DATA-DRIVEN INSIGHTS INTO GENTRIFICATION: A PYTHON-BASED ANALYSIS OF SOCIODEMOGRAPHIC TRENDS IN PESHAWAR PAKISTAN

Abstract

The research paper examines the gentrification effects and their consequences on public health in the city of Peshawar, Pakistan regarding the sociodemographic. The data was collected based on 500-1000 individuals in both gentrified and non-gentrified settings with the mixed-methods approach. The survey was conducted on the demographical data, the attitude to gentrification, and its influence on social cohesion and health. The correlation analysis, sentiment analysis and cluster analysis were used in analysis of the data using Python-based tools. The findings showed that 80 percent of the respondents agreed that gentrified places are more attractive compared to the non-gentrified ones. They were 90 percent sure that, gentrification had provided facilities to the ordinary people and 80 percent said staffing is a factor in the forming of cities. But 80 percent of the respondents indicated that gentrification enhances crime and 55 percent indicated that it adds to eviction. 100 percent indicated that gentrification generates division of classes. Cluster analysis has found three clusters, namely Affluent Supporters (40%), Middle Ground (35%), and Displaced/Concerned (25 percent). Correlation analysis presented the moderately strong connections of age and income (0.61) and the good connections between income and health outcomes (0.85). The results of this have indicated a discordance in imbalanced effects of gentrification and the necessity of inclusive urban policies.