https://doi.org/10.4081/gh.2025.1277

Moran’s I and Geary’s C: investigation of the effects of spatial weight matrices for assessing the distribution of infectious diseases

Vol. 20 No. 1 (2025)
Published: 7 April 2025
Moran’s I, Geary’s C, spatial autocorrelation, COVID-19
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Authors

Sarah Isnan
https://orcid.org/0000-0003-1511-6869
Department of Biological and Agricultural Engineering, Faculty of Engineering, University Putra Malaysia, Serdang; Department of Maritime Technology, Faculty of Defence Science and Technology, National Defence University of Malaysia, Cheras, Kuala Lumpur, Malaysia.
Ahmad Fikri bin Abdullah
International Institute of Aquaculture and Aquatic Sciences (I-AQUAS), Universiti Putra Malaysia, Port Dickson, Malaysia.
Abdul Rashid Shariff
https://orcid.org/0000-0003-4626-4995
Department of Biological and Agricultural Engineering, Faculty of Engineering, University Putra Malaysia, Serdang, Malaysia.
Iskandar Ishak
iskandar_i@upm.edu.my
Universiti Putra Malaysia, Malaysia.
Sharifah Norkhadijah Syed Ismail
Department Environmental and Occupational Health, Faculty of Medicine and Health Sciences, University Putra Malaysia, Serdang, Selangor, Malaysia.
Maheshwara Rao Appanan
Ministry of Health Malaysia, Putrajaya, Malaysia.

The COVID-19 outbreak has precipitated severe occurrences on a global scale. Hence, spatial analysis is crucial in determining the relationships and patterns of geospatial data. Moran’s I and Geary’s C are prominent methodologies used to measure the spatial autocorrelation of geographical data. Both measure the degree of similarity or dissimilarity between nearby locations based on attribute values in such a way that the selection of distance techniques and weight matrices significantly impact the spatial autocorrelation results. This paper aimed at carrying out the spatial epidemiological characteristics analysis of the pandemic comparing the results of Moran’s I and Geary’s C with different parameters to gain a comprehensive understanding of the spatial relationship of COVID-19 cases. We employed distance-based techniques, K-nearest neighbour, and Queen contiguity techniques to assess the sensitivity of the different parameter configurations for both Moran’s I and Geary’s C. The findings revealed that former provided more reliable and robust results compared to the latter, with consistent results of spatial autocorrelation (positive spatial autocorrelation). The distance weight of 0.05 using the Manhattan method of Moran’s I is the recommended distance weight, as it outperformed other weight matrices (Moran’s I = 0.0152, Z-value= 110.8844 and p-value=0.001).

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Supporting Agencies

Ministry of Health Malaysia

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Moran’s I and Geary’s C: investigation of the effects of spatial weight matrices for assessing the distribution of infectious diseases. (2025). Geospatial Health, 20(1). https://doi.org/10.4081/gh.2025.1277
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