This methodology involves leveraging geospatial analysis techniques, including random forest algorithms and regression with the main target to assign variable values to new areas overlapping with former infrastructure.
In the future, a transition from highways and roadways to boulevards is anticipated due to a projected decrease in the number of cars. This shift is expected to address several issues commonly associated with highways, particularly in lower-income neighborhoods, such as reduced noise pollution and improved livability. This project aims to develop a data-driven urban model using Python to predict optimal areas and their characteristics for road infrastructure replacement with housing complexes.
In terms of data aggregation, data will be formatted in cells of 100x100m. Every known cell is described by 4 main entities containing a set of data:
a)Characteristics: Spatial data both is numeric and categorical values describing the dominant urban attributes of each cell. Data as such can be the building height and density as well as the dominant building use, materiality or typology.
b)Assets: Data of quantified urban qualities such as the transportation hubs, public amenities, or the green areas cover per each cell.
c)Demand: This category refers to the population and lot reserve values per cell.