In this paper, we propose a method for predicting the impression evaluation of buildings’ exterior appearance using street image big data, and we demonstrate its applicability to architectural design. First, we conduct a large-scale impression evaluation web questionnaire using building exterior images extracted mechanically from street image big data in Ota Ward, Tokyo. Next, by training a deep learning model using the results, we can quantify the impression evaluation of building exterior images taken from different building uses and angles. Furthermore, we analyze the impression evaluation scores predicted by the model and demonstrate this method in architectural design through several case studies.

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.