Population Distribution and Poverty

2016 ◽  
pp. 485-504 ◽  
Author(s):  
Rachel E. Dwyer ◽  
Daniel Sanchez
Keyword(s):  
Population Distribution

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.

Clinical Epidemiology of Coronavirus Disease 2019:Defined on Current Research

2020 ◽  
pp. 54-72 ◽  
Author(s):  
Fengyu Zhang ◽  
Claude Hughes
Keyword(s):  
Clinical Presentation ◽  
Population Distribution ◽  
Clinical Epidemiology ◽  
Case Fatality ◽  
Immunological Response ◽  
Prevention Strategies ◽  
Clinical Type ◽  
Global Pandemic ◽  
Potential Therapeutics

Coronavirus disease 2019 (COVID-19) is a new infectious respiratory disease that has caused the ongoing global pandemic. The primary purpose of this article is to describe evolving clinical epidemiology of COVID-19, including 1) infection and testing, 2) clinical spectrum including classification of clinical type, asymptomatic cases, severe cases and comorbidity, and clinical and immunological response, 3) regional variation in clinical presentation, 4) population distribution by age, sex, and occupation, and finally, 5) case-fatality. This content may provide important information on detailed clinical type and presentation of the disease, in which appropriate clinical outcomes can be derived for developing prevention strategies and clinical studies or trials that aim to test potential therapeutics or products for different patient populations.

Wastewater management strategies for the black sea coast of Turkey

1999 ◽  
Vol 39 (8) ◽  
pp. 169-176
Author(s):  
I. Ozturk ◽  
E. Yuksel ◽  
A. Tanik
Keyword(s):  
Black Sea ◽  
Management Strategy ◽  
Population Distribution ◽  
Management Strategies ◽  
The Black Sea ◽  
Wastewater Management ◽  
Black Sea Coast ◽  
The Cost ◽  
Solid Waste Landfills ◽  
Sea Coast

The Black Sea, surrounded by six riparian countries, is under the threat of severe pollution, giving rise to the need of taking precautions to protect it from further deterioration. In this paper, an effort putting forth a wastewater treatment and management strategy is outlined for the Black Sea coast of Turkey, including both the technical and financial aspects. The present situation of the coast in terms of land-based pollution and infrastructure is stated, followed by an applicable management strategy. The strategy developed for the coastal settlements involves various stagewise treatment schemes based on population distribution and densities along the coastline, and on the availability of land in a specified period of thirty years. Similar strategies are proposed for the control of pollution originating from industries, for those carried by rivers joining the sea, and for leachate of solid waste landfills. The cost estimations of various treatment schemes are also given in terms of population equivalents.

scholarly journals Modeling Spatiotemporal Population Changes by Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data in Chongqing, China

2021 ◽  
Vol 13 (2) ◽  
pp. 284
Author(s):  
Dan Lu ◽  
Yahui Wang ◽  
Qingyuan Yang ◽  
Kangchuan Su ◽  
Haozhe Zhang ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Relative Error ◽  
Urban Areas ◽  
Large Scale ◽  
Population Distribution ◽  
Spatial Optimization ◽  
Distribution Data ◽  
Mountainous Areas ◽  
Mean Relative Error ◽  
Nighttime Light

The sustained growth of non-farm wages has led to large-scale migration of rural population to cities in China, especially in mountainous areas. It is of great significance to study the spatial and temporal pattern of population migration mentioned above for guiding population spatial optimization and the effective supply of public services in the mountainous areas. Here, we determined the spatiotemporal evolution of population in the Chongqing municipality of China from 2000–2018 by employing multi-period spatial distribution data, including nighttime light (NTL) data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). There was a power function relationship between the two datasets at the pixel scale, with a mean relative error of NTL integration of 8.19%, 4.78% less than achieved by a previous study at the provincial scale. The spatial simulations of population distribution achieved a mean relative error of 26.98%, improved the simulation accuracy for mountainous population by nearly 20% and confirmed the feasibility of this method in Chongqing. During the study period, the spatial distribution of Chongqing’s population has increased in the west and decreased in the east, while also increased in low-altitude areas and decreased in medium-high altitude areas. Population agglomeration was common in all of districts and counties and the population density of central urban areas and its surrounding areas significantly increased, while that of non-urban areas such as northeast Chongqing significantly decreased.

scholarly journals Development of Digital Map System for Poor Population Distribution

2021 ◽  
Vol 1807 (1) ◽  
pp. 012018
Author(s):  
Wahyu Yanuartha ◽  
Mohammad Fadli ◽  
Tina Tri Wulansari ◽  
Abiratno ◽  
Nariza Wanti Wulan Sari ◽  
...  
Keyword(s):  
Population Distribution ◽  
Digital Map ◽  
Poor Population

scholarly journals Modeling Population Spatial-Temporal Distribution Using Taxis Origin and Destination Data

2021 ◽  
Vol 13 (7) ◽  
pp. 3727
Author(s):  
Fatema Rahimi ◽  
Abolghasem Sadeghi-Niaraki ◽  
Mostafa Ghodousi ◽  
Soo-Mi Choi
Keyword(s):  
Regression Analysis ◽  
Mean Squared Error ◽  
Population Distribution ◽  
Temporal Distribution ◽  
Coefficient Of Determination ◽  
Temporal Modeling ◽  
Location Data ◽  
Time Period ◽  
Proposed Model

During dangerous circumstances, knowledge about population distribution is essential for urban infrastructure architecture, policy-making, and urban planning with the best Spatial-temporal resolution. The spatial-temporal modeling of the population distribution of the case study was investigated in the present study. In this regard, the number of generated trips and absorbed trips using the taxis pick-up and drop-off location data was calculated first, and the census population was then allocated to each neighborhood. Finally, the Spatial-temporal distribution of the population was calculated using the developed model. In order to evaluate the model, a regression analysis between the census population and the predicted population for the time period between 21:00 to 23:00 was used. Based on the calculation of the number of generated and the absorbed trips, it showed a different spatial distribution for different hours in one day. The spatial pattern of the population distribution during the day was different from the population distribution during the night. The coefficient of determination of the regression analysis for the model (R2) was 0.9998, and the mean squared error was 10.78. The regression analysis showed that the model works well for the nighttime population at the neighborhood level, so the proposed model will be suitable for the day time population.

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