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https://orcid.org/0000-0003-0831-1543
This study aims to clarify the impact of the COVID-19 pandemic on home range. The home range is the area that individuals traverse in conducting their daily activities, such as working and shopping. In Japan, the central government declared the first state of emergency in April 2020. This study analyzed the panel data for mobile phone GPS location history from April 2019 to April 2020 in Ibaraki City, Osaka Metropolitan area. The study applied the minimum convex polygon method to analyze the data. The results show that the home range decreased significantly between April 2019 and April 2020. Specifically, the home range in 2020 decreased to approximately 50% of that in 2019 because of COVID-19 infection control measures, preventing people from traveling far from their homes and only allowing them to step outside for the bare minimum of daily activities and necessities. The results suggest that the emergency reduced people’s home ranges to the neighborhood scale. Therefore, it is necessary to consider designing new walkable neighborhood environments after the COVID-19 pandemic era.
Haruka Kato; Atsushi Takizawa; Daisuke Matsushita. Impact of COVID-19 Pandemic on Home Range in a Suburban City in the Osaka Metropolitan Area. Sustainability 2021, 13, 8974 .
AMA StyleHaruka Kato, Atsushi Takizawa, Daisuke Matsushita. Impact of COVID-19 Pandemic on Home Range in a Suburban City in the Osaka Metropolitan Area. Sustainability. 2021; 13 (16):8974.
Chicago/Turabian StyleHaruka Kato; Atsushi Takizawa; Daisuke Matsushita. 2021. "Impact of COVID-19 Pandemic on Home Range in a Suburban City in the Osaka Metropolitan Area." Sustainability 13, no. 16: 8974.
We developed a method to generate omnidirectional depth maps from corresponding omnidirectional images of cityscapes by learning each pair of an omnidirectional and a depth map, created by computer graphics, using pix2pix. Models trained with different series of images, shot under different site and sky conditions, were applied to street view images to generate depth maps. The validity of the generated depth maps was then evaluated quantitatively and visually. In addition, we conducted experiments to evaluate Google Street View images using multiple participants. We constructed a model that predicts the preference label of these images with and without the generated depth maps using the classification method with deep convolutional neural networks for general rectangular images and omnidirectional images. The results demonstrate the extent to which the generalization performance of the cityscape preference prediction model changes depending on the type of convolutional models and the presence or absence of generated depth maps.
Atsushi Takizawa; Hina Kinugawa. Deep learning model to reconstruct 3D cityscapes by generating depth maps from omnidirectional images and its application to visual preference prediction. Design Science 2020, 6, 1 .
AMA StyleAtsushi Takizawa, Hina Kinugawa. Deep learning model to reconstruct 3D cityscapes by generating depth maps from omnidirectional images and its application to visual preference prediction. Design Science. 2020; 6 ():1.
Chicago/Turabian StyleAtsushi Takizawa; Hina Kinugawa. 2020. "Deep learning model to reconstruct 3D cityscapes by generating depth maps from omnidirectional images and its application to visual preference prediction." Design Science 6, no. : 1.
When an underground mall is flooded, the shoppers should be evacuated to a building connected to the mall. However, the number of evacuees from a large-scale underground mall will exceed the capacity of the evacuation center. Furthermore, the evacuation time may be delayed. This paper proposes a mathematical programming problem that minimizes the evacuation completion time on a general planar graph of a partitioned evacuation area with a specified sink capacity. We also propose a workflow for translating the general geometric spatial data to graphical data. The problem is applied to the real spatial data and evacuation setting of Umeda underground mall in Osaka, Japan. The problem’s performance is compared with that of the conventional problem that minimizes the total evacuation distance, and its accuracy is confirmed in a multi-agent simulation. The validity of the proposed method is also discussed.
Ryo Yamamoto; Atsushi Takizawa. Partitioning Vertical Evacuation Areas in Umeda Underground Mall to Minimize the Evacuation Completion Time. The Review of Socionetwork Strategies 2019, 13, 209 -225.
AMA StyleRyo Yamamoto, Atsushi Takizawa. Partitioning Vertical Evacuation Areas in Umeda Underground Mall to Minimize the Evacuation Completion Time. The Review of Socionetwork Strategies. 2019; 13 (2):209-225.
Chicago/Turabian StyleRyo Yamamoto; Atsushi Takizawa. 2019. "Partitioning Vertical Evacuation Areas in Umeda Underground Mall to Minimize the Evacuation Completion Time." The Review of Socionetwork Strategies 13, no. 2: 209-225.