The link between the independent factors and the dependent variable’s log-odds (binary outcome) is represented by the coefficients in logistic regression.
When the logistic regression model is being trained, the coefficients are estimated.
The projected probabilities are then obtained by applying the logistic function, also known as the sigmoid function, to these log-odds.
Logistic regression coefficients in the context of geographical data can be interpreted in a manner similar to that of ordinary logistic regression, but with a spatial context.
The link between the spatially distributed independent variables and the likelihood that an event will occur (binary outcome) will be attempted to be captured by the logistic regression model.
I used the formula log-odds = B0 + B1 * LATITUDE + B2 * LONITUDE +… + Bn * LONGITUDE because our data includes both longitude and latitude.
In this case, log-odds represents the odds’ natural logarithm.
