Today we went over how the spatial element is crucial in many analyses, from social to physical.

One major issue faced in geography is that of scale. Selecting the correct scale is crucial in any project and may completely change the nature of one’s results. This is commonly seen in the Modifiable Areal Unit Problem (MAUP), whereby selecting in inappropriately small or large scale results in skewed analysis results.

Selecting spatial unit boundaries is another issue that often occurs alongside scale-based problems and often serves to confound many projects. Using different boundary schemes may yield completely different, yet still correct, results. This in turn often leads to disagreements, as the data may be used for different purposes.

An example of this is gerrymandering, a practice created by 19th Century American politicians, where constituency lines are drawn in such a way as to ensure victory in a first past the post electoral system.

The extent of the study area must also be clearly defined, as selecting an overly large or small extent upsets the scale.

Spatial analysis must also take into account the effects of spatial autocorrelation. This is when a presence of some quantity expressed in one spatial unit is also seen to an extent in the immediately surrounding neighbour units. Spatial autocorrelation is non-random, meaning that there is often an underlying cause to it, and the degree of autocorrelation can be measured using Moran’s I, which provides a numerical score between -1 and +1.

In ecology, autocorrelation can often be explained by three neighbourhood models.

The first is a grouping model, where similar individuals choose to be located in a certain area, or are constrained in some way to be there.

The second is a group dependent model, in which the individuals in a given area are subject to similar external influences, such as a species of flower prospering in an area due to ideal physical conditions.

The final model is the feedback model. This is when individuals interact with and influence one another to become similar. For example, people who live together will inevitably develop shared interests or behaviours.

The main issue for spatial analysis is determining which cause is at work, especially since they may all be simultaneously true at differing scales.