Rural–urban scaling of age, mortality, crime and property reveals a loss of expected self-similar behaviour

Abstract

The urban scaling hypothesis has improved our understanding of cities; however, rural areas have been neglected. We investigated rural–urban population density scaling in England and Wales using 67 indicators of crime, mortality, property, and age. Most indicators exhibited segmented scaling about a median critical density of 27 people per hectare. Above the critical density, urban regions preferentially attract young adults (25–40 years) and lose older people (> 45 years). Density scale adjusted metrics (DSAMs) were analysed using hierarchical clustering, networks, and self-organizing maps (SOMs) revealing regional differences and an inverse relationship between excess value of property transactions and a range of preventable mortality (e.g. diabetes, suicide, lung cancer). The most striking finding is that age demographics break the expected self-similarity underlying the urban scaling hypothesis. Urban dynamism is fuelled by preferential attraction of young adults and not a fundamental property of total urban population.

Homogeneous and heterogeneous propagation of COVID-19 from super-spreading to super-isolation

“…This behaviour is consistent with the age demographics in England and Wales. Previous work has documented that populations dense regions serve and a magnet for young people while rural regions tend to have a greater proportion of elderly people [11]. The scaling exponents for death throughout are consistent with those seen for scaling of people 60 and above in England and Wales.…”

“…The data were analysed using the statistical software R version (3.6.2) [19] with the sf (0.9-1) [20], raster (3.0-12) [21], dplyr (0.8.5) [22], spData (0.3.5) [23], tmap (2.3-2) [24], ggplot2 (3.3.0) [25][26][27][28], xlsx (0.5.7) [29], gplots (3.0.4) [30] , httr (1.4.2) [31], plyr (1.8.5) [32], png (0.1-7) [33], rgdal (1. [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] [34], rgeos (0.5-5) [35], lubridate (1.7.9.2) [36], fitdistrplus (1.1-3) [37], fgarch (3042.83.2) [38] and glogis (1.0-1) [39] packages.…”

“…The effects of population size on COVID-19 dynamics have been investigated previously including aspects of population density effects [6][7][8][9]. Investigations of population density effects have been limited to a relatively small number of time points aggregated over a period of time, usually a month or year [10][11][12][13][14][15][16]. Daily granularity of data is not easily accessible; however, the COVID-19 pandemic has provided a unique and evolving data set with daily updates.…”

Scaling of mortality in 742 metropolitan areas of the Americas

“…More details on these three steps are available elsewhere ( 65 ). We also adjusted for the compositional effect of different age structures across cities, which have been shown to drive some scaling patterns ( 18 ) and which have a strong effect on mortality patterns. For this, we calculated the proportion of city residents aged 0 to 14, 15 to 39, 40 to 64, and 65 years and above, for the years of the study.…”

Population density and spreading of COVID-19 in England and Wales

“…In some circumstances Eq 3 needs to be adjusted to account for a breakpoint to allow for a segmented fit. Empirically, the breakpoint, d *, for a range of indicators usually occurs in the range of 10–70 people per hectare [ 19 , 21 ]. Thus, Eq 3 can be adapted to allow for such fit and is given by Where β L and Y 0 are the exponent and pre-exponential factors below the breakpoint; β H and Y 1 are the exponent and pre-exponential factor above the breakpoint.…”