Conventional GIScience in favor of objectivity and rationality has focused mainly on locations in the physical environment while falling short of dealing with mental space and human perceptions, feelings, and emotions. Emotional factors are minimized in the majority of GIS algorithms as such factors are viewed as obstructive to spatial analysis. Nevertheless, emotion about place, or sense of place, is fundamental in people’s everyday lives as an essential aspect of geospatial experiences. They affect humans’ decision-making, spatial behaviors, and mental well-being. Nowadays, it is encouraged to humanize modern GIS that has been predominantly technocentric and has overlooked human livelihoods. This dissertation investigates dynamic human emotions toward geographic environments using different approaches. Chapter 2 delves into the conceptualization of emotion in GIS studies and summarizes three frequently used approaches to measure emotions, namely, interpretive, psychophysiological, and data-driven approaches. A mixed methods approach is further suggested to holistically capture the richness and complexity of human emotions. The potential of a new sensing approach has been discussed, which quantitatively studies human emotions toward geographic environments by integrating EEG (electroencephalogram)-based emotions into GIScience. As a non-invasive and wireless brain recording technique, EEG collects signals from the surface of the human brain, where emotions are processed by brain structures. Through the analysis of EEG signals, instantaneous, continuous, and bodily emotional responses can be inferred. Following this approach, Chapter 3 dives into both neurophysiological responses and subjective feelings of individuals toward geographic environments, investigating evidence from EEG recordings, short descriptions of emotions, emotional ratings, and personal accounts of feelings using mixed methods. It applies an EEG headset to collect the brainwaves of participants who watched a range of first-person perspective videos presenting various geographic environments, develops machine learning algorithms to infer emotions from brainwaves, and compares them with subjective explanations of emotions. Using the same experiment data, Chapter 4 examines how the dynamic senses of place of individuals can be explained by both perceived geographic environments and demographics using deep learning. Place semantics are extracted from first-person perspective video frames using the state-of-the-art semantic segmentation architecture. Gradient boosting models are built to explain emotions per participant per video frame using participant demographics and place semantics that indicate perceived geographic environments. Overall, this dissertation encourages closer attention to psychophysiological and subjective experiences of place and space, and offers insights into synthesizing human subjective and personal experiences into GIS applications and services.