Explaining the variation in historical trends for agriculture and population density using soil, climate, and topography data

2021 ◽  
pp. 1-19
Author(s):  
Yannik E. Roell ◽  
Niels M. Jacobsen ◽  
Morten Graversgaard ◽  
Nele Lohrum ◽  
Amélie Beucher ◽  
...  
Keyword(s):  
Population Density ◽  
Historical Trends ◽  
Soil Climate ◽  
Topography Data

Use of Inferential Statistics in JEAB: Historical Trends

2010 ◽  
Author(s):  
Laurence D. Smith ◽  
Lisa A. Best ◽  
Alan Stubbs
Keyword(s):  
Inferential Statistics ◽  
Historical Trends

Historical Trends in Women's Career Interests

2007 ◽  
Author(s):  
Neeta Kantamneni ◽  
Paul A. Gore
Keyword(s):  
Career Interests ◽  
Historical Trends

Convergence of regional population density in Russia over 120 years

2020 ◽  
pp. 133-158
Author(s):  
K. A. Kholodilin ◽  
Y. I. Yanzhimaeva
Keyword(s):  
Population Density ◽  
Spatial Dependence ◽  
Population Distribution ◽  
Regional Level ◽  
Capital City ◽  
County Level ◽  
Spatial Effects ◽  
Russian Regions ◽  
Regional Population ◽  
Regional Population Density

A relative uniformity of population distribution on the territory of the country is of importance from socio-economic and strategic perspectives. It is especially important in the case of Russia with its densely populated West and underpopulated East. This paper considers changes in population density in Russian regions, which occurred between 1897 and 2017. It explores whether there was convergence in population density and what factors influenced it. For this purpose, it uses the data both at county and regional levels, which are brought to common borders for comparability purposes. Further, the models of unconditional and conditional β-convergence are estimated, taking into account the spatial dependence. The paper concludes that the population density equalization took place in 1897-2017 at the county level and in 1926—1970 at the regional level. In addition, the population density increase is shown to be influenced not only by spatial effects, but also by political and geographical factors such as climate, number of GULAG camps, and the distance from the capital city.

Extension of the Spatial PDI Model to Three Dimensions

1997 ◽  
Vol 84 (1) ◽  
pp. 176-178
Author(s):  
Frank O'Brien
Keyword(s):  
Population Density ◽  
Dimensional Space ◽  
Finite Lattice ◽  
Three Dimensional ◽  
Upper Bounds ◽  
Three Dimensions ◽  
Lower And Upper Bounds ◽  
Density Index ◽  
Three Dimensional Space

The author's population density index ( PDI) model is extended to three-dimensional distributions. A derived formula is presented that allows for the calculation of the lower and upper bounds of density in three-dimensional space for any finite lattice.

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