Graph-Based Hotspot Detection of Socio-Economic Data Using Rough-Set

Mohd Shamsh Tabarej, Sonajharia Minz, Anwar Ahamed Shaikh, Mohammed Shuaib, Fathe Jeribi () and Shadab Alam
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Mohd Shamsh Tabarej: School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi 110067, India
Sonajharia Minz: School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi 110067, India
Anwar Ahamed Shaikh: Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522502, India
Mohammed Shuaib: Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan 45142, Saudi Arabia
Fathe Jeribi: Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan 45142, Saudi Arabia
Shadab Alam: Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan 45142, Saudi Arabia

Mathematics, 2024, vol. 12, issue 13, 1-24

Abstract: The term hotspot refers to a location or an area where the occurrence of a particular phenomenon, event, or activity is significantly higher than in the surrounding areas. The existing statistical methods need help working well on discrete data. Also, it can identify a false hotspot. This paper proposes a novel graph-based hotspot detection using a rough set (GBHSDRS) for detecting the hotspots. This algorithm works well with discrete spatial vector data. Furthermore, it removes the false hotspot by finding the statistical significance of the identified hotspots. A rough set theory is applied to the graph of the spatial polygon data, and the nodes are divided into lower, boundary, and negative regions. Therefore, the candidate hotspot belongs to the lower region of the set, and the boundary value analysis will ensure the identification of the hotspots if the hotspot is present in the dataset. The p -value is used to find the statistical significance of the hotspots. The algorithm is tested on the socioeconomic data of Uttar Pradesh (UP) from 1991 on medical facilities. The average gain in density and Hotspot Prediction Accuracy Index (HAPI) of the detected hotspots is 26.54% and 23.41%, respectively. An average reduction in runtime is 27.73%, acquired compared to all other methods on the socioeconomic data.

Keywords: geospatial data; graph; hotspot; rough set; tree; DBSCAN (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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