Great insight Tom. I might argue that geospatial analysis has always been about change detection. Feature detection at its core is change detection over spatial dimensions. Temporal change adds complexity, particularly if spatial resolutions vary. So, I guess I would contend that central vector is a composite of space and time. Just focusing on time - assumes the feature is stationary - which is not true for features such as hurricanes, tornadoes, coastal inundation (yes I work at NOAA😬).
Space and time! I guess I was thinking about looking at changes to a fixed location, and you are reminding me to also consider features that move through space. Both are enormously valuable.
Great insight Tom. I might argue that geospatial analysis has always been about change detection. Feature detection at its core is change detection over spatial dimensions. Temporal change adds complexity, particularly if spatial resolutions vary. So, I guess I would contend that central vector is a composite of space and time. Just focusing on time - assumes the feature is stationary - which is not true for features such as hurricanes, tornadoes, coastal inundation (yes I work at NOAA😬).
Space and time! I guess I was thinking about looking at changes to a fixed location, and you are reminding me to also consider features that move through space. Both are enormously valuable.
Kim at GeoCento has some interesting thoughts on the Double Middle, as you describe it… Great piece!
Thanks, Tom! I do think stopping at the pixel is a massive barrier to broader adoption.