Factors influencing the individual shopping behaviour: The case of Switzerland

Transcription

1 Factors influencing the individual shopping behaviour: The case of Switzerland Submission Date : Word Count : 6165 Authors: Dr. Anja Simma (corresponding author) ARE Swiss Federal Office for Spatial Development Kochergasse 10 CH3003 Berne, Switzerland Phone: +41 (0) Fax : +41 (0) anja.simma@are.admin.ch Pietro Cattaneo ARE Swiss Federal Office for Spatial Development Kochergasse 10 CH3003 Berne, Switzerland Phone: +41 (0) pietro.cattaneo@are.admin.ch Myriam Baumeler ARE Swiss Federal Office for Spatial Development Kochergasse 10 CH3003 Berne, Switzerland Phone: +41 (0) myriam.baumeler@are.admin.ch Prof. Dr. Kay W. Axhausen ETH Zürich Institut für Verkehrsplanung und Transportsysteme (IVT) CH8093 Zürich, Switzerland Phone: +41 (0) axhausen@ivt.baug.ethz.ch

2 Simma, A. 2 ABSTRACT The literature on the impact of the spatial structure on shopping behaviour reveals a divergence of opinion. Two opposing positions are very clearly identifiable: some believe shopping behaviour is not dependent on the spatial structure while others believe the opposite is true. These contrasting positions constituted the grounds for conducting a more detailed exploration of shopping behaviour as part of the interpretation of the 2000 Swiss survey of the population s travel behaviour. The underlying hypothesis is that, in addition to socioeconomic factors, the characteristics of the individual neighbourhood and the supply with shopping facilities have a relevant impact on shopping behaviour. Models were estimated using SEM modelling, whereby shopping behaviour was defined by five variables namely the share of shopping trips on all trips, the number of shopping trips by slow modes, by motorised vehicles respectively by public transport and by the daily shopping distance. The findings reveal that shopping behaviour is dependent on exogenous variables, especially the number of trips by motorised vehicles could be explained fairly well. The most important exogenous variables were the various socioeconomic variables and the supply with shopping facilities in the municipality and in the neighbourhood.

3 Simma, A. 3 INTRODUCTION A complex web of short and longterm decisions determines individual travel behaviour and consequently also the individual shopping behaviour. Key elements of this web include decisions regarding car availability and the ownership of public transport season ticket, the choice of destinations and activities as well as modes of transport. The choice of where to live, and what type of accommodation to live in, also plays a role. Various travel surveys identify the differences that exist among people in terms of all these variables as well as their typical patterns. The noticeable differences in people s shopping behaviour raise the question about the inherent causes of these differences (1)(2). It is assumed, that different socioeconomic situations and shopping habits play an undoubted role, whereby the shopping habits again are influenced by socioeconomic variables. Additionally, the time factor is certainly important. According to the typical weekly rhythm, which is common in our society, shopping trips are distributed differently over the week. Furthermore, the spatial environment in which people live and the supply with shopping facilities can be assumed as decisive factors as well. This paper provides a detailed exploration of the relative weight of these factors. Since the spatial anchoring of this analysis is essential, a specific area had to be selected. In the present case, Switzerland was chosen as the study area, for the following reasons. Availability of a suitable dataset: The Swiss survey of the population s travel behavior (3) is a dataset containing individual information on the socioeconomic status and travel behaviour of approximately 30,000 people. This dataset additionally includes information about the location of homes in the form of addressspecific geocodes that facilitate linkage with local spatial variables (4). Availability of spatial variables : Switzerland has a hectarebyhectare, geocoded spatial grid containing a raft of information (5). For instance, it includes the results of Swiss Labor Force Survey. The prime objective of this paper is to identify points of reference for planning measures targeting a more sustainable travel behaviour. Therefore, it is important to identify those spatial characteristics that can be expected to exert an influence on people s shopping behavior. Spatial characteristics influencing travel behavior as a whole were already analyzed in a former paper of the authors, other trip purposes will be analyzed in the near future. The first two chapters contain a literature overview and a brief introduction to the method applied. The following chapter looks at the selected variables on the exogenous and endogenous model side. Then the selection of observations is described. The model is restricted to shopping mobile individuals in the 18plus age group. Chapter 5 deals with the model hypotheses. Using all this information, the core topic of this paper can be presented: the development, estimation and interpretation of a model. Finally conclusions are drawn. STATE OF THE ART Transport and spatial planning has been based on the theoretical acceptance that spatial variables influence the individuals shopping behaviour. Although there is no dispute about the existence of a relationship at a theoretical level, there are conflicting empirical findings on the question of whether any causal relationship exists at all. The following two positions can be identified. Shopping behaviour is not dependent on the spatial structure: Some studies found no association between land use patterns and nonwork trip frequency and therefore support the theory of inelastic travel demand (6)(7). Shopping behaviour does depend on the spatial structure: Studies have found that land use patterns have a significant impact on the decisions related to homebased shopping trip frequency (8)(9). These two positions do not stem so much from any pronounced differences in the underlying assumption. They are more likely to be due to the choice and definition of the spatial variables involved as well as the spatial level of analysis. The following section describes several variables in more detail. Influence of spatial variables In studies, which identify a clear statistical dependence of travel behaviour on spatial characteristics, a high degree of importance is generally attributed to accessibility of facilities (10)(11)(12). For example, households residing at locations with greater accessibility tended to make more onestop shopping tours (8)(9). But these peo

4 Simma, A. 4 ple also tended to generate fewer onestop driving tours (13). Furthermore, the length of shopping trips turned out to be negatively correlated to accessibility (14). The most influential attributes in the context of daily shopping trips were identified as the supply of shops for the destination choice, the travel time of bus for the mode choice and the parking costs for the parking choice. The most influential attributes in the context of nondaily shopping trips were found to be the supply of shops, the travel time by bike and the parking duration (15). Many people seem to identify their neighbourhood on the basis of their shopping area. Therefore they appreciated a range of convenience goods and services in the proximity of their home, even more than bus stops, post offices, banks and places of work (16). But the findings of several studies show that the nearest centre wass not necessarily the one chosen (17)(18)(19) The shopping distance seems to be dependent from the size of convenience stores as well (20). Furthermore, transport mode and travel attributes turned out to be more important in shopper s choice of destinations than the stores attributes (21). As far as mode choice is concerned, land use mix show no significant impact. Whereas walking and public transport seem to be highly correlated with employment and population density (22). Influence of socioeconomic variables A recent study has shown that trip rates depend primarily upon socioeconomic variables and secondarily on land use variables (23). For example, households with more members are more likely to make more frequent trips and higher income households are less likely to make trips (24). THE METHOD USED: STRUCTURAL EQUATION MODELLING (SEM) SEM modelling is a method of simultaneous analysis of relationships between several variables (25)(26)(27). This means it is particularly well suited for examining the complex issues adressed in this paper. The method contains two excellent features. First, the modelling process does not use the individual observed values but the covariances or correlations between the variables; second, the method is so universal that many other methods constitute a special case in SEM modelling. An SEM model consists of three submodels: two measuring models and one structural model. The measuring models are based on the factor analytical approach, while the structural model follows the regression and path analysis approach (28). Only structural models are used here, since modelling attempts have shown that travel behaviour as understood in this context or spatial structure and socioeconomic status cannot be illustrated clearly using latent variables (hypothetical constructs). The structural model contains the relationships between the exogenous (independent) variables and the endogenous (dependent) variables explained within the model (see FIGURE 1). Several endogenous variables may be considered. The structural model is defined as follows: η = Βη + Γξ + ζ Legend: η (eta) ξ (ksi) ζ (zeta) B (beta) Γ (gamma) m*1 vector of the (latent) endogenous variables n*1 vector of the (latent) exogenous variables m*1 vector of the error variables m*m coefficient matrix of the postulated relationships between the endogenous variables m*n coefficient matrix of the relationships between the exogenous and endogenous variables The endogenous variables are consequently a function of the (m) endogenous variables (Β matrix) and (n) exogenous variables (Γ matrix). The user defines which elements of the three matrixes Β, Γ and Ψ (covariances between the endogenous error variables) are free, i.e. which relationships are estimated in the explanatory model. The free parameters are estimated simultaneously. A series of algorithms is available for the model estimation. In this paper we use the maximum likelihood method, since this method is quick in providing efficient estimators and is relatively robust in relation to breaches of the assumption of normal distribution. The results of an estimation are the direct as well as total effects between the variables. The latter comprise the direct and indirect relationships between the variables.

5 Simma, A. 5 SELECTION OF MODEL VARIABLES Target of this paper is a model which identifies factors influencing shopping behaviour. To reach this target, shopping behaviour must be specified in a way that it can be handled in the model. Additionally, variables describing the exogenous side of the model must be selected as efficient as possible. Efficient means that they should be able to explain a high portion of the variance of the endogenous variables. Shopping variables The literature review has shown that different variables could be of interest. For example, the number of shopping days within a longer time period, the shopping durations, the chaining of trips, the travelled distances, the choice of modes and destinations as well as the importance of shopping compared to other activities. But the choice of variables is limited by the available database. In the Swiss travel survey only the behaviour on one specific day is reported. Furthermore the calculation of shopping durations and of the chaining of trips is not possible, because the trip purpose return home was replaced by coding according to the trip purpose of the longest trip within the journey. Recoding was deemed to be too error prone. A problem also exists with the destinations. For the majority of the trips only information about the destination municipality is in the database, which is too rough for detailed analyses. For these reasons the variable groups mode choice, travel distance and the importance of shopping were selected. These variables were operationalised as follows. Importance of shopping: The number of shopping trips compared to all trips Mode choice: The number of shopping trips by slow modes, by public transport and by motorised vehicles Travel distance: The distance traveled for shopping purposes per day and per person Socioeconomic and situational variables The choice of socioeconomic variables is based on theoretical considerations and the available database. The variables are chosen in such a way that they reflect the characteristics, resources and obligations of an individual and their household in the best possible way. Gender, age (under 30 years), number of working hours per week, marital status (married), number of infants in the household, household income, car availability and the possession of a public transport season ticket as well as the living situation (living in a single/dualfamily dwelling, number of years in the municipality) were chosen. Additionally, one situational variable is used in the models, namely the variable Saturday, which indicates if the reported day was a Saturday or not. Hereby, it is assumed that the shopping behaviour is different on Saturdays compared to other days, because traditionally Saturday is the traditional shopping day after 5 workdays. This assumption was supported by descriptive analyses of the Swiss travel survey showing that many and comparatively long trips are typically made on Saturdays. Spatial variables First, variables are specified, which describe the supply with shopping facilities. As shopping trips encompasses trips for the daily provision (food, etc.), for consumption goods (clothes, shoes, etc) and for investment goods (jewellery, etc.), shop variables at different spatial levels are necessary. Second, variables which describe the individual neighbourhood are specified. For models addressing Switzerland a language variable should be added to account for the different language regions. Shopping supply Switzerland comprises around municipalities, which differ, apart from other characteristics, in terms of their shopping supply. On the one hand the supply within the municipality is different, on the other hand the accessibility of relevant facilities. The shopping supply within a municipality is described by the total sales area in the municipality; the accessibility structure by the distance to the nearest agglomeration, by the accessible sales area and by the accessibility of other key services (bank, post office, pharmacy, GP). The accessibility of shops is measured by an accessibility model which takes into account both the size of a unit as well as the distance between locations. There are myriad opportunities for operationalising accessibility (10).

6 Simma, A. 6 To depict the shopping situation of each individual household, gravitybased measures are used in this instance and operationalized in the following way: Acc i = j = 444 j= 1 A * e j α * cij Legend: Acc i accessibility of shopping area to the household i A j sales area of a store or shopping centre j c ij crowfly distance between household i and shop j α constant used to define resistance The calculation of the availability of shop area was based on the geocoded location of the respondent s household, as well as the location (based on the hectare grid) and size of each shop (30). The size of constant α was identified using regression analysis for the frequency distribution of the length of shopping trips travelled on foot, as given in the Swiss travel survey. The resulting value of 1 may seem high at first glance (31); however, it is plausible considering the limited context of the neighbourhood. For the other key services a combined overall variable is calculated that enables conclusions to be drawn about the general level of accessibility of the local facilities provided (sum of the different distances). The compilation of an overall figure helps to ensure that each individual facility is considered. Using GIS, the distances between home and the respective facilities were calculated for each individual in the survey in the form of straightline distances. Characteristics of the neighborhood The choice of variables and the determination of the buffer are of crucial importance for the description of the neighbourhood. Many authors (32)(33)(34) define a distance of up to 300 m or a walking time less than six minutes as the maximum value for an optimal foot distance. Therefore a radius of 300 m from the location of the home is taken as respective buffer. The variables were chosen in a way that they reflect the density (number of inhabitants) and the shopping supply in the neighbourhood. The shopping supply was differentiated in the number of supermarkets and the number of other food stores in the respective buffer. In addition, a variable describing the location within the municipality was expressed by the distance to the municipal centre. A variable describing the location in relation to the nearest public transport station/stop was also included. SELECTION OF OBSERVATIONS As the focus of this paper is the individual shopping behaviour, only persons with at least one shopping trip during their specific day were selected from the whole database. A second restriction concerns the age of persons, a third one their mode choice (only slow modes, public transport and motorised vehicles). Only persons older than 17, who could be allowed to drive a car, are in the respective database persons (about one third of all respondents) fulfilled these three criteria and form a sufficient database. The following models consequently consider only these respondents and say nothing about the other persons. As it is interesting to know who these persons are, a binary regression analysis was made to answer the question of who is shopping mobile or not. The variables selected for the SEMmodel were also used in this analysis. The results obtained after a simplification process had a relatively low explanatory power, but show interesting relationships. Decisive for being shopping mobile or not are socioeconomic variables. Persons working, being male and young have a low probability to make a shopping trip (see TABLE 1). MODEL HYPOTHESES Creating hypotheses for the shopping behavior is because of the conflicting assumptions concerning the relationships anything but trivial. But as SEM modelling is a structurechecking procedure, a base model is a prerequisite. Therefore a type of model is set out that is feasible in the view of the authors (see TABLE 2). The following thoughts were essential considerations:

7 Simma, A. 7 a) The importance of shopping is mainly influenced by socioeconomic variables. The spatial variables do not affect the importance of shopping trips. b) The importance of shopping has a positive influence on the number of trips independent of the mode of transport. c) There are substitutive relationships between the different modes. d) A good local accessibility promotes the usage of slow modes and reduces the usage of other modes. This also has a negative effect (reduction) on the travelled distances. Due to the uncertainties with regard to the relationships, an explorative approach is also taken during the course of the analysis. Initial model results are used to assess which relationships can be realeased and which cannot. That means which modifications to the relationships are recommended from a statistical point of view. Nevertheless, such changes are only made if they can be explained by further theoretical considerations. RESULTS The model that emerges after several modifications has a high degree of fit (see TABLE 3). Various fit criteria, such as the NFI and CFI, which compare the estimated model with a base model (excluding relationships), indicate nearly optimal values. If the fit indices of the postulated model are compared with those of the final model, it is apparent that the fit indices are nearly equal, but the degree of freedom varies. This means that several simplifications of the postulated model were possible without losing information. In addition to the postulated and final models, two other models were estimated. The third model only included spatial variables, the fourth only socioeconomic variables. Accordingly these models show the degree of explanatory power provided by the spatial variables in comparison with that of the socioeconomic variables. It is evident that the socioeconomic model is better than the spatial model, but the differences are slight. Not only the differences between these two models are slight, but also the difference to the other two models indicating that several relations are captured by different variables. The multiple correlation coefficients for the endogenous variables are relatively similar in all four models. They take values between 0.07 and 0.55 (see TABLE 5). The greatest degree of explanatory power was achieved in the case of the number of trips by motorized vehicles (55% of variance), the lowest for the daily shopping distance. This means the exogenous variables are at least able to explain the modal split comparatively well. The daily distance and the importance are not well explained by the model. In the final model, both the direct and total effects, which can be seen in TABLE 5 and in the path diagram (see FIGURE 2), are highly significant (at the level). Parameters in the Β and Γ matrixes were released. The model is recursive, this means that there are no feedback effects within the Β matrix. Some postulated exogenous variables were deleted during the modification process namely the number of infants, the accessible sales area, the distance to the public transport stop and the distance to municipal centre, because no paths started from them. INTERPRETATION OF RESULTS With regard to the Β matrix (see TABLE 4) the following can be stated. First, the importance of shopping trips has an influence on the other endogenous variables. This can be explained by the fact that each trip causes a trip by a specific mode. The effect on the trips by motorised vehicles is the greatest one. Second, substitutive relationships exist between the three modal variables indicating that each trip can only be performed by one mode. Third, the importance of shopping trips and the trip variables excepting the trips by slow modes have a positive effect on the daily shopping distance. The endogenous variables influence one another. Additionally, the exogenous variables have an impact on them. Importance of shopping trips: As postulated, this variable is mainly dependent on the socioeconomic variables. Persons who are married have a higher probability to make a shopping trip compared to all trips, persons who work, are young respectively have a high income and/or a car, have a lower probability. A variable which is also important in this case is the situational variable Saturday. This fact points to a weekly shopping rhythm with Saturday as the main shopping day. Shopping trips by slow modes: Socioeconomic (caravailability, work hours, years of residence, living in a single/dualfamily house) as well as spatial variables (German speaking, number of other food

8 Simma, A. 8 stores and supermarkets, the provision) are important: Especially the high importance of the number of shops within the 300mbuffer is very interesting. A high number of shops promote the usage of slow modes. Shopping trips by public transport: This variable is mainly influenced by the mobility tool situation, whereby the possession of a public transport season ticket has a positive effect, the availability of a car a negative one. Interesting to know is that only one spatial variable is directly connected with the trips by public transport, namely the total sales area in the municipality indicating the size of a municipality and consequently the quality of the public transport system. Furthermore, the indirect spatial effects are comparatively small. Shopping trips by motorized vehicles: Most of the socioeconomic variables have an impact on this variable, whereby the availability of a car is particularly important. An interesting finding is that being married as well as being young have a positive effect. But it can be assumed that the reasons for using a vehicle are different married persons often have to manage big food purchases, young people make rather special purchases which are easier to handle by a vehicle. Shopping distances: Like the shopping trips by public transport, this variable is relatively independent of the spatial variables. But the socioeconomic variables have also small importance. Only the other endogenous variables can be used to explain this endogenous variable. This fact is also one reason for the comparatively small multiple correlation coefficient. CONCLUSIONS The prime objective of this paper was to demonstrate empirical evidence of the hypothesis of a statistical relationship between the spatial environment and selected aspects of shopping behavior (importance of shopping trips, mode choice, travel distance) with a view to identifying points of reference for spatial planning activity. The model estimations on the basis of the 2000 Swiss travel behavior survey have shown that the supply with shopping facilities especially the total sales area and number of supermarkets in a 300 m buffer has an influence on mode choice. The total sales area in the municipality has a positive effect on the number of trips by public transport and a negative effect on the number of trips by motorized vehicles, the number of supermarkets and other food stores have a positive effect on the number of trips by slow modes. This means that a good supply with shopping facilities promote the usage of environmentally friendly modes. Therefore one means for promoting environmentally friendly modes could be a good provision with local stores. At the moment local administrations do not have specific policies to encourage the presence of shopping facilities, but given the general trend to centralization it might be worthwhile for them to subsidize some of the costs of these facilities to retain them not only for transport, but also for community building reasons. Although some spatial variables are of relevance, it must be stated that socioeconomic characteristics such as age, sex, weekly working hours and household income are more important for the individual shopping behavior than the spatial variables. In the models calculated, socioeconomics and the spatial environment can so far explain only part of shopping travel behavior in the statistical sense. While the fit of the final model is very good, there remain unexplained shares in variance of between 93% (trips) and 45% (daily distance) in the case of the endogenous variables. At the same time, this means that the main determinants of the shopping travel behavior examined here are still unknown or that the linear models used fail to provide an adequate mapping of any nonlinear relationships between the variables. The relationships may well be stronger for other shopping behavior characteristics that have not yet been studied. In evaluating the identified factors that influence travel behavior, it should be noted that typical constellations of socioeconomic and spatial conditions are in evidence due to the sociospatial differentiation of society. The overlapping influence of social and spatial factors on travel behavior is one reason for the identifiable differences in the shopping travel behavior of residents of specific spatial units (aggregate comparisons). From the empirical point of view, the model examined is a crosssectional analysis. The study compared individuals and their spatial environments at a specific point in time in the year It is therefore not possible to draw any conclusions about changes in the relationship between spatial characteristics, socioeconomic characteristics and shopping travel behavior over the course of time. The inclusion of analyses on the migration of households and on spatial changes would be important in this context. From a dynamic perspective, the spatial structure factor may well be accorded greater significance than from a pure crosssectional viewpoint.

9 Simma, A. 9 Acknowledgements: The authors express their gratitude to Raffael Hilber for providing and calculating the spatial data.

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12 Simma, A. 12 LIST OF TABLES AND FIGURES TABLE 1 Results of a binary regression for being shopping mobile or not TABLE 2 Postulated direct effects TABLE 3 GoodnessofFit of the four different models TABLE 4 Estimated standardized direct (italics) and total effects on the endogenous variables TABLE 5 Explained Variance of the endogenous variables FIGURE 1 Example of a structural model FIGURE 2 Path Diagram of the direct effects of the final model

13 Simma, A. 13 TABLES AND FIGURES FIGURE 1 Example of a structural model x1 e1 1 e2 1 y z x2 x3 x4 e3 1 w Legend: x1, x2, x3, x4 = exogenous variables y, z, w = endogenous variables e1, e2, e3 = endogenous error variables

14 Simma, A. 14 TABLE 1 Results of a binary regression for being shopping mobile or not B tvalue Sig. Step 1(a) number of infants germanspeaking Switzerland dist. to post, bank, GP, pharmacy male year age group household income married accessible sales area working hours per week constant a Variable(s) entered on step 1: anz_kk, deutsch, versorg, male, jugendlich, hh_einkommen, married, verkflae, f51000.

15 Simma, A. 15 TABLE 2 Postulated direct effects shopping behaviour shopping trips/all trips shoppingtrips by slow modes shoppingtrips by public transport shoppingtrips by vehicles daily shopping distance shoppingtrips/all trips shoppingtrips by slow modes shoppingtrips by public transport shoppingtrips by motorised vehicles socioeconomics shopping on a Saturday male 1830 year age group working hours per week household income married number of infants car availability possession of a public transport season ticket living in a single/dualfamily dwelling years of residence (no. of years in municipality) spatial characteristics Germanspeaking distance to nearest agglomeration accessible sales area total sales area inhabitants per ha (r = 300m) number of supermarkets (r = 300m) number of other food stores (r = 300m) dist. to post, bank, GP, pharmacy distance to municipal centre dist. to public transport stop

16 Simma, A. 16 TABLE 3 GoodnessofFit of the four different models goodnessoffit of the postulated model goodnessoffit of the final model goodnessoffit of the spatial model goodnessoffit of the socioeconomic model sample size number of exogenous variables chi 2 or min. discrepancy value degrees of freedom number of parameters RMSEA (root mean square error of approximation) normed fit index (NFI) comparative fit index (CFI) Source: Secondary analyses of Swiss travel survey, relates to shopping mobile persons aged 18 years and over.

17 Simma, A. 17 FIGURE 2 Path Diagram of the direct effects of the final model saturday male arb_h hheinko socioeconomics married age 1830 av_auto sh_suw abo w_lv wohn_d w_oev haus1_2 w_miv german spatial characteristics ein_tot di_agglo supermar solmlade versorg eink_d positive impact negative impact Legend: saturday shopping on a Saturday wohn_d years of residence male male haus1_2 living in a single/dualfamily dwelling arb_h working hours per week german germanspeaking Switzerland hheinko household income ein_tot accessible sales area married married di_agglo distance to nearest agglomeration age year age group supermar number of supermarkets (r = 300m) av_auto car availability solmlade number of other food stores (r = 300m) abo possession of a public transport season ticket versorg distance to post, bank, GP, pharmacy Source: Secondary analyses of Swiss travel survey, relates to shopping mobile persons aged 18 years and over.

18 Simma, A. 18 TABLE 4 Estimated standardized direct (italics) and total effects on the endogenous variables shopping behaviour shoppingtrips/all trips shoppingtrips by slow modes shoppingtrips by public transport shoppingtrips by motorised vehicles socioeconomics shopping on a Saturday male 1830 year age group working hours per week household income married car availability possession of a public transport season ticket living in a single/dualfamily dwelling years of residence (no. of years in municipality) spatial characteristics Germanspeaking distance to nearest agglomeration accessible sales area number of supermarkets (r = 300m) number of other food stores (r = 300m) dist. to post, bank, GP, pharmacy shopping trips/all trips shoppingtrips by slow modes shoppingtrips by public transport Source: Secondary analyses of Swiss travel survey, relates to shopping mobile persons aged 18 years and over. shoppingtrips by vehicles daily shopping distance

19 Simma, A. 19 TABLE 5 Explained Variance of the endogenous variables Explained variance in the postulated model Explained variance in the final model Explained variance in the. spatial model Explained variance in the. spatial model shoppingtrips/all trips shoppingtrips by slow modes shoppingtrips by public transport shoppingtrips by motorised vehicles Daily shopping distance Source: Secondary analyses of Swiss travel survey, relates to shopping mobile persons aged 18 years and over.