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Fuzzy Entropy-Based Spatial Hotspot Reliability.

Ferdinando Di Martino^1,2, Salvatore Sessa^1,2

¹Dipartimento di Architettura, Università degli Studi di Napoli Federico II, Via Toledo 402, 80134 Napoli, Italy.

Entropy (Basel, Switzerland)

|April 30, 2021

Summary

This summary is machine-generated.

This study introduces a new method to measure the reliability of detected hotspots using Fuzzy Entropy, enhancing spatial analysis for decision-makers. The approach improves hotspot identification accuracy and reliability in disease mapping.

Keywords:

EFCM FCM fuzzy clustering fuzzy entropy hotspots reliability

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Area of Science:

Spatial analysis
Data mining
Fuzzy logic

Background:

Cluster techniques, like Fuzzy C-means, identify hotspots as circular areas but lack reliability measures.
Assessing hotspot reliability is crucial for effective decision-making in spatial analysis.
Existing methods do not quantify the confidence in detected hotspot shapes and locations.

Purpose of the Study:

To propose a novel method for measuring the reliability of detected hotspots.
To integrate Fuzzy Entropy with an extended Fuzzy C-means algorithm for hotspot analysis.
To provide decision-makers with a reliable assessment of identified hotspots.

Main Methods:

Utilized an extension of the Fuzzy C-means clustering algorithm.
Incorporated De Luca and Termini's Fuzzy Entropy to quantify hotspot reliability.
Applied the method to a disease analysis case study in Naples, Italy.

Main Results:

The proposed method successfully measures the reliability of detected hotspots.
A dependency was observed between hotspot reliability and the fluctuation of belonging values.
The study demonstrated the practical application in identifying areas with high patient populations.

Conclusions:

The Fuzzy Entropy-based method enhances hotspot analysis by adding a crucial reliability measure.
This approach improves the accuracy and trustworthiness of spatial hotspot identification.
Reliable hotspot detection supports better-informed decisions in public health and spatial planning.