Identify hot spots, cold spots & outliers
Detect statistically significant hotspots, cold spots and outliers. Make decisions based on these observed patterns. Answer questions such as, “Where are clusters of high expenditures on electronic goods?” or “Where are the hot spots of cancer deaths?”
Detect natural groups or clusters
Find clusters in your data by combining location information with multiple variables. For example, find areas with similar vulnerability characteristics based on socioeconomic status, governance, population density and climate change.
Identify change over time
Gain additional knowledge by adding temporal data. Analyse how spatial patterns (such as hotspots, low spots, clusters and outliers) have changed in both time and space. Answer question based on what you see: “Are rich and poor communities becoming clustered?” or “Have pine beetle outbreak hotspots grown larger or smaller?”
Agriculture and Agri-Food Canada (AAFC)
How AAFC helps agricultural producers in Canada better understand crop patterns and make decisions that are economically and environmentally viable.