Authors : Ketaki Katre; Aditya Deshmukh; Dinesh Rathod; Mayank Mehta; Tanisha Adhav; Akshay Singhal

Volume/Issue : Volume 10 - 2025, Issue 3 - March

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Google Scholar : https://tinyurl.com/3jdp25ux

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DOI : https://doi.org/10.38124/ijisrt/25mar2003

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Abstract : This survey explores the role of Geographic Information Systems (GIS) and location intelligence in optimizing industrial and food business expansion. While GIS has been widely used for site selection, challenges remain in integrating multi-objective criteria, real-time constraints, and dynamic market factors. This paper reviews existing methodologies, highlighting key GIS applications in demographic analysis, competitor mapping, and infrastructure assessment. By synthesizing recent advancements, this survey provides insights into how GIS-driven decision-making enhances market penetration, profitability, and operational efficiency.

Keywords : GIS, Location Intelligence, Business Expansion, Site Selection, Market Analysis

References :

1. K.S.G. Ramos and E.C. Peramo, "Optimizing Healthcare Accessibility: An Integration of GIS and Machine Learning for Strategic Hospital Facility Location Planning," 2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 158-163, IEEE, 2024.
2. A. Rikalovic, I. Cosic, R.D. Labati, and V. Piuri, "Intelligent Decision Support System for Industrial Site Classification: A GIS-Based Hierarchical Neuro-Fuzzy Approach," IEEE Systems Journal, vol. 12, no. 3, pp. 2970- 2981, 2017.
3. X. Zhang, L. Zhou, S. Zhao, and J. Zhang, "Machine Learning and GIS for Emergency Response Optimization: A Case Study on Spatial Data Integration," Journal of Environmental Management, vol. 234, pp. 72-80, Elsevier, 2019.
4. H. Chen and L. Zhang, "Urban Land Use Classification Using GIS and Machine Learning Techniques," Computers, Environment and Urban Systems, vol. 74, pp. 50-60, Elsevier, 2019.
5. A. Mitra and A. Khamparia, "A Smart City Planning Framework with Deep Learning and GIS Integration," 2020 IEEE International Conference on Smart City Innovations (SCI), pp. 98-105, IEEE, 2020
6. M. Alharbi and H. Kim, "Sustainable Urban Development through GIS-Based Environmental Impact Assessments," Sustainability, vol. 12, no. 7, pp. 1-15, MDPI, 2020.
7. Y. Hu, Y. Zhou, M. Li, and X. Li, "GIS-Based Flood Risk Assessment Using Machine Learning Approaches: A Case Study in Coastal Areas," Environmental Earth Sciences, vol. 79, no. 3, pp. 45-59, Springer, 2020.
8. R. Smith and T. Brown, "Evaluating Urban Green Spaces Using a GIS-Based Framework: Implications for Land Use Planning," Landscape and Urban Planning, vol. 202, pp. 103-112, Elsevier, 2020.
9. W. Liu, Y. Chen, Z. Yang, and H. Zhang, "A GIS-Based Decision Support System for Environmental Sustainability: Integrating Machine Learning and Environmental Indicators," Journal of Cleaner Production, vol. 254, pp. 1- 12, Elsevier, 2020.
10. Y. Wang and X. Zhang, "Big Data and GIS in Smart City Development: Real-Time Spatial Data Integration for Decision Making," International Journal of Geo- Information, vol. 8, no. 11, pp. 1-20, MDPI, 20