The map representation is one of the most important methods used in geography. In many cases, maps are projected onto two-dimensional planes. Considering the problem of plotting the distance data for the given n locations, two coordinates are required for each location. Accordingly, the ploblem of mapping the potential structure of interpoint distance in which dimensions are required to be represented spatially, may be solved by that n two-dimensional position vectors are derived from n times n distance matrix.
In this study, the mapping of time distance between forty six cities which are the prefectural capitals in Japan is attempted under the assumption that one travels between the cities by the Japanese National Railways only. And, hypothesizing that the potential structure of time distances between the cities may be expressed in a two-dimensional time-space, Tobler's multilateration method for multidimensional scaling is applied. If the hypothesis mentioned above is accepted, the transportation system represented by the National Railways in Japan may be visualized on a two-dimensional map and the possibility of visual consideration in the researches on transportation system is suggested.
Investigation of the hypothesis is undertaken as follows:
1. The spatial configurations of cities are reconstructed onto a two-dimensional time-space map, and the profile of the map is interpreted visually.
2. For October 1961 and April 1980, the spatial configurations of cities are analyzed respectively. And according to the time-serial comparison between them, the problems how the transportation system in Japan has developed for these nineteen years are considered.
3. Concerning such problems, the usefulness of Tobler's method is examined. The resulting time-space maps are illustrated in Fig. 4. According to the evaluation of a goodness-of-fit, it may safely be asserted that the input data of time distances are sufficiently reconstructed on the two-dimensional Euclidean space, E². On the other hand, it can be regarded that Tobler's method is sufficiently useful on the condition that the input data of interpoints are metric and that the space in which the points are reconstructed is E2. Therefore, the possibility of visualization of the complicated data may be suggested.
According to the time-serial comparison of the time-space maps, the time distances have been decreasing all over the country. Especially the Shinkansen, the trunk line, with very highspeed, of the Japanese National Railways, has much affected to shorten the time distances. In terms of a goodness-of-fit for each city, the well-fitted cities are distributed along the main rail lines on the Pacific coast (i.e. the Tokaido and Sanyo Lines). Conversely, for the cities located in the other areas such as the coast of the Japan Sea, the inland areas and Shikoku, and also in the peripheral areas (i.e. Hokkaido, Tohoku and Kyushu), a goodness-of-fit is poor.

View full abstract

The purpose of this paper is to clarify in detail the influence of map pattern effect on distance parameters in spatial interaction models. This issue has previously been stated by Curry (1972). This paper is based on a study of migration in 1978, and journey-to-work in 1975, which crosses shi (city), machi (town) and mura (village) boundaries in Shiga Prefecture. Figure 1 shows the distribution of these municipal units and their boundaries in Shiga Prefecture. The study uses a doubly-constrained entropy type model, as shown in equation (3). Distance parameter a is calculated using equation (6), which is developed by Kadas and Klafszky (1976). Table 1 shows the results obtained.
A map pattern effect consists of the following three elements:
(1) inter-point distances (leading element)
(2) variation of the value of O or D (supplementary elemants)
(3) covariance of values of O and D at the same point
Nearest neighbor measure R, spatial autocorrelation statistic (standard deviate of Moran coefficient I) and correlation coefficient r provide a simple technique for measuring (1), (2) and (3) respectively.
Matrix of distances (cost of travelling) c_ij is obtained from both road distance and time from i to j, because it is expected that the physical barrier of Lake Biwa, located in the center of the study area, may distort inter-point distances measured by a straight line. Therefore, element (1) and (2) were investigated using a two dimensional configuration of multi dimensional scaling (Fig. 2). Stress is 6.4% with road distance data, and 9.1% with time distance data. Table 2 shows the map pattern effect in relation to the empirical data. According to this analysis, 50 points show a clustered pattern rather than a purely random pattern (R=1).
A simulation model was developed to examine more a comprehensive influence of map pattern effect and to overcome the restraints of intra-urban journey-to-work in previous studies. Three basic patterns were assumed, each pattern including 25 points (Fig. 3). Hypothetical urban area configurations, shown by dotted lines, were obtained from cluster analysis of the locations, which are indicated by X and Y coordinates. Therefore, spatial interaction in a regular pattern can be regarded as only inter-urban, that in a random pattern as both inter-urban and intra-urban, and that in a clustered pattern as only intra-urban.
The following assumptions are made:
(1) A positive autocorrelation effect operates at an intra-urban level.
(2) A positive or negative autocorrelation effect operates at an inter-urban level.
(3) For that interaction occurring only at an intra-urban level, sum of O is equal to that of D in an urban area. These assumptions reflect the differences between the two types of interaction. One is a phenomenon occurring only at an intra-urban level, for example, journey-to-work. The other is a phenomenon occurring at both the inter-urban and the intra-urban levels, as represented by migration. In assumption (2), positive autocorrelation is concerned with the latter type of interaction, while negative autocorrelation is concerned with the former.
A flow chart of the simulation model is shown in Fig. 4. One hundred distance parameter values per pattern were generated and a total of three hundred values were obtained. An analysis of variance (F=114.95^*) to test the significant difference in variation of distance parameters shows the strong influence of the map pattern effect. Absolute values of the distance parameters are lowest in the random pattern and highest in the clustered pattern. That metropolitanization is now under way in Shiga Prefecture means that a nearest neighbor measure approaching a clustered pattern should occur in the future. That value should consequently become larger eventually.

View full abstract

This study examines the spatial structure of nodal regions at a microscopic scale and analyses the relationship between the pattern of the daily movement of persons and the regional characteristics within Seoul from the stand-points of socio-economic dimensions and trip purposes. A general field theory of spatial behavior is extensively applied for this study.
The main results of this study can be summarized as follows:
1: Since the cartographic analysis, using the origin/destination data being constituted of the daily movement flows of persons by 123×123 districts, is impossible to reveal a distinct pattern of flows, the method of the largest flows is adapted to distinguish the major flow patterns and cluster analysis is used to delimit nodal regions. As the results, it is possible to identify that a closed pattern of flows is formed within one of the areas. The flows from outside of the city (zone number from 112 to 123 in Fig. 1) are generated or pulled within peripheral zone (Fig. 2).
Meanwhile, interregional similarities of the movements are defined by correlation analysis. (Fig. 3). It suggests a neighbourhood influence at work in a system, whereby the greatest similarities are to be found between adjacent zones. This correlation analysis of each column or destination vector in the OD matrix is highly related to each other, yielding a 111 X 111 matrix of coefficient. The similarities of the central parts of Seoul are more significant than the periphery zones in particular, and also in proportion to the degree of urbanization.
2. In order to delimit the nodal regions, cluster analysis of origin zones after transformating from the OD matrix to correlation matrix is applied. By examining the loss of detail or similarity, the writer delimited 7 regions in the first stage but 17 regions in the second stage ; the regions are available at 0. 75 of similarity index, and the sub-regions are 0. 80. Boundary of the nodal regions correspond with natural obstacles (mountain or river) and administrative divisions (Gu boundary). The nodal regions and sub-regions are as follows (Fig. 4);
Nodal region A……Central area (31 zones)
Nodal region B……Dobong-Seongbug area (26 zones): sub-regions B₁_??_B₅
Nodal region C……Yeongdeungpo area (15 zones): sub-regions C₁_??_C₂
Nodal region D……Gangnam area (13 zones): sub-regions D₁_??_D₂
Nodal region E……Gwanag area (6 zones): sub-regions E₁_??_E₂
Nodal region F……Seodaemun area (15 zones): sub-regions F₁_??_F₃
Nodal region G……Cheonho area (5 zones): sub-regions G₁_??_G₂
Hierarchy of centers were ranked by grading of centers for all nodes having the largest flows. Thirty-eight centers were required by the method. It is remarkable that the intervals between the centers maintain regular spaces each other, and in the same time the intervals are different in accordance with the hierarchy of centers. But the hierarchy of nodal centers Because in Seoul, southern areas in particular, is not yet differentiated thoroughly (Fig. 4). nodalitY is identified as a behavioral act of man, it is not simply a geometric point or a intersect of traffic network.
3. Subsequently, the relationship between the nodal regions and urban structural characteristics or different purpose trips are summarized as follows:
In order to analyse the regional characteristics of Seoul, 30 variables were recognized by factor analysis and 6 different types of trips were identified by Nelson's classification method. By comparing both, it was possible to estimate relationships between spatial behavior resulted from the character of places and spatial interactions resulted from the character of places in Seoul.

View full abstract

Recent temperature trends in the Southern Hemisphere are investigated by analyzing the hemispheric patterns of temperature differences between the three decades from the 1950's to the 1970's.
In January (summer), increasing temperature trends were found in Antarctica and on the east coast region of Australia during the period 1951_??_1978. On the other hand, decreasing temperature trends were shown in the northeastern part of South America and in a part of Africa. Hemispherically, temperature trends from the 1950's to the 1960's seemed to be compensated by the inverse trends from the 1960's to the 1970's.
As for July (winter), such compensatory trends were not apparent. From the 1960's to the 1970's, temperature increased remarkably in Antarctica, Australia and New Zealand.
Change in the atmospheric circulation, which was characterized by the 500mb height field, was examined from the 1960's to the 1970's. The polar vortex (cold air mass) was reduced and weakened in the 1970's. Meridional heat transfer increased around the mid-latitudes. These trends at the 500mb level were related with the increasing temperature trend in the higher latitudes and the decreasing temperature trends in the mid-latitudes.
In the Northern Hemisphere, the polar vortex had an expanding trend from the 1960's to the 1970's. This suggests that the compensatory trend also exists between the Arctic and the Antarctic.
More detailed studies will become possible with the accumulation of the climatological data in the Southern Hemisphere.

View full abstract