Remote sensing, a field that deals with tons of spatial data extracted and processed from satellite images, aerial photos, and other sensor-based technologies, or any field using data with spatial features, presents a non-trivial challenge. When we analyze all this data, we have to deal with spatial dependencies (i.e., how things that are close together can influence each other). As Crawford (2009) aptly puts it:
Spatial dependence refers to the degree of spatial autocorrelation between independently measured values observed in geographical space.
These spatial dependencies can often lead to autocorrelated errors in statistical models, where observations near each other tend to exhibit similar error characteristics not captured by the explanatory variables alone.
To dig deep into spatial autocorrelation, check a nice YouTube video of a lecture by Luc Anselin at the University of Chicago (October 2016). I also invite you to briefly check my previous post, Spatial Cross-Validation in Geographic Data Analysis (March 22, 2024), where I expose the importance of modeling spatial relationships accounting for spatial correlation to improve the performance, reliability, and predictive power of a model.