This class outlined the importance of scale (the spatial domain) to any research with a geographic focus. Different scales will have different degrees of influence on a pattern or process, and explanatory models are inherently scale-dependent.

Spatial Statistics

Spatial Autocorrelation: This is a spatial statistic which determines the extent to which a quantity on of one spatial. If positive, the variables are visible in clusters and if negative, they will be visible as equidistant. The non-random distribution of organisms on the planet means that in fields such as ecology, the research will have an inherently spatial dimension.

Kriging: This is a geostatistical interpolation tool which applies a smoothing function  to fitted values.

Moran’s I value: This is a weighted product moment correlation coefficient – effectively, a similar tool to Pearson’s-r, but suitable for spatial data.

Modifiable Areal Unit Problem (MAUP)

MAUP is an issue in all spatial analyses consisting of the uncertainty about what constitutes the objects of spatial study, and the introduction of bias dependent on the scale selected for research. Running statistics on the same data using different scales can generate different statistics – how can we be confident that we have selected the correct spatial domain? This is especially problematic considering that we usually receive data on only one scale, so we have no flexibility in altering the scale and comparing statistics.

MAUP also involves the issue of groups being used to represent the behaviours of an individual (the ecological fallacy) and individuals being used to represent the behaviours of a group (the individualistic fallacy).

Simpson’s Paradox

This is the issue that multiple variables can statistically correlate, but are actually commonly influenced by a shared variable that may not always be captured in analysis. For example, while the number bars and churches in cities appear to be correlated, they are in actuality influenced by the size of the overall population. Thus, it is important to critically consider the potential for misinterpretation that may arise from statistical analysis (such as spatial autocorrelation).