Emotional habitat: mapping the global geographic distribution of human emotion with physical environmental factors using a species distribution model

ABSTRACT

Human emotion is an intrinsic psychological state that is influenced by human thoughts and behaviours. Human emotion distribution has been regarded as an important part of emotional geography research. However, it is difficult to form a global scaled map reflecting human emotions at the same sampling density because various emotional sampling data are usually positive occurrences without absence data. In this study, a methodological framework for mapping the global geographic distribution of human emotion is proposed and applied, combining a species distribution model with physical environment factors. State-of-the-art affective computing technology is used to extract human emotions from facial expressions in Flickr photos. Various human emotions are considered as different species to form their ‘habitats’ and predict the suitability, termed as ‘Emotional Habitat’. To our knowledge, this framework is the first method to predict emotional distribution from an ecological perspective. Different geographic distributions of seven dimensional emotions are explored and depicted, and emotional diversity and abnormality are detected at the global scale. These results confirm the effectiveness of our framework and offer new insights to understand the relationship between human emotions and the physical environment. Moreover, our method facilitates further rigorous exploration in emotional geography and enriches its content.

KEYWORDS:

• Affective computing
• emotional distribution
• species distribution model
• geosocial data
• maximum entropy modelling

Acknowledgments

We would like to thank Prof. May Yuan, Prof. Stephen Hirtle and all anonymous reviews for their useful comments and suggestions that significantly strengthened this manuscript.

Data and codes availability statement

The codes that support the findings of the present study are available on Figshare at http://doi.org/10.6084/m9.figshare.11841372.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Open Fund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University under Grant No. [19E02].

Notes on contributors

Yizhuo Li

Yizhuo Li is a master candidate in the School of Resource and Environmental Sciences at Wuhan University. His research focuses on urban data mining and spatial analysis.

Teng Fei

Teng Fei is currently an Associate Professor in the School of Resource and Environmental Sciences at Wuhan University. His research focuses on remote sensing, urban data analysis, social sensing and ecological modelling.

Yingjing Huang

Yingjing Huang is a master candidate in the School of Resource and Environmental Sciences at Wuhan University. Her research focuses on urban data mining and emotional computing.

Jun Li

Jun Li is a master candidate in the School of Resource and Environmental Sciences at Wuhan University. His research focuses on spatial analysis and urban computing.

Xiang Li

Xiang Li is currently an Associate Professor in the Institute of Survey and Mapping at Information Engineering University in China. His research focuses on geographic information analysis and visualization.

Fan Zhang

Fan Zhang is currently an Associate Professor in State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing at Wuhan University. His research focuses on LIDAR remote sensing and applications.

Guofeng Wu

Guofeng Wu is currently a Professor in Shenzhen Key Laboratory of Spatial Smart Sensing and Services at Shenzhen University. His research focuses on remote sensing applications on natural resources and ecological environments.