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

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 E...

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Published in:	Urban studies (Edinburgh, Scotland) Vol. 57; no. 10; pp. 2147 - 2162
Main Authors:	Qiang, Yi, Xu, Jinwen, Zhang, Guohui
Format:	Journal Article
Language:	English
Published:	London, England Sage Publications, Ltd 01.08.2020 SAGE Publications Sage Publications Ltd
Subjects:	Cities City centres Commuting Crowdsourcing Decentralization Dislocated workers Economic analysis Economic conditions Economic models Employment Euclidean geometry Exponential functions Housing Housing costs Housing prices Independent variables Morphology Population (statistical) Population decline Population density Population distribution Population growth Population studies Residential patterns Spatial data Statistics Technological change Travel time Urban areas Urban population Urban populations United States > US 就业集聚劳动力人口分布 population distribution agglomeration employment transport 众包地理空间数据 crowdsourced geospatial data labour 土地使用 land use urbanisation 城市化交通
ISSN:	0042-0980, 1360-063X
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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. 从市中心到郊区的人口密度下降的规律在美国城市中已被广泛观察到。这种规律体现了房价/通勤成本和就业之间的权衡。然而，以往对城市人口密度函数的研宄大多基于欧式距离，没有考虑城市通勤成本。本研宄以前往市中心的出行时间为自变量，对美国382个大都市统计区的经典人口密度函数进行了实证评估。本研宄的主要发现是：（I)负指数函数总体上最适合大都市统计区（MSA)的人口密度；（2)高斯函数和指数函数倾向于适合较大的MSA,而幂函数更适合较小的MSA; (3)在1990-2016年期间，大多数MSA似乎显示出去中心化趋势，较大的MSA倾向于具有较高的去中心率。这项研宄利用众包地理空间数据提供经典城市经济模型的经验证据。这些发现将增加我们对城市形态、人口-工作转移和城市去中心化的理解。这些发现还为监测和预测未来环境和技术变化可能导致的城市人口分布变化趋势提供了基线信息。
AbstractList	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. 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. 从市中心到郊区的人口密度下降的规律在美国城市中已被广泛观察到。这种规律体现了房价/通勤成本和就业之间的权衡。然而，以往对城市人口密度函数的研宄大多基于欧式距离，没有考虑城市通勤成本。本研宄以前往市中心的出行时间为自变量，对美国382个大都市统计区的经典人口密度函数进行了实证评估。本研宄的主要发现是：（I)负指数函数总体上最适合大都市统计区（MSA)的人口密度；（2)高斯函数和指数函数倾向于适合较大的MSA,而幂函数更适合较小的MSA; (3)在1990-2016年期间，大多数MSA似乎显示出去中心化趋势，较大的MSA倾向于具有较高的去中心率。这项研宄利用众包地理空间数据提供经典城市经济模型的经验证据。这些发现将增加我们对城市形态、人口-工作转移和城市去中心化的理解。这些发现还为监测和预测未来环境和技术变化可能导致的城市人口分布变化趋势提供了基线信息。
Author	Xu, Jinwen Zhang, Guohui Qiang, Yi
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Keywords	就业集聚劳动力人口分布 population distribution agglomeration employment transport 众包地理空间数据 crowdsourced geospatial data labour 土地使用 land use urbanisation 城市化交通
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Snippet	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...
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SubjectTerms	Cities City centres Commuting Crowdsourcing Decentralization Dislocated workers Economic analysis Economic conditions Economic models Employment Euclidean geometry Exponential functions Housing Housing costs Housing prices Independent variables Morphology Population (statistical) Population decline Population density Population distribution Population growth Population studies Residential patterns Spatial data Statistics Technological change Travel time Urban areas Urban population Urban populations
Subtitle	Revisiting the classic population density functions using crowdsourced geospatial data
Title	The shapes of US cities
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Volume	57
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