Inbound tourism plays an important role in local economies. To stimulate local economies, it is necessary to attract foreign tourists to various areas of a country. This research aims to develop a method of extracting the locations of tourist destinations in a country and to understand what characteristics foreign tourists expect of areas near tourist attractions compared with what domestic tourists expect. In this paper, a tourist destination is defined as a small area that has places of interests for tourists such as historic sites, theme parks, hotels, and restaurants. The methods proposed in this paper are applied to data acquired from Twitter and Foursquare in Japan. The proposed method successfully extracts the locations of tourist destinations and characterizes those locations based on the points of interest in the neighborhood. The results indicate that foreign tourists who come to Japan expect nightlife spots (bars, nightclubs, etc.) to be located in the neighborhood of tourist destinations, in contrast to the expectations of domestic tourists. The proposed methods are applicable to not only Japan, but to any country.

Visual impression of urban landscape has been investigated in detail through behavioral experiments and questionnaire surveys in the field of architecture. However, in order to give an incentive to build and maintain a good residential environment, an economic consideration of the urban landscape across space is also an important aspect. Employing a semantic segmentation approach using Google Street View images, we investigate the relationship between urban landscapes and property prices in low-rise residential areas in a suburban city of Tokyo, Japan. Such visual images are used to represent the landscape of both surrounding districts in general and the street-level landscape; more specifically, the latter is derived after controlling for the district fixed effect. We first show that greenery, openness and visual enclosure are positively correlated with property price at street level. We also investigate the value of urban landscapes commonly seen in Japan: (i) the presence of a power pole is negatively correlated with property price at district level; and (ii) the presence of a road shoulder or farmland, either of which may disrupt the continuity of a residential area, does not exhibit negative correlations with property price at either district or street level.

In recent years, mobility data from smart cards, mobile phones and sensors have become increasingly available. However, they often lack some of the key information including the purposes of trips for each individual user. Information on trip purposes is crucial for projecting the future travel patterns as well as understanding the characteristics of each area of a city and how it is changing. This paper proposes a method called EAT-CD (Extraction of Activity Types and Change Detection). It estimates the volume of passengers by activity types (e.g. commuting, leisure) using non-negative matrix factorization and detects changes in the number of visitors for each activity (e.g. increase in shopping trips triggered by the development of a new commercial facility). Validity of EAT-CD is tested through empirical analysis using smart card data of public transportation in Western Japan. The results showed that EAT-CD is effective in deriving activity patterns, which showed strong correlation with travel survey data. The results also confirmed that EAT-CD detects changes in travel patterns (e.g. start and end of semesters) and land uses (e.g. establishment of new facilities).