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The effect of location on demand for urban recreation trips
Tmnspn Res. Vol. 16A. No. I. 99. 25-34. 1982 Printed in Great Britain.
0191-2Mn/82!01002~-1o1o3.oo10 0 1982 Pergamon Press Ltd
THE EFFECT OF LOCATION ON DEMAND FOR URBAN RECREATIONTRIPS? PETER R. STOPHER Schimpeler.
Corradino Associates, Coral Gables, FL 33134, U.S.A. and
G~KMEN ERG~IN University
of Petroleum and Minerals, Saudi Arabia (Received 9 June 1980)
Abstract-This paper is concerned with the development of models for forecasting and predicting the L,loices of urban residents for urban recreational and cultural activities and, hence, the travel for such activities. In earlier work, reported elsewhere, some preliminary choice models, using the multinomial logit model, were reported with one-way segmentations on each of geographic location, perceived attractiveness, and stage in the family life cycle. Geographic segmentation was found to be statistically the most significant segmentation scheme which is an undesirable result. It was postulated subsequently, that geographic segmentation was a proxy for social and economic differences in the populations of the geographic units used. This paper reports on the results of two-way segmentations using location as one dimension and various sociodemographic variables as the second dimension. In all cases, it was still found that the effects of location were significant, although seemingly less so than in the one-way segmentation schemes. Lack of a sufficiently large sample prevented the investigation of more complex segmentation schemes than the two-way schemesreported here. It must he concluded that there is no evidence lo reject the postulate that geographic location affects recreation-travel behavior. BACKGROUND
groups of the population. Each of these methods holds distinct advantages and disadvantages. If the characteristics of the individual are, indeed, assumed to result in taste variations, then the third method-segmentation-seems to be the soundest conceptually. Segmentation of the population clearly permits each group of individuals to exhibit different weights for each attribute describing a choice alternative and may even allow choice alternatives to be described differently for different segments (Watson and Stopher, 1974; Louviere et a/., 1976; Hensher, 1976; inter ah). However, segmentation is also very data-intensive, requiring sufficient sample sizes to permit appropriate segmentation of the population, with the size of the smallest group defined still being large enough for reliable statistical modeling. Furthermore, the need for multiple models results in added complexity and cumbersomeness for application to policy analysis. Because of the nature of the models used, the addition of characteristics of the individual to the linear-additive utility seems less than realistic. It is generally possible to associate a given characteristic of an individual with the utility of only one alternative. As a result of this restriction and the linear-additive nature of the inclusion of such variables, a model specified in this way does not reflect taste variations, in the sense of varying weights for (importances of or preferences for) different attributes of the choice alternatives. However, the procedure is quite data-efficient and only one model is generated. Similarly, while the multiplication of an attribute by an individual characteristic also still results in only one model being produced, it is again a method that is seriously restricted. In this case, the restriction is that any given characteristic of an individual can be used with
This
paper is concerned with the development of models for forecasting and predicting the choices of urban residents for urban recreational and cultural activities. Two reasons have been cited (Stopher and Ergfin, 1979) for a concern with this topic: the previous concentration of research and development of models for nonurban recreation and vacation activities and the concomitant lack of work on urban activities, despite major governmental concerns with urban activities; and the possibility that fuel shortages and fuel-price increases will lead to a substitution of urban activities for nonurban ones. The modeling approach undertaken was to apply techniques of individual choice modeling from travel forecasting (Stopher and Meyburg, 1975; Spear 1976; Domencich and McFadden, 1975) to the choice of urban recreation activity. Thus, the basic modeling hypotheses are that choices of individuals (or households) should be studied, and that these choices are based upon economic theories of utility maximization. The utility of alternative recreation activities is assumed to consist of attributes of the activities and to be modified by characteristics of the individual (taste variations). In order to reflect these taste variations, transportation researchers have used three different mechanisms: inclusion of individuals’ characteristics as linear additive terms in the linear utility function; inclusion of the characteristics as multipliers of attributes of the choice alternatives; and segmentation of the population by characteristics of the individual, leading to separate models for presumed homogeneous sub+This research was supported by the National Science Foundation. Division of Research Applied to Naticnal Needs, under grant APR 76-19086. 25
26
P.R.
STOPHER and G. ERGUN
only one attribute. Use in two or more generally results in the production of undesirable multi-collinearities among the independent variables and to inefficient (or even incorrect) estimates of coefficients. Both of these methods are also restricted by the need for cardinal measures for individual characteristics, whereas many such measures (e.g. education, life-cycle stage, income, etc.) are traditionally determined only on an ordinal scale and, in some cases, can only be measured on such a scale. This limits the set of available characteristics that can be used by these methods. On the basis of these arguments, this research used segmentation as the means to enter individual characteristics into the models of recreation-activity choice. The models developed are logit models of choice, as shown in eqn (1)
where pi” = probability of an individual from group (segment) k choosing recreation alternative j, ZJF, U/‘ = utilities of alternatives j and i respectively for members of segment k, and c = summation over the choice set of I
recreation alternatives. The models were fitted using a standard maximumlikelihood procedure (Berkman, et al., 1972). from which the models can be assessed statistically using likelihoodratio tests and t tests of the coefficients. In an earlier paper (Stopher and Ergiln, 1979). the authors reported on some initial one-way segmentations, using the variables of geographic location, perceived attractiveness, and life-cycle stage as segmentation variables. Each of these variables was found to produce significant differences for the segmented models, with the strongest, statistically, being the geographic segmentation. This result was considered to be somewhat undesirable, because a fundamental notion of individualchoice modeling is that it is possible to develop fullytransferable (i.e. geographically-transferable) models provided one can specify the environment of decision making to a sufficient degree. The models themselves seemed to exhibit a relatively high degree of specification, so that a higher degree of geographic transferability was hoped for. It was noted, however, that there were significant differences in the socioeconomic profile of the two geographic locations used. It was hypothesized, therefore, that the significant geographic variation noted in this early work was a function of the variation in socioeconomic characteristics that were not specified in the earlier models. To test this hypothesis, a series of twoway segmentations were undertaken, in which one segmentation variable was location and the other variable was education, recreation, attractiveness, or income. (The reasons for the choice of these three variables are described later in this paper.) By segmenting in this way, one can test whether geographic segmentation improves the models significantly beyond the segmentation of the
other variable. If it does not. then the hypothesis that the differences in geographic location are due to variations in socioeconomic characteristics cannot be rejected. It should be noted that a complete test of the hypothesis requires multiway segmentation. With the available sample, this was not possible. Even with a two-way segmentation, some cells were found to have too few observations for satisfactory models to be built. DATA PROCESSING
AND DEVELOPMENT
OF BASE MODELS
The data The data were collected in the late summer-early fall of 1977 in two suburbs of Chicago-Evanston and Des Plaines. The data-collection instrument was a self-administered mail-out, mail-back form that collected information on the perceptions of attributes of selected recreation activities; availability, attractiveness, and annual and seasonal participation in a list of activities; and socioeconomic characteristics of the respondents. Initially, the data consisted of 812 cases, split fairly evenly between the two suburbs. These 812 cases were used in the earlier work (Stopher and Ergtin, 1979). However, several problems were detected subsequently in the data and these were dealt with prior to the commencement of this research. First, while the survey had asked for details on urban-recreation trips, it was found that some reported trips were clearly long-distance trips. Thus, any trip over lOOmiles in length was eliminated. The location of the beginning of the trip was requested and, to limit the data to trips in the Chicago region, those trips reported to have started from anywhere other than home or work were eliminated. Finally, it was desired to work with a consistent-sized data set in the modeling to avoid the need to adjust likelihood-ratio statistics (Stopher and Ergiin, 1979), resulting from sample-size variation because of missing information on a segmentation variable. Therefore all cases were deleted that contained missing information on any of the variables to be used directly in the models or as segmentation variables. This resulted in a data set of 638 cases, for each of which participation in between two and ten recreation activities was reported and for which data were complete for this entire modeling activity. The ten activities used, the number of participations in each and the number of cases reporting participation in the activity are shown in Table I. Participation information was restricted only to summer participation, although the questionnaire requested participation for each season for the past year. Most activities are summer activities, and prior work suggested that some unreliability existed in recall of participation during earlier seasons than summer, where summer represented the immediate past season. The data of Table I indicate that, on average, respondents reported participating in almost 2.3 activities and that the average number of times an individual participated in recreation activities from the list was 33.2. In addition, Table I provides information that shows that, for each activity participated in an individual took part in it approx. 14.5 times during the summer season, or a little more than once a week. The activities undertaken with the greatest frequency are clearly bicycling (25 times
21
The effect of location on demand for urban recreation trips Table I. Participation Activity
statistics for the combined Evanston and Des Plaines data No. of cases reporting Participation
Bowling
145
725
Bicycling
276
6789
SWIminS
190
4116
Tennis
174
3468
calf
135
1913
40
459
Fishing Going to Hovies
180
818
Going to Theatre, opera, concerts
136
492
Watching Sports
08
Playing in Team sports
99
Averageper
case
average per person), swimming, tennis, golf, and playing in team sports. Likewise more people reported participating in bicycling than any other activity, but this was followed by swimming, going to movies, tennis, bowling, going to theatre, opera and concerts, and golf. Development
Total number of times activity engaged in
of the base models
To be able to determine the effects of segmentation it is necessary to be able to compare the sets of models developed for different segments of the population with a single, unsegmented model. This model represents the best fit that can be obtained on the total data set, within the limitations of available variables. Statistical tests of segmentation require all models to have the same specification, so one specification must be sought for the entire exercise. Three sets of variables were available and considered suitable for use in the models: situational variables, factor scores, and other variables. The situational variables comprise three measures reported by respondents about their recreation activities: distance from the trip origin to the activity site (DIST), duration of the activity in hours (DURN), and the fee paid for the activity, divided by the duration of the activity and the household income (FEINDR), i.e. the fee per hour adjusted for income. The factor scores were obtained from a factor analysis of the perceptual information about the recreation activities. (A complete description of the development of these perceptual dimensions is provided in Peterson et ol., 1978.) The selected factor analytic solution was one consisting of four factors, which were labelled as extroversion (the ability to interact with other people while engaged in the activity-EXTM), achievement (personal achievement derived from the activity-ACHV4). pastoralism (the ability to get close to nature and away from urban life in the activityWsing a frequency as the dependent variable results in the creation of as many cases as there are total days of participation in all activities. with the logit model predicting the probability that a given activity will be chosen on one occasion.
712 1668
33.20
2.29
PAST4), and escapism (the ability to get away from day-to-day demands and pressures in the activityESCP4). The other variables were two in number and consisted of a scaled response on the availability of the recreation activity (AVAIL) and an attractiveness rating for each recreation activity (ATTR). In interpreting the models, it should be noted that the factor scores and attractiveness were scaled so that higher values indicated a higher magnitude of each of the variables, while higher values on availability indicated a less-available activity. After extensive testing two model specifications were chosen, as shown in Table 2, with fitted coefficients, t statistics, and likelihood statistics. The models, designated models 6 and 7, were chosen as providing the best statistical performance. Model 6 was used because the variable of attractiveness was also used as a segmentation variable, at which time it cannot also be used in the utility function. All models fitted included a full set of alternative-specific dummies, although these are not shown in the tables in this paper. These models are not discussed in detail here, because they are used primarily as the basis for comparison and are not assumed necessarily to represent the absolute-best specification that could be achieved. It should just be noted that both models 6 and 7 are significantly better than the model with constants alone (the market-share model) and that all significant variables have the expected signs. Only distance and escapism are consistently not significant at a 95% confidence level. It should be noted that, because the dependent variable used in the modeling was a continuous frequency variable-days of participation-and not a 0, 1 type variable,t the maximum achievable log likelihood will not be equal to zero. In this case, it was - 12,499 indicating that the fitted models succeed in explaining nearly half the variation in the data, as shown by the adjusted likelihood-ratio index. The next step in the process was to examine the value of different segmentation variables. Because of the length
of
this was
the
list
of
potential
segmentation
variables,
done in two steps. In the first step, two-way
P. R. STOPHER and G. ERGL’N
28
Table 2. The base models Variable
Model
DIST
0.0003 (0.30)
6
Hodel
7
-1.013 (4.29)
-1.220 (5.11)
nLlP.N
-0.0248 (2.04)
-0.0389 (3.16)
ExTR4
0.1678 (7.88)
0.1559 (7.29)
ACHY4
0.2580 (12.64)
0.2256 (10.95)
PAST4
0.1156
0.1227
(5.29)
(5.65)
0.0172 (0.84)
AVAIL
-0.0123 (0.60)
-0.0755
-0.0591 (4.53)
(5.86)
0.2662 (15.60)
AlTR
Log
Only
0.0004 (0.44)
FEINDR
ESCPO
constants
-17,870
Likelihood Zero Likelihood at Convergence at
Log
Likelihood-Ratio Statistic to
Adjusted Index Percent
-15,580
Harket
-15,450
-15,710
Share 260
511
.426
.450
Likelihood-Ratio
Correctly
.402
Predicted 61.1
62.6
analysis of variance was applied to obtain a short list of potential segmentation variables. In the second step, choice models were built for the segments identified as being potentially useful by the ANOVA and its associated test statistics. To build models for all potential segmentation schemes would have been prohibitively expensive and time-consuming. The,initial elimination of potential variables was done by a two-way analysis of variance on choice vs the various socioeconomic and situational variables available for segmentation. The model used for the two-way analyses of variance is shown in eqn (2) Yij~=~+Tj+Alr+(7A)j~ttij~
(2)
where Yiik= total participation reported by the ith individual’ in the jth category for the kth recreation activity, /L = grand mean, Ti = segmentation-variable effect, At = recreation-activity effect, (rA), = interaction effect, and liik = random error The element of interest is the segmentation-variable effect, zi. Of course, this effect is only meaningful if the interaction term is found to be insignificant. For all ten activities, the interaction effect was, indeed, found to be insignificant. Therefore, the main effects of segmentation could be examined for each candidate variable. Different analyses of variance were undertaken also for different
60.5
groupings of each of the variables and the best grouping selected. The main effects for the best groupings are shown in Table 3 and the groupings for the selected segmentation variables are shown in Table 4. It can be seen that location is the most significant segmentation variable, followed by attractiveness, education, and income. This confirms the results of the earlier one-way segmentations with a larger data set, as reported in Stopher and Ergtin, 1979. Nevertheless, models were built for one-way segmentations on the reduced (638 cases) data set and are reported elsewhere (Peterson et al., 1978). The one-way segmentation model for location is shown in Table 5, however, for comparison with the two-way segmentations. The likelihood-ratio statistics between the segmented and unsegmented models are 291 and 333 for models 6 and 7 respectively, with 17 and I8 degrees of freedom, respectively. (Details of this test are given in Stopher and Ergiin, 1979.)These values are very much larger than the 99.95% table values of $, indicating that segmentation by location provides significantly-increased explanatory power. Comparing the coefficients across the segments, using t tests, shows that all coefficients except those for DURN and PAST 4 in Model 6 and DURN in Model 7 are significantly different beyond 99% (Peterson, et al., 1978). Hence, location segmentation appears to be extremely powerful and suggests that the choice proces-
The effect of location on demand for urban recreation trips Table 3. Summary of main effects from two-way ANOVA Meall square
Segmentation Variable
29
on senmentation variables F
Significance of F
As=
3765
2.5
.04
Sex
798
.5
.46
.5
.40
Marital
status
724
Education
5916
4.1
.003
Occupation
3421
2.3
.02
Income Attractiveness Life-cycle
stage
Lopat ion
5104
3.5
.004
10649
7.2
.OOl
3386
2.3
.02
24722
17.0
.OOl
Table 4. Groupings for selected segmentation variables segmentation Variable
segments
Locat ion
1. 2.
Des Planes Evanston
Income
1. 2. 3. 4.
< $12,500 $12,500 $22,500 $37,500 -
1. 2. 3.
Some high school College Graduate Graduate Degree
1. 2. 3. 4. 5.
< 25 25 5 Age < 35 35iAge<45 45 < Age ( 55 55 and over
1. 2.
Young (s 35 years) unnarried Young, married, with no children or oldest child under 5 Married, oldest child over 5 Older, married, no children at home Older, unmarried
Education
Life
Cycle
3. 4. 5.
Attractiveness 1. 2. 3. 4.
ses of people living in Evanston are significantly different from those of people living in Des Plaines, again confirming what was established with the larger data set (Stopher and Ergtin, 1979). TWO-WAYSEGMENTATIONS
The analysis-of-variance results on the segmentation variables showed attractiveness, education, and income to be the most significant segmentation variables after location and these were selected for the two-way segmentations with location. It was also found that age and life cycle, which each generated ten cells for two-way segmentation, presented problems of insufficient data for model-building. As is noted in this section, some of the
Attractiveness Social-Cultural LOW LOW High High
22,499 37,500 55,000
.
Score Outdoor Sports LOW High LOW High
models for the six- and eight-cell schemes resulting from the attractiveness, education, and income segmentations failed to converge because of too little data or poorlyconditioned data. Table 6 shows the results of the two-way segmentation of location and education for model 7. Results were very similar for model 6 and so are not shown here. Table 7 summarizes the likelihood-ratio tests for the segmented models. The x2 values indicated under the pooled models are those obtained from the one-way segmentations. Because of the failure of one of the Evanston models to converge, a complete comparison cannot be made. (With only ten observations in that cell, a reliable model could not have been built under any circumstances.)
30
P. R. STOPHER and G. ERGLY Table 5. Results of one-way segmentation by location Variable
Des Plaines Model 6 Model 7
Evanston Xodel 6 Model 7
-0.0056 (3.53)
-0.0059
(3.64)
0.0060 (4.51)
0.0066 (A.92)
FEINDR
-2.229 (4.90)
-2.50 (5.04)
-0.3040 (1.06)
-0.5683 (1.96)
Dl!RN
-0.0136 (0.80)
-0.0327 (1.91)
-0.0237 (1.25)
-0.0399 (2.09)
EXTR4
0.2487 (6.92)
0.2792 (7.67)
0.0792 (2.87)
0.0480 (1.72)
ACHV4
0.3934 (9.92)
0.3533 (8.63)
0.1379 (7.58)
0.1616 (6.48)
PAST4
0.3028 (8.16)
0.3128 (8.43)
-0.0049 (0.17)
-0.0037 (0.12)
ESCP4
-0.1141 (3.32)
-0.1435 (4.96)
0.0798 (3.02)
0.0702 (2.65)
AVAIL
-0.2278 (10.88)
-0.2059 (9.69)
0.0395 (2.20)
0.0539 (2.97)
DIST
ATTR
0.3485 (14.16)
No. of Observations Log Likelihood at convergence Likelihood-Ratio Statistic to Market Share
0.2225 (9.15)
696
696
767
767
-6414
-6311
-9018
-8975
2204
2411
2633
2749
Table 6. Models for two-way segmentation by location and education Variable
Up to
High School
DIST
-0.0566 (2.23)
Des Plaines Graduate College Degree Grad.
-0.0074 (4.30)
up to High School
Evanston College Grad.
Graduate Degree
0.0397 (5.07)
F A
0.0091 (4.26)
-0.0001
L E
0.0473 (0.13)
0.8095 (1.55)
(0.06)
FEINDR
-0.9630 (0.20)
-2.434 (4.80)
-14.14 (6.45)
DUP.N
-0.1360 (1.20)
-0.0279 (1.38)
-0.0173 (0.24)
0.1382 (4.85)
-0.1055 (3.62)
EXTR4
0.4566 (1.15)
0.3258 (7.67)
0.4816 (3.06)
-0.1393 (5.15)
0.1644 (4.22)
ACHV4
0.7919 (1.46)
0.3261 (6.65)
-0.2322 (1.50)
0.0014 (0.04)
0.2463 (6.24)
PAST4
1.931 (4.41)
0.1102 (2.49)
0.9834 (8.99)
-0.0178 (0.46)
-0.0638 (1.35)
ESCP4
-0.7715 (2.32)
-0.0961 (2.27)
-0.1927 (1.46)
0.0844 (2.30)
0.1052 (2.52)
AVAIL
-0.5499 (2.96)
-0.1691 (6.98)
-0.0033 (0.04)
0.1464 (6.00)
-0.0907 (3.01)
ATTR
1.112 (5.53)
0.4278 (13.50)
0.2430 (2.98)
0.1575 (5.40)
0.3567 (7.53)
543
108
419
338
-4626
-4014
No. of Observations 45 Log Likelihood at convergence -464.3
-4801
-874.9
10
The effect of location on demand for urban recreation trips
31
Table 7. x2 values for location and education segments up to High School
LOCATION
EDUCATION College Graduate
Graduate Degree
410.7
1864
EVZXlStOll
Failed to converge
1280
1754
3144
2231
2648
1965
Pooled Models
443.5
477.0
496 (18)
x2 (d.f.)
Pooled Models
2 &.)
Des Plaines
COlUmm sums
ROW Sums
2411
340.7 (36)
3034
2749
285 (36)
266 (18)
From the results of Table 7, it must be concluded that both geographic and education segmentations add to the explanatory power of the models, because the x2 values are highly significant in comparing two-way and one-way segmentations, as shown by the last column and last row of Table 7. Although the sample sizes shown in Table 6 clearly demonstrate the differences in education-level distributions between Evanston and Des Plaines, controlling for education does not remove the significance of location and there are still marked and significant differences between the models for any given education level between the two locations. The differences between models were also analyzed by means of t tests on the coefficients. Comparing education segments within Des Plaines, five of the nine coefficients were found to be significantly different between each Table 8. Models for two-way
2751.7
pair of models. Similarly, for the two education segments in Evanston, six of the coefficients were significantly different. This confirms the earlier findings of significance of education as a segmentation variable within each location. Comparing models from each location for a given education level, all coefficients are significantly different between Evanston and Des Plaines for college graduates and five of the nine are significantly different for those with graduate degrees. Table 8 shows the results of two-way segmentation with location and attractiveness for model 6. Model 7 could not be used for this segmentation, because it includes the segmentation variable of attractiveness. The measurement and grouping of attractiveness is described by Stopher and Ergiin, (1979). The x2 values are summarized in Table 9. Again, it is apparent from the x2
segmentation by location and attractiveness
Des Plaines Aft. 2 Att. 3
Aft. 4
Att. 1
EVBttStOn Att. 2 Att. 3
-0.0037 (0.89)
-0.0075 -0.0028 (0.83) (2.50)
-0.0082 (2.02)
0.0343 (1.53)
0.0070 -0.0005 (2.88) (0.12)
FEINDR
-0.9586 (1.01)
-4.074 -2.531 -2.354 (3.99) (2.43) (2.35)
-2.657 (1.79)
0.6408 (1.32)
0.7631 -0.7776 (1.35) (0.99)
DIJRR
-0.1267 (2.64)
-0.0067 -0.0793 (0.20) (2.11)
0.0481 (1.09)
0.3952 (2.70)
0.0700 (2.09)
0.0524 -0.2610 (1.18) (7.51)
0.4845 (6.27)
0.1692 (1.35)
-0.2217 (0.92)
0.3166 -0.0898 (5.10) (1.72)
0.2660 (5.29)
1.023 (3.25)
0.6140 -0.0140 (10.81) (0.29)
0.1251 (2.75)
Variable Att. 1*
DIST
Att. 4
0.0094
(3.71)
EX-ZR4
0.3187 (4.08)
-0.0360 (0.42)
ACHV4
0.2624 (2.56)
0.3466 (3.90)
0.4236 -0.0113 (5.34) (0.11)
PAST4
-0.0928 (0.98)
0.4647 (5.52)
0.5903 (6.89)
0.1854 (2.06)
-0.7376 (2.38)
0.1687 -0.1719 (2.64) (3.32)
0.2115 (3.91)
ESCP4
-0.1919 (2.58)
0.0701 -0.2358 (0.71) (3.69)
0.0238 (0.26)
-0.0769 (0.37)
0.0772 (1.42)
0.3049 (4.74)
0.0001 (0.00)
AVAIL
-0.1824 (3.53)
-0.2447 -0.1720 -0.2891 (5.67) (3.95) (5.19)
-0.5290 (3.42)
0.0444 (1.32)
0.0742 -0.1393 (1.96) (3.89)
so.
of Obs.
198
Log Likelihood at convergence -1393.
179
189
-1773.
-1715.
* See Table 4 for definitions
130
-1372.
71
225
-4977. -2790.
239
-2187.
-3221.
32
P.
R. STOPHER and
G. ERGCK
Table 9. ,$ values for location and attractiveness
segments
Aceracciveness 1
7
k
3
Row
Pooled Mode 1s
1.2
sums
2525.6
2204
321.6 (34)
13f)
Location
561.2
Plaines
Des
635.9
683.0
values that both segmentation variables are highly significant. However, the values for the segmentation by attractiveness within location are substantially higher than those for location within attractiveness, suggesting that the attractiveness segmentation does account for a major portion of the variability. The results of the t tests are shown in Table 10. Again, it is clear that attractiveness segmentation within each location produces a substantial number of significantlydifferent coefficients. Between 2 and 5 of the 8 coefficients are different within the Des Plaines models (segments 141, and between 4 and 7 are different for the Evanston models. However, between locations, the models for a given attractiveness segment are also different, with between 3 and 7 of the 8 coefficients being significantly different at or beyond the 95% confidence level. It must be concluded that attractiveness segmentation does not account for the geographic differences in choice processes, although attractiveness segmentation does seem to reduce the significance of geographic (locational) segmentation. The final two-way segmentation was by income and location, the models for which-using model 7 specification-are shown in Table 11 and the x2 values in Table
IO. Summary
of
I
tests on coefficients
645.4
Table 12. As in the previous two cases, all segmentations are highly significant, although insufficient data in the highest group for Des Plaines prevented a model from being constructed for that group. As far as it can be ascertained with the missing model, the income segmentation seems to reduce the significance of geographic segmentation, given the large values of x2 for income segmentation within location. The t tests on the coefficients are shown in summary form in Table 13. These tests confirm the value of the income segmentation within each geographic location, where, with the exception of the top two income groups in Evanston, there are from 3 to 7 significant differences for the 9 variables in the models. Similarly, comparing income groups across locations, there are also from 3 to 7 significant differences. Hence, it must also be concluded that, while income segmentation improves the explanatory power of the models beyond location alone, it does not account for the locational variability. CONCLUSIONS
The high significance of the original segmentation and the analysis of variance on location suggested some potential problems of transferability for the urban
of models
for location
and attractiveness
segments
Variable segment
DIST
FEINDR
DURN
cc
2
EXTR4
1 and 1 and 1 and 2 and 2 and 3 and
3 4 3 4 4
+t
+t
cc
5 5 5 6 6 7
and and and and and and
6 7 8 7 8 8
+t
*
+t
*
-I+
1 2 3 4
and and and and
5 6 7 8
I+
Significant
KIN4
I+
PAST4
*
+t
957.
*
or
beyond
*
+t
fc
ar
AVAIL
+t ++
+t
ESCPL
+t ++
4-t +I-
cc
-H
++
cc ++ cc ++
4-h
*
4-b
4-k
+t tc
++ cc
* ++
cc
33
The effect of location on demand for urban recreation trips Table 11. Models variable Inc. 1*
for two-waysegmentationby location and income
De0 PhilIe Inc. 2 Inc. 3
EVmm ton
Inc. 4
Inc. 1
Ins. 2
Ii-c.3
Inc. 4
0.0417 -0.0101 -0.0024 (4.32) (0.73) (2.47)
0.0017 -0.ooo7 0.0117 0.0133 (0.40) (0.21) (4.54) (4.09)
FEINDR
-3,974 -0.2253 -7.092 (5.77) (0.15) (6.17)
2.111 1.829 -4.577 -2.908 (3.62) (3.09) (5.64) (1.56)
DURN
-0.2256 (4.04)
0.0335 -0.1033 (1.32) (3.09)
EXTR4
0.5710 (6.04)
0.0713 0.2244 (1.25) (2.93)
ACHV4
0.4261 (4.00)
0.2422 0.3696 (4.31) (3.62)
0.0848 0.1439 0.1324 0.1285 (1.09) (2.96) (2.79) (1.73)
PAST4
-0.4612 (4.02)
0.3907 0.4204 (7.21) (4.65)
0.3686 -0.2944 0.0893 0.0610 (4.41) (5.76) (1.64) (0.66)
ESCP4
-0.0908 -0.2932 -0.1790 (0.80) (5.47) (2.66)
0.1779 0.4705 -0.0715 0.1399 (1.76) (8.07) (1.75) (1.49)
AVAIL
0.1334 -0.2269 -0.2198 (2.06) (7.22) (4.41)
-0.0648 0.1204 0.0663 0.0630 (1.06) (2.92) (2.25) (1.18)
ATTR
0.6464 (7.17)
DIST
NO.
of Obs.
0.4780 -0.0155 -0.0086 0.1461 (3.57) (0.31) (0.16) (2.01)
0.2792 0.4608 (7.76) (9.51)
118
372
Log Likelihood at convergence -1110.
*
-0.1579 -0.1518 -0.0896 0.0120 (2.63) (4.33) (2.80) (0.15)
T 0
190
-3063.
0.5863 0.0755 O-.2601 0.3533 (6.13) (1.72) (6.06) (3.99) 16
-1861.
124
-
-1214.
237
306
100
-2574. -3534. -1358.
See Table 4 for definition of income groups.
Table 12. y* values for location and income segments
1
Income 2 3
4
Pooled Models
x2 (df)
976.6 Failed 2622.3 t0 COIlVerge
2411
211.3 (54)
951.9
696.4
2749
590.9 (54)
ROW Sums
Location
Des Plaines
466.7
Evanston
753.7
937.9
Column Sums
1220.4
2116.9
1928
696.4
Pooled Models
1020
1793
1757
743.0
x2 (df)
200 (16)
1179
323.9 (18)
171 (18)
recreation-choice models reported in this paper and elsewhere (Peterson et al., 1978; Stopher and Ergiin, 1979). To test the extent to which real locational biases exist between the population groups examined, some two-way segmentations were designed to test whether the locational differences might be due, instead, to differences, in the distribution of sociodemographic variables. The results of the two-way segmentations suggest, however, that the variables used here cannot account alone for the locational differences observed in a one-way segmentation. In each case, segmentation by location adds significant information beyond that of segmentation on each of the sociodemographic variables. It is. of course, possible that the use of two or more
3339.9
-
sociodemographic variables might remove the need for locational segmentation and might lead to satisfactory geographic transferability of the models. For reasons of sample size, however, it is not possible to investigate this. Based on the available data, it must be concluded that there are significant variations in decision making for urban recreation activities between people living in different locations of an urban area. There are also significant differences in the urban recreation-choice behavior of people in different sociodemographic groups and joint segmentation on location and sociodemographic variables has been shown to provide substantial improvement in the statistical performance of the choice
34
P. R. STOPHER and G. ERG~JN Table 13. Summary of t tests on coefficients of models for location and income segments Variable Segments
1 and L and 2 and
2 3 3
5 5 5 6 6 7
and and and and and and
6 7 8 7 8 8
1 and 2 and 3 and
5 6 7
DIST
* i-b
++ +t i+
i-k *
FEINDR
+t +t *
DURN
+t
ACHVL
+t -4-b
PAST4
ESCP4
AVAIL
* +t
++ +I-
++
+-I++ 4+ ++
+t +I*
EXTR4
XTTR
x ++
++ +I*
+I* tc +t i-t +t +t
cc *
+t
cc
+Ice
models. Given the small data set used for the two-way segmentation, it is not possible to state what are optimal groupings of the data, nor to investigate multiway segmentations, beyond the two-way segmentations reported here. It is clear from the research reported here that further investigation is warranted on the effect of location on urban recreation-activity choices. However, such research will require both a larger data set than that gathered for this research and should, preferably, have a data set that is gathered via a stratified sampling design, whereby sufficient data points can be obtained within each cell of multiway stratifications of the data. The results reported here and in Peterson et al., (1978) provide a reasonable basis for selecting appropriate segments for such data collection.
REFERENCES
Berkman Jerry et al. (1972)QUAIL 3.0, Llser’s Manual, Dept. of Economics, University of California, California.
++ +t
++ c+
+t +t
++ *
++
t-t *
t+ *
Domencich T. A. and McFadden D. (1975) Urban Trace/ Demand. North Holland, Amsterdam. Hensher D. A. (1976) Use and application of market segmentation. In Behavioral Travel-Demand Mode/s (Edited by P. R. Stopher and A. H. Meyburg). D. C. Heath, Lexington, Mass. Louviere J. J., Ostresh L. M., Jr., Henley D. H.. Meyer R. J. (1976) Travel demand segmentation. some theoretical considerations related to behavioral modeling. In Behavioral Travel-Demand Models, (Edited by P. R. Stopher and A. H. Meyburg). D. C. Heath, Lexington, Mass. Peterson G. L. et al. (1978) Prediction of Urban Recreation Demand, Final Report to the National Science Foundation, Dept. of Civil Engineering, Northwestern University. Spear B. D. (1976)Application of New Travel Demand Forecasting Techniques to Transportation Planning, U.S. Department of Transportation, Federal Highway Administration, Washington, D.C. Stopher P. R. and Meyburg, A. H. (1975) Urban Transportation Modeling and Planning. DC. Heath, Lexington, Mass. Stopher P. R. and Ergiin G. (1979) Population segmentation in urban recreation choices. Transp. Res. Rec. 728, 59-65. Watson P. L. and Stopher P. R. (1974)The effect of income on the usage and valuation of transport modes. Transp. Res. Forum Proc. XV. 460-469.
0191-2Mn/82!01002~-1o1o3.oo10 0 1982 Pergamon Press Ltd
THE EFFECT OF LOCATION ON DEMAND FOR URBAN RECREATIONTRIPS? PETER R. STOPHER Schimpeler.
Corradino Associates, Coral Gables, FL 33134, U.S.A. and
G~KMEN ERG~IN University
of Petroleum and Minerals, Saudi Arabia (Received 9 June 1980)
Abstract-This paper is concerned with the development of models for forecasting and predicting the L,loices of urban residents for urban recreational and cultural activities and, hence, the travel for such activities. In earlier work, reported elsewhere, some preliminary choice models, using the multinomial logit model, were reported with one-way segmentations on each of geographic location, perceived attractiveness, and stage in the family life cycle. Geographic segmentation was found to be statistically the most significant segmentation scheme which is an undesirable result. It was postulated subsequently, that geographic segmentation was a proxy for social and economic differences in the populations of the geographic units used. This paper reports on the results of two-way segmentations using location as one dimension and various sociodemographic variables as the second dimension. In all cases, it was still found that the effects of location were significant, although seemingly less so than in the one-way segmentation schemes. Lack of a sufficiently large sample prevented the investigation of more complex segmentation schemes than the two-way schemesreported here. It must he concluded that there is no evidence lo reject the postulate that geographic location affects recreation-travel behavior. BACKGROUND
groups of the population. Each of these methods holds distinct advantages and disadvantages. If the characteristics of the individual are, indeed, assumed to result in taste variations, then the third method-segmentation-seems to be the soundest conceptually. Segmentation of the population clearly permits each group of individuals to exhibit different weights for each attribute describing a choice alternative and may even allow choice alternatives to be described differently for different segments (Watson and Stopher, 1974; Louviere et a/., 1976; Hensher, 1976; inter ah). However, segmentation is also very data-intensive, requiring sufficient sample sizes to permit appropriate segmentation of the population, with the size of the smallest group defined still being large enough for reliable statistical modeling. Furthermore, the need for multiple models results in added complexity and cumbersomeness for application to policy analysis. Because of the nature of the models used, the addition of characteristics of the individual to the linear-additive utility seems less than realistic. It is generally possible to associate a given characteristic of an individual with the utility of only one alternative. As a result of this restriction and the linear-additive nature of the inclusion of such variables, a model specified in this way does not reflect taste variations, in the sense of varying weights for (importances of or preferences for) different attributes of the choice alternatives. However, the procedure is quite data-efficient and only one model is generated. Similarly, while the multiplication of an attribute by an individual characteristic also still results in only one model being produced, it is again a method that is seriously restricted. In this case, the restriction is that any given characteristic of an individual can be used with
This
paper is concerned with the development of models for forecasting and predicting the choices of urban residents for urban recreational and cultural activities. Two reasons have been cited (Stopher and Ergfin, 1979) for a concern with this topic: the previous concentration of research and development of models for nonurban recreation and vacation activities and the concomitant lack of work on urban activities, despite major governmental concerns with urban activities; and the possibility that fuel shortages and fuel-price increases will lead to a substitution of urban activities for nonurban ones. The modeling approach undertaken was to apply techniques of individual choice modeling from travel forecasting (Stopher and Meyburg, 1975; Spear 1976; Domencich and McFadden, 1975) to the choice of urban recreation activity. Thus, the basic modeling hypotheses are that choices of individuals (or households) should be studied, and that these choices are based upon economic theories of utility maximization. The utility of alternative recreation activities is assumed to consist of attributes of the activities and to be modified by characteristics of the individual (taste variations). In order to reflect these taste variations, transportation researchers have used three different mechanisms: inclusion of individuals’ characteristics as linear additive terms in the linear utility function; inclusion of the characteristics as multipliers of attributes of the choice alternatives; and segmentation of the population by characteristics of the individual, leading to separate models for presumed homogeneous sub+This research was supported by the National Science Foundation. Division of Research Applied to Naticnal Needs, under grant APR 76-19086. 25
26
P.R.
STOPHER and G. ERGUN
only one attribute. Use in two or more generally results in the production of undesirable multi-collinearities among the independent variables and to inefficient (or even incorrect) estimates of coefficients. Both of these methods are also restricted by the need for cardinal measures for individual characteristics, whereas many such measures (e.g. education, life-cycle stage, income, etc.) are traditionally determined only on an ordinal scale and, in some cases, can only be measured on such a scale. This limits the set of available characteristics that can be used by these methods. On the basis of these arguments, this research used segmentation as the means to enter individual characteristics into the models of recreation-activity choice. The models developed are logit models of choice, as shown in eqn (1)
where pi” = probability of an individual from group (segment) k choosing recreation alternative j, ZJF, U/‘ = utilities of alternatives j and i respectively for members of segment k, and c = summation over the choice set of I
recreation alternatives. The models were fitted using a standard maximumlikelihood procedure (Berkman, et al., 1972). from which the models can be assessed statistically using likelihoodratio tests and t tests of the coefficients. In an earlier paper (Stopher and Ergiln, 1979). the authors reported on some initial one-way segmentations, using the variables of geographic location, perceived attractiveness, and life-cycle stage as segmentation variables. Each of these variables was found to produce significant differences for the segmented models, with the strongest, statistically, being the geographic segmentation. This result was considered to be somewhat undesirable, because a fundamental notion of individualchoice modeling is that it is possible to develop fullytransferable (i.e. geographically-transferable) models provided one can specify the environment of decision making to a sufficient degree. The models themselves seemed to exhibit a relatively high degree of specification, so that a higher degree of geographic transferability was hoped for. It was noted, however, that there were significant differences in the socioeconomic profile of the two geographic locations used. It was hypothesized, therefore, that the significant geographic variation noted in this early work was a function of the variation in socioeconomic characteristics that were not specified in the earlier models. To test this hypothesis, a series of twoway segmentations were undertaken, in which one segmentation variable was location and the other variable was education, recreation, attractiveness, or income. (The reasons for the choice of these three variables are described later in this paper.) By segmenting in this way, one can test whether geographic segmentation improves the models significantly beyond the segmentation of the
other variable. If it does not. then the hypothesis that the differences in geographic location are due to variations in socioeconomic characteristics cannot be rejected. It should be noted that a complete test of the hypothesis requires multiway segmentation. With the available sample, this was not possible. Even with a two-way segmentation, some cells were found to have too few observations for satisfactory models to be built. DATA PROCESSING
AND DEVELOPMENT
OF BASE MODELS
The data The data were collected in the late summer-early fall of 1977 in two suburbs of Chicago-Evanston and Des Plaines. The data-collection instrument was a self-administered mail-out, mail-back form that collected information on the perceptions of attributes of selected recreation activities; availability, attractiveness, and annual and seasonal participation in a list of activities; and socioeconomic characteristics of the respondents. Initially, the data consisted of 812 cases, split fairly evenly between the two suburbs. These 812 cases were used in the earlier work (Stopher and Ergtin, 1979). However, several problems were detected subsequently in the data and these were dealt with prior to the commencement of this research. First, while the survey had asked for details on urban-recreation trips, it was found that some reported trips were clearly long-distance trips. Thus, any trip over lOOmiles in length was eliminated. The location of the beginning of the trip was requested and, to limit the data to trips in the Chicago region, those trips reported to have started from anywhere other than home or work were eliminated. Finally, it was desired to work with a consistent-sized data set in the modeling to avoid the need to adjust likelihood-ratio statistics (Stopher and Ergiin, 1979), resulting from sample-size variation because of missing information on a segmentation variable. Therefore all cases were deleted that contained missing information on any of the variables to be used directly in the models or as segmentation variables. This resulted in a data set of 638 cases, for each of which participation in between two and ten recreation activities was reported and for which data were complete for this entire modeling activity. The ten activities used, the number of participations in each and the number of cases reporting participation in the activity are shown in Table I. Participation information was restricted only to summer participation, although the questionnaire requested participation for each season for the past year. Most activities are summer activities, and prior work suggested that some unreliability existed in recall of participation during earlier seasons than summer, where summer represented the immediate past season. The data of Table I indicate that, on average, respondents reported participating in almost 2.3 activities and that the average number of times an individual participated in recreation activities from the list was 33.2. In addition, Table I provides information that shows that, for each activity participated in an individual took part in it approx. 14.5 times during the summer season, or a little more than once a week. The activities undertaken with the greatest frequency are clearly bicycling (25 times
21
The effect of location on demand for urban recreation trips Table I. Participation Activity
statistics for the combined Evanston and Des Plaines data No. of cases reporting Participation
Bowling
145
725
Bicycling
276
6789
SWIminS
190
4116
Tennis
174
3468
calf
135
1913
40
459
Fishing Going to Hovies
180
818
Going to Theatre, opera, concerts
136
492
Watching Sports
08
Playing in Team sports
99
Averageper
case
average per person), swimming, tennis, golf, and playing in team sports. Likewise more people reported participating in bicycling than any other activity, but this was followed by swimming, going to movies, tennis, bowling, going to theatre, opera and concerts, and golf. Development
Total number of times activity engaged in
of the base models
To be able to determine the effects of segmentation it is necessary to be able to compare the sets of models developed for different segments of the population with a single, unsegmented model. This model represents the best fit that can be obtained on the total data set, within the limitations of available variables. Statistical tests of segmentation require all models to have the same specification, so one specification must be sought for the entire exercise. Three sets of variables were available and considered suitable for use in the models: situational variables, factor scores, and other variables. The situational variables comprise three measures reported by respondents about their recreation activities: distance from the trip origin to the activity site (DIST), duration of the activity in hours (DURN), and the fee paid for the activity, divided by the duration of the activity and the household income (FEINDR), i.e. the fee per hour adjusted for income. The factor scores were obtained from a factor analysis of the perceptual information about the recreation activities. (A complete description of the development of these perceptual dimensions is provided in Peterson et ol., 1978.) The selected factor analytic solution was one consisting of four factors, which were labelled as extroversion (the ability to interact with other people while engaged in the activity-EXTM), achievement (personal achievement derived from the activity-ACHV4). pastoralism (the ability to get close to nature and away from urban life in the activityWsing a frequency as the dependent variable results in the creation of as many cases as there are total days of participation in all activities. with the logit model predicting the probability that a given activity will be chosen on one occasion.
712 1668
33.20
2.29
PAST4), and escapism (the ability to get away from day-to-day demands and pressures in the activityESCP4). The other variables were two in number and consisted of a scaled response on the availability of the recreation activity (AVAIL) and an attractiveness rating for each recreation activity (ATTR). In interpreting the models, it should be noted that the factor scores and attractiveness were scaled so that higher values indicated a higher magnitude of each of the variables, while higher values on availability indicated a less-available activity. After extensive testing two model specifications were chosen, as shown in Table 2, with fitted coefficients, t statistics, and likelihood statistics. The models, designated models 6 and 7, were chosen as providing the best statistical performance. Model 6 was used because the variable of attractiveness was also used as a segmentation variable, at which time it cannot also be used in the utility function. All models fitted included a full set of alternative-specific dummies, although these are not shown in the tables in this paper. These models are not discussed in detail here, because they are used primarily as the basis for comparison and are not assumed necessarily to represent the absolute-best specification that could be achieved. It should just be noted that both models 6 and 7 are significantly better than the model with constants alone (the market-share model) and that all significant variables have the expected signs. Only distance and escapism are consistently not significant at a 95% confidence level. It should be noted that, because the dependent variable used in the modeling was a continuous frequency variable-days of participation-and not a 0, 1 type variable,t the maximum achievable log likelihood will not be equal to zero. In this case, it was - 12,499 indicating that the fitted models succeed in explaining nearly half the variation in the data, as shown by the adjusted likelihood-ratio index. The next step in the process was to examine the value of different segmentation variables. Because of the length
of
this was
the
list
of
potential
segmentation
variables,
done in two steps. In the first step, two-way
P. R. STOPHER and G. ERGL’N
28
Table 2. The base models Variable
Model
DIST
0.0003 (0.30)
6
Hodel
7
-1.013 (4.29)
-1.220 (5.11)
nLlP.N
-0.0248 (2.04)
-0.0389 (3.16)
ExTR4
0.1678 (7.88)
0.1559 (7.29)
ACHY4
0.2580 (12.64)
0.2256 (10.95)
PAST4
0.1156
0.1227
(5.29)
(5.65)
0.0172 (0.84)
AVAIL
-0.0123 (0.60)
-0.0755
-0.0591 (4.53)
(5.86)
0.2662 (15.60)
AlTR
Log
Only
0.0004 (0.44)
FEINDR
ESCPO
constants
-17,870
Likelihood Zero Likelihood at Convergence at
Log
Likelihood-Ratio Statistic to
Adjusted Index Percent
-15,580
Harket
-15,450
-15,710
Share 260
511
.426
.450
Likelihood-Ratio
Correctly
.402
Predicted 61.1
62.6
analysis of variance was applied to obtain a short list of potential segmentation variables. In the second step, choice models were built for the segments identified as being potentially useful by the ANOVA and its associated test statistics. To build models for all potential segmentation schemes would have been prohibitively expensive and time-consuming. The,initial elimination of potential variables was done by a two-way analysis of variance on choice vs the various socioeconomic and situational variables available for segmentation. The model used for the two-way analyses of variance is shown in eqn (2) Yij~=~+Tj+Alr+(7A)j~ttij~
(2)
where Yiik= total participation reported by the ith individual’ in the jth category for the kth recreation activity, /L = grand mean, Ti = segmentation-variable effect, At = recreation-activity effect, (rA), = interaction effect, and liik = random error The element of interest is the segmentation-variable effect, zi. Of course, this effect is only meaningful if the interaction term is found to be insignificant. For all ten activities, the interaction effect was, indeed, found to be insignificant. Therefore, the main effects of segmentation could be examined for each candidate variable. Different analyses of variance were undertaken also for different
60.5
groupings of each of the variables and the best grouping selected. The main effects for the best groupings are shown in Table 3 and the groupings for the selected segmentation variables are shown in Table 4. It can be seen that location is the most significant segmentation variable, followed by attractiveness, education, and income. This confirms the results of the earlier one-way segmentations with a larger data set, as reported in Stopher and Ergtin, 1979. Nevertheless, models were built for one-way segmentations on the reduced (638 cases) data set and are reported elsewhere (Peterson et al., 1978). The one-way segmentation model for location is shown in Table 5, however, for comparison with the two-way segmentations. The likelihood-ratio statistics between the segmented and unsegmented models are 291 and 333 for models 6 and 7 respectively, with 17 and I8 degrees of freedom, respectively. (Details of this test are given in Stopher and Ergiin, 1979.)These values are very much larger than the 99.95% table values of $, indicating that segmentation by location provides significantly-increased explanatory power. Comparing the coefficients across the segments, using t tests, shows that all coefficients except those for DURN and PAST 4 in Model 6 and DURN in Model 7 are significantly different beyond 99% (Peterson, et al., 1978). Hence, location segmentation appears to be extremely powerful and suggests that the choice proces-
The effect of location on demand for urban recreation trips Table 3. Summary of main effects from two-way ANOVA Meall square
Segmentation Variable
29
on senmentation variables F
Significance of F
As=
3765
2.5
.04
Sex
798
.5
.46
.5
.40
Marital
status
724
Education
5916
4.1
.003
Occupation
3421
2.3
.02
Income Attractiveness Life-cycle
stage
Lopat ion
5104
3.5
.004
10649
7.2
.OOl
3386
2.3
.02
24722
17.0
.OOl
Table 4. Groupings for selected segmentation variables segmentation Variable
segments
Locat ion
1. 2.
Des Planes Evanston
Income
1. 2. 3. 4.
< $12,500 $12,500 $22,500 $37,500 -
1. 2. 3.
Some high school College Graduate Graduate Degree
1. 2. 3. 4. 5.
< 25 25 5 Age < 35 35iAge<45 45 < Age ( 55 55 and over
1. 2.
Young (s 35 years) unnarried Young, married, with no children or oldest child under 5 Married, oldest child over 5 Older, married, no children at home Older, unmarried
Education
Life
Cycle
3. 4. 5.
Attractiveness 1. 2. 3. 4.
ses of people living in Evanston are significantly different from those of people living in Des Plaines, again confirming what was established with the larger data set (Stopher and Ergtin, 1979). TWO-WAYSEGMENTATIONS
The analysis-of-variance results on the segmentation variables showed attractiveness, education, and income to be the most significant segmentation variables after location and these were selected for the two-way segmentations with location. It was also found that age and life cycle, which each generated ten cells for two-way segmentation, presented problems of insufficient data for model-building. As is noted in this section, some of the
Attractiveness Social-Cultural LOW LOW High High
22,499 37,500 55,000
.
Score Outdoor Sports LOW High LOW High
models for the six- and eight-cell schemes resulting from the attractiveness, education, and income segmentations failed to converge because of too little data or poorlyconditioned data. Table 6 shows the results of the two-way segmentation of location and education for model 7. Results were very similar for model 6 and so are not shown here. Table 7 summarizes the likelihood-ratio tests for the segmented models. The x2 values indicated under the pooled models are those obtained from the one-way segmentations. Because of the failure of one of the Evanston models to converge, a complete comparison cannot be made. (With only ten observations in that cell, a reliable model could not have been built under any circumstances.)
30
P. R. STOPHER and G. ERGLY Table 5. Results of one-way segmentation by location Variable
Des Plaines Model 6 Model 7
Evanston Xodel 6 Model 7
-0.0056 (3.53)
-0.0059
(3.64)
0.0060 (4.51)
0.0066 (A.92)
FEINDR
-2.229 (4.90)
-2.50 (5.04)
-0.3040 (1.06)
-0.5683 (1.96)
Dl!RN
-0.0136 (0.80)
-0.0327 (1.91)
-0.0237 (1.25)
-0.0399 (2.09)
EXTR4
0.2487 (6.92)
0.2792 (7.67)
0.0792 (2.87)
0.0480 (1.72)
ACHV4
0.3934 (9.92)
0.3533 (8.63)
0.1379 (7.58)
0.1616 (6.48)
PAST4
0.3028 (8.16)
0.3128 (8.43)
-0.0049 (0.17)
-0.0037 (0.12)
ESCP4
-0.1141 (3.32)
-0.1435 (4.96)
0.0798 (3.02)
0.0702 (2.65)
AVAIL
-0.2278 (10.88)
-0.2059 (9.69)
0.0395 (2.20)
0.0539 (2.97)
DIST
ATTR
0.3485 (14.16)
No. of Observations Log Likelihood at convergence Likelihood-Ratio Statistic to Market Share
0.2225 (9.15)
696
696
767
767
-6414
-6311
-9018
-8975
2204
2411
2633
2749
Table 6. Models for two-way segmentation by location and education Variable
Up to
High School
DIST
-0.0566 (2.23)
Des Plaines Graduate College Degree Grad.
-0.0074 (4.30)
up to High School
Evanston College Grad.
Graduate Degree
0.0397 (5.07)
F A
0.0091 (4.26)
-0.0001
L E
0.0473 (0.13)
0.8095 (1.55)
(0.06)
FEINDR
-0.9630 (0.20)
-2.434 (4.80)
-14.14 (6.45)
DUP.N
-0.1360 (1.20)
-0.0279 (1.38)
-0.0173 (0.24)
0.1382 (4.85)
-0.1055 (3.62)
EXTR4
0.4566 (1.15)
0.3258 (7.67)
0.4816 (3.06)
-0.1393 (5.15)
0.1644 (4.22)
ACHV4
0.7919 (1.46)
0.3261 (6.65)
-0.2322 (1.50)
0.0014 (0.04)
0.2463 (6.24)
PAST4
1.931 (4.41)
0.1102 (2.49)
0.9834 (8.99)
-0.0178 (0.46)
-0.0638 (1.35)
ESCP4
-0.7715 (2.32)
-0.0961 (2.27)
-0.1927 (1.46)
0.0844 (2.30)
0.1052 (2.52)
AVAIL
-0.5499 (2.96)
-0.1691 (6.98)
-0.0033 (0.04)
0.1464 (6.00)
-0.0907 (3.01)
ATTR
1.112 (5.53)
0.4278 (13.50)
0.2430 (2.98)
0.1575 (5.40)
0.3567 (7.53)
543
108
419
338
-4626
-4014
No. of Observations 45 Log Likelihood at convergence -464.3
-4801
-874.9
10
The effect of location on demand for urban recreation trips
31
Table 7. x2 values for location and education segments up to High School
LOCATION
EDUCATION College Graduate
Graduate Degree
410.7
1864
EVZXlStOll
Failed to converge
1280
1754
3144
2231
2648
1965
Pooled Models
443.5
477.0
496 (18)
x2 (d.f.)
Pooled Models
2 &.)
Des Plaines
COlUmm sums
ROW Sums
2411
340.7 (36)
3034
2749
285 (36)
266 (18)
From the results of Table 7, it must be concluded that both geographic and education segmentations add to the explanatory power of the models, because the x2 values are highly significant in comparing two-way and one-way segmentations, as shown by the last column and last row of Table 7. Although the sample sizes shown in Table 6 clearly demonstrate the differences in education-level distributions between Evanston and Des Plaines, controlling for education does not remove the significance of location and there are still marked and significant differences between the models for any given education level between the two locations. The differences between models were also analyzed by means of t tests on the coefficients. Comparing education segments within Des Plaines, five of the nine coefficients were found to be significantly different between each Table 8. Models for two-way
2751.7
pair of models. Similarly, for the two education segments in Evanston, six of the coefficients were significantly different. This confirms the earlier findings of significance of education as a segmentation variable within each location. Comparing models from each location for a given education level, all coefficients are significantly different between Evanston and Des Plaines for college graduates and five of the nine are significantly different for those with graduate degrees. Table 8 shows the results of two-way segmentation with location and attractiveness for model 6. Model 7 could not be used for this segmentation, because it includes the segmentation variable of attractiveness. The measurement and grouping of attractiveness is described by Stopher and Ergiin, (1979). The x2 values are summarized in Table 9. Again, it is apparent from the x2
segmentation by location and attractiveness
Des Plaines Aft. 2 Att. 3
Aft. 4
Att. 1
EVBttStOn Att. 2 Att. 3
-0.0037 (0.89)
-0.0075 -0.0028 (0.83) (2.50)
-0.0082 (2.02)
0.0343 (1.53)
0.0070 -0.0005 (2.88) (0.12)
FEINDR
-0.9586 (1.01)
-4.074 -2.531 -2.354 (3.99) (2.43) (2.35)
-2.657 (1.79)
0.6408 (1.32)
0.7631 -0.7776 (1.35) (0.99)
DIJRR
-0.1267 (2.64)
-0.0067 -0.0793 (0.20) (2.11)
0.0481 (1.09)
0.3952 (2.70)
0.0700 (2.09)
0.0524 -0.2610 (1.18) (7.51)
0.4845 (6.27)
0.1692 (1.35)
-0.2217 (0.92)
0.3166 -0.0898 (5.10) (1.72)
0.2660 (5.29)
1.023 (3.25)
0.6140 -0.0140 (10.81) (0.29)
0.1251 (2.75)
Variable Att. 1*
DIST
Att. 4
0.0094
(3.71)
EX-ZR4
0.3187 (4.08)
-0.0360 (0.42)
ACHV4
0.2624 (2.56)
0.3466 (3.90)
0.4236 -0.0113 (5.34) (0.11)
PAST4
-0.0928 (0.98)
0.4647 (5.52)
0.5903 (6.89)
0.1854 (2.06)
-0.7376 (2.38)
0.1687 -0.1719 (2.64) (3.32)
0.2115 (3.91)
ESCP4
-0.1919 (2.58)
0.0701 -0.2358 (0.71) (3.69)
0.0238 (0.26)
-0.0769 (0.37)
0.0772 (1.42)
0.3049 (4.74)
0.0001 (0.00)
AVAIL
-0.1824 (3.53)
-0.2447 -0.1720 -0.2891 (5.67) (3.95) (5.19)
-0.5290 (3.42)
0.0444 (1.32)
0.0742 -0.1393 (1.96) (3.89)
so.
of Obs.
198
Log Likelihood at convergence -1393.
179
189
-1773.
-1715.
* See Table 4 for definitions
130
-1372.
71
225
-4977. -2790.
239
-2187.
-3221.
32
P.
R. STOPHER and
G. ERGCK
Table 9. ,$ values for location and attractiveness
segments
Aceracciveness 1
7
k
3
Row
Pooled Mode 1s
1.2
sums
2525.6
2204
321.6 (34)
13f)
Location
561.2
Plaines
Des
635.9
683.0
values that both segmentation variables are highly significant. However, the values for the segmentation by attractiveness within location are substantially higher than those for location within attractiveness, suggesting that the attractiveness segmentation does account for a major portion of the variability. The results of the t tests are shown in Table 10. Again, it is clear that attractiveness segmentation within each location produces a substantial number of significantlydifferent coefficients. Between 2 and 5 of the 8 coefficients are different within the Des Plaines models (segments 141, and between 4 and 7 are different for the Evanston models. However, between locations, the models for a given attractiveness segment are also different, with between 3 and 7 of the 8 coefficients being significantly different at or beyond the 95% confidence level. It must be concluded that attractiveness segmentation does not account for the geographic differences in choice processes, although attractiveness segmentation does seem to reduce the significance of geographic (locational) segmentation. The final two-way segmentation was by income and location, the models for which-using model 7 specification-are shown in Table 11 and the x2 values in Table
IO. Summary
of
I
tests on coefficients
645.4
Table 12. As in the previous two cases, all segmentations are highly significant, although insufficient data in the highest group for Des Plaines prevented a model from being constructed for that group. As far as it can be ascertained with the missing model, the income segmentation seems to reduce the significance of geographic segmentation, given the large values of x2 for income segmentation within location. The t tests on the coefficients are shown in summary form in Table 13. These tests confirm the value of the income segmentation within each geographic location, where, with the exception of the top two income groups in Evanston, there are from 3 to 7 significant differences for the 9 variables in the models. Similarly, comparing income groups across locations, there are also from 3 to 7 significant differences. Hence, it must also be concluded that, while income segmentation improves the explanatory power of the models beyond location alone, it does not account for the locational variability. CONCLUSIONS
The high significance of the original segmentation and the analysis of variance on location suggested some potential problems of transferability for the urban
of models
for location
and attractiveness
segments
Variable segment
DIST
FEINDR
DURN
cc
2
EXTR4
1 and 1 and 1 and 2 and 2 and 3 and
3 4 3 4 4
+t
+t
cc
5 5 5 6 6 7
and and and and and and
6 7 8 7 8 8
+t
*
+t
*
-I+
1 2 3 4
and and and and
5 6 7 8
I+
Significant
KIN4
I+
PAST4
*
+t
957.
*
or
beyond
*
+t
fc
ar
AVAIL
+t ++
+t
ESCPL
+t ++
4-t +I-
cc
-H
++
cc ++ cc ++
4-h
*
4-b
4-k
+t tc
++ cc
* ++
cc
33
The effect of location on demand for urban recreation trips Table 11. Models variable Inc. 1*
for two-waysegmentationby location and income
De0 PhilIe Inc. 2 Inc. 3
EVmm ton
Inc. 4
Inc. 1
Ins. 2
Ii-c.3
Inc. 4
0.0417 -0.0101 -0.0024 (4.32) (0.73) (2.47)
0.0017 -0.ooo7 0.0117 0.0133 (0.40) (0.21) (4.54) (4.09)
FEINDR
-3,974 -0.2253 -7.092 (5.77) (0.15) (6.17)
2.111 1.829 -4.577 -2.908 (3.62) (3.09) (5.64) (1.56)
DURN
-0.2256 (4.04)
0.0335 -0.1033 (1.32) (3.09)
EXTR4
0.5710 (6.04)
0.0713 0.2244 (1.25) (2.93)
ACHV4
0.4261 (4.00)
0.2422 0.3696 (4.31) (3.62)
0.0848 0.1439 0.1324 0.1285 (1.09) (2.96) (2.79) (1.73)
PAST4
-0.4612 (4.02)
0.3907 0.4204 (7.21) (4.65)
0.3686 -0.2944 0.0893 0.0610 (4.41) (5.76) (1.64) (0.66)
ESCP4
-0.0908 -0.2932 -0.1790 (0.80) (5.47) (2.66)
0.1779 0.4705 -0.0715 0.1399 (1.76) (8.07) (1.75) (1.49)
AVAIL
0.1334 -0.2269 -0.2198 (2.06) (7.22) (4.41)
-0.0648 0.1204 0.0663 0.0630 (1.06) (2.92) (2.25) (1.18)
ATTR
0.6464 (7.17)
DIST
NO.
of Obs.
0.4780 -0.0155 -0.0086 0.1461 (3.57) (0.31) (0.16) (2.01)
0.2792 0.4608 (7.76) (9.51)
118
372
Log Likelihood at convergence -1110.
*
-0.1579 -0.1518 -0.0896 0.0120 (2.63) (4.33) (2.80) (0.15)
T 0
190
-3063.
0.5863 0.0755 O-.2601 0.3533 (6.13) (1.72) (6.06) (3.99) 16
-1861.
124
-
-1214.
237
306
100
-2574. -3534. -1358.
See Table 4 for definition of income groups.
Table 12. y* values for location and income segments
1
Income 2 3
4
Pooled Models
x2 (df)
976.6 Failed 2622.3 t0 COIlVerge
2411
211.3 (54)
951.9
696.4
2749
590.9 (54)
ROW Sums
Location
Des Plaines
466.7
Evanston
753.7
937.9
Column Sums
1220.4
2116.9
1928
696.4
Pooled Models
1020
1793
1757
743.0
x2 (df)
200 (16)
1179
323.9 (18)
171 (18)
recreation-choice models reported in this paper and elsewhere (Peterson et al., 1978; Stopher and Ergiin, 1979). To test the extent to which real locational biases exist between the population groups examined, some two-way segmentations were designed to test whether the locational differences might be due, instead, to differences, in the distribution of sociodemographic variables. The results of the two-way segmentations suggest, however, that the variables used here cannot account alone for the locational differences observed in a one-way segmentation. In each case, segmentation by location adds significant information beyond that of segmentation on each of the sociodemographic variables. It is. of course, possible that the use of two or more
3339.9
-
sociodemographic variables might remove the need for locational segmentation and might lead to satisfactory geographic transferability of the models. For reasons of sample size, however, it is not possible to investigate this. Based on the available data, it must be concluded that there are significant variations in decision making for urban recreation activities between people living in different locations of an urban area. There are also significant differences in the urban recreation-choice behavior of people in different sociodemographic groups and joint segmentation on location and sociodemographic variables has been shown to provide substantial improvement in the statistical performance of the choice
34
P. R. STOPHER and G. ERG~JN Table 13. Summary of t tests on coefficients of models for location and income segments Variable Segments
1 and L and 2 and
2 3 3
5 5 5 6 6 7
and and and and and and
6 7 8 7 8 8
1 and 2 and 3 and
5 6 7
DIST
* i-b
++ +t i+
i-k *
FEINDR
+t +t *
DURN
+t
ACHVL
+t -4-b
PAST4
ESCP4
AVAIL
* +t
++ +I-
++
+-I++ 4+ ++
+t +I*
EXTR4
XTTR
x ++
++ +I*
+I* tc +t i-t +t +t
cc *
+t
cc
+Ice
models. Given the small data set used for the two-way segmentation, it is not possible to state what are optimal groupings of the data, nor to investigate multiway segmentations, beyond the two-way segmentations reported here. It is clear from the research reported here that further investigation is warranted on the effect of location on urban recreation-activity choices. However, such research will require both a larger data set than that gathered for this research and should, preferably, have a data set that is gathered via a stratified sampling design, whereby sufficient data points can be obtained within each cell of multiway stratifications of the data. The results reported here and in Peterson et al., (1978) provide a reasonable basis for selecting appropriate segments for such data collection.
REFERENCES
Berkman Jerry et al. (1972)QUAIL 3.0, Llser’s Manual, Dept. of Economics, University of California, California.
++ +t
++ c+
+t +t
++ *
++
t-t *
t+ *
Domencich T. A. and McFadden D. (1975) Urban Trace/ Demand. North Holland, Amsterdam. Hensher D. A. (1976) Use and application of market segmentation. In Behavioral Travel-Demand Mode/s (Edited by P. R. Stopher and A. H. Meyburg). D. C. Heath, Lexington, Mass. Louviere J. J., Ostresh L. M., Jr., Henley D. H.. Meyer R. J. (1976) Travel demand segmentation. some theoretical considerations related to behavioral modeling. In Behavioral Travel-Demand Models, (Edited by P. R. Stopher and A. H. Meyburg). D. C. Heath, Lexington, Mass. Peterson G. L. et al. (1978) Prediction of Urban Recreation Demand, Final Report to the National Science Foundation, Dept. of Civil Engineering, Northwestern University. Spear B. D. (1976)Application of New Travel Demand Forecasting Techniques to Transportation Planning, U.S. Department of Transportation, Federal Highway Administration, Washington, D.C. Stopher P. R. and Meyburg, A. H. (1975) Urban Transportation Modeling and Planning. DC. Heath, Lexington, Mass. Stopher P. R. and Ergiin G. (1979) Population segmentation in urban recreation choices. Transp. Res. Rec. 728, 59-65. Watson P. L. and Stopher P. R. (1974)The effect of income on the usage and valuation of transport modes. Transp. Res. Forum Proc. XV. 460-469.