The availability of vast amounts of location-based data from social media platforms such as Twitter has enabled us to look deeply into the dynamics of human movement. The aim of this paper is to leverage a large collection of geo-tagged tweets and the street networks of two major metropolitan areas—London and Tokyo—to explore the underlying mechanism that determines the heterogeneity of human mobility patterns. For the two target cities, hundreds of thousands of tweet locations and road segments were processed to generate city hotspots and natural streets. User movement trajectories and city hotspots were then used to build a hotspot network capable of quantitatively characterizing the heterogeneous movement patterns of people within the cities. To emulate observed movement patterns, the study conducts a two-level agent-based simulation that includes random walks through the hotspot networks and movements in the street networks using each of three distance types—metric, angular and combined. Comparisons of the simulated and observed movement flows at the segment and street levels show that the heterogeneity of human urban movements at the collective level is mainly shaped by the scaling structure of the urban space.

The built year and structure of individual buildings are crucial factors for estimating and assessing potential earthquake and tsunami damage. Recent advances in sensing and analysis technologies allow the acquisition of high-resolution street view images (SVIs) that present new possibilities for research and development. In this study, we developed a model to estimate the built year and structure of a building using omnidirectional SVIs captured using an onboard camera. We used geographic information system (GIS) building data and SVIs to generate an annotated built year and structure dataset by developing a method to automatically combine the GIS data with images of individual buildings cropped through object detection. Furthermore, we trained a deep learning model to classify the built year and structure of buildings using the annotated image dataset based on a deep convolutional neural network (DCNN) and a vision transformer (ViT). The results showed that SVI accurately predicts the built year and structure of individual buildings using ViT (overall accuracies for structure = 0.94 [3 classes] and 0.96 [2 classes] and for age = 0.68 [6 classes] and 0.90 [3 classes]). Compared with DCNN-based networks, the proposed Swin transformer based on ViT architectures effectively improves prediction accuracy. The results indicate that multiple high-resolution images can be obtained for individual buildings using SVI, and the proposed method is an effective approach for classifying structures and determining building age. The automatic, accurate, and large-scale mapping of the built year and structure of individual buildings can help develop specific disaster prevention measures.

This article describes the development of a comprehensive simulation model that integrates property damages (building-collapse, fire-spreading, and street-blockage) and various activities (rescue activity, firefighting activity, and wide-area evacuation activity) by local residents in the event of a large earthquake. Using this model, we analyze the effect and risk of rescue and firefighting activities carried out by local residents under the assumption that a large earthquake directly hits the Tokyo area. Furthermore, we attempt to find new knowledge on the situations where there are many fatal casualties caused by people failing to evacuate, being trapped on streets, and so on.

It is very important in disaster prevention planning to estimate the level of human damage after large earthquakes under various scenarios that takes into account the day of week, the time of the disaster, weather conditions, earthquake intensity, etc. There have been many previous studies based on the spatial characteristics of urban areas about evaluating protection against fires, evacuation risks, and the safety of evacuation routes to designated areas. However, no study so far has integrated models of property damage (building collapse, fire spread, and street blockage) and human behavior (rescue activities, firefighting activities, and wide-area evacuation behavior), and carries out simulations in order to analyze human damage in detail. In this paper, we present a survey of previous studies of the methods of evaluating urban-area characteristics, rescue and firefighting activities, and wide-area evacuation, all of which have been discussed as separate issues. We summarize the findings within the respective fields, their methods of evaluation and modeling, and identify their issues. Based on this survey, we point out that the construction of an integrated simulation model requires six important activities. They are to: 1) carry out evaluations on a microscopic scale at the block or street level; 2) use an evaluation index that allows a direct grasp of the expected level of human damage; 3) take into consideration many detailed and concrete disaster scenarios; 4) take into consideration the interactions among rescue participants, firefighting participants and wide-area evacuees, along with the effects of property damage; 5) incorporate the concept of time; and 6) set up comparative scenarios that allow the quantitative evaluation of the effects of various measures or policies. Therefore, it is necessary to construct a model based on the concept of multi-agent simulation (MAS).