The method proposed is to use statistical analysis of time series between your data and the climate/environmental variables.

Figure 1: Time series analysis between a particular data (in this case: disease) and a climate/environmental variable.

In the above interactive graph, the X axis represents time. The Y axis could represent rainfall in millimeter or temperature in degree Celsius or vegetation index (no unit), etc.

The example above shows seasonality between the disease (red line) and a climate variable (green line). The seasonality between these two might have a lag; move the cursor to show different lags.

For your particular research, the spatial and temporal relationship between your data (red line) and climate/environmental variables (green line) might take on different forms. It is important to remember that, when the relationship between your data and climate/environmental variables shows a seasonality, you must:

1. Remove the seasonality and work on anomalies (see Lesson 11).
2. Run statistical analysis with different lags (see Lesson 6).