This presentation is a comparison of different clustering based on their computational time. This is the first step in creating open source and bespoke Geodemographic classifications in near real time.

1. Real time Geodemographics: Requirements and Challenges Muhammad Adnan, Paul Longley

4. What are real time Geodemographics ? Specification Estimation Testing

8. K-means (100 runs of k-means on OAC data set for k=4)

9. An example of bad clustering result (K-means)

10. An example of bad clustering result (K-means)

11. An example of bad clustering result (K-means)

15. Comparing computational efficiency (Z-scores) PAM, and GA on the three geographic aggregations of a dataset covering London. Figure 1: OA (Output Area) level results Figure 2 : LSOA (Lower Super Output Area) level results Figure 3 : Ward level results

16. Comparing computational efficiency (Range Standardisation) PAM, and GA on the three geographic aggregations of a dataset covering London. Figure 4: OA (Output Area) level results Figure 5 : LSOA (Lower Super Output Area) level results Figure 6 : Ward level results

17. Comparing computational efficiency (PCA) PAM, and GA on the three geographic aggregations of a dataset covering London. Figure 7: OA (Output Area) level results Figure 8 : LSOA (Lower Super Output Area) level results Figure 9 : Ward level results

18. Algorithm Stability (w.r.t. Computational time) Figure 10: Running k-means on OA (Output Area) for 120 times on each iteration Figure 11: Running CLARA on OA (Output Area) for 120 times on each iteration Figure 12: Running GA on OA (Output Area) for 120 times on each iteration