Analysis of shopping behavior characteristics in the Keihanshin metropolitan area in Japan based on a person trip survey

Figures & data

Figure 1. Target area. (This map is created based on content from the Keihanshin Metropolitan Transportation Planning Council 2019 homepage.)

Table 1. The person trip (PT) Survey summary

Survey data	The 4th (2000) Keihanshin metropolitan area PT Survey (Weekday) (392,673 people)
Target area	Osaka prefecture, a part of Shiga prefecture, Kyoto prefecture, Hyogo prefecture, Nara prefecture, and Wakayama prefecture
Movement purpose	Shopping behaviors
Target data	Route not including stop-by action (home → shopping place → home) (44,454 people)
Survey items	Age, sex, occupation, transportation means, departure time, travel time, travel distance, and duration of time at the destination

Figure 2. Aggregate results for age (n = 44,454)

Figure 3. Aggregate results for sex (n = 44,454)

Figure 4. Aggregate results for occupations (n = 44,454)

Figure 5. Aggregate results for transportation means (n = 61,201, multiple answers)

Figure 6. Departure time from home in six-hour categories (n = 44,454)

Figure 7. Travel time in five-minute intervals (n = 44,454)

Figure 8. Travel distance in 1 km intervals (n = 44,454)

Figure 9. Duration of time at the destination in 15 min intervals (n = 44,454)

Table 2. Results of the chi-square test and residual analysis involving occupation and transportation means (p < 0.001)

Identity	Walking	Bicycle	Motorcycle	Car	Bus	Railway
Employed	- – -	- -	+	+ + +
Student		-				+
Housewife		+ + +	-	- – -
Unemployed	+ + +			- – -

Two-sided significance level: + + +(- – -); 1% (quartile 2.58), + +(- -); 5% (1.96), +(-); 10% (1.65)

“+” or “-” means strong or weak tendency.

Table 3. Results of the correspondence analysis involving occupation and transportation means

Axis	Singular value	Eigenvalue	Contribution rate	Cumulative contribution rate
First axis	0.0366	0.0013	0.8306	0.8306
Second axis	0.0137	0.0002	0.1171	0.9477
Significance test	Chi-square value	Degree of freedom	P-value
First axis	80.7128	7	P < 0.001
Second axis	11.3773	5	0.0444

Figure 10. Visual diagram of the correspondence analysis involving occupation and transportation means

Table 4. Results of the chi-square test and residual analysis involving travel time and transportation means (p < 0.001)

Time	Walking	Bicycle	Motorcycle	Car	Bus	Railway
0–5 min	+ +	+ + +		- – -
5–10 min		+ + +
10–15 min				+ +	- – -	- – -
15–20 min		- – -		+ + +
20 min+		- – -			+ + +	+ + +

Two-sided significance level: + + +(- – -); 1% (quartile 2.58), + +(- -); 5% (1.96), +(-); 10% (1.65)

“+” or “-” means strong or weak tendency.

Table 5. Result of the correspondence analysis involving travel time and transportation means

Axis	Singular value	Eigenvalue	Contribution rate	Cumulative contribution rate
First axis	0.0480	0.0023	0.7565	0.7565
Second axis	0.0259	0.0007	0.2211	0.9775
Significance test	Chi-square value	Degree of freedom	P-value
First axis	138.8736	8	P < 0.001
Second axis	40.5501	6	P < 0.001

Figure 11. Visual diagram of the correspondence analysis involving travel time and transportation means

Table 6. Results of the chi-square test and residual analysis involving duration of time at the destination and transportation means (p < 0.001)

Time	Walking	Bicycle	Motorcycle	Car	Bus	Railway
0–15 min	+ +			- -	- -
15–30 min	+ + +			- – -
30–45 min		+ + +		- -	-	-
45–60 min				+ +		- -
60 min+	- – -	- – -		+ + +	+ + +	+ + +

Two-sided significance level: + + +(- – -); 1% (quartile 2.58), + +(- -); 5% (1.96), +(-); 10% (1.65)

“+” or “-” means strong or weak tendency.

Table 7. Results of the correspondence analysis involving duration of time at the destination and transportation means

Axis	Singular value	Eigenvalue	Contribution rate	Cumulative contribution rate
First axis	0.0426	0.0018	0.8681	0.8681
Second axis	0.0148	0.0002	0.1044	0.9725
Significance test	Chi-square value	Degree of freedom	P-value
First axis	109.4383	8	P < 0.001
Second axis	13.1481	6	0.0407

Figure 12. Visual diagram of the correspondence analysis involving duration of time at the destination and transportation means

Table 8. Cluster center results and number of cases for each cluster

Time	Early departure and long stay type	Late departure and home surrounding type	Early departure and home surrounding type	Early departure and long trip type
Departure time	–0.53	0.99	–0.76	–0.45
Travel time	0.86	–0.25	–0.14	3.59
Stay time	2.97	–0.26	–0.15	1.04
Number of cases for each cluster	2240	18,714	21,909	1591

Figure 13. Plot results of the cluster analysis

Table 9. Results of the multiple regression analysis using the logarithm of duration of time at the destination as the dependent variable and travel time log as the independent variable (R² = 0.915)

	B	beta	Significance probability	VIF
travel time (min) (log)	1.506	0.957	***	1.000

***: p < 0.001

Table 10. Results of the multiple regression analysis using the logarithm of duration of time at the destination as the dependent variable and several independent variables (R² = 0.955)

Time	B	beta	Significance probability	VIF
12–6 am (D)	1.403	0.009	***	1.008
6–12 pm (D)	1.475	0.267	***	6.247
12–18 pm (D)	1.339	0.251	***	6.285
Male (D)	0.086	0.010	***	1.579
10s (D)	0.134	0.007	**	4.742
20s–30s (D)	0.278	0.036	***	1.924
40s–50s (D)	0.245	0.040	***	2.486
Employed (D)	0.323	0.039	***	2.097
Student (D)	0.392	0.022	***	4.822
Housewife (D)	0.257	0.049	***	4.172
Walking (D)	0.276	0.051	***	3.150
Bicycle (D)	0.297	0.048	***	2.443
Motorcycle (D)	0.284	0.017	***	1.182
Car (D)	0.315	0.047	***	2.234
Bus (D)	0.090	0.005	***	1.132
Railway (D)	–0.008	–0.001		1.327
Travel time (min) (log)	0.672	0.427	***	8.692

***: p < 0.001, **: p < 0.01, (D): Dummy variable

Keihanshin Metropolitan Transportation Planning Council. 2019. Outline of Person-Lip Survey. Accessed February 7 2019. http://www.kkr.mlit.go.jp/plan/pt/research_pt/h12/p1.html

 Google Scholar

Data availability statement

The data that support the findings of this study are available from Center for Spatial Information Science (CSIS), www.csis.u-tokyo.ac.jp/english/. The University of Tokyo, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of CSIS.