This week featured an introduction to best practices for preparing a map and dataset for a spatial statistical analysis. This included evaluating the spatial distribution of data collected (as per the map linked below), including calculating the mean center and median center of the data points. If they are in close proximity, it is an indicator that the data may be normally distributed, whereas if they are not the data likely has some sort of spatial skewing going on. The oval representing the directional distribution of the data points below is calculated based on 1 standard deviation of the data point's location calculated along the x and the y axes. In this case, the data tends to be spread out further along a more or less east to west axis, although the axis have a slight tilt towards the southwest from the northeast. Normally distributed data would also tend to have about 68% of its data points lying within such an ellipse.
From there, we looked at ways to determine how the values of the data are distributed. Both histograms and qq plots were looked at as ways to evaluate whether the data values are normally distributed and also whether there are any outliers in the data. From there, we looked at ways to explore the variation in our data, with an eye toward making sure that the data has a locational element to it. This involved voronoi maps and semivariogram clouds. These also act, again, as methods that can help point out outliers in the data that may need to be removed from the dataset.
Finally, we were exposed to methods that can be used to do a trend analysis to look for any spatially oriented trends inherent in the data.
An Analysis of the Spatial Distribution of Temperature Data in Western Europe
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