Clarify “Scale” in Different Research Domain

In the paper of Dungan et al. 2002, the definition of the terminology “scale” is examined in spatial research domains. They explore “scale” with the phenomenon being studied, the spatial unit or sampling unit, and data analysis. Within different research domains, they find different synonyms for “scale”, including extent, gain, resolution, lag, support and cartographic ratio. Case studies are provided to illustrate different definition of “scale” in different research topics. Modifiable Area Unit Problem (MAUP) is identified, and authors present several suggestions to avoid it.

Most of the examples in this paper come from ecology studies, so the diversity of “scale” is not fully explored. They have mentioned “scale” in remote sensing, and refer it as the synonym of “resolution”. But “resolution” in remote sensing is involved with spatial resolution, spectral resolution and temporal resolution. In image data analysis, the word “scale” is more often utilized as statistical scale, which is related to the analysis unit rather than the observational or sampling unit. For geospatial database design and implementation, the word “scale”, or “large-scale” have significantly different meaning. The large scale data do not only mean huge volume, but also heterogeneity (e.g., different spectral and spatiotemporal resolution) and complexity (e.g., data with different format, noisy rate, and distributed storage) as well. Therefore, I agree with the authors in this paper, that “scale” should be specified with respect to the context that it is used.

Different scales give us different approaches to study our targets. By changing the scale, we actually change our methodology and observation methods. Therefore, more attention should be given to “scale “itself, not the definition.



One Response to “Clarify “Scale” in Different Research Domain”

  1. climateNYC says:

    I think you make a strong point here because scale can be so influential in underlying any and all research questions. Interesting to think about this concept in GIS too as it has such different connotations in other fields.