This assignment will focus on some of the more advanced spatial analyses techniques learned in class. With one or more datasets you are exploring for your final project, do one of the following:
- Find tendencies for spatial clustering in your data by conducting a spatial autocorrelation analysis. Your results must include a global Moran's I statistic, followed by a local spatial autocorrelation with a moran's plot that indicates a P-value and a scatterplot with HH, HL, LH, and LL values. Produce a final output in the form of a map that indicates the location of statistically significant spatial clusters.
and/or...
- Conduct a thorough point pattern analysis of your dataset. Your notebook should include a KDE plot, as well as a centrography analysis, indicating central tendencies of your data (mean, median, ellipse). Include different "slices" of your data to produce meaningful results (e.g. kde plots for different categorical values).
Complete your notebook with a markdown cell that succinctly explains the map's interpretation, what you are visualizing and why it is interesting: tell us its story. This story should be accompanied by any relevant descriptive statistics, as needed, to round out the picture.
At the end of the notebook, include a markdown cell that identifies each group member and describes their contribution to this assignment (one sentence each).
Make sure your notebook runs from the top without any errors and that all the visuals can be seen inline (without me having to re-run your notebook).
Post your assignment here