Introduction Definition of Probability Probability is a subfield of mathematics that entails numerous models that help to assess how likely an event is to happen and how reliable such a proposition could be. Most interpretations of probability theory treat discrete probability distributions and continuous probability distributions separately. Discrete probability theory is useful for analyzing events that happen in countable sample spaces, with the most widely used examples being throwing dice or tossing coins (Murphy & Katz, 2019). Within this approach, probability can be described as a ratio of the number of favorable cases to the number of total outcomes, provided the experiment is set in an equiprobable sample space. In turn, a continuous probability distribution is supported by an uncountable set (Murphy & Katz, 2019). An example would be a distribution of female heights in a population. A chart depicting such distribution would allow one to figure out the average height but not the exact probability for any one woman. Spatial Applications Spatial probabilistic models find extensive use in a variety of fields, including criminology, cellular networking, and natural disasters (fires, floods, plant diseases, droughts, and others) management. For instance, Ristea et al. (2020) describe the use of spatial probability in predicting crime and violence at sports events. In particular, Ristea et al. (2020) point out the mechanism of crime displacement on crowded event days due to heightened traffic to the so-called specific activity nodes such as sporting arenas and sports bars. There is a possibility that such nodes will turn criminogenic and attract or generate crime (Ristea et al., 2020). Spatial crime distribution that uses socioeconomic, environmental, behavioral, and even social media geospatial data helps to predict crimes at certain locations and strengthen law enforcement when necessary. Elmahdy et al. (2016) show how a weighted probability model can empower geographic information systems to predict landslides that are a constant threat in several mountainous regions in Malaysia. Automated algorithms allow for the construction of improved geological maps that are based on remote sensing data. Such maps helped Elmahdy et al. (2016) calculate the elevation and slope range of areas that pose the most threat to the residents. Lastly, there are numerous studies such as the one by Phillipson and Erdbrink (2015) that demonstrate the capacity of spatial probability in improving the cost-efficiency of cellular networks for mobile operators by analyzing the spatial traffic. Discussion Advantages of Spatial Probability An inarguable benefit of spatial probabilistic methods is their flexibility in identifying the explicit spatial relations of objects and events with regard to their distance, direction, and topology (Murphy & Katz, 2019). In addition, the spatial probability models help to uncover implicit spatial dependencies (Murphy & Katz, 2019). However, first and foremost, spatial probability mitigates risks and uncertainty that are indispensable from the decision-making process regardless of the industry. When applied to cellular network development, probability can help optimize investments by improving radio traffic planning. Ensuring safety is another advantage of spatial probability application. Natural hazard prediction not only saves live