Urban and rural population size and growth at the country level

2019 ◽  
pp. 33-53
Author(s):  
Keyword(s):  
Population Size ◽  
Rural Population ◽  
Country Level

SECTORAL SPECIAL FEATURES OF TERRITORIAL DEVELOPMENT OF PETERSBURG CITY AGGLOMERATION

2021 ◽  
Vol 2 (65) ◽  
pp. 101-112
Author(s):  
Victor V. Solodilov ◽  
Keyword(s):  
Population Size ◽  
Rural Population ◽  
Territorial Development ◽  
Present Stage ◽  
Structural Units ◽  
Core Zone

The article presents the structure of the St. Petersburg city agglomeration at the present stage of its development: agglomeration core, zone of satellites, planning sectors. The problems and trends, sectoral special features of agglomeration development are described. The author describes the parameters of territorial development of the St. Petersburg city agglomeration, the main characteristics of its structural units: the population size, its density, the proportion of urban and rural population.

Globalisering in popmuziekhitlijsten in negen landen, 1960-2010

2013 ◽  
Vol 41 (4) ◽  
Author(s):  
Marc Verboord ◽  
Amanda Brandellero
Keyword(s):  
Population Size ◽  
Pop Music ◽  
Global Trends ◽  
Cultural Globalization ◽  
Star Power ◽  
Country Level ◽  
Cultural Proximity ◽  
Level Data ◽  
The Us ◽  
Media Systems

Going global. Trends in pop music charts 1960-2010 Going global. Trends in pop music charts 1960-2010 This paper studies the cultural globalization of pop music by (a) describing trends in pop music single charts in nine countries in the period 1960-2010, and (b) explaining global success using a double comparative design in which multiple origin groups are observed in multiple destinations. Our explanatory analyses thus comprise country level data (degree of cultural centrality of music industry, cultural proximity, media systems, political context, GDP, population size) and artist level data (language, star power) which affect global flows of pop music. The results show that pop charts are increasingly globalizing, with the exception of the US. Centrality of production in the origin country is highly important, yet after 1990 many European countries also host more domestic music. In addition, we find clear effects of cultural proximity. Artists’ star power as well as the language they perform in also impacts global success.

scholarly journals Estimating the size of undetected cases of the SARS-CoV-2 outbreak in Europe: An upper bound estimator

2020 ◽  
Author(s):  
Irene Rocchetti ◽  
Dankmar Boehning ◽  
Heinz Holling ◽  
Antonello Maruotti
Keyword(s):  
Lower Bound ◽  
Population Size ◽  
Upper Bound ◽  
Population Based ◽  
Parametric Models ◽  
European Countries ◽  
Data Driven ◽  
Open Population ◽  
Country Level ◽  
True Number

Background: While the number of detected SARS-CoV-2 infections are widely available, an understanding of the extent of undetected cases is urgently needed for an effective tackling of the pandemic. The aim of this work is to estimate the true number of SARS-CoV-2 (detected and undetected) infections in several European Countries. The question being asked is: How many cases have actually occurred? Methods: We propose an upper bound estimator under cumulative data distributions, in an open population, based on a day-wise estimator that allows for heterogeneity. The estimator is data-driven and can be easily computed from the distributions of daily cases and deaths. Uncertainty surrounding the estimates is obtained using bootstrap methods. Results: We focus on the ratio of the total estimated cases to the observed cases at April 17th. Differences arise at the Country level, and we get estimates ranging from the 3.93 times of Norway to the 7.94 times of France. Accurate estimates are obtained, as bootstrap based intervals are rather narrow. Conclusions: Many parametric or semi-parametric models have been developed to estimate the population size from aggregated counts leading to an approximation of the missed population and/or to the estimate of the threshold under which the number of missed people cannot fall (i.e. a lower bound). Here, we provide a methodological contribution introducing an upper bound estimator and provide reliable estimates on the dark number, i.e. how many undetected cases are going around for several European Countries, where the epidemic spreads differently.

Plotting biological data in various ways.

2021 ◽  
pp. 37-78
Author(s):  
Donald L. J. Quicke ◽  
Buntika A. Butcher ◽  
Rachel A. Kruft Welton
Keyword(s):  
Population Size ◽  
Rural Population ◽  
Biological Data ◽  
Statistical Testing ◽  
Line Graphs ◽  
Confidence Limits ◽  
Trematode Infection ◽  
Error Bars

Abstract This chapter introduces plotting line graphs, bar charts, pie charts, box and whisker plots. It will troubleshoot the main areas where you are likely to encounter problems. It will show how to create log plots, add legends, error bars, notches and confidence limits, and introduce confidence limits and statistical testing. Examples are given, including bryophytes up a mountain; relationship between rural population size and the potential remaining intact forest; dietary differences between hornbill species (Buceros bicornis, Rhyticeros undulatus, Anthracoceros albirostris and Anorrhinus (Ptilolaemus) tickelli); and study of the level of trematode infection in various species of fish in Thailand.

scholarly journals Fair allocation of healthcare research funds by the European Union?

2018 ◽  
Author(s):  
Zoltán Kaló ◽  
Loek Hendrik Matheo van den Akker ◽  
Zoltán Vokó ◽  
Marcell Csanádi ◽  
György János Pitter
Keyword(s):  
Population Size ◽  
Disease Burden ◽  
Healthcare Research ◽  
Gdp Per Capita ◽  
Regression Analyses ◽  
Research Grants ◽  
Country Level ◽  
Per Capita ◽  
Grant Allocation ◽  
Research Excellence

AbstractThis study aimed to investigate the distribution of European Union (EU) healthcare research grants across EU countries, and to study the effect of the potential influencing factors on grant allocation. We analysed publicly available data on healthcare research grants from the 7th Framework Programme and the Horizon 2020 Programme allocated to beneficiaries between 2007 and 2016. Grant allocation was analysed at the beneficiary-, country-, and country group-level (EU-15 versus newer Member States, defined as EU-13). The investigated country-level explanatory variables included GDP per capita, population size, overall disease burden, and healthcare research excellence. Grant amounts per 100,000 inhabitants was used as an outcome variable in the regression analyses.Research funds were disproportionally allocated to EU-15 versus the EU-13, as 96.9% of total healthcare grants were assigned to EU-15 countries. At the beneficiary level, EU funding was positively influenced by participating in previous grants. The average grant amount per beneficiary was higher for EU-15 organizations. In univariate regression analyses at the country level, higher GDP per capita (p<0.001) and better medical research excellence (p<0.001) were associated with more EU funding, and a higher disease burden was associated with less EU funding (p=0.003). In the multiple regression analysis GDP per capita (p=0.002) and research excellence (p<0.001) had a significant positive association with EU funding. Population size had an inverted U-shaped relationship with EU funding for healthcare research, having the largest per capita funding in second and the third quartiles (p=0.03 and p=0.02).The uneven allocation of healthcare research funds across EU countries was influenced by GDP per capita, medical research excellence and population size. Wealthier countries with an average population size and strong research excellence in healthcare had more EU funding for healthcare research. Higher disease burden apparently was not associated with more EU research funding.

scholarly journals Mathematical modeling of the hierarchical structure of the spatial distribution of the medieval rural population

2020 ◽  
Author(s):  
S. Shpirko ◽  
Keyword(s):  
Mathematical Modeling ◽  
Spatial Distribution ◽  
Population Size ◽  
Rural Population ◽  
Variational Approach ◽  
The Hierarchical Structure ◽  
The Subject ◽  
High Degree ◽  
The Relationship ◽  
Density Population

The subject of paper is the mathematical modeling of the spatial distribution of the medieval rural population. On the basis of the variational approach, two models of the hierarchy of centers are being developed, allowing with a high degree of reliability to identify the factors of the development of the settlement structure and to describe quantitatively the relationship between its most important parameters, such as density, population size and area.

scholarly journals Rural population dependence on the level of agricultural development: panel data analysis of Leningrad oblast

POPULATION ◽  
2021 ◽  
Vol 24 (1) ◽  
pp. 90-102
Author(s):  
Yulia Nikulina ◽  
Tatiana Yurchenko ◽  
Vladimir Surovtsev
Keyword(s):  
Panel Data ◽  
Rural Development ◽  
Population Size ◽  
Rural Areas ◽  
Rural Population ◽  
Agricultural Production ◽  
Agricultural Development ◽  
Small Scale ◽  
Leningrad Oblast ◽  
Agricultural Producers

Rural development has been and remains a relevant government task. Dynamic structural and technological changes in the agricultural sector lead to the need of reassessing the mutual influence of the level of development of agricultural production and rural areas. The study deals with quantitative assessment of the dependence of rural population size as an integral indicator of socio-economic well-being of rural areas on selected factors and indicators that characterize the level of agricultural development, its sectoral specifics and the structure of agricultural producers. Empirical estimates were obtained from panel data of municipal districts in Leningrad oblast for 2012-2018. The greatest positive impact on the rural population size among the considered characteristics of agriculture is determined for the factor of sown areas that is associated with the specifics of agricultural sub-sectors, their different needs for such factors as land and labor, the development potential for small-scale farming. It was found that the concentration of agricultural production in the large commercial sector has a negative impact on the rural population size. This is explained by difference in employment dynamics and redistribution of resources between categories of agricultural producers. Modeling results showed that agrarian subsidies received by agricultural producers have a statistically insignificant impact on rural population that justifies the need to adjust the orientation and forms of agricultural state support to achieve a synergetic effect on rural development.

Plotting biological data in various ways.

2021 ◽  
pp. 37-78
Author(s):  
Donald L. J. Quicke ◽  
Buntika A. Butcher ◽  
Rachel A. Kruft Welton
Keyword(s):  
Population Size ◽  
Rural Population ◽  
Biological Data ◽  
Statistical Testing ◽  
Line Graphs ◽  
Confidence Limits ◽  
Trematode Infection ◽  
Error Bars

Abstract This chapter introduces plotting line graphs, bar charts, pie charts, box and whisker plots. It will troubleshoot the main areas where you are likely to encounter problems. It will show how to create log plots, add legends, error bars, notches and confidence limits, and introduce confidence limits and statistical testing. Examples are given, including bryophytes up a mountain; relationship between rural population size and the potential remaining intact forest; dietary differences between hornbill species (Buceros bicornis, Rhyticeros undulatus, Anthracoceros albirostris and Anorrhinus (Ptilolaemus) tickelli); and study of the level of trematode infection in various species of fish in Thailand.

scholarly journals Estimating the size of undetected cases of the COVID-19 outbreak in Europe: an upper bound estimator

2020 ◽  
Vol 9 (s1) ◽  
Author(s):  
Irene Rocchetti ◽  
Dankmar Böhning ◽  
Heinz Holling ◽  
Antonello Maruotti
Keyword(s):  
Lower Bound ◽  
Population Size ◽  
Upper Bound ◽  
Population Based ◽  
Parametric Models ◽  
European Countries ◽  
Data Driven ◽  
Open Population ◽  
Country Level ◽  
True Number

Abstract Background While the number of detected COVID-19 infections are widely available, an understanding of the extent of undetected cases is urgently needed for an effective tackling of the pandemic. The aim of this work is to estimate the true number of COVID-19 (detected and undetected) infections in several European countries. The question being asked is: How many cases have actually occurred? Methods We propose an upper bound estimator under cumulative data distributions, in an open population, based on a day-wise estimator that allows for heterogeneity. The estimator is data-driven and can be easily computed from the distributions of daily cases and deaths. Uncertainty surrounding the estimates is obtained using bootstrap methods. Results We focus on the ratio of the total estimated cases to the observed cases at April 17th. Differences arise at the country level, and we get estimates ranging from the 3.93 times of Norway to the 7.94 times of France. Accurate estimates are obtained, as bootstrap-based intervals are rather narrow. Conclusions Many parametric or semi-parametric models have been developed to estimate the population size from aggregated counts leading to an approximation of the missed population and/or to the estimate of the threshold under which the number of missed people cannot fall (i.e. a lower bound). Here, we provide a methodological contribution introducing an upper bound estimator and provide reliable estimates on the dark number, i.e. how many undetected cases are going around for several European countries, where the epidemic spreads differently.

scholarly journals Geoinformation and Cartographic Methods in the Research of the Rural Population Dynamics

2021 ◽  
pp. 20-26
Author(s):  
Natalya Khavanskaya
Keyword(s):  
Population Dynamics ◽  
Spatial Analysis ◽  
Population Size ◽  
Rural Population ◽  
Population Decline ◽  
Population Census ◽  
Rural Settlements ◽  
Automated Mapping ◽  
Eastern Zone ◽  
Volgograd Region

The article deals with the dynamics of the rural population of Volgograd region for 1969–2010. The source materials of the study were archival statistical data on the population size of villages and rural settlements in 1969 and the results of the 2010 All-Russian Population Census. The main purpose of the study has two components: the study of trends in the change of the rural population size and the spatial analysis of villages and rural settlements with different directions of the population dynamics. The main methods were the geoinformation and cartographic method, combining the possibilities of automated mapping according to classified indicators. The results of the work are two maps describing the dynamics of the population of villages and rural settlements. The author used such methods of cartographic representation as the method of cartodiagrams and the method of cartograms based on the classification of the numeric fields of attribute tables. The design and composition of maps was carried out in ArcGis 10.3 geographic information system. The generalized conclusion based on the materials of the work is the prevalence of population decline trends in villages and rural settlements, the strengthening of this trend in the direction from east to west of the region. Natural and geographical areas with a predominance of the tendency for the reduction of the rural population by 50% or more are highlighted: the coast of the rivers Khoper, Buzuluk, Tersa. An increase in the rural population is observed in the districts – suburban areas of Volgograd, the Volga-Ilovlinsky interfluve. The spatial analysis of the rural population dynamics made it possible to distinguish two zones: the western zone, in which the processes of the rural population reduction are the most intense, and the eastern zone, in which, along with a decrease in the population in a number of villages and settlements, its increase is observed.

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