In logistic regression, the coefficients represent the relationship between the independent variables and the log-odds of the dependent variable (binary outcome).The coefficients are estimated during the training of the logistic regression model. The logistic function (sigmoid function) is then applied to these log-odds to obtain the predicted probabilities.
In the context of geospatial data, logistic regression coefficients can be interpreted similarly to logistic regression in general, but with a spatial context. The logistic regression model will try to capture the relationship between the spatially distributed independent variables and the probability of an event occurring (binary outcome).
Since our data has both longitude and latitude, i used the formula
log-odds = B0 +B1 * LATITUDE + B2 * LONITUDE + … + Bn * LONGITUDE.
here,
log-odds is the natural logarithm of the odds of the event occurring.
B0 is the intercept
B1 and B2 are the coefficients associated with latitude and longitude.
The coefficients are estimated during the training of the logistic regression model. The logistic regression model would then predict the probability of an event occurring at different locations in your geospatial dataset.
In the code, used the longitude and latitude variables to predict if the event occurs. Later, trained the logistic regression model and coefficients were displayed.