Renovation and management of slum housing and communities: Baqubah city as a case study

• Main Authors: Abd, Abbas M., Hussein, Nuha A., Yas, Samaan M.
• Format: Conference Proceeding
• Language: English
• Subjects: Cities; Correlation coefficients; Demographic variables; Dependent variables; Economic conditions; Housing; Income; Independent variables; Regression analysis; Regression models; Unemployment; Urban planning; Variables
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0212792

One of the biggest challenges facing the Third World today is slum housing. The phenomenon of slum expansion is increasing every day, which leads to a deterioration in the quality of life and negatively affects the urban planning of the city. The city of Baqubah is one of the cities that suffered and suffers from the problem of slum expansion, which resulted in slum gatherings resulting from demographic, social, and economic variables that negatively affected the organization of the city’s various uses of land, specifically residential uses. This research aims to know the effect of factors (population, unemployment rate, income level) on the increase of slum housing and the extent of their impact on them in the future. The data were collected during the time period from 2013 to 2021 in terms of the number of slum housing units and gatherings. Was noted an increase in slums in Baqubah city due to the lack of necessary measures to reduce this phenomenon. was used SPSS version 25 program, multiple linear regression analysis to find out the relationship between the dependent variable and the independent variables. The results showed a multiple linear regression analysis, Where the highest correlation coefficient between slum housing and the was Unemployment rate 0.925 and the correlation coefficient with the population was 0.903. While the correlation coefficient with the income level was -0.770 Where the per capita income is inversely proportional to the increase in slum housing units. The results showed that the linear regression model is a significant regression model, with an F-value (107.415) in terms of sig. (0.001), which means that there is an influence of a independent variables on a dependent variable.