A distance cartogram is a diagram that visualizes the proximity indices between points in a network, such as time-distances between cities. The Euclidean distances between the points on the distance cartogram represent the given proximity indices. This is a useful visualization tool for the level of service of transport, e.g. difference in the level of service between regions or points in a network and its improvement in the course of time. The two previously proposed methods-multidimensional scaling (MDS) and network time-space mappinghave certain advantages and disadvantages. However, we observe that these methods are essentially the same, and the merits of both these methods can be combined to formulate a generalized solution. In this study, we first formulate the time-space mapping problem, which includes the key features of both of the above stated methods, and propose a generalized solution. We then apply this solution to the time-distances of Japan's railway networks to confirm its applicability.

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This study assessed the measurable features of proptosis in Japanese patients with DO and contributes to the understanding by correlating symptoms and signs of DO.

SITA has greater patient acceptability than the Full Threshold strategy. However, the difference in sensitivity can be considerable in a serial comparison of one patient's fields tested by Full Threshold and SITA.

A quadrilateral table cartogram is a rectangle-shaped figure that visualizes table-form data; quadrilateral cells in a table cartogram are transformed to express the magnitude of positive weights by their areas, while maintaining the adjacency of cells in the original table. However, the previous construction method is difficult to implement because it consists of multiple operations that do not have a unique solution and require complex settings to obtain the desired outputs. In this article, we propose a new construction for quadrilateral table cartograms by recasting the construction as an optimization problem. The proposed method is formulated as a simple minimization problem to achieve mathematical clarity. It can generate quadrilateral table cartograms with smaller deformation of rows and columns, thereby aiding readers to recognize the correspondence between table cartograms and original tables. In addition, we also propose a means of sorting rows and/or columns prior to the construction of table cartograms to reduce excess shape deformation. Applications of the proposed method confirm its capability to output table cartograms that clearly visualize the characteristics of datasets.