When I analyzed the PCA plot of the simulated crime data, I noticed clear patterns related to urbanization and crime rates. The First Principal Component (PC1) captured the majority of the variance in the data, and I saw it was heavily influenced by variables like UrbanPop, AssaultRate, and RapeRate. This told me that urban areas are strongly linked to higher occurrences of certain crimes, particularly assault and rape. The Second Principal Component (PC2) showed patterns that PC1 didn’t explain. I noticed that MurderRate had a unique relationship, moving in a different direction compared to the other variables. This made me think that murder might not always follow the same trends as assault or rape and could be influenced by other factors beyond urbanization.

Vẽ biểu đồ với ggplot 2 - Tập huấn phân tích số liệu cơ bản ngày 2 tại BV SIS Cần Thơ 02-06/1/2024

Evaluating several methods for dividing valley into regions based on river reaches.