Lab 6

Part 1

Significance Map guerry_dotn_sig

Cluster Map guerry_dotn_cluster

Natural Breaks Map guerry_dotn_natbrk

Significance Map with 999 Randomizations guerry_dotn_sig_random_pre99999

Significance Map with 99999 Randomizations guerry_dotn_sig_random_post99999

Cluster Map with 999 Randomizations guerry_dotn_clust_random_pre99999

Cluster Map with 99999 Randomizations guerry_dotn_clust_random_post99999

Selecting High-High Regions by Scatterplot Quadrant guerry_dotn_clustscatter_select_hh

Selecting High-High Regions Manually guerry_dotn_clust_select_hh

Selecting Low-High Regions from the Menu guerry_dotn_clust_select_lh

Adding LISA Stats to Table guerry_dotn_clust_lisastats_table

Selection Using Significance Filter guerry_dotn_clust_select_sig_filter

Highlighting Multiple Significance Factors guerry_dotn_sig_mult_sigs

Selection Using Custom FDR Significance Filter guerry_dotn_clust_custom_sig_filter

Selection Using Custom FDR Significance Filter guerry_dotn_clust_fdr

Displaying Cores and Neighbors cluster_cores_and_neighbors

Overlaying Cores and Neighbors cluster_cores_and_neighbors_overlaid

Selecting Significance from Cores and Neighbors cluster_cores_and_neighbors_signif

Conditional Cluster Map guerry_dotn_clust_cond

Greary Significance Map guerry_greary_sig

Greary Cluster Map guerry_greary_cluster

Selecting High-High Regions in Greary Scatterplot by Quadrant guerry_greary_dotn_clustscatter_select_hh

Selecting Low-Low Regions from Menu guerry_greary_dotn_clust_select_ll

Selecting Negative Correlation Regions from Menu guerry_greary_dotn_clust_select_neg

Greary Significance Map (varies from Moran I) guerry_greary_dotn_clust_diff

Greary Cluster Map with Significance of p = 0.1 guerry_greary_dotn_clust_01sig

Greary Cluster Map with Custom Significance of p = 0.00012 guerry_greary_dotn_clust_custom_sig

Gi Significance Map guerry_gi_sig

Gi Cluster Map guerry_gi_cluster

Gi Cluster Map with Significance of p = 0.1 guerry_gi_dotn_clust_01sig

Greary Significance Map (varies from Moran I and Greary) guerry_gi_dotn_signif_diff

Gi Cluster Map with Custom FDR Significance guerry_gi_dotn_clust_fdr

Part 2

For my part 2 analysis I ran a regression on the Baltimore City dataset to see if family size was correlated with poverty level.

pt2_size_pov_regr

The regression report calculated a Moran’s I value of 0.00016, suggesting that the two were strongly correlated. I then examined the values for Lagrange Lag and error, which were as follows:

Lag = 0.42748

Error = 0.00086

These results would indicate that the error model results are extremely significant, thus it is a better fit.

pt2_size_pov_regr_report

I then ran the error model, which yielded a probability of 0.00252, confirming my hypothesis of correlation.

pt2_hhsize_pov_regr_errormod_report

The resulting Bivariate Moran’s I Maps are displayed below.

Cluster Map pt2_hhsize_pov_regr_moran_cluster

Significance Map pt2_hhsize_pov_regr_moran_signif

Scatterplot pt2_hhsize_pov_regr_moran_scatter

Conditional Significance Map pt2_hhsize_pov_regr_moran_conditional

Part 3