Using Social Media Camping Data for Evaluating, Quantifying, and Understanding Recreational Ecosystem Services in Post-COVID-19 Megacities: A Case Study from Beijing
2. Materials and Methods
2.1. Study Area and Camping Trend in Post-COVID-19
2.2. Data Collection and Preprocessing
2.3. Data Analysis
2.3.1. Kernel Density Analysis
2.3.2. Cluster/Outlier Analysis for Hot/Cold Point Classification
2.3.3. Correspondence Analysis
3.1. The Spatial Pattern of Camping Behaviors
3.2. Distribution of Hot and Cold Points of RES Perceived by the Public
3.3. Relationship between Hot/Cold Spots, Land Cover Features, and Visitor Genders
4.1. The Spatial Pattern of Camping Behaviors
4.2. The Spatial Pattern of Public Perceived RES Value
4.3. Impact Factors towards Public-Perceived RES Value
4.4. Advantages, Limitations, and Future Potentials for CES Evaluation Based on Mapping Geo-Tagged Camping Notes from Social Media Data
- According to the spatial pattern of camping habits, both suburban and urban central areas have given certain camping support services to the public since the implementation of COVID-19 restrictions. However, much more clusters were detected in the suburbs than in the central districts, indicating that existing urban green spaces do not provide enough camping facilities.
- Hot points associated with high-RES values were mainly identified in suburban areas. Despite the high density of camping noted in the central urban area, the green spaces in this area provide relatively low RES value for the public. We believe this is due to the restricted public leisure activities supported by these urban parks as a result of their current management policies. Therefore, additional optimization is required.
- Differing from previous studies, the lower RES value was correlated with waterbodies in Beijing. Here, we think this is due to local recreation restorations of public water bodies. Higher RES were closely related to grassland and forests. In addition, female visitors tend to camp in grassland while males tend to bare land and built-up areas, which could be explained by the gender differences in landscape preferences, recreational use of nature, and concerns about conflicts.
Data Availability Statement
Conflicts of Interest
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|No.||Locations||Districts||Landscapes||Total Camping Notes||Percentage||Kernel Density (Mean)|
|1||Jinghaihu scenic area; Weilan Valley camping park||Pinggu||Lake, mountain, forests, lawn||356||12.28%||0.0162|
|2||Chaoyang Park||Chaoyang (central urban area)||Urban park, lake, forest, lawn, European buildings||504||16.69%||0.0084|
|3||Baihewan scenic area||Huairou||Mountain, forests, riverside, bare land||183||6.15%||0.0093|
|4||Daxiangludao resort||Pinggu||Lawn, woods, lake,||107||3.60%||0.0070|
|5||Tiankai Reservoir, Tiankai Farm||Fanshan||Mountain, bare land, cultivated land, pond||158||5.32%||0.0081|
|6||Shuangying camping park||Fanshan||Grassland, bare land||137||4.61%||0.0083|
|No.||Poi Name||Landscape Features||Recreation during Camping||n = Like|
|1||Yudushan scenic area and local Yudu camping park||Mountain, forests, lake, grassland, lawn, pool, waterfall||Tenting, picnicking, boating, hiking, dog walking, barbecues, sawanobori||6661|
|2||Jinhaihu scenic area and local Weilan Valley camping park||Lake, mountain, forests, lawn||Tenting, picnicking, boating, carriage, horse riding, flying kites, walking dogs, and playing Frisbee, and grassland concert events||5079|
|3||Darehuangye||Grassland, bare land, mountain||Tenting, picnicking, campfire, barbecues, motorbikes, concerts, and market events||2726|
|No.||Poi Name||Landscape Features||Recreation during Camping||n = Like|
|1||Huangye No.7 camping park, Jinhaihu town||Mountain, lake, grassland||Tenting, picnicking, hiking, dog walking||242|
|2||Xiaoqinghe||Woods, wetland, forests, riverside, bare land||Tenting, picnicking, BBQ||121|
|3||Wenyuhe||Woods, riverside, bare land, sand land||Tenting, picnicking, dog walking||117|
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Xu, H.; Zhao, G.; Liu, Y.; Miao, M. Using Social Media Camping Data for Evaluating, Quantifying, and Understanding Recreational Ecosystem Services in Post-COVID-19 Megacities: A Case Study from Beijing. Forests 2023, 14, 1151. https://doi.org/10.3390/f14061151
Xu H, Zhao G, Liu Y, Miao M. Using Social Media Camping Data for Evaluating, Quantifying, and Understanding Recreational Ecosystem Services in Post-COVID-19 Megacities: A Case Study from Beijing. Forests. 2023; 14(6):1151. https://doi.org/10.3390/f14061151Chicago/Turabian Style
Xu, Haiyun, Guohan Zhao, Yan Liu, and Meng Miao. 2023. "Using Social Media Camping Data for Evaluating, Quantifying, and Understanding Recreational Ecosystem Services in Post-COVID-19 Megacities: A Case Study from Beijing" Forests 14, no. 6: 1151. https://doi.org/10.3390/f14061151