scholarly journals Correlating Urban Population Density and Sustainability Using the Corona Index Method

2021 ◽  
Vol 12 (1) ◽  
pp. 25-33
Author(s):  
Tanushri KAMBLE ◽  
Sarika BAHADURE
Keyword(s):  
Population Density ◽  
Urban Population ◽  
High Density ◽  
Disease Spread ◽  
Spatial Density ◽  
Index Method ◽  
Study Approach ◽  
Density Maps ◽  
Severity Of The Disease ◽  
Crisis Conditions

A high population density is considered beneficial for sustainable urban planning. In crisis conditions, such as the present COVID-19 pandemic, the role of population density needs to be clearly understood in order to deal with the situation and also plan the future pandemic interventions. The paper presents a case study approach in a Nagpur, India to understand the relation between urban population density and the COVID-19 spread during the first wave. Spatial density maps and COVID-19 patient data for five consecutive critical months have been correlated using the corona index method. The corona index helps to determine the severity of the disease spread in neighbourhoods of varying population densities. The study reveals a high corona index in high-density areas and a low corona index in low- and medium-density areas. It shows that although high-density planning is sustainable, it proves hazardous for public health during pandemics. The study reveals that high-density areas are at a greater risk of disease spread during pandemics.

Mapping the population density of a rarely populated region (the example of the Irkutsk region)

2019 ◽  
Vol 946 (4) ◽  
pp. 32-38
Author(s):  
A.N. Vorobiev
Keyword(s):  
Population Density ◽  
Urban Population ◽  
Populated Area ◽  
Rural And Urban ◽  
Density Maps ◽  
Irkutsk Region ◽  
Populated Region

The principles and methodology of mapping the population density of a sparsely populated area has been developed using the example of the Irkutsk region. For the most part of the Irkutsk region is not characterized by a continuous area settlement, but linear and focal with an extremely rare network of permanent settlements, except for its southern part along the Trans-Siberian railway. Of the three possible types of population density maps there are contours, choropleths and quantitative areas for a rarely inhabited and little populated region, and only the latter type is suitable. The borders of the inhabited territory are carried out in a formal way. To draw the boundaries of the area populated area by the method of spots we need the radius of the buffer 3 km. Inhabited zones, formed by chains of settlements along the transport routs, adjoin the entirely populated territory. A quantitative map of the density by areas of the rural and urban population was constructed. The map helps you visualize the quantitative differentiation of the populated areas seeing population density and distinguish the unsettled territories. A map of inhabited areas can be the basis for mapping demographic, ethnographic, ecological and socio-economic characteristics of the population.

scholarly journals An analysis of COVID-19 clusters in India

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pooja Sengupta ◽  
Bhaswati Ganguli ◽  
Sugata SenRoy ◽  
Aditya Chatterjee
Keyword(s):  
Public Health ◽  
Cluster Analysis ◽  
Population Density ◽  
Case Studies ◽  
Compartment Model ◽  
Health Interventions ◽  
Disease Spread ◽  
Second Phase ◽  
Public Health Interventions ◽  
Homogeneous Groups

Abstract Background In this study we cluster the districts of India in terms of the spread of COVID-19 and related variables such as population density and the number of specialty hospitals. Simulation using a compartment model is used to provide insight into differences in response to public health interventions. Two case studies of interest from Nizamuddin and Dharavi provide contrasting pictures of the success in curbing spread. Methods A cluster analysis of the worst affected districts in India provides insight about the similarities between them. The effects of public health interventions in flattening the curve in their respective states is studied using the individual contact SEIQHRF model, a stochastic individual compartment model which simulates disease prevalence in the susceptible, infected, recovered and fatal compartments. Results The clustering of hotspot districts provide homogeneous groups that can be discriminated in terms of number of cases and related covariates. The cluster analysis reveal that the distribution of number of COVID-19 hospitals in the districts does not correlate with the distribution of confirmed COVID-19 cases. From the SEIQHRF model for Nizamuddin we observe in the second phase the number of infected individuals had seen a multitudinous increase in the states where Nizamuddin attendees returned, increasing the risk of the disease spread. However, the simulations reveal that implementing administrative interventions, flatten the curve. In Dharavi, through tracing, tracking, testing and treating, massive breakout of COVID-19 was brought under control. Conclusions The cluster analysis performed on the districts reveal homogeneous groups of districts that can be ranked based on the burden placed on the healthcare system in terms of number of confirmed cases, population density and number of hospitals dedicated to COVID-19 treatment. The study rounds up with two important case studies on Nizamuddin basti and Dharavi to illustrate the growth curve of COVID-19 in two very densely populated regions in India. In the case of Nizamuddin, the study showed that there was a manifold increase in the risk of infection. In contrast it is seen that there was a rapid decline in the number of cases in Dharavi within a span of about one month.

scholarly journals Correlating elements content in mosses collected in 2015 across Germany with spatially associated characteristics of sampling sites and their surroundings

2019 ◽  
Vol 31 (1) ◽  
Author(s):  
Stefan Nickel ◽  
Winfried Schröder
Keyword(s):  
Land Use ◽  
Population Density ◽  
Atmospheric Deposition ◽  
Vegetation Structure ◽  
Urban Areas ◽  
Explanatory Power ◽  
Spatial Density ◽  
Area Index ◽  
Explanatory Variables ◽  
Tree Layer

Abstract Background The aim of the study was a statistical evaluation of the statistical relevance of potentially explanatory variables (atmospheric deposition, meteorology, geology, soil, topography, sampling, vegetation structure, land-use density, population density, potential emission sources) correlated with the content of 12 heavy metals and nitrogen in mosses collected from 400 sites across Germany in 2015. Beyond correlation analysis, regression analysis was performed using two methods: random forest regression and multiple linear regression in connection with commonality analysis. Results The strongest predictor for the content of Cd, Cu, Ni, Pb, Zn and N in mosses was the sampled species. In 2015, the atmospheric deposition showed a lower predictive power compared to earlier campaigns. The mean precipitation (2013–2015) is a significant factor influencing the content of Cd, Pb and Zn in moss samples. Altitude (Cu, Hg and Ni) and slope (Cd) are the strongest topographical predictors. With regard to 14 vegetation structure measures studied, the distance to adjacent tree stands is the strongest predictor (Cd, Cu, Hg, Zn, N), followed by the tree layer height (Cd, Hg, Pb, N), the leaf area index (Cd, N, Zn), and finally the coverage of the tree layer (Ni, Cd, Hg). For forests, the spatial density in radii 100–300 km predominates as significant predictors for Cu, Hg, Ni and N. For the urban areas, there are element-specific different radii between 25 and 300 km (Cd, Cu, Ni, Pb, N) and for agricultural areas usually radii between 50 and 300 km, in which the respective land use is correlated with the element contents. The population density in the 50 and 100 km radius is a variable with high explanatory power for all elements except Hg and N. Conclusions For Europe-wide analyses, the population density and the proportion of different land-use classes up to 300 km around the moss sampling sites are recommended.

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 Population Density or Populations Size. Which Factor Determines Urban Traffic Congestion?

2021 ◽  
Vol 13 (8) ◽  
pp. 4280
Author(s):  
Yu Sang Chang ◽  
Sung Jun Jo ◽  
Yoo-Taek Lee ◽  
Yoonji Lee
Keyword(s):  
Population Density ◽  
Population Size ◽  
Traffic Congestion ◽  
Critical Role ◽  
Small Population ◽  
High Density ◽  
Urban Traffic ◽  
Contradictory Result ◽  
Low Density

A large number of articles have documented that as population density of cities increases, car use declines and public transit use rises. These articles had a significant impact of promoting high-density compact urban development to mitigate traffic congestion. Another approach followed by other researchers used the urban scaling model to indicate that traffic congestion increases as population size of cities increases, thus generating a possible contradictory result. Therefore, this study examines the role of both density and population size on traffic congestion in 164 global cities by the use of Stochastic Impacts by Regression on Population, Affluence and Technology model. We divide 164 cities into the two subgroups of 66 low density cities and 98 high density cities for analysis. The findings from the subgroups analysis indicated a clear-cut difference on the critical role of density in low-density cities and the exclusive role of population size in high-density cities. Furthermore, using threshold regression model, 164 cities are divided into the two regions of large and small population cities to determine population scale advantage of traffic congestion. Our findings highlight the importance of including analysis of subgroups based on density and/or population size in future studies of traffic congestion.

Urban population density function of two polar variables

1975 ◽  
Vol 9 (1) ◽  
pp. 63-68
Author(s):  
Emile Sanders
Keyword(s):  
Population Density ◽  
Density Function ◽  
Urban Population

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

scholarly journals Spatial analysis of dengue hemorrhagic fever in Talaud Islands regency

2020 ◽  
Vol 8 (1) ◽  
pp. 104
Author(s):  
Chreisye K. F. Mandagi ◽  
Angela F. C. Kalesaran ◽  
Febi K. Kolibu
Keyword(s):  
Spatial Analysis ◽  
Population Density ◽  
Dengue Hemorrhagic Fever ◽  
Secondary Data ◽  
Hemorrhagic Fever ◽  
Spatial Description ◽  
District Health ◽  
Study Approach ◽  
Spatial Risk ◽  
Risk Areas

Background: The number of dengue hemorrhagic fever (DHF) cases in Indonesia from January to February 2016 was 8,487 with 108 deaths. DHF is an infectious disease that continues to increase from 2014 until 2016 in Manado city. DHF cases in Talaud Islands Regency from 2014 to 2016 were 143 cases. Regional spatial analysis would simplify the distribution of DHF cases in high-risk areas. To be aware of the DHF outbreak cycle, it is necessary to model spatial risk factors based on geographic information systems (GIS) to tackle and eradicate DHF cases by region.Methods: This study aimed to analyze the spread of DHF in Talaud regency based on age, sex, population density and area height. The design of this research is qualitative analytic by using an ecological study approach. The research scope was 19 districts in Talaud regency. Secondary data are used which consists of case number, age, sex, population density, and area height taken from the Talaud district health office with 66 DHF cases in 2018-2019 and analyzed using the GIS approach through spatial analysis.Results: Based on the number of DHF cases that is most in the age group of 5-11 years. Male gender is more likely to suffer from DHF than female. Spatial description of the condition of the altitude in the Talaud Islands regency at risk of suffering from DHF is>50 meters above sea level. Spatial description of population density with most DHF cases is not densely populated area with less than 1,620 inhabitants per km.Conclusions: The health office of Talaud islands regency needs to actively promote health by providing information about eradicating mosquitoes.

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