Spatial analysis of population density and its effects during the Covid-19 pandemic in Sanandaj, Iran

Irandoost, Kayoumars; Alizadeh, Hooshmand; Yousefi, Zahed; Shahmoradi, Behzad

doi:10.1080/13467581.2022.2047983

Cited by 17 publications

(8 citation statements)

References 22 publications

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“…Meanwhile, a lower record of COVID-19 cases will be observed in a less populous sub-district. This scenario was empirically proven in other research analyses ( Irandoost et al., 2023 ; Wong et al., 2023 ). Thus, the impact of population density will also be observed in this study.…”

Section: Resultssupporting

confidence: 59%

Spatio-temporal clustering analysis of COVID-19 cases in Johor

Foo,

Abdul Rahman,

Shaik Abdullah

et al. 2024

Infectious Disease Modelling

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Section: Resultssupporting

confidence: 59%

Spatio-temporal clustering analysis of COVID-19 cases in Johor

Foo,

Abdul Rahman,

Shaik Abdullah

et al. 2024

Infectious Disease Modelling

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“…The correlation coefficient for this relationship is reported to be 0.01. This observation implies a positive correlation between the prevalence of COVID-19 infections and both population size and residential density in urban regions (Irandoost et al, 2023). Numerous prior investigations have demonstrated a positive correlation between urban areas characterized by greater population density and the propensity for SARS-CoV-2 transmission.…”

Section: Resultsmentioning

confidence: 81%

“…There is a positive correlation between population density and the likelihood of SARS-CoV-2 transmission in urban areas. This potentiality arises from the heightened degree of human interaction within densely populated communities, which exhibits a notable association with rates of illness (Irandoost et al, 2023;Moosa & Khatatbeh, 2021;Yin et al, 2021). Furthermore, the extent of interpersonal interaction is not the sole determinant of the rapid transmission of the virus in highly populated regions; the specific viral variation also exhibits a favorable correlation with this phenomenon (Md Iderus et al, 2022).…”

Section: This Is Particularly Pertinent As Approximately One-third Of...mentioning

confidence: 99%

COVID-19 pandemic and population density problem in Indonesia: transmigration policy as an alternative program

Saleh,

Khadafi,

Nurmandi

et al. 2023

otoritas

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The objective of this study is to examine the associations between levels of population density and the transmission of COVID-19 in Indonesia. Descriptive analysis is employed to determine the data distribution of the response variable (dependent variable Y) as well as the independent variables X1 and X2. Therefore, inferential analysis is a quantitative technique that involves examining a sample in order to draw conclusions about a larger population. The present investigation has revealed a statistically significant positive connection (r = 0.954) between the incidence of COVID-19 patients and population density in Indonesia. The Model Summary provides the R value (Correlation Coefficient) of 0.959, indicating a strong positive correlation between the variables. Additionally, the multiple correlation coefficient r (Multiple R) is 0.920, which represents the determination index or the proportion of the influence of X on Y. Therefore, it can be asserted that 92% of COVID-19 cases are driven by population density and the number of individuals experiencing employment terminations, whilst the remaining 8% is affected by additional factors. The regression equation can be constructed from the Coefficients table in the following manner: The equation can be expressed as . The calculation of the Standard Error of the Estimate (SE) yields a value of 8151.076 or 8151. The standardized coefficient (Beta) of 1.381 represents the extent of association between the number of COVID-19 infection cases and population density.

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“…However, there are also other approaches, such as using net density (population/urban area), as done by [22] Jamshidi et al Meanwhile, a spatial, physical approach can be seen in research [23] which uses building density variables with remote sensing analysis. In another case, [24] combined these variables: population density and building density. The result is that both influence the spread of Covid-19, but there is no significant difference between population density and building density.…”

Section: Study Methodsmentioning

confidence: 99%

The Influence of Density and Mixed Land Use on The Spread of Covid-19 in Surakarta City

Istanabi

Miladan

Werdiningtyas

2023

IOP Conf. Ser.: Earth Environ. Sci.

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Covid-19 has a significant risk of spreading in urban areas because of the aglomeration of built-up areas and people. It frequently contains a mix of land uses and is accessible to urban amenities. Due to the area’s extensive usage of mixed land uses, it is better able to provide internal urban services on its own. Greater use of area lockdown and social separation strategies could result from this situation. The most populous city in the province of Central Java, Surakarta, has a significant risk of contracting COVID-19. The purpose of this study is to evaluate the impact of density and levels of mixed land use on the Covid-19 distribution in Surakarta City.Population density is used to calculate density. The entropy index approach was used to measure the amount of mixed land use. It is a method for calculating the balance between each form of land use. The availability of current land use data being processed by the spatial analysis with the Arc GIS application provided help for the analysis. Additionally, it makes use of information on Covid-19 cases in relation to the general populace that is supplied by the Surakarta Municipality. The relationship between mixed land use and Covid-19 risk was analyzed using a linear regression approach. The study’s findings indicated a minor influence between density and the spread of COVID-19. Meanwhile, the level of mixed land use does not influence the spread of the Covid-19 virus in Surakarta City.

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Spatial analysis of population density and its effects during the Covid-19 pandemic in Sanandaj, Iran

Cited by 17 publications

References 22 publications

Spatio-temporal clustering analysis of COVID-19 cases in Johor

Spatio-temporal clustering analysis of COVID-19 cases in Johor

COVID-19 pandemic and population density problem in Indonesia: transmigration policy as an alternative program

The Influence of Density and Mixed Land Use on The Spread of Covid-19 in Surakarta City

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