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Mapping global human presence for nature conservation using geotagged social media data

文献类型：期刊论文

作者Luo, Peixian1,2; Yi, Jiawei1,2; Du, Yunyan1,2; Huang, Sheng1,2; Wang, Nan1,2; Tu, Wenna1,2; Hu, Dingchen1,2; Wei, Haitao3
刊名BIOLOGICAL CONSERVATION
出版日期2025-11-01
卷号311页码:111404
关键词Human presence Geotagged social media data Random forest Global conservation Anthropogenic pressures
ISSN号0006-3207
DOI10.1016/j.biocon.2025.111404
产权排序1
文献子类Article
英文摘要Quantifying human presence in natural areas is crucial for understanding anthropogenic pressures on biodiversity and informing conservation strategies, yet monitoring at global scales remains challenging due to limited data and spatial sampling bias. This study presents a data-driven approach to mapping global human presence at a 0.01-degree resolution using geotagged social media data. We developed a human presence indicator (HPI) that categorizes locations into four intensity levels: no presence, occasional presence, frequent presence, and sustained presence. Using over 195 million geotagged microblogs from China and 76 covariate layers representing natural and social factors, we trained a random forest model to predict human presence worldwide. The model's effectiveness was validated through comprehensive cross-validation with external datasets, including manually labeled global samples, data from X (formerly Twitter), and global human settlement and population distributions. The inferred HPI map showed detectable human presence through social media covering at least 13.41 % of Earth's terrestrial surface, with substantial regional variations across continents and biodiversity hotspots. Analysis of 1995 strictly protected areas showed that while 67 % had minimal human presence (<1 % of their area), 163 protected areas exhibited human presence in over 10 % of their domain, indicating potential conservation challenges. Despite limitations in data quality and sampling rates, this dataset provides valuable estimates of global human presence, particularly for remote or poorly monitored protected areas. The trained model and dataset, which we make freely available, can support consistent cross-regional comparisons and evidence-based conservation planning globally.
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WOS关键词CULTURAL ECOSYSTEM SERVICES ; DISTRIBUTION MODELS ; DIGITAL FOOTPRINTS ; RECREATION ; PATTERNS ; LAND ; MAP
WOS研究方向Biodiversity & Conservation ; Environmental Sciences & Ecology
语种英语
WOS记录号WOS:001560826200001
出版者ELSEVIER SCI LTD
源URL[http://ir.igsnrr.ac.cn/handle/311030/216117]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Yi, Jiawei
作者单位1.Univ Chinese Acad Sci, Beijing 101408, Peoples R China;
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China;
3.Shandong Univ Sci & Technol, Coll Resources, Taian Campus, Tai An 271019, Peoples R China
推荐引用方式
GB/T 7714		 Luo, Peixian,Yi, Jiawei,Du, Yunyan,et al. Mapping global human presence for nature conservation using geotagged social media data[J]. BIOLOGICAL CONSERVATION,2025,311:111404.
APA Luo, Peixian.,Yi, Jiawei.,Du, Yunyan.,Huang, Sheng.,Wang, Nan.,...&Wei, Haitao.(2025).Mapping global human presence for nature conservation using geotagged social media data.BIOLOGICAL CONSERVATION,311,111404.
MLA Luo, Peixian,et al."Mapping global human presence for nature conservation using geotagged social media data".BIOLOGICAL CONSERVATION 311(2025):111404.

入库方式： OAI收割

来源：地理科学与资源研究所

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