This article emphasizes the importance of spatial scale in research and defines important concepts like space and scaling. Written in 1999, this article continues to be relevant to problems of scale presented by new technologies like drones. Marceau states “nor is a single scale sufficient to investigate phenomena that are inherently hierarchical in space.” She explains that doing this can severely jeopardize your research by hiding the modifiable areal unit problem. One of the important contributions of remote sensing, and more recently programmable drones, is the ability to rapidly collect data on phenomenon at multiple scales. In terms of mitigating the MAUP, the use of a drone to collect imagery could allow the researcher to perform a more robust sensitivity analysis.
I found the discussion on the difference between relative space and absolute space. The author writes that scale is the window in which we view the world, and that scales within relative space are more difficult to define than scales in absolute space, for example in remote sensing. As we move towards more advanced remote sensing using autonomous drones, I wonder how these concepts of space are programmed into AI. For example, traditional remote sensing uses GPS based imagery that is georeferenced in absolute space. But research is moving towards drones that can navigate absent of GPS coordinates, using computer vision to extract features from the landscape. This way, the drone can navigate around obstacles with only references to relative distance based on velocity and no computation of absolute space. Defining scale in such studies becomes difficult when the lines between absolute and relative space are blurred.