The shapes of US cities: Revisiting the classic population density functions using crowdsourced geospatial data

Abstract

The declining pattern of population density from city centres to the outskirts has been widely observed in American cities. Such a pattern reflects a trade-off between housing price/commuting cost and employment. However, most previous studies in urban population density functions are based on the Euclidean distance, and do not consider commuting cost in cities. This study provides an empirical evaluation of the classic population density functions in 382 metropolitan statistical areas (MSA) in the USA using travel times to city centres as the independent variable. The major findings of the study are: (1) the negative exponential function has the overall best fit for population density in the MSAs; (2) the Gaussian and exponential functions tend to fit larger MSAs, while the power function has better performance for small MSAs; (3) most of the MSAs appear to show a decentralisation trend during 1990–2016, and larger MSAs tend to have a higher rate of decentralisation. This study leverages crowdsourced geospatial data to provide empirical evidence of the classic urban economic models. The findings will increase our understanding about urban morphology, population–job displacement and urban decentralisation. The findings also provide baseline information to monitor and predict the changing trend of urban population distribution that could be driven by future environmental and technological changes.

Magnets of creative talents in Yangtze River Delta Bay area

“…For Jiangsu, the most significant indicator represents housing affordability, surpassing that of scientific infrastructure, women’s rights and medical care amenities, revealing the vital effect of real estate on migration and residence of creative talents in this area. Housing price and commuting cost could pose a push effect on population density and cause decentralization in cities (Qiang et al , 2020). Therefore, proper policies of commercial housing are in urgent demand for talent retaining and talents inflow encouragement.…”

Big Data in the city

“…It can, for example, illuminate the daily rhythms and activities of the city, creating ‘rich databases of neighbourhood and other place-based contexts’ (Sampson, 2013: 9). Qiang et al (2020) provide one such example, in their examination of the urban population density function. This function, long thought to be the outcome of trade-off between housing price, commuting cost and employment, has previously not been possible to fully operationalise and test due to a lack of suitable commuting cost data.…”

Is the monocentric urban economic model still empirically relevant? Assessing urban econometric predictions in 192 cities on five continents

“…As in Qiang et al (2020), we use commuting times and distances from Google Maps' data. We are going two steps further by considering two transportation modes and by accounting for both opportunity and monetary costs.…”

“…These papers use distance to the city center as a rough proxy for commuting costs. The recent paper by Qiang et al (2020) on density profiles of US cities, is, to our knowledge, the only study going further and using actual transportation times as a proxy for transportation generalized costs. Several papers also checked the aggregated predictions of the SUM on the size of urbanized areas, i.e.…”