A big data approach to modelling urban population density functions: from monocentricity to polycentricity

Abstract

Urban studies have a long tradition of examining the regularity of urban structure by modelling urban population density functions and probing the theoretical or behavioural foundation behind it. Previous studies commonly used census data in areal units such as census tracts or census block groups, which varied a great deal in area size and shape and led to the zonal and scale effects, commonly referred to as the modifiable areal unit problem (MAUP). This study uses big data of individual vehicle trips in Tampa, Florida, to define the precise population and employment distribution locations, and then aggregates them with uniform areal units such as squares, triangles, and hexagons to examine and mitigate the scale and zonal effects. Both monocentric and polycentric models are employed in the analysis of urban population density functions. The results suggest that the exponential density function remains the best fitting monocentric function in most areal units including census units and designed uniform units. The polycentric model reveals two centres (downtown and University of South Florida) exerting influences on the areawide population density pattern. The zonal effect is not significant in the designed uniform units, but the scale effect remains evident in all areal units