## Table 2 - Input Parameters for 38 Trial Cluster Analyses on Land-Use Distress

Case | Distance | Linage | Standardization | No. of clusters | Comments |
---|---|---|---|---|---|

1 | Euclidean | Single | None | 20 | Produced single large clusters of over 300 districts with no cluster larger than 5 districts; chaining effect took place |

2 | Euclidean | Single | None | 15 | Produced single large clusters of over 300 districts with no cluster larger than 5 districts; chaining effect took place |

3 | Euclidean | Single | None | 25 | Produced single large clusters of over 300 districts with no cluster larger than 5 districts; chaining effect took place |

4 | Euclidean | Average | None | 20 | Smaller main clusters and larger minor clusters but not much diversity in residential scenarios |

5 | Euclidean | Average | None | 30 | Smaller main clusters and larger minor clusters but not much diversity in residential scenarios |

6 | Euclidean | Average | None | 18 | Smaller main clusters and larger minor clusters but not much diversity in residential scenarios |

7 | Euclidean | Centroid | None | 20 | Relatively poor distribution of cluster sizes and poor diversity in residential scenarios |

8 | Euclidean | Centroid | None | 30 | Relatively poor distribution of cluster sizes and poor diversity in residential scenarios |

9 | Euclidean | Complete | None | 20 | Somewhat more diversity in residential scenarios |

10 | Euclidean | Complete | None | 17 | Somewhat more diversity in residential scenarios |

11 | Euclidean | McQuitty | None | 20 | Little diversity in residential scenarios |

12 | Euclidean | Median | None | 20 | Large mega-cluster, like with single linkage |

13 | Euclidean | Ward | None | 20 | Relatively equal cluster sizes; good diversity |

14 | Pearson | Average | None | 20 | Pearson and Squared Pearson linkages created mega-clusters; Manhattan seemed almost as good as Euclidean; Squared Euclidean was nearly identical to Euclidean |

15 | Manhattan | Average | None | 20 | Pearson and Squared Pearson linkages created mega-clusters; Manhattan seemed almost as good as Euclidean; Squared Euclidean was nearly identical to Euclidean |

16 | Sq. Euclidean | Average | None | 20 | Pearson and Squared Pearson linkages created mega-clusters; Manhattan seemed almost as good as Euclidean; Squared Euclidean was nearly identical to Euclidean |

17 | Sq. Pearson | Average | None | 20 | |

18 | Pearson | Complete | None | 20 | Pearson and Squared Pearson linkages created identical mega-clusters; Manhattan created slightly more diversity than Euclidean; Squared Euclidean identical with Euclidean |

19 | Manhattan | Complete | None | 20 | Pearson and Squared Pearson linkages created identical mega-clusters; Manhattan created slightly more diversity than Euclidean; Squared Euclidean identical with Euclidean |

20 | Sq. Euclidean | Complete | None | 20 | Pearson and Squared Pearson linkages created identical mega-clusters; Manhattan created slightly more diversity than Euclidean; Squared Euclidean identical with Euclidean |

21 | Sq. Pearson | Complete | None | 20 | |

22 | Pearson | McQuitty | None | 20 | Changing the distance measure has similar effects as with average and complete linkages |

23 | Manhattan | McQuitty | None | 20 | Changing the distance measure has similar effects as with average and complete linkages |

24 | Sq. Euclidean | McQuitty | None | 20 | Changing the distance measure has similar effects as with average and complete linkages |

25 | Sq. Pearson | McQuitty | None | 20 | Changing the distance measure has similar effects as with average and complete linkages |

26 | Pearson | Ward | None | 20 | Pearson and Squared Pearson produced slightly less diversity in residential and agricultural scenarios; Squared Euclidean lumped agricultural scenarios together; Manhattan similar to Euclidean |

27 | Manhattan | Ward | None | 20 | Pearson and Squared Pearson produced slightly less diversity in residential and agricultural scenarios; Squared Euclidean lumped agricultural scenarios together; Manhattan similar to Euclidean |

28 | Sq. Euclidean | Ward | None | 20 | Pearson and Squared Pearson produced slightly less diversity in residential and agricultural scenarios; Squared Euclidean lumped agricultural scenarios together; Manhattan similar to Euclidean |

29 | Sq. Pearson | Ward | None | 20 | |

30 | Sq. Euclidian | Ward | Z-scores | 20 | Similar diversity to case without standardization (case 28) but oddly distributed variables better represented |

31 | Euclidean | Complete | Z-scores | 20 | Much poorer diversity than in case 9 |

32 | Euclidean | Average | Z-scores | 20 | Forms mega-cluster; worse than case 4 |

33 | Euclidean | Ward | Z-scores | 20 | More diverse in some areas than with case 13 |

34 | Sq. Euclidean | Ward | Z-scores | 30 | Improved diversity over case 30 |

35 | Euclidean | Ward | Z-scores | 30 | More diverse than case 33 |

36 | Euclidean | Ward | None | 30 | Similar diversity to case 35 but oddly distributed variables like R3, R4 not as well represented |

37 | Euclidean | Ward | Scaled percentages | 30 | Oddly distributed variables well-represented but not enough of an improvement in variable bounds |

38 | Sq. Euclidean | Ward | Z-scores | 35 | Number of clusters increase to 35 to separate a few odd groupings |

Updated: Saturday, May 20, 2017