The model is predicting the number of 911 calls is dependent on latitudes and longitude...naturally, it would be expected that the number of 911 calls be strongly correlated with the amount of potential victims. ("The model score (R2) is 0.81, so its accuracy is significant" is an incorrect interpretation of the R2; it indicated the amount of variation explains by the model, which is much different from an accuracy metric, and the high value could be explained by the correlation)

I built a similar model which predicts the types of crimes in San Francisco using LightGBM (better than xgboost which is better than scikit-learn's GBMs/GBTs), filling lat/long, month, day-of-week, hour, and year (http://minimaxir.com/2017/02/predicting-arrests/). The classification aspect is much tricker than a simple regression. But even then, latitude and longitude constituted 70% of the Gain in the GBM model.

(as an aside, day-of-week/hour should likely be encoded as categorical variables using one-hot-encoding, although when I tested that in my post, the results were unchanged, oddly)