This paper aims to clarify the component of impression of townscape while walking and to verify the effect of sky factor and the change on the components above-mentioned. The results are; (1) according to the factor analysis, "Calmness", "Openness", and "Activity" compose the impression of the city, (2) neither the relation between "Calmness" and sky factor nor change of sky factor are significant, (3) sky factor and change of it define "Openness" of the city significantly, (4) "Activity" of the city appears to be influenced by change of sky factor rather than sky factor itself.
This study compared the impression structure of streetscapes gained from field experiments with that from head-mounted display virtual reality experiments. The results were as follows: (1) the field experiment experience had a slight influence on the impressions reported from the head-mounted display experiment; (2) few items exhibited significant differences in impression values for the two experiments; (3) the impression structure comprised three factors—“Openness,” “Balance,” and “Activity”—in both experiments, and (4) no significant difference appeared between the two experiments relative to the subscale scores of these three factors. These results show that head-mounted display experiments can substitute field experiments.
In recent years, big data entered use in various fields. Google Street View (hereinafter called “GSV”) can be regarded as open big data, and its images can be obtained using API. The streets can be viewed 360° horizontally and 290° vertically from each point on the web. In addition to those, zooming up is available, and the viewpoint can be moved approximately 10 m forward or backward to/from the current point. The original image to provide the view is the panoramic image associated with the latitude and longitude information on the street consecutively at intervals of 10 m, and they exist as massive data on the web. We determine the area of the sky using these images from GSV. In this research, we calculate the sky view factor (hereinafter called “SVF”) in an extended area by defining the area of the sky with the SVF and utilizing the computer.
<p><strong>Abstract.</strong> We applied a computation method of calculating the sky view factor (SVF) using Google Street View to Shibuya area, Tokyo, for the purpose of examining the relation between the SVF/SVF change and physical elements. The distribution of the SVF calculated by the above method was visualized, and the statistical process showed the tendency of a high SVF in quasi-residential districts and roadsides of high-graded trunk roads. The difference in the SVF change was small at 10-m intervals. The SVF change tended to be more apparent near an intersection and at different elevations.</p>
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