Using Python to Streamline Regional Land Use and Parcel Data Processing
Among some of the most critical datasets MAG produces are Regional Land Use and a set of Parcel Information Tables, which detail residential and non-residential building space across the region. These datasets serve as key inputs for socioeconomic modeling and analysis. In an effort to meet increasing demands for timely and accurate data, Python programming is utilized to automate the bulk of the processing on these very large datasets. In concert with ArcPy, the Pandas data analysis library proved to be instrumental in streamlining the production of these datasets and improving their quality.