigjscoreig � scorei þ SDiðscoreÞ� �

ð5ÞLDg ¼ ng ðscenarioSDgÞ ð6Þ

where

I

III II

Fig. 7. Area-ratio of score class divided into equal intervals


g: grid g;i: scenario i;SDi (score): standard deviation of a set of scores for scenario i;n (scenario): the number of scenarios, 0 � n � 18

If LDg is relatively high, within upper limit of the total number of scenariosunder consideration, the location (i.e. a set of grids) has consistentlyfavorable livability over different criteria. Conversely, a low LDg indicatesthe location is suitable for only limited preference types. Fig. 8 demonstratesa spatial distribution of LDg values classified equally into five brackets,comparing with similarly classified hedonic land prices in 1996. Although aninterpretation of LDg depends on applying scenarios owing to the charac-teristics of the index, we drew the following viewpoints:

� Public open space (e.g. large parks, dry riverbeds) have twofold aspectsand make weighting effects very small. Viewpoint of risk highlights lesspossibility of catastrophic building structural failure, while beneficial

Fig. 8. Spatial distribution of livable diversity index and land price


point of view emphasize a good accessibility to large parks and a highgreen-cover ratio;

� A distribution of livable diversity is distinct from hedonic land priceestimated in 1996. The high score area around open spaces on the LivableDiversity Index map are not visible on the Land Price map, that has adifferent high valued area around terminal stations. The real estate marketin central Tokyo emphasizes business aspects rather than open spaces.

(3) Threshold p for FSMAs stated in Sect. 2.4, introduction of theFSM is one of themain characteristicsof our proposed method, and threshold p controls the subordinate degree ofthe FSM. Fig. 9 shows how levels of hierarchy are removed from upper levelsas threshold p increases. Weighting values come under the influence ofthese changes in a fuzzy structure. A higher p value implies graduallyequalizing livable preferences. This sensitivity of threshold p indicates:

� The preference structure can be uniquely determined depending on athreshold p;

� This system supports decisions of the most appropriate threshold p valuesreferring to spatially distributed effects after being weighted by fuzzyhierarchies;

� Equal weighting rate between the isolated level set (Lis) and the top levelset (Lt) is necessary to be reconsidered, as Lis has no hierarchies.

4.2 Evaluation cases: supposed livable preference types

In this section, three use-cases of this system are demonstrated under a thefollowing hypothetical evaluation criteria: Urban-aware type, Nature-waretype, and Hazard-avoidance type. In this use-case comparison, the selectionof adopting environmental profiles is not considered in order to examine howutility functions and weighting values (preference structures) affect the totalscore as a whole. We used the same set of profiles in developing the scenarios.All variable parameters for the utility functions were not altered from the

Fig. 9. Fuzzy diagram (left) and weighting value (right) depending on threshold p


default settings for easy comparison. Table 4 shows three types of subor-dinate matrices for the FSM and weighting values. Fig. 10 illustrates sets ofutility function types as the other input in the evaluation and fuzzy diagraphsand total score class maps as outputs in the evaluation.Each fuzzy diagraph derived from the subordination matrix relatively

depicts every possible preference type. From an interpretation of a total score-class map of the Urban-aware type, high score classes are distributed aroundterminal stations of the main railway, called the Yamanote-line. The possiblefactors of this concentration would be accessibility to the rail station whosebuffering features can be seen on the map, and the floor area ratio related tothose buffers. For the Nature-aware type, the distribution tends to beotherwise compared with Urban-aware type and more similar to a primaryindex of green cover ratio with a high score-class area near large parks aroundoutside of the wards. It can be said that hazard-avoidance type has amoderately corresponding distribution with respect to the Nature-aware type.Reasons for this similar distribution can be the selection of environmentalprofiles, catastrophic building structural failure during earthquakes, irritationfacilities such as plants and factories, and air pollution by automobiles.Assuming that some kind of risk is derived from non-social (natural) start-points (for example rates of earthquake occurrence, volcano eruption, orflood), a different distribution of score-class could result.As we have seen in these use-cases, we confirm that types of utility function

and subordinate matrices contribute to produce original criteria for livable

Table 4. Subordinate matrix and weighting values for supposed evaluation types

E A F G P C T Weighting value

Urban-aware

E 0.00 0.50 0.44 0.40 0.31 0.76 0.85 0.14630938

A 0.58 0.00 0.46 0.41 0.45 0.78 0.87 0.15015963

F 0.75 0.64 0.00 0.42 0.59 0.95 0.93 0.09760375

G 0.74 0.60 0.40 0.00 0.65 1.00 0.81 0.07037481

P 0.74 0.58 0.53 0.28 0.00 0.86 0.93 0.10826894

C 0.24 0.17 0.07 0.12 0.22 0.00 0.82 0.19251233

T 0.25 0.16 0.23 0.03 0.15 0.36 0.00 0.23477115

Nature-aware

E 0.00 0.94 0.41 0.74 0.62 0.02 0.15 0.09393605

A 0.28 0.00 0.42 0.84 0.57 0.13 0.21 0.20201302

F 1.40 0.81 0.00 0.85 0.59 0.20 0.21 0.16363055

G 0.29 0.43 0.19 0.00 0.46 0.02 0.04 0.24049167

P 0.57 0.75 0.40 0.85 0.00 0.08 0.18 0.15150976

C 0.79 0.76 0.62 1.00 0.82 0.00 0.55 0.07420948

T 0.79 0.73 0.66 0.87 0.71 0.37 0.00 0.07420948

Hazard-avoidance

E 0.00 0.28 0.03 0.16 0.31 0.00 0.32 0.21286932

A 0.88 0.00 0.21 0.59 0.38 0.05 0.71 0.13451211

F 1.00 0.64 0.00 0.67 0.58 0.21 0.76 0.18945369

G 0.85 0.42 0.32 0.00 0.65 0.37 0.72 0.07509071

P 0.81 1.31 0.42 0.44 0.00 0.48 0.23 0.11207569

C 0.95 0.77 0.43 0.55 0.72 0.00 0.76 0.17545414

T 0.89 0.22 0.17 0.30 0.21 0.33 0.00 0.10357433


environment, which suggests an effectiveness of our proposed evaluationmethodology.

4.3 System validation

The effects of the proposed evaluation methodology were confirmed throughsensitivity analysis. However, for a validation of the system working in a

Fig. 10. Fuzzy graph and score map for supposed evalution types


Web GIS environment, it is necessary to apply this system through practicaluse in real world. Moreover, it is desirable to make continuous experimentsopened to a broad range of evaluating users. The objectives of thoseexperiments are twofold. Firstly, to improve the social effectiveness of thedeveloping system and adding spatial information, and secondly, to followhow livable preferences are changing as functions of both social andindividual attitudes related to the environment.As the first step towards a long-term goal, after simplifying the evaluating

process and improving the user interfaces for use by general citizens, anempirical study in collaboration with a public developer commenced in late2002. During the study, public participation was invited through thedeveloper’s website from based on advertisements in other related media.Evaluation of the user’s information was monitored through the systemaccess log and from personal profiles supplied by the system users. Thesystem access log provides usability (e.g. functions used, access times, etc.)information and feedback on the effectiveness of the Web GIS at eachdecision step (e.g. spatial data context, scale, area). The user profile containsinformation about the user’s individual attributes (e.g. age, occupation,family structure, income, present address) and livable preferences (e.g. utilityfunction type or subordinate matrix).The empirical study described above lasted approximately nine weeks.

Evaluation and analysis of these results are ongoing. From this study weexpected to derive effective marketing practices on the potential relationshipbetween individual attributes and livable preferences, and on the spatialsegmentation corresponding to the livable preferences.

5 Conclusion

In this paper we have proposed an exploratory methodology evaluating theintegration of SMCA and FSM for the identification of an ambiguouspreference structure for livable environments. To test our methodology, wehave implemented a Web GIS support system using interactive userinterfaces for the manipulation of spatial information, statistics, utilityfunctions, and fuzzy diagrams. As an effective test of our proposedmethodology, we analyzed the sensitivities of utility function cases, weightingvalues and threshold of the FSM to verify the results of use inputpreferences. Finally for validation, we introduced the project developmentthat has been completed and described the empirical study undertaken withreal users and analysis of those results that are currently ongoing.This research has achieved some positive results, however many lessons

and future tasks are also highlighted. Some of the following tasks are feasibleand should be addressed:

� Input of the subordinate matrix by every paired comparison is trouble-some, and should be made more simple;

� Total score should be transferred into relative value such as Willing ToPay (WTP) because it has an unclear meaning as an absolute value.

In addition the following, more difficult tasks, remain to be addressed:

� Evaluation depends on a prepared range of environmental profiles;


� Feasibility of satisfying a constraint, required by the SAW, of indepen-dent preferences and utilities imposed in selecting environmental profilesis scarce in the real world;

� There are a many varieties of utility function cases known amongevaluating users;

� Theory and methodology for scaling environmental values by economicunits is still far from practical for use in real decision problems.

Assuming some improvements for the tasks mentioned above and having anunderstanding of the system limits, the potential of the system can be realizedin various use cases by consumers, developers, venture capitalists, environ-mental educators, and policy managers.Furthermore, this system will be applied to both individual decisions and

group decision making problems such as regional development, environ-mental policy making, or risk communication. As current decisions arebecoming more complex and difficult to reach agreements, it is the intentionthat the system and research presented here will continue to evolve as one ofthe ‘‘social sensing tools’’, and that this tool will continue to meet therequirements of users and assist them in their decision making process.

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