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Toward a framework for examining the utilization of a public facility: Health-care centres in Kuala Lumpur, Malaysia
Geoforum, Vol. 8, pp.113-119,
1977. PergamonPress.Printedin Great Britain.
Toward a Framework for Examining the Utilization of a Public Facility: Health-Care Centres in Kuala Lumpur, Malaysia
BRYAN H. MASSAM and DIANA BOUCHARD,*
but a detailed planning study should search for the reasons for differences among the utilization of existing centres. Perhaps by supplying more information about the current facilities, by modifying administrative procedures, or by altering staffing arrangements, the attractiveness of a centre can be improved. These strategies should complement the strategy of re-arranging the location pattern by opening new centres in the overall exercise of making the best use of investments in the public sector.
Introduction
MAHADEV
and RAO (1974)
Canada
offer a procedure for
evaluating the available supply of public services health, education, recreation and shopping facilities in neighbourhoods in a non-Western city, where they claim that utilization is primarily determined by accessibility, with distance as the critical variable. A linear programming model is built in order to classify the neighbourhoods in order of accessibility to the services. Such a classification, it is suggested, could be used for planning purposes. For example, those neighbourhoods with low accessibility scores could receive priority for government investments, with the aim of enhancing neighbourhoods and community development by equalizing the supply of public services available among the neighbourhoods. This planning strategy is appropriate only if the city is homogeneous in ethnic and social characteristics and aspiration levels, if distance is the dominant variable influencing utilization, and if information regarding the location of services is widely available and understood by all. Studies of the utilization of public services (ANTIPODE, 1971; BROOKS, 1974; Ross, 1972), suggest that if an individual is given a free choice among two or more alternate locations for a particular service, then some individuals patronize the nearest facility while others are prepared to trade off extra travel cost, time, or inconvenience against perceived higher quality of service at the more distant centre. We would suggest that in order to classify the current arrangement of supply centres for a public facility, it is necessary to understand the ways in which distance and perceived quality are traded off. Furthermore, in a multi-ethnic city it is appropriate for planning purposes to examine differences within and among the population groups in the ways this trade-off is made. In order to enhance the well-being of the residents of a city it may be useful to increase the supply of public facilities by adding new centres,
The study discussed in this paper is a preliminary attempt to provide a classification of a set of healthcare facilities in a non-Western city, Kuala Lumpur, Malaysia. The classification is based upon observed aggregate utilization patterns. Further work is needed to explain the classifications produced beyond the simplistic conclusions offered here. It is our belief that prior to any expensive data collection exercise it is necessary to provide a framework for organizing the information such that comparative studies can be undertaken and hopefully policy-making can be based upon a clear understanding of the reasons why people choose a particular centre for a specific service. An initial application of the procedure to be discussed has been made to a western city, Montreal (MASSAM and BOUCHARD, 1976). The stimulus to examine a non-Western city was provided by the excellent paper on the health-care situation in Kuala Lumpur by MEADE and WECELIN (1975). Data Analysis and Results
Patient travel data were available on a map showing aggregate patient flows from squatter settlements and housing projects to health-care facilities. These flows represent information from the 550 in-depth interviews, 275 each from rehoused and non-rehoused squatters, conducted by MEADE and WECELIN (1975) in 1973. The rehoused families had been living in new housing for two years and were matched with squatter families that were comparable on a number of household attributes.
Data for this study were kindly provided in map form by E. Wegelin (University of Amsterdam) and Melinda S. Meade (University of Georgia at Athens) from their recent paper (Meade and Wegelin, 1975). They are not responsible for errors in this paper. We are grateful to Prof. R. Aiken (Concordia, Montreal and ex. University of Kuala Lumpur), and also to E. Wegelin, for reading and commenting on an earlier draft. The financial assistance of the MinistAre d’Education du Queb& (FCAC program) is also gratefully acknowledged. * Department of Geography, York University, Canada.
Each origin-destination path on the map was drawn using one of several styles of line which symbolized a numerical range containing the actual number of individuals using that path; therefore the precise number of trips from each origin to each destination was not known. However, the analytic procedures to be used required that this information be disaggre-
113
Geoforum/Volume
114
Table 1 l
Nearest
Order of nearest neighbour 1 -10 11 20 21~ 30 31 40 41 50 51-m60 Over 60
8/Number
3/1977
Table 2
place behaviour
of patient groups, Percentage
All patients 31.8 21.3 15.6 13.5 3.6 11.5 2.7
Kuala
Lumpur
of patients
Rehoused squatters
Non-rehoused squatters
30.9 17.1 15.6 15.6 3.3 13.8 3.6
32.7 25.5 15.6 11.3 4.0 9.1 1.8
gated to a set of individual origin--destination flows. This was carried out with the assistance of a computer program which evaluated a given, manually generated origin-destination flow pattern for consistency with the maximum and minimum values for each link in the trip network and also with the constraints on total number of patients. The output indicated links which were ‘out of range’ and thereby areas where manual reallocation could produce a new, more consistent flow pattern. Six iterations of this procedure yielded a consistent pattern.
The next stage in the analysis involved examination of the nearest-neighbour characteristics of the patients’ travel patterns. Due to the large number of destinations involved (82), it was necessary to aggregate orders of nearest neighbour by tens to obtain meaningful results. (The order of nearest neighbour indicates how many closer destinations an individual bypassed in order to reach the facility of his choice). Percentages of patients in the aggregate categories are shown in Table 1, and percentages by distance travelled in Table 2. It is clear that non-rehoused squattters tend to bypass few alternative sources of medical care and to travel shorter distances than the rehoused squatters; this observation will be discussed later at more length. Observation of travel patterns is, however, not sufficient to determine patterns of preference among facilities, since it does not give information on the set of alternatives considered by any individual. Reconstruction of the actual choice sets is impossible from the flow map, but it may be reasonably assumed that people prefer the destination they actually choose to any which are closer, and that the set of the actual destination and all closer destinations is a good approximation to the set of alternatives considered. A method for ordering trip destinations on an attractiveness scale, based on the above assumptions, has been developed by ROSS (1972) and used by BROOKS (1974) and MASSAM, BROOKS, and BOUCH ARD (1974). Clearly the spatial arrangement of origins and destinations influences the number of comparisons which can be inferred.
l
Cumulative percentages Kuala Lumpur
Distance band (miles)
of patients
by distance
travellcd.
Percentage All patients
o-
1.00 l.Ol- 2.00 2.01- 3.00 3.01~ 4.00 4.01~~5.00 5.01-6.00 6.01 -7.00 7.Ol~m8.00 8.01&9.00
Rehoused squatters
10.0 27.9 51.2 72.5 81.1 88.3 90.7 99.3 100.0
Non-rehoused squatters
5.8 22.9 41.1 65.8 70.9 83.3 86.5 98.5 100.0
14.2 33.1 61.5 79.3 91.3 93.5 94.9 100.0 100.0
The set of comparisons derived from a large number of individuals can be aggregated into a matrix of the style shown in Figure 1. The fractional entries mean, for example, that out of 25 comparisons made between centres C and A, C is preferred 20 times whereas A is preferred 5 times. ROSS used this matrix to produce an attractiveness score for each destination which could be used for ranking. Two measures of the consistency with which individuals choose among alternatives arc available: the index of unanimity (K index) is the proportion of transitive rankings made by individuals, and the E index measures the percentage of judgements in the matrix which are consistent with (i.e. could be predicted from) the attractiveness scale. A I
B I
C I
D I
1
$-/q-q ... I
.
Figure 1 0
Example
of comparison
matrix.
In order for the scale to reflect accurately the prcference relations in the data, the number of destinations must be small relative to the number of origin destination flows, so that each cell of the comparison matrix contains several occurrences and single instan-
Geoforum/Volume 8/Number 3/1977
115
8
l
Figure 2
ces of comparison do not unduly affect the results. Since the present study involves 82 destinations and only 550 origin-destination flows, the destinations had to be grouped, but in such a way that differences in spatial behaviour would still show up as differences in attractiveness of health facility groups. An initial partitioning into seven groups, distinguishing hospitals, clinics, and private practitioners, and singling out the downtown/Pudu and Bangsar Road concentrations, gave results which had no evident interpretation, but the large number of entries in most cells of the comparison matrix suggested that a finer spatial breakdown would be possible. The second grouping, which proved more amenable to interpretation, was the following: 1. Assunta hospital 2. University hospital 3. Tong Shin hospital 4. General hospital 5. Downtown ~ clinics and private practitioners >, 3, ” 6. Pudu ~ 3, 7, ” 7. Bangsar >> ‘> 8. Assunta north -” 3, 7, 9. Assunta south -” 3, ,, 10. Peripheral west -~
11. Peripheral south 12. Peripheral north -
,, 3,
1, 2,
Note that considering each hospital separately, but lumping clinics and private practitioners together as eliciting similar behaviour, seems to have been more successful than the earlier approach. Locations of facility groups are given in Figure 2. and the results of the attractiveness scaling appear in Table 3. Visual inspection of the attractiveness scales suggest that they are similar, an observation which, when considered together with the observed differences in nearest place behaviour between rehoused and nonrehoused squatters, strongly suggests that familiar sources of health care continue to be used in spite of relocation. By and large, the facilities that were most attractive to squatters before rehousing are still preferred, though rehoused squatters travel farther and bypass more alternatives to get to them. Note that in this case the population proved to be homogeneous with regard to attractiveness judgements but heterogeneous with regard to nearest place behaviour, a fact which underlines the need for care in the selection of populations and subpopulations in preference studies.
Geoforum/Volume
116
Table 3 l
Attractiveness
Useful as the ROSS attractiveness scale is in extracting an order of preference from a large number of
scales twelve health i’;Lcilitygroups
Numbers are the identifying given on previous page.
numbers
All patients
Rehoused squatters
12 9 5 8 10 7
1 4 2 9 x 10 5 12 7
11 6 3
11 6 3
85.57
86.26
4
I 2
‘%)inferred judgements consistent @ index) Index of unanimity (K index) ‘i: of proportions matrix occupied No. of origin records
of the facility
0.840 100.00
0.839 100.00
groups
;LS
Non-rehoused squatters 4 12 1 2 5 9 10 8 7 6 3
11 87.29 0.878 98.48
212 203
99 95
113 108
No. of individual comparisons (excluding diagonal)* 13037
6796
6241
No. of useful flows
8/Number 3/1977
* This stipulation had to be made in the analysis because
in a comparison of facility groups instead of individual facilities, it is possible for comparisons to occur within a group, yet these comparisons yield no information on relative group attractiveness.
individual choices, it is a one-dimensional ordinal scale only and therefore cannot represent the more complex interrelationships which may exist in the determination of site attractiveness. Multidimensional scaling uses a similarity matrix to establish perceptual distances between facilities and thus to assign them locations in a perceptual space of a given dimension. whose axes constitute ratio scales and represent the factors which are most important in the perception of distinctions among the facilities. The similarity matrix may be constructed from the comparison matrix as defined above by placing in the ii- th cell the absolute difference 10.5 - L’ijI. where cij is the ii th entry in the comparison matrix. For further details of the procedure see BROOKS (1974). GOLLEDGI! (1968).
In this study (ROSKAM and
RUSHTON
(1972),
GLITTMAN
the MINISSA computer LONGOES, 1970) was
program
used to produce one and two-dimensional scales and plots of the two-dimensional scales: the latter are reproduced in Figure 3. An initial run with twelve destination groups produced scales whose axes proved impossible to identify with any apparent characteristics of the groups. In a second run, groups 3, 1 1, and 12, which tended to behave as ‘exceptional points’ and which involved very few origin- destination flows, were eliminated, resulting in a more meaningful scale. Stress coefficients, which tneasure the extent to which a given configuration of points cannot be accommodated in a space of given dimensionality (GOLLEDGF and RUSHTON, dirnensional
The large hospitals are top-ranked by all groups, except for Tong Shin which was very poorly represented (three patients), probably due to the fact that Tong Shin is strongly Chinese in affiliation and people of Chinese origin were generally underrepresented in the study. Among the rehoused squatters, second choice after the major hospitals seemed to be facilities in the west end, around Assunta Hospital and Sungai Way. This pattern strongly suggests continued patronage of facilities used before rehousing. since many of the settlements where these people formerly lived were in the western part of the city. Next in importance was the downtown area, which has a high concentration of doctors’ offices. Among the non-rehoused squatters, downtown was more attractive than the west end in terms of alternatives bypassed. The remaining facilities were low-ranked either because they were chosen relatively few times or because they serve mainly a local clientele ; Bangsar and Pudu fall into the latter category. Indices of consistency and unanimity are high in all cases, although a slight drop in both was noted with rchousthat the continuance of previous ing, indicating choice patterns was not universal.
and
1972), were solution but
rather
high
for
the
one-
low for two dimensions.
and are given in Table 4. Table 4 l
Stress values for multidimensional
scales
Stress Values (Weak Monotonicity)
All patients Rehoused squatters Non-rehoused squatters
1 Dim.
2 Dim.
0.144 0.205 0.218
0.088 0.097 0.075
The congruence among the three maps was measured for two-dimensional regressionusing a routine correlation analysis developed by TOBLER .* The results are shown in Table 5. In an attempt to identify the axes of these twodimensional spaces, SPEARMAN rank correlations * We are grateful to Professor P. Gould of Pennsylvania State University for drawing our attention to this routine.
Geoforum/Volume
I/Number
3/1977
117
~;eoforum/Volu~ne
11x
tion matrix (which is of course symmetric) in Table 6.
Table 5 .
I(/Number
Two-dimensional
correlation
Rehoused squatters
Non-rehoused Fquatters
0.4x2 0.65
I
were computed between the orderings of facility groups on the X- and Y-axes of each pattern and the following other orderings: Number of patients patronizing the facility group (descending order) Centrality (closeness to downtown) Hospitals first, remaining groups by centrality East to west North to south Average distance travelled to the facility group (descending order) Percentage of patients using the group going to orders 1~ 10 of nearest neighbour facility (descending order) Closeness to Bangsar Attrsctiveness scale ~~all patients (9 groups) Attractiveness scale ~ rehoused squatters (9 groups) Attractiveness scale non-rehoused squatters (9 groups) The lower triangular
is shown
coefficients
All patients All patients Rehoused squatters Non-rehoused squatters
3/1977
portion
of the resulting correla-
The first point to note is that although the axes derived from the choice patterns of any one of the patient groups are generally highly correlated with the corresponding axes (i.e. X with X and Y with Y) for the other groups. in the case of the non-rehoused squatters both X and Y-axes are reversed. This affected a later stage of the analysis, as will be seen shortly. The one exception to high correlations among corresponding axes was the low correlation (0.183) between the Y-axes for all patients and rehoused squatters. Clearly the X-axis is in all cases an ‘attractiveness axis’, being strongly correlated with all the attractiveness scales and also with average distance travelled, with the scale placing hospitals first and then other facilities by centrality of location, and negatively with nearest place behaviour. This scale measures the propensity to travel extra distance and bypass altcrnatives to patronize a preferred facility. The Y-axes of the various plots are more difficult to interpret and their interpretation varies from one patient group to another. The Y-axis for all patients is correlated positively with nearest place behaviour and number of patients and negatively with average distance travelled; it seems to represent the extent to which a facility group is used locally by large numbers of people. However, for the rehoused squatters the only significant correlation with the Y-axis is a positive relationship with closeness to the Banpsar
Table 6 Spearman rank correlations Multidimensional scaling axes and attribute
l
3
4
5
b
Scale
I
1
I .OOO
2
0.417**1.000
3
0.917
-0.250
1.000
4
0.300
0.183
0.150
5
0.933
0.383
6
0.583%
0.683
0.433
0.700
7
0.300
0.617
0.400
0.383
0.233
8
0.133
0.333
0.333
0.250
0.n
9
0.583
0.167
0.517
10
0.017
0.467
0.233
II
0.117
0 433
12 13
0.850 0.733
I4
2
-0.900
scales 7
8
9
10
II
12
13
14
15
16
1.000 0.100
1.000 -0.467
1.000 0.533
1.000
~-0.433
0.800
1.000
0.717
0.083
0.317
0.533
I.000
0.033
0.0
0.267
0.883
0 883
0.533
1.000
0.217
0.217
0.050
-0.367
0.700
0.850
0.483
0.783
I.000
0.583 0.717
0.700 0.500
0.067 0.183
0.867 0.683
0.467 -0.533
0.200 0.267
0.100 0.083
0.517 0.283
0033 0.017
0.100 0.067
1.000 -0.933
1.000
0.467
0.200
0.517
0.567
0.267
0.517
0.617
0.617
0.167
0.417
0.300
-0.283
0.350
1.000
15
0.967
0 367
0.950
0.983
0.500
-0.267
0.650
0.050
0.833
0.650
0.350
1.000
I6 17
0.900 0.917
0.317 0.400
0.983 0 883
0.883 O.Y83
0.500
m-O.483
0.400
0.500
0.317
0.333
0.667
0.467
m~O.483
0.933
1.000
0.567
~0.267
0.050
0.683
0.017
0.067
0.833
~0.667
-0.333
0.967
0.867
Scale Numbers I. X-skis all patients 2. Y-au1s dl1 patients 3. X-axic rebuused squatters 4. Y-au1s rchrused squatters 5. non-rehoused sq. 6 Y-ax1s non-rehoused sq
X-ax1s
17
-0.100
-0.133 0.217 0.183
7 Number 8. 9. 10. 1 I. 12.
-0.117
of patients
Centrality Hospitals, then centrality !-as1 to west North to south Average distance travelled
13. 14. 15. 16. 17.
-0.067
Nearest place behavmur Closeness to Bangsar Attractweness all Attractiveness rehoused Attractwzmess non-reh
Significance
levels: * 10% ** 5%
1 .ooo
119
Geoforum/Volume 8/Number 3/1977
district, which probably reflects the fact that many of the rehoused squatters originally lived there and still orient their behaviour to some extent with respect to their former neighbourhood. The Y-axis for nonrehoused squatters is negatively correlated with number of patients, nearest place behaviour, and closeness to Bangsar, and positively correlated with the attractiveness scales. If the signs of the coefficients are reversed, the result is a Y-scale which is a composite of the two others, that is, a measure of large-scale local facility use, but specifically oriented towards Bangsar.
Finally, it would be presumptuous for us to offer firm conclusions explaining the position of healthcare centres on the attractiveness scales presented here on the basis of aggregate data. Rather, we offer the general procedure and the four points raised above as a framework for further detailed study of the utilization patterns of public facilities in the hope that planning in the public sector in western and nonwestern cities will be based upon a clear understanding of the utilization of facilities currently available. References
Conclusions
In order to strengthen the utility of the basic procedure discussed here, we would suggest that the following four areas deserve further work. First, the geometrical aspects of the particular configuration of origins and destinations makes certain comparisons more probable than others. What effect does this have on the attractiveness scale? In order to examine this, a series of simulation exercises could be conducted. By maintaining the flows between origins and destinations at constant levels and by altering the relative positions of the origins and destinations, the effects of modifications to the point configuration on the attractiveness scale could be examined. Second, specific infomration on the alternatives rejected needs to be incorporated into the comparison matrix. Such information could be derived from interviews. Third, details of the attributes of the individuals and the alternate centres are needed in order to examine group differences and to explain the attractiveness scales. Fourth, a time framework needs to be incorporated into the study to examine the ways in which utilization patterns change as the attributes of the centres are altered, the transport network is modified, individuals gain experience of using the centres, and information about alternatives diffuses through the city.
Access to public services (1971) Special edition of the journal Antipode. AIKEN S. R. and LEIGH C. H. (1975) Malaysia’s emerging conurbation, AAAG 65,546-563. BROOKS S. (1974) Spatial preferences in a medical care context, M.A. thesis McGill University, Department of Geography.
GOLLEDGE R. and RUSHTON G. (1972)Multidimensional
Scaling: Review and Geographical Applications (Washington, DC.: Association of American Geographers, TP-IO). GUTTMAN L. (1968) A general nonmetric technique for finding the smallest coordinate space for a configuration of points, Psychometrika 33,469-506. MAHADUR P. D. and RAO K. R. (1974) A model for location of service facilities in a non-Western urban environment YEATES M. (ed.) Proceedings of the I.G.U. Conference on Quantitative Geography Montreal: McGill(Queens Press) 164 -182. MASSAM B. H. and BOUCIIARD D. (1976) A comparison of observed and hypothetical choice behaviour, Environ. Plan. A 8,367-373. MASSAM B. H., BROOKS S. and BOUCHARD D. (1974) The analysis of movement patterns, Area 6, 174-179. MEADE M. S. and WEGELIN E. A. (1975) Some aspects of the health environments of squatters and rehoused squatters in Kuala Lumpur, Malaysia, J. Trap. Geog. 41,45-58. ROSKAM E. and LINGOES J. C. (1970) MINISSA-I: A FORTRAN IV(G) program for the smallest space analysis of square symmetric matrices, Behav. Sci. 15, 204-205. ROSS J. (1972) A measure of site attraction. Ph.D. thesis, Department of Geography, University of Western Ontario, London, Ontario.
1977. PergamonPress.Printedin Great Britain.
Toward a Framework for Examining the Utilization of a Public Facility: Health-Care Centres in Kuala Lumpur, Malaysia
BRYAN H. MASSAM and DIANA BOUCHARD,*
but a detailed planning study should search for the reasons for differences among the utilization of existing centres. Perhaps by supplying more information about the current facilities, by modifying administrative procedures, or by altering staffing arrangements, the attractiveness of a centre can be improved. These strategies should complement the strategy of re-arranging the location pattern by opening new centres in the overall exercise of making the best use of investments in the public sector.
Introduction
MAHADEV
and RAO (1974)
Canada
offer a procedure for
evaluating the available supply of public services health, education, recreation and shopping facilities in neighbourhoods in a non-Western city, where they claim that utilization is primarily determined by accessibility, with distance as the critical variable. A linear programming model is built in order to classify the neighbourhoods in order of accessibility to the services. Such a classification, it is suggested, could be used for planning purposes. For example, those neighbourhoods with low accessibility scores could receive priority for government investments, with the aim of enhancing neighbourhoods and community development by equalizing the supply of public services available among the neighbourhoods. This planning strategy is appropriate only if the city is homogeneous in ethnic and social characteristics and aspiration levels, if distance is the dominant variable influencing utilization, and if information regarding the location of services is widely available and understood by all. Studies of the utilization of public services (ANTIPODE, 1971; BROOKS, 1974; Ross, 1972), suggest that if an individual is given a free choice among two or more alternate locations for a particular service, then some individuals patronize the nearest facility while others are prepared to trade off extra travel cost, time, or inconvenience against perceived higher quality of service at the more distant centre. We would suggest that in order to classify the current arrangement of supply centres for a public facility, it is necessary to understand the ways in which distance and perceived quality are traded off. Furthermore, in a multi-ethnic city it is appropriate for planning purposes to examine differences within and among the population groups in the ways this trade-off is made. In order to enhance the well-being of the residents of a city it may be useful to increase the supply of public facilities by adding new centres,
The study discussed in this paper is a preliminary attempt to provide a classification of a set of healthcare facilities in a non-Western city, Kuala Lumpur, Malaysia. The classification is based upon observed aggregate utilization patterns. Further work is needed to explain the classifications produced beyond the simplistic conclusions offered here. It is our belief that prior to any expensive data collection exercise it is necessary to provide a framework for organizing the information such that comparative studies can be undertaken and hopefully policy-making can be based upon a clear understanding of the reasons why people choose a particular centre for a specific service. An initial application of the procedure to be discussed has been made to a western city, Montreal (MASSAM and BOUCHARD, 1976). The stimulus to examine a non-Western city was provided by the excellent paper on the health-care situation in Kuala Lumpur by MEADE and WECELIN (1975). Data Analysis and Results
Patient travel data were available on a map showing aggregate patient flows from squatter settlements and housing projects to health-care facilities. These flows represent information from the 550 in-depth interviews, 275 each from rehoused and non-rehoused squatters, conducted by MEADE and WECELIN (1975) in 1973. The rehoused families had been living in new housing for two years and were matched with squatter families that were comparable on a number of household attributes.
Data for this study were kindly provided in map form by E. Wegelin (University of Amsterdam) and Melinda S. Meade (University of Georgia at Athens) from their recent paper (Meade and Wegelin, 1975). They are not responsible for errors in this paper. We are grateful to Prof. R. Aiken (Concordia, Montreal and ex. University of Kuala Lumpur), and also to E. Wegelin, for reading and commenting on an earlier draft. The financial assistance of the MinistAre d’Education du Queb& (FCAC program) is also gratefully acknowledged. * Department of Geography, York University, Canada.
Each origin-destination path on the map was drawn using one of several styles of line which symbolized a numerical range containing the actual number of individuals using that path; therefore the precise number of trips from each origin to each destination was not known. However, the analytic procedures to be used required that this information be disaggre-
113
Geoforum/Volume
114
Table 1 l
Nearest
Order of nearest neighbour 1 -10 11 20 21~ 30 31 40 41 50 51-m60 Over 60
8/Number
3/1977
Table 2
place behaviour
of patient groups, Percentage
All patients 31.8 21.3 15.6 13.5 3.6 11.5 2.7
Kuala
Lumpur
of patients
Rehoused squatters
Non-rehoused squatters
30.9 17.1 15.6 15.6 3.3 13.8 3.6
32.7 25.5 15.6 11.3 4.0 9.1 1.8
gated to a set of individual origin--destination flows. This was carried out with the assistance of a computer program which evaluated a given, manually generated origin-destination flow pattern for consistency with the maximum and minimum values for each link in the trip network and also with the constraints on total number of patients. The output indicated links which were ‘out of range’ and thereby areas where manual reallocation could produce a new, more consistent flow pattern. Six iterations of this procedure yielded a consistent pattern.
The next stage in the analysis involved examination of the nearest-neighbour characteristics of the patients’ travel patterns. Due to the large number of destinations involved (82), it was necessary to aggregate orders of nearest neighbour by tens to obtain meaningful results. (The order of nearest neighbour indicates how many closer destinations an individual bypassed in order to reach the facility of his choice). Percentages of patients in the aggregate categories are shown in Table 1, and percentages by distance travelled in Table 2. It is clear that non-rehoused squattters tend to bypass few alternative sources of medical care and to travel shorter distances than the rehoused squatters; this observation will be discussed later at more length. Observation of travel patterns is, however, not sufficient to determine patterns of preference among facilities, since it does not give information on the set of alternatives considered by any individual. Reconstruction of the actual choice sets is impossible from the flow map, but it may be reasonably assumed that people prefer the destination they actually choose to any which are closer, and that the set of the actual destination and all closer destinations is a good approximation to the set of alternatives considered. A method for ordering trip destinations on an attractiveness scale, based on the above assumptions, has been developed by ROSS (1972) and used by BROOKS (1974) and MASSAM, BROOKS, and BOUCH ARD (1974). Clearly the spatial arrangement of origins and destinations influences the number of comparisons which can be inferred.
l
Cumulative percentages Kuala Lumpur
Distance band (miles)
of patients
by distance
travellcd.
Percentage All patients
o-
1.00 l.Ol- 2.00 2.01- 3.00 3.01~ 4.00 4.01~~5.00 5.01-6.00 6.01 -7.00 7.Ol~m8.00 8.01&9.00
Rehoused squatters
10.0 27.9 51.2 72.5 81.1 88.3 90.7 99.3 100.0
Non-rehoused squatters
5.8 22.9 41.1 65.8 70.9 83.3 86.5 98.5 100.0
14.2 33.1 61.5 79.3 91.3 93.5 94.9 100.0 100.0
The set of comparisons derived from a large number of individuals can be aggregated into a matrix of the style shown in Figure 1. The fractional entries mean, for example, that out of 25 comparisons made between centres C and A, C is preferred 20 times whereas A is preferred 5 times. ROSS used this matrix to produce an attractiveness score for each destination which could be used for ranking. Two measures of the consistency with which individuals choose among alternatives arc available: the index of unanimity (K index) is the proportion of transitive rankings made by individuals, and the E index measures the percentage of judgements in the matrix which are consistent with (i.e. could be predicted from) the attractiveness scale. A I
B I
C I
D I
1
$-/q-q ... I
.
Figure 1 0
Example
of comparison
matrix.
In order for the scale to reflect accurately the prcference relations in the data, the number of destinations must be small relative to the number of origin destination flows, so that each cell of the comparison matrix contains several occurrences and single instan-
Geoforum/Volume 8/Number 3/1977
115
8
l
Figure 2
ces of comparison do not unduly affect the results. Since the present study involves 82 destinations and only 550 origin-destination flows, the destinations had to be grouped, but in such a way that differences in spatial behaviour would still show up as differences in attractiveness of health facility groups. An initial partitioning into seven groups, distinguishing hospitals, clinics, and private practitioners, and singling out the downtown/Pudu and Bangsar Road concentrations, gave results which had no evident interpretation, but the large number of entries in most cells of the comparison matrix suggested that a finer spatial breakdown would be possible. The second grouping, which proved more amenable to interpretation, was the following: 1. Assunta hospital 2. University hospital 3. Tong Shin hospital 4. General hospital 5. Downtown ~ clinics and private practitioners >, 3, ” 6. Pudu ~ 3, 7, ” 7. Bangsar >> ‘> 8. Assunta north -” 3, 7, 9. Assunta south -” 3, ,, 10. Peripheral west -~
11. Peripheral south 12. Peripheral north -
,, 3,
1, 2,
Note that considering each hospital separately, but lumping clinics and private practitioners together as eliciting similar behaviour, seems to have been more successful than the earlier approach. Locations of facility groups are given in Figure 2. and the results of the attractiveness scaling appear in Table 3. Visual inspection of the attractiveness scales suggest that they are similar, an observation which, when considered together with the observed differences in nearest place behaviour between rehoused and nonrehoused squatters, strongly suggests that familiar sources of health care continue to be used in spite of relocation. By and large, the facilities that were most attractive to squatters before rehousing are still preferred, though rehoused squatters travel farther and bypass more alternatives to get to them. Note that in this case the population proved to be homogeneous with regard to attractiveness judgements but heterogeneous with regard to nearest place behaviour, a fact which underlines the need for care in the selection of populations and subpopulations in preference studies.
Geoforum/Volume
116
Table 3 l
Attractiveness
Useful as the ROSS attractiveness scale is in extracting an order of preference from a large number of
scales twelve health i’;Lcilitygroups
Numbers are the identifying given on previous page.
numbers
All patients
Rehoused squatters
12 9 5 8 10 7
1 4 2 9 x 10 5 12 7
11 6 3
11 6 3
85.57
86.26
4
I 2
‘%)inferred judgements consistent @ index) Index of unanimity (K index) ‘i: of proportions matrix occupied No. of origin records
of the facility
0.840 100.00
0.839 100.00
groups
;LS
Non-rehoused squatters 4 12 1 2 5 9 10 8 7 6 3
11 87.29 0.878 98.48
212 203
99 95
113 108
No. of individual comparisons (excluding diagonal)* 13037
6796
6241
No. of useful flows
8/Number 3/1977
* This stipulation had to be made in the analysis because
in a comparison of facility groups instead of individual facilities, it is possible for comparisons to occur within a group, yet these comparisons yield no information on relative group attractiveness.
individual choices, it is a one-dimensional ordinal scale only and therefore cannot represent the more complex interrelationships which may exist in the determination of site attractiveness. Multidimensional scaling uses a similarity matrix to establish perceptual distances between facilities and thus to assign them locations in a perceptual space of a given dimension. whose axes constitute ratio scales and represent the factors which are most important in the perception of distinctions among the facilities. The similarity matrix may be constructed from the comparison matrix as defined above by placing in the ii- th cell the absolute difference 10.5 - L’ijI. where cij is the ii th entry in the comparison matrix. For further details of the procedure see BROOKS (1974). GOLLEDGI! (1968).
In this study (ROSKAM and
RUSHTON
(1972),
GLITTMAN
the MINISSA computer LONGOES, 1970) was
program
used to produce one and two-dimensional scales and plots of the two-dimensional scales: the latter are reproduced in Figure 3. An initial run with twelve destination groups produced scales whose axes proved impossible to identify with any apparent characteristics of the groups. In a second run, groups 3, 1 1, and 12, which tended to behave as ‘exceptional points’ and which involved very few origin- destination flows, were eliminated, resulting in a more meaningful scale. Stress coefficients, which tneasure the extent to which a given configuration of points cannot be accommodated in a space of given dimensionality (GOLLEDGF and RUSHTON, dirnensional
The large hospitals are top-ranked by all groups, except for Tong Shin which was very poorly represented (three patients), probably due to the fact that Tong Shin is strongly Chinese in affiliation and people of Chinese origin were generally underrepresented in the study. Among the rehoused squatters, second choice after the major hospitals seemed to be facilities in the west end, around Assunta Hospital and Sungai Way. This pattern strongly suggests continued patronage of facilities used before rehousing. since many of the settlements where these people formerly lived were in the western part of the city. Next in importance was the downtown area, which has a high concentration of doctors’ offices. Among the non-rehoused squatters, downtown was more attractive than the west end in terms of alternatives bypassed. The remaining facilities were low-ranked either because they were chosen relatively few times or because they serve mainly a local clientele ; Bangsar and Pudu fall into the latter category. Indices of consistency and unanimity are high in all cases, although a slight drop in both was noted with rchousthat the continuance of previous ing, indicating choice patterns was not universal.
and
1972), were solution but
rather
high
for
the
one-
low for two dimensions.
and are given in Table 4. Table 4 l
Stress values for multidimensional
scales
Stress Values (Weak Monotonicity)
All patients Rehoused squatters Non-rehoused squatters
1 Dim.
2 Dim.
0.144 0.205 0.218
0.088 0.097 0.075
The congruence among the three maps was measured for two-dimensional regressionusing a routine correlation analysis developed by TOBLER .* The results are shown in Table 5. In an attempt to identify the axes of these twodimensional spaces, SPEARMAN rank correlations * We are grateful to Professor P. Gould of Pennsylvania State University for drawing our attention to this routine.
Geoforum/Volume
I/Number
3/1977
117
~;eoforum/Volu~ne
11x
tion matrix (which is of course symmetric) in Table 6.
Table 5 .
I(/Number
Two-dimensional
correlation
Rehoused squatters
Non-rehoused Fquatters
0.4x2 0.65
I
were computed between the orderings of facility groups on the X- and Y-axes of each pattern and the following other orderings: Number of patients patronizing the facility group (descending order) Centrality (closeness to downtown) Hospitals first, remaining groups by centrality East to west North to south Average distance travelled to the facility group (descending order) Percentage of patients using the group going to orders 1~ 10 of nearest neighbour facility (descending order) Closeness to Bangsar Attrsctiveness scale ~~all patients (9 groups) Attractiveness scale ~ rehoused squatters (9 groups) Attractiveness scale non-rehoused squatters (9 groups) The lower triangular
is shown
coefficients
All patients All patients Rehoused squatters Non-rehoused squatters
3/1977
portion
of the resulting correla-
The first point to note is that although the axes derived from the choice patterns of any one of the patient groups are generally highly correlated with the corresponding axes (i.e. X with X and Y with Y) for the other groups. in the case of the non-rehoused squatters both X and Y-axes are reversed. This affected a later stage of the analysis, as will be seen shortly. The one exception to high correlations among corresponding axes was the low correlation (0.183) between the Y-axes for all patients and rehoused squatters. Clearly the X-axis is in all cases an ‘attractiveness axis’, being strongly correlated with all the attractiveness scales and also with average distance travelled, with the scale placing hospitals first and then other facilities by centrality of location, and negatively with nearest place behaviour. This scale measures the propensity to travel extra distance and bypass altcrnatives to patronize a preferred facility. The Y-axes of the various plots are more difficult to interpret and their interpretation varies from one patient group to another. The Y-axis for all patients is correlated positively with nearest place behaviour and number of patients and negatively with average distance travelled; it seems to represent the extent to which a facility group is used locally by large numbers of people. However, for the rehoused squatters the only significant correlation with the Y-axis is a positive relationship with closeness to the Banpsar
Table 6 Spearman rank correlations Multidimensional scaling axes and attribute
l
3
4
5
b
Scale
I
1
I .OOO
2
0.417**1.000
3
0.917
-0.250
1.000
4
0.300
0.183
0.150
5
0.933
0.383
6
0.583%
0.683
0.433
0.700
7
0.300
0.617
0.400
0.383
0.233
8
0.133
0.333
0.333
0.250
0.n
9
0.583
0.167
0.517
10
0.017
0.467
0.233
II
0.117
0 433
12 13
0.850 0.733
I4
2
-0.900
scales 7
8
9
10
II
12
13
14
15
16
1.000 0.100
1.000 -0.467
1.000 0.533
1.000
~-0.433
0.800
1.000
0.717
0.083
0.317
0.533
I.000
0.033
0.0
0.267
0.883
0 883
0.533
1.000
0.217
0.217
0.050
-0.367
0.700
0.850
0.483
0.783
I.000
0.583 0.717
0.700 0.500
0.067 0.183
0.867 0.683
0.467 -0.533
0.200 0.267
0.100 0.083
0.517 0.283
0033 0.017
0.100 0.067
1.000 -0.933
1.000
0.467
0.200
0.517
0.567
0.267
0.517
0.617
0.617
0.167
0.417
0.300
-0.283
0.350
1.000
15
0.967
0 367
0.950
0.983
0.500
-0.267
0.650
0.050
0.833
0.650
0.350
1.000
I6 17
0.900 0.917
0.317 0.400
0.983 0 883
0.883 O.Y83
0.500
m-O.483
0.400
0.500
0.317
0.333
0.667
0.467
m~O.483
0.933
1.000
0.567
~0.267
0.050
0.683
0.017
0.067
0.833
~0.667
-0.333
0.967
0.867
Scale Numbers I. X-skis all patients 2. Y-au1s dl1 patients 3. X-axic rebuused squatters 4. Y-au1s rchrused squatters 5. non-rehoused sq. 6 Y-ax1s non-rehoused sq
X-ax1s
17
-0.100
-0.133 0.217 0.183
7 Number 8. 9. 10. 1 I. 12.
-0.117
of patients
Centrality Hospitals, then centrality !-as1 to west North to south Average distance travelled
13. 14. 15. 16. 17.
-0.067
Nearest place behavmur Closeness to Bangsar Attractweness all Attractiveness rehoused Attractwzmess non-reh
Significance
levels: * 10% ** 5%
1 .ooo
119
Geoforum/Volume 8/Number 3/1977
district, which probably reflects the fact that many of the rehoused squatters originally lived there and still orient their behaviour to some extent with respect to their former neighbourhood. The Y-axis for nonrehoused squatters is negatively correlated with number of patients, nearest place behaviour, and closeness to Bangsar, and positively correlated with the attractiveness scales. If the signs of the coefficients are reversed, the result is a Y-scale which is a composite of the two others, that is, a measure of large-scale local facility use, but specifically oriented towards Bangsar.
Finally, it would be presumptuous for us to offer firm conclusions explaining the position of healthcare centres on the attractiveness scales presented here on the basis of aggregate data. Rather, we offer the general procedure and the four points raised above as a framework for further detailed study of the utilization patterns of public facilities in the hope that planning in the public sector in western and nonwestern cities will be based upon a clear understanding of the utilization of facilities currently available. References
Conclusions
In order to strengthen the utility of the basic procedure discussed here, we would suggest that the following four areas deserve further work. First, the geometrical aspects of the particular configuration of origins and destinations makes certain comparisons more probable than others. What effect does this have on the attractiveness scale? In order to examine this, a series of simulation exercises could be conducted. By maintaining the flows between origins and destinations at constant levels and by altering the relative positions of the origins and destinations, the effects of modifications to the point configuration on the attractiveness scale could be examined. Second, specific infomration on the alternatives rejected needs to be incorporated into the comparison matrix. Such information could be derived from interviews. Third, details of the attributes of the individuals and the alternate centres are needed in order to examine group differences and to explain the attractiveness scales. Fourth, a time framework needs to be incorporated into the study to examine the ways in which utilization patterns change as the attributes of the centres are altered, the transport network is modified, individuals gain experience of using the centres, and information about alternatives diffuses through the city.
Access to public services (1971) Special edition of the journal Antipode. AIKEN S. R. and LEIGH C. H. (1975) Malaysia’s emerging conurbation, AAAG 65,546-563. BROOKS S. (1974) Spatial preferences in a medical care context, M.A. thesis McGill University, Department of Geography.
GOLLEDGE R. and RUSHTON G. (1972)Multidimensional
Scaling: Review and Geographical Applications (Washington, DC.: Association of American Geographers, TP-IO). GUTTMAN L. (1968) A general nonmetric technique for finding the smallest coordinate space for a configuration of points, Psychometrika 33,469-506. MAHADUR P. D. and RAO K. R. (1974) A model for location of service facilities in a non-Western urban environment YEATES M. (ed.) Proceedings of the I.G.U. Conference on Quantitative Geography Montreal: McGill(Queens Press) 164 -182. MASSAM B. H. and BOUCIIARD D. (1976) A comparison of observed and hypothetical choice behaviour, Environ. Plan. A 8,367-373. MASSAM B. H., BROOKS S. and BOUCHARD D. (1974) The analysis of movement patterns, Area 6, 174-179. MEADE M. S. and WEGELIN E. A. (1975) Some aspects of the health environments of squatters and rehoused squatters in Kuala Lumpur, Malaysia, J. Trap. Geog. 41,45-58. ROSKAM E. and LINGOES J. C. (1970) MINISSA-I: A FORTRAN IV(G) program for the smallest space analysis of square symmetric matrices, Behav. Sci. 15, 204-205. ROSS J. (1972) A measure of site attraction. Ph.D. thesis, Department of Geography, University of Western Ontario, London, Ontario.