Spatio-temporal characteristics of human activities using location big data in Qilian Mountain National Park

Human activities significantly impact the environment. Understanding the patterns and distribution of these activities is crucial for ecological protection. With location-based technology advancement, big data such as location and trajectory data can be used to analyze human activities on finer temporal and spatial scales than traditional remote sensing data. In this study, Qilian Mountain National Park (QMNP) was chosen as the research area, and Tencent location data were used to construct time series data. Time series clustering and decomposition were performed, and the spatio-temporal distribution characteristics of human activities in the study area were analyzed in conjunction with GPS trajectory data and land use data. The study found two distinct human activity patterns, Pattern A and Pattern B, in QMNP. Compared to Pattern B, Pattern A had a higher volume of location data and clear nighttime peaks. By incorporating land use and trajectory data, we conclude that Pattern A and Pattern B represent the activity patterns of the resident and tourist populations, respectively. Moreover, the study identified seasonal variations in human activities, with human activity in summer being approximately two hours longer than in winter. We also conducted an analysis of human activities in different counties within the study area.

Automated summary

Human activities have a significant impact on the environment, making it crucial to understand their patterns and distribution for ecological protection. With advancements in location-based technology, big data such as location and trajectory data can be utilized to analyze human activities at finer temporal and spatial scales than traditional remote sensing data. This study focuses on Qilian Mountain National Park (QMNP) and utilizes Tencent location data to construct time series data. By analyzing the spatio-temporal distribution characteristics of human activities in QMNP, two distinct patterns were identified, one representing residents and the other representing tourists. The study also discovered seasonal variations in human activities and conducted an analysis of human activities in different counties within QMNP.