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<p>The COVID-19 pandemic has highlighted the critical need for data driven approaches in public health management. The integration of big data technologies with geospatial data analysis offers powerful tools for visualizing and managing the spread of infectious diseases. This study explores the integration of big data analytics and Geographic Information Systems (GIS) to detect COVID-19 hotspots and plan interventions in Mohali, Punjab, India. Utilizing a dataset of COVID-19 cases, this paper demonstrates the application of QGIS for spatial analysis, hotspot identification, and intervention zone mapping. The dataset covers confirmed COVID-19 cases from March 2020 to December 2021. It includes preprocessing steps to ensure data quality. The methodology incorporates data utilization using QGIS, heat map creation, hotspot detection through reclassification and intervention zone delineation. The results showcase the potential of GIS tools in visualizing disease spread patterns and identifying high risk areas. The findings emphasize the importance of leveraging technological advancements in spatial analysis and big data for effective public health decision making. The integration of big data enables more comprehensive and dynamic spatial analyses.</p>

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Role of big data in geospatial analysis for public healthcare management applications

  • 期刊论文

  • 发表日期：

    2025年11月28日

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  • Manvinder Sharma,
  • Rajneesh Talwar,
  • Satyajit Anand,
  • Danvir Mandal,
  • Binay Kumar Pandey,
  • Digvijay Pandey

摘要

The COVID-19 pandemic has highlighted the critical need for data driven approaches in public health management. The integration of big data technologies with geospatial data analysis offers powerful tools for visualizing and managing the spread of infectious diseases. This study explores the integration of big data analytics and Geographic Information Systems (GIS) to detect COVID-19 hotspots and plan interventions in Mohali, Punjab, India. Utilizing a dataset of COVID-19 cases, this paper demonstrates the application of QGIS for spatial analysis, hotspot identification, and intervention zone mapping. The dataset covers confirmed COVID-19 cases from March 2020 to December 2021. It includes preprocessing steps to ensure data quality. The methodology incorporates data utilization using QGIS, heat map creation, hotspot detection through reclassification and intervention zone delineation. The results showcase the potential of GIS tools in visualizing disease spread patterns and identifying high risk areas. The findings emphasize the importance of leveraging technological advancements in spatial analysis and big data for effective public health decision making. The integration of big data enables more comprehensive and dynamic spatial analyses.