Spatial Data Analysis of Complex Urban Systems

Cities are complex systems that are constantly evolving to better allow people to connect with one another. Moreover, and similar to countless natural phenomena, cities exhibit inherent orders that can be captured and expressed through complex analyses of their components. Using a variety of large datasets, this work offers a ring-buffer approach to analyze the spatial characteristics of four components of Chicago urban system, namely: roads, intersections, buildings, and population+ employment. The complex nature of these four components manifests itself in power-law relationships, represented by their fractal dimensions. Results show that road length and number of intersections, and to a larger degree, population+ employment count and building gross floor area exhibit significantly similar properties. The proposed method could further be used to analyze large demographic, socio-economic, and other geospatial datasets with the aim to study their impacts on relevant urban systems characteristics, including mobility, connectivity, and accessibility to name a few.