Beijing is one of the most developed cities in China and has experienced a series of environmental problems. In accordance with the Major Function Zone planning, Beijing is divided into four zones in an attempt to coordinate development between urban areas and the eco-environment. Classic coupling model uses statistical data to evaluate the interactions of these two subsystems; however, it lacks the capability to express dynamic changes to land cover. Thus, we extracted land cover data from Landsat images and examined the urbanization and eco-environment level as well as the coupling coordination in Beijing and its functional zones. The main conclusions are as follows. (1) Between 2001 and 2011, both urbanization and the eco-environment level in Beijing and its functional zones grew steadily. Different zones coordinated together according to their own characteristics, and the overall coupling coordination of the city transformed from the “basically balanced” to the “superiorly balanced” stage of development. (2) After 2011, the condition of the eco-environment worsened in Beijing and in most of the function zones, while the coordination between increased urbanization and the worsened eco-environment may be a result of environmental lag. This study integrated land cover data into the coupling mode and fully utilized the advantages of spatiotemporal analysis and the coupling model. In other words, the spatiotemporal analysis explains the land cover changes visually over the research period, while the coupling model explores the interaction mechanisms between urbanization and the eco-environment. The land cover data enriches the coupling theory and provides a reference for evaluating the effectiveness of local development policy.

An important component of research in cultural geography involves the exploration and analysis of the laws of regional cultural differences. This topic has considerable significance in the discovery of distinctive cultures, protection of regional cultures, and in-depth understanding of cultural differences. In recent years, with the “spatial turn” of sociology, scholars have focused increasing attention to implicit spatial information in social media data, as well as the social phenomena and laws they reflect. Grasping sociocultural phenomena and their spatial distribution characteristics through texts is an important aspect. Using machine learning methods, such as the popular natural language processing (NLP) approach, this study extracts hotspot cultural elements from text data and accurately detects the spatial interaction patterns of specific cultures, as well as the characteristics of emotions toward non-native cultures. Through NLP, this study examines cultural differences among people from South and North China by analyzing 6128 answers to the question, “What are the differences between South and North China that you ever know?” posted on the Zhihu Q&A platform. Moreover, this study probes individuals’ emotions and cognition of cultural differences between South and North China in three aspects, namely, spatial interaction patterns of hotspot cultural elements, components of hotspot cultures, and emotional characteristics under the influence of cultural differences between the two regions. Results reveal that: (1) people from North and South China exhibit considerable differences in recognizing each other’s culture; (2) among numerous cultural differences, food culture is the most popular; and (3) people tend to have a negative attitude toward food cultures that differ from their own. These factors can shed light on regional cultural differences and help address cultural conflicts. In addition, this study provides effective solutions from a macro perspective, which has been challenging for new cultural geography.