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Why are Spatial Data Special?

Why are Spatial Data Special?. The Pitfalls and Potential of Spatial Data. Why are Spatial Data Special?. The Pitfalls of Spatial Data. Spatial Autocorrelation Tobler’s 1 st Law of Geography All features are related and near features are more related than distant ones.

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Why are Spatial Data Special?

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  1. Why are Spatial Data Special? The Pitfalls and Potential of Spatial Data Why are Spatial Data Special?

  2. Why are Spatial Data Special? Why are Spatial Data Special?

  3. The Pitfalls of Spatial Data • Spatial Autocorrelation • Tobler’s 1st Law of Geography • All features are related and near features are more related than distant ones. • Without spatial autocorrelation, Geography would be irrelevant. Why are Spatial Data Special?

  4. The Pitfalls of Spatial Data • Spatial Autocorrelation • One of the most basic of assumptions of conventional statistical analyses is that the data are random. • Spatial autocorrelation means that there is redundancy in the data. • This can affect the calculation of confidence intervals, etc. • There is a strong case for assessing the degree of spatial autocorrelation in a data set before doing any conventional statistics at all. • Joins count statistic, Moran’s I, Geary’s C, (semi-)variograms Why are Spatial Data Special?

  5. The Pitfalls of Spatial Data • Spatial Autocorrelation • Describing and modelling patterns of variation across a study area – i.e. measuring the autocorrelation present – is of primary importance in spatial analysis. Why are Spatial Data Special?

  6. The Pitfalls of Spatial Data • Modifiable Areal Unit Problem (MAUP) • The definition of a boundary is arbitrary with respect to the feature being mapped. • If you change the boundary, the reported values for the region may change too. Why are Spatial Data Special?

  7. The Pitfalls of Spatial Data • Modifiable Areal Unit Problem (MAUP) Why are Spatial Data Special?

  8. The Pitfalls of Spatial Data • Ecological Fallacy • Occurs when it is inferred that data for areas under study can be applied to individuals in that area. • Do all the people living in a census tract have the mean income? • Are all the locations in an elevation zone have the mean elevation? Why are Spatial Data Special?

  9. The Pitfalls of Spatial Data • Scale • The scale at which we work affects the representations we use and the spatial analyses we apply. • The correct (or appropriate) scale for a study is usually impossible to determine beforehand. Why are Spatial Data Special?

  10. The Pitfalls of Spatial Data • Nonuniformity of Space • Arises because features are not evenly distributed everywhere. • By simply plotting break & enter locations we may see clusters in the data that have little to do with crime, e.g. in high density residential neighbourhoods. We may also see gaps in the data in parks and over lakes. Why are Spatial Data Special?

  11. The Pitfalls of Spatial Data • Edge Effects • Arise along artificial boundaries. • If we are trying to identify neighbours of a particular feature, those features along the edges of the study area will have far fewer neighbours than those in the central area. Why are Spatial Data Special?

  12. The Potential of Spatial Data • Distance • Euclidean distance • Perceptual distance Why are Spatial Data Special?

  13. The Potential of Spatial Data • Adjacency • Whether 2 objects are situated next to each other. • How do you decide? • Within a fixed distance? • Nearest neighbour? • Connectivity? Why are Spatial Data Special?

  14. The Potential of Spatial Data • Interaction • A combination of distance and adjacency. • A mathematical formulation of Tobler’s Law. • Generally represented as a number between 0 (no interaction) and 1 (tightly coupled interaction) Why are Spatial Data Special?

  15. The Potential of Spatial Data • Neighbourhood • The set of all features adjacent to another feature. • The set of all features within a given distance of another feature. Why are Spatial Data Special?

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