The frequency and intensity of extreme heat wave events have increased in the past several decades and are likely to continue to increase in the future under the influence of human-induced climate change. Exposure refers to people, property, systems, or other elements present in hazard zones that are thereby subject to potential losses. Exposure to extreme heat and changes therein are not just determined by climate changes but also population changes. Here we analyze output for three scenarios of greenhouse gas emissions and socio-economic growth to estimate future exposure change taking account of both climate and population factors. We find that for the higher emission scenario (RCP8.5-SSP3), the global exposure increases nearly 30-fold by 2100. The average exposure for Africa is over 118 times greater than it has been historically, while the exposure for Europe increases by only a factor of four. Importantly, in the absence of climate change, exposure is reduced by 75–95% globally and across all geographic regions, as compared with exposure under the high emission scenario. Under lower emission scenarios RCP4.5-SSP2 and RCP2.6-SSP1, the global exposure is reduced by 65% and 85% respectively, highlighting the efficacy of mitigation efforts in reducing exposure to extreme heat.

This research is motivated by the increasing threat of urban heat waves that are likely worsened by pervasive global warming and urbanization. Different regions of the city including urban, borderland and rural area will experience different levels of heat health risk. In this paper, we propose an improved approach to quantitatively assess Beijing’s heat health risk based on three factors from hazard, vulnerability and especially environment which is considered as an independent factor because different land use/cover types have different influence on ambient air temperatures under the Urban Heat Island effect. The results show that the heat health risk of Beijing demonstrates a spatial-temporal pattern with higher risk in the urban area, lower risk in the borderland between urban and rural area, and lowest risk in the rural area, and the total risk fluctuated dramatically during 2008–2011. To be more specific, the heat health risk was clearly higher in 2009 and 2010 than in 2008 and 2011. Further analysis with the urban area at sub-district level signifies that the impervious surface (urban area such as buildings, roads, et al.) ratio is of high correlation with the heat health risk. The validation results show that the proposed method improved the accuracy of heat health risk assessment. We recommend that policy makers should develop efficient urban planning to accomplish Beijing’s sustainable development.

Despite the now-ubiquitous two-dimensional (2D) electronic maps, three-dimensional (3D) globe viewers, or 3D geo-browsers such as Google Earth and NASA World Wind have gained much attention. However, the effect of such interactive 3D geo-browsers on spatial knowledge acquisition and decision-making is not well known. This study aims to explore the potential benefits of using interactive 3D geo-browsers in three processes of pedestrian navigation (self-localization, spatial knowledge acquisition, and decision-making) in digital environments. We employed eye tracking to show differences of visual attention in pedestrian navigation between a 2D map (Google Map) and a 3D geo-browser (Google Earth). The results indicated that benefits and drawbacks of 3D representations are task dependent. Participants using the 3D geo-browser had an extensively visual search resulting in significantly longer response time than the 2D participants for spatial knowledge acquisition, whereas 3D users performed a more efficient visual search and resulted in a better navigation performance at complex decision points. We speculate that the inefficient knowledge acquisition when using the 3D geo-browser was most probably due to information overload and obstructed views. Landmarks in photorealistic 3D models assisted recall of spatial knowledge from mental maps, which contributed to efficient decision-making at a complex turning point. These empirical results can be helpful to improve the usability of pedestrian navigation systems.

Motivated by growing heat-related morbidity and mortality in a warming climate, this paper assesses global heat health risk in order to understand the challenges to sustainability in the 21st century, using four Representative Concentration Pathways (RCPs) of the HadGEM2-ES climate model and five Shared Socio-Economic Pathways (SSPs). Factors influencing global heat health risk were reviewed and risks were estimated based on heat hazard and socio-economic vulnerability. Hazard, vulnerability, risk and in particular, populations at different risk levels, were analyzed quantitatively at both global and regional scales. The results show that under an RCP8.5-SSP3 scenario, the world will be subject to the highest heat health risk, with rapidly increasing hazard levels and vulnerability over the century. Less developed or developing regions, such as Africa and Southeast Asia, are at the highest risk. The heat risk under an RCP2.6-SSP1 scenario will first increase and then fall, resulting in the lowest heat-health-risk pattern. We found that heat health risk will increase during the century under all RCP-SSP scenarios, with a higher frequency, higher intensity, longer duration and expanding spatial reach. Significant differences were observed across regions. The results make clear that the increasing risk poses significant challenges to sustainable human health. To meet these challenges, more attention and effective actions are urgently needed from both policy makers and individuals.

Eye movement data convey a wealth of information that can be used to probe human behaviour and cognitive processes. To date, eye tracking studies have mainly focused on laboratory-based evaluations of cartographic interfaces; in contrast, little attention has been paid to eye movement data mining for real-world applications. In this study, we propose using machine-learning methods to infer user tasks from eye movement data in real-world pedestrian navigation scenarios. We conducted a real-world pedestrian navigation experiment in which we recorded eye movement data from 38 participants. We trained and cross-validated a random forest classifier for classifying five common navigation tasks using five types of eye movement features. The results show that the classifier can achieve an overall accuracy of 67%. We found that statistical eye movement features and saccade encoding features are more useful than the other investigated types of features for distinguishing user tasks. We also identified that the choice of classifier, the time window size and the eye movement features considered are all important factors that influence task inference performance. Results of the research open doors to some potential real-world innovative applications, such as navigation systems that can provide task-related information depending on the task a user is performing.

Finding one’s way is a fundamental daily activity and has been widely studied in the field of geospatial cognition. Immersive virtual reality (iVR) techniques provide new approaches for investigating wayfinding behavior and spatial knowledge acquisition. It is currently unclear, however, how wayfinding behavior and spatial knowledge acquisition in iVR differ from those in real-world environments (REs). We conducted an RE wayfinding experiment with twenty-five participants who performed a series of tasks. We then conducted an iVR experiment using the same experimental design with forty participants who completed the same tasks. Participants’ eye movements were recorded in both experiments. In addition, verbal reports and postexperiment questionnaires were collected as supplementary data. The results revealed that individuals’ wayfinding performance is largely the same between the two environments, whereas their visual attention exhibited significant differences. Participants processed visual information more efficiently in RE but searched visual information more efficiently in iVR. For spatial knowledge acquisition, participants’ distance estimation was more accurate in iVR compared with RE. Participants’ direction estimation and sketch map results were not significantly different, however. This empirical evidence regarding the ecological validity of iVR might encourage further studies of the benefits of VR techniques in geospatial cognition research.