As a symbol of Chinese culture, Chinese traditional-style architecture defines the unique characteristics of Chinese cities. The visual qualities and spatial distribution of architecture represent the image of a city, which affects the psychological states of the residents and can induce positive or negative social outcomes. Hence, it is important to study the visual perception of Chinese traditional-style buildings in China. Previous works have been restricted by the lack of data sources and techniques, which were not quantitative and comprehensive. In this paper, we proposed a deep learning model for automatically predicting the presence of Chinese traditional-style buildings and developed two view indicators to quantify the pedestrians’ visual perceptions of buildings. Using this model, Chinese traditional-style buildings were automatically segmented in streetscape images within the Fifth Ring Road of Beijing and then the perception of Chinese traditional-style buildings was quantified with two view indictors. This model can also help to automatically predict the perception of Chinese traditional-style buildings for new urban regions in China, and more importantly, the two view indicators provide a new quantitative method for measuring the urban visual perception in street level, which is of great significance for the quantitative research of tourism route and urban planning.

Urban function refers to various roles and activities that take place in an urban region or a public facility. Previous research only focused on the types of urban function and its quantitative characterization did not attract enough attention. In this paper, we highlight the issue of quantifying the urban function and divide it into two aspects: one is the intensity of the urban function, which can be seen as the capacity to accommodate people engaging in activities related to the function, and the other is its influence on the surrounding area. Due to the cause-and-effect dynamics between urban functions and geographical flows, which refer to the human or objects moving between locations, geographical flows have been used to estimate the intensity and influence of urban function. Based on the review of recent updates, this paper provides a research framework for utilizing flow data to quantify urban function in terms of intensity and influence. Regarding the intensity, the indices are mainly based on the volume of inflow/outflow and their ratio. Regarding the influence, there are two types of indices for evaluating it: one for planar facilities and the other for linear facilities. To describe the intensity and influence of the urban function simultaneously, the volume along with the length of flows are considered into the indices, which can quantify the importance or irreplaceability of a place or facility. The analysis shows that the geographical flows provide a new perspective for quantifying urban function in terms of intensity and influence.

The fine-grained population distributions of different age groups are crucial for urban planning applications. With the development of information and communication technology (ICT), detailed population data retrieved from various big data sources, especially on a fine scale, have been extensively used for urban planning. However, studies estimating the detailed population distributions of different age groups are still lacking. This study constructs a framework to generate fine-grained population data for different age groups and explores the influence of various factors on the distributions of different age groups. The population is divided into the following four age groups: (1) early adulthood people: 18 ≤ age ≤ 24, (2) young people: 25 ≤ age ≤ 39, (3) middle-aged people: 40 ≤ age ≤ 59, and (4) elderly people: 60 ≤ age. The results indicate that education and accommodation factors have a major influence on the distributions of early adulthood and elderly people, respectively. Business, restaurant, and accommodation factors are the main factors influencing the population distributions of young and middle-aged people. The accommodation factor plays a major controlling role at night, and its explanatory power gradually decreases during the day, while the explanatory powers of the business and restaurant factors increase and become leading factors during the day. Specifically, the hospital factor has a greater effect on the distribution of elderly people. The entertainment factor has very little explanatory power for the population distributions of the different age groups.