Global Human Presence Intensity Dataset (2017)

<h1>Overview</h1> <ul> <li>Understanding global patterns of human presence is crucial for monitoring anthropogenic pressures on ecological integrity, optimizing tourism management, and informing decision-making in various domains. However, existing datasets on human presence are insufficient and primarily limited to local scales.</li> <li>We combined massive geotagged microblogs and multi-covariates to infer the global human presence in 2017 at a fine spatial resolution of 0.01 degrees. Specifically, we proposed a Human Presence Indicator (HPI) to quantify human presence. It categorizes the intensity of human presence at a location in four levels based on year-long statistics of geotagged data. HPI-0, HPI-1, HPI-2, and HPI-3 represent no human presence, occasional human presence, frequent human presence, and sustained human presence, respectively.</li> <li>The model achieved a macro-F1 score of 0.72 on a test set comprising over 1.9 million grids in China and 0.84 on the manually labeled samples available worldwide. Cross-validation with external datasets, including geotagged social media data from X and global human settlement and population data, corroborated the model's effectiveness.</li> </ul> <h1>Data Directory Contents</h1> <p>The complete dataset is organized into four main components, each available as a separate compressed <code>.zip</code> file for download:</p> <ul> <li> <p><strong>Gridded HPI Data:</strong> Contains the primary data product showing global human presence intensity (on a 0-3 scale) at a 0.01-degree resolution in GeoTIFF format.</p> </li> <li> <p><strong>Covariate Layers:</strong> Contains all 76 predictor variables used for model training, resampled to the same 0.01-degree resolution.</p> </li> <li> <p><strong>Validation Data:</strong> Contains the manually labeled samples that were used to assess model performance.</p> </li> <li> <p><strong>Model Files:</strong> Contains the final trained random forest model in <code>.joblib</code> format, ready for use by Python practitioners.</p> </li> </ul> <h1>Citation</h1> <p>Please cite both the dataset and the related article when using these data:</p> <ul> <li>Luo, P., Yi, J., Du, Y., Huang, S., Wang, N., Tu, W., Hu, D., &amp; Wei, H. (2025). Global Human Presence Intensity Dataset (2017) (1.2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.16499251</li> <li> <p><span lang="EN-US">Luo, P., Yi, J., Du, Y., Huang, S., Wang, N., Tu, W., Hu, D., &amp; Wei, H. (2025). Mapping global human presence for nature conservation using geotagged social media data. Biological Conservation, 311, 111404. https://doi.org/10.1016/j.biocon.2025.111404</span></p> </li> </ul>