# How to use LAT/LNG as predictor variables

I'm working on geographic data where I need to predict the average income per geo key/zip code. The data I have consisted of more than 30 million unique geo keys in Zip+4 format. As per my understanding, this many geokeys won't be a good predictor so, I converted them into geo points (LAT/LNG). So the data looks like this,

LAT LNG Avg_inc
39.829506 105.013535 47374.5

I tried Linear regression, Random Forest, and SGD Regressor on this data but the results are not looking good. Also, I build a BigQuery model for the same but the I am not getting good results. I am so confused now, I want to know how to use these geographic data (LAT/LNG) for any regression/classification problem. Please suggest.

• I would bet on Random Forest. Try LAT + LNG and LAT - LNG as extra features Jun 10 at 18:21
• Thanks, @IvanReshetnikov. It actually worked for me! I also added another feature, Household Count for each pair of lat and long. Jun 14 at 14:20