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.

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In order to solve the problem of inaccurate estimation of expressway travel time, this paper proposes a method of highway travel time estimation based on feature matching. Firstly, historical detector data is used as input to establish historical state library for each road segment, searching states which are similar to the current states. then estimate the travel time of the road segment in the state based on the K-nearest neighbor search algorithm. The advantage of this method is that complicated traffic modeling process is avoided, and the historical state library does not need to be updated in general. The method is based on vissim simulation of various traffic states to verify that the error is within the allowable range, and the results show that the method has strong stability.

Electromagnetic geographic environment is closely related to human life. With continuous popularization of public infrastructures and daily electronic instruments, such as electric power communication systems and household appliances, electromagnetic radiation sources have increased sharply in the geographic environment, which leads to increasingly serious electromagnetic radiation pollution. Thus, it is significant to monitor, evaluate, and analyze the electromagnetic radiation condition and explore its changing law in the environment. However, the traditional monitoring method can only detect anomalies within certain frequencies in the fixed stations. To fill this gap, this research first develops a vehicle-mounted electromagnetic environment monitoring system to collect both spatial positioning data and electromagnetic data of the whole frequency range. The acquired data are then used to construct the location-based frequency-intensity curve to reflect the variation of electromagnetic radiation at different frequency ranges. On this basis, a curve similarity measurement method is introduced to analyze the similarity of different curves, which is effective to diagnose time-varying sources from both global and local perspectives. This research provides a real-time mobile monitoring method, which is significant to know the dynamic variation of local electromagnetic environment and promotes subsequent comprehensive geographic analyses.