Geographically Weighted Regression (GWR) is a local version of spatial regression that captures spatial dependency in regression analysis. GWR has many application in practice as a visualization and prediction tool for spatial exploration (e.g in climate, economy, medical). However, this locally regression model is slow in process upon the volume of calculations and the spatial getting bigger. Improving performance of GWR is a critical issue, but their distributed implementations have not been studied. Recently, with the advent of Spark as well MapReduce framework, the development of machine learning applications and parallel programming becomes easier. In this article, we propose several large-scale implementations of distributed GWR, leveraging Spark framework. We implemented and evaluated these approaches with large datasets. To our best knowledge, this is the first work addressing GWR at large-scale.