Week Two: Why is Geography Important?

Here is a summary of what we talked about in our lecture on the importance of geography!

In this lecture, we learnt about the importance of geography and the way it is applied to address problems. As we have learnt in week 1, geography is important in analyzing problems within various topics such as landscape ecology, health geography, and crime analysis. However, we must also acknowledge the fundamental issue found in determining the patterns and processes that are scale dependent. In other words, the problem is that we need to be able to work on different scales of space, time and ecological organization without a single natural scale at which ecological phenomena should be studied. Analyzing the geography of a problem is important because of the following factors:

  • The modifiable areal unit problem
  • Scale, grain, and extent of a study
  • Nature of boundaries of a study area
  • Spatial dependence / heterogeneity

We were introduced to the terminology and concepts of the factors listed above. The modifiable areal unit problem (MAUP) was of particular interest. MAUP exists because geographical areas are made from groupings of species, individuals, or households that are similar. Species, individuals, or households outside of the area tend to be less alike. This is explained by the neighbourhood model. There are three classes of neighbourhood models:

  • Grouping: similar individuals / households choose, or are constrained, to locate in the same area / group, either when those groups are formed or through migrations.
  • Group development: individuals / households in the same area / group are subject to similar external influences.
  • Feedback models: individuals / households interact with each other and influence each other, and the frequency / strength of such interaction is likely to be greater between individuals in the same area / group than between individuals in different areas

Neighbourhoods are composed of unique combinations of biological and physical environments and no combination of statistical manipulations may be able to unpack such a complex set of 'actors'.

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