Chinese calligraphy is a symbol of cultural heritage; it is widely used in visual design (e.g., film posters and modern fashion) for its aesthetic value. In human-computer interaction, visual design complexity strongly influences users’ preferences. Indeed, the relevance of visual complexity to aesthetic preference has been confirmed. However, the visual complexity of calligraphy artworks has seldom been investigated and the process of how visual complexity affects aesthetic preference remains vague. Therefore, in this work we proposed a computational method to evaluate complexity and conducted several perception studies. Results showed that layout features and calligraphy style (regular script, running script, and cursive script) affected visual complexity. The level of visual complexity (low, medium, and high) affected aesthetic preference but calligraphy style did not. Furthermore, Chinese calligraphy with moderate visual complexity evokes strong aesthetic preference. The present findings can help designers redesign websites and interfaces for high aesthetic preference and can provide insights for developing the theoretical design of calligraphic art for advanced interaction in cultural heritage, such as in interactive systems for teaching calligraphy based on aesthetic cognition.

Round and square are important concepts in Chinese aesthetics. The legacy of Chinese calligraphy aesthetics has led the way to modern design. In HCI, interface typefaces influence users’ reading experiences. Users’ aesthetic preferences may influence their reading experiences as well. The investigation of how human aesthetic preferences are affected by character visual features (e.g., character size and character weight) in type design has received substantial attention. However, it is difficult to accurately study human aesthetic preferences through these visual features in Chinese calligraphy-type design. How Chinese character visual features affect human aesthetic preferences is not clear. Inspired by graphic design, different visual features (e.g., color and texture) can cause changes in impression expressions (e.g., modern and classical), thus affecting human aesthetic preferences. In this article, we introduced a character impression analysis method to investigate how human aesthetic preferences are affected by Chinese character visual features. First, we proposed two impression expressions of character (round and square) from the concepts of Chinese aesthetics; Then, for these two impression expressions, we modeled four visual features of calligraphy characters from the principles of Gestalt psychology. Finally, we conducted two studies to investigate how visual features affect impression expressions and explored the relationship between aesthetic preferences. The results clearly explain how character visual features affect human aesthetic preferences. The present study can give designers a deep understanding of how visual features affect human aesthetic preferences based on character impression analysis. It can guide designers to make better type design decisions to achieve a high aesthetic preference for users. Furthermore, the findings of the present study can potentially provide new insights into Latin type design.

Real estate prices have a significant impact on individuals, families, businesses, and governments. The general objective of real estate price prediction is to identify and exploit socioeconomic patterns arising from real estate transactions over multiple aspects, ranging from the property itself to other contributing factors. However, price prediction is a challenging multidimensional problem that involves estimating many characteristics beyond the property itself. In this paper, we use multiple sources of data to evaluate the economic contribution of different socioeconomic characteristics such as surrounding amenities, traffic conditions and social emotions. Our experiments were conducted on 28,550 houses in Beijing, China and we rank each characteristic by its importance. Since the use of multisource information improves the accuracy of predictions, the aforementioned characteristics can be an invaluable resource to assess the economic and social value of real estate. Code and data are available at: https://github.com/IndigoPurple/PATE.

The study of socioeconomic status has been reformed by the availability of digital records containing data on real estate, points of interest, traffic and social media trends such as micro-blogging. In this paper, we describe a heterogeneous, multi-source, multi-modal, multi-view and multi-distributional dataset named "H4M". The mixed dataset contains data on real estate transactions, points of interest, traffic patterns and micro-blogging trends from Beijing, China. The unique composition of H4M makes it an ideal test bed for methodologies and approaches aimed at studying and solving problems related to real estate, traffic, urban mobility planning, social sentiment analysis etc. The dataset is available at: https://indigopurple.github.io/H4M/index.html.