A bias in estimating urban population density functions

1978 ◽  
Vol 5 (1) ◽  
pp. 35-45 ◽  
Author(s):  
Mark W. Frankena
Keyword(s):  
Population Density ◽  
Urban Population ◽  
Density Functions

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

Urban Studies ◽  
2019 ◽  
Vol 57 (10) ◽  
pp. 2147-2162 ◽  
Author(s):  
Yi Qiang ◽  
Jinwen Xu ◽  
Guohui Zhang
Keyword(s):  
Population Density ◽  
Urban Population ◽  
Population Distribution ◽  
Empirical Evaluation ◽  
Urban Morphology ◽  
Geospatial Data ◽  
Job Displacement ◽  
Density Functions ◽  
Negative Exponential Function ◽  
City Centres

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.

Form Follows Function: Reformulating Urban Population Density Functions

Urban Studies ◽  
1992 ◽  
Vol 29 (7) ◽  
pp. 1043-1069 ◽  
Author(s):  
Michael Batty ◽  
Kwang Sik Kim
Keyword(s):  
Population Density ◽  
Urban Population ◽  
Density Functions

Some tests of alternative urban population density functions

1976 ◽  
Vol 3 (3) ◽  
pp. 242-252 ◽  
Author(s):  
John F. McDonald ◽  
H.Woods Bowman
Keyword(s):  
Population Density ◽  
Urban Population ◽  
Density Functions

scholarly journals Effectiveness of Government Policies in Controlling COVID-19 in India

2020 ◽  
pp. 002073142098374
Author(s):  
Ashutosh Pandey ◽  
Nitin Kishore Saxena
Keyword(s):  
Population Density ◽  
Rural Areas ◽  
Rural Population ◽  
Urban Areas ◽  
Urban Population ◽  
Demographic Factors ◽  
Poverty Rate ◽  
Government Policies ◽  
Cumulative Number ◽  
Population Number

The purpose of this study is to find the demographic factors associated with the spread of COVID-19 and to suggest a measure for identifying the effectiveness of government policies in controlling COVID-19. The study hypothesizes that the cumulative number of confirmed COVID-19 patients depends on the urban population, rural population, number of persons older than 50, population density, and poverty rate. A log-linear model is used to test the stated hypothesis, with the cumulative number of confirmed COVID-19 patients up to period [Formula: see text] as a dependent variable and demographic factors as an independent variable. The policy effectiveness indicator is calculated by taking the difference of the COVID rank of the [Formula: see text]th state based on the predicted model and the actual COVID rank of the [Formula: see text]th state[Formula: see text]Our study finds that the urban population significantly impacts the spread of COVID-19. On the other hand, demographic factors such as rural population, density, and age structure do not impact the spread of COVID-19 significantly. Thus, people residing in urban areas face a significant threat of COVID-19 as compared to people in rural areas.

scholarly journals PENGARUH FAKTOR EKONOMI, SOSIAL EKONOMI DAN IKLIM TERHADAP BENCANA ALAM DI INDONESIA

Jurnal Ecogen ◽  
2019 ◽  
Vol 1 (3) ◽  
pp. 539
Author(s):  
Surya Irmayani ◽  
Zul Azhar ◽  
Melti Roza Adry
Keyword(s):  
Economic Growth ◽  
Population Density ◽  
Natural Disasters ◽  
Urban Population ◽  
Participation Rate ◽  
Least Square ◽  
Ordinary Least Square ◽  
Descriptive Research ◽  
Negative Effect ◽  
Education Participation

This purpose of the research  are to the analyse the Economic Growth, Education Participation Rate, Urban Population, Population Density, Number of Rainfall in terms of Damage Natural Disasters in Indonesia. This type of research is associative descriptive research. This study is based on data 2015 obtained from institutions and related institution. Methods that being used are Ordinary Least Square (OLS). The estimation results show that Economic Growth has a significant negative effect the Damage Natural Disasters in Indonesia, Education Participation Rate has a not significant effect the Damage Natural Disasters in Indonesia, Urban Population has a significant positive effect the Damage Natural Disasters in Indonesia, Population Density has a not significant effect the Damage Natural Disasters in Indonesia, Number of rainfall has a not significant effect the Damage Natural Disasters in Indonesia. Keywords: Economic Growth, Education Participation Rate, Urban Population, Population Density, Number of Rainfall

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