高層因子分析法による京阪神大都市圏の機能地域区分

This paper proposes an improved procedure for factor analysis applied to flow data in terms of solving relevant technical problems, and attempts to systematize it (see Fig. 5).<br>After confirming the proposed procedure's validity, the utility of higher-order factor analysis is examined, taking as a case study automobile traffic flows in the Keihanshin Metropolitan Area with a complicated connectivity structure. Compared with the orthogonal rotation which results in identifying uncorrelated functional regions, higher-order factor analysis has advantages of being able to extract correlated functional regions and to clarify the hierarchical structure of functional regions.<br>The proposed procedure of factor analysis applied to the Origin-Destination data matrix is as follows: i) as to the form of the O-D data matrix, an asymmetrical matrix including diagonal elements or intra-flows is preferable; ii) as to the input data matrix in extracting the initial factors, the result of factor analysis of a hypothetical O-D data matrix reveals that the cross-product standardized by the sum of squares (ΣX_j²=1.0) matrix may be more adequate than the correlation matrix; iii) as to the number-of-factors which is one of the most intractable problems on factor analysis, the number of factors showing the most interpretable factor structure are regarded as the number of common factors to be extracted, based on three criteria of 1) each area's communality value of greater than 0.1, 2) the change of percentage of the accumulated variance explained and 3) existence or not of bipolar factors; iv) in interpreting factors, under the assumption that the high factor loadings may specify groups of destinations receiving trips from common origins, and that the high factor scores specify these (groups of) origins, the internal structure of each functional region (the primate central area-type, the multiprimate central areas-type and the interdependent-type) is identified based on the ranking of the factor scores and their differences from the first ranking factor score.<br>Next, higher-order factor analysis with oblique rotation is applied to the 212×212 O-D data matrix of automobile traffic flows in the Keihanshin Metropolitan Area in 1979. As a result of the analysis, it turned out that the Keihanshin Metropolitan Area consisted of 30 first-order functional regions with the above-mentioned internal structures, and that these first-order functional regions were integrated into 13 relatively independent second-order functional regions.<br>First, as for the first-order functional regions (see Fig. 9), the northern part of Osaka City whose central area is Osaka-Kita Ward, is classified as the primate central area-type, the southern part of Osaka City is classified as the interdependent-type, Kyoto City is classified as the interdependent-type, and Kobe City whose central areas are Kobe-Ikuta and Kobe-Hyogo Wards is classified as the multi-primate central areas-type. They are identified as functional regions corresponding to the three metropolitan areas of Osaka, Kyoto and Kobe respectively.<br>In addition, ‘satellite cities’ of Himeji, Nara and Wakayama located in periphery of the Metropolitan Area are characterized as functional regions classified as the primate central area-type. One set of ‘satellite cities’ surrounding Osaka City such as Higashi-Osaka, Sakai and Tondabayashi, and another set of ‘satellite cities’ such as Toyonaka-Suita-Ibaragi, Hirakata-Neyagawa-Moriguchi-Kadoma and Amagasaki-Nishinomiya, show different internal structures. The former is the primate central area-type, and the latter is the multi-primate central areas-type.<br>Second, as for the second-order functional regions (see Fig. 10), it could be pointed out that they integrate a few neighboring first-order functional regions, and that some boundaries between them correspond to the boundaries of prefectures.