Moran’s I and Geary’s C: investigation of the effects of spatial weight matrices for assessing the distribution of infectious diseases
Published: 7 April 2025
Abstract Views: 209
PDF: 68
Supplementary materials: 7
Supplementary materials: 7
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
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).
Altmetrics
Downloads
Download data is not yet available.
Citations
Ayouni I, Maatoug J, Dhouib W, Zammit N, Fredj SB, Ghammam R, Ghannem H, 2021. Effective public health measures to mitigate the spread of COVID-19: a systematic review. BMC Public Health 21:1015. DOI: https://doi.org/10.1186/s12889-021-11111-1
Bangira T, Alfieri SM, Menenti M, van Niekerk A, 2019. Comparing thresholding with machine learning classifiers for mapping complex water. Remote Sens 11:1351. DOI: https://doi.org/10.3390/rs11111351
Chen Y, 2021. An analytical process of spatial autocorrelation functions based on Moran’s index. PLoS ONE 16:0249589 DOI: https://doi.org/10.1371/journal.pone.0249589
Chen Y, 2023. Spatial autocorrelation equation based on Moran’s index. Sci Rep 13:1–14. DOI: https://doi.org/10.1038/s41598-023-45947-x
da Silva CFA, Silva MC, dos Santos AM, Rudke AP, do Bonfim CV, Portis GT, de Almeida Jr PM, Coutinho MBS, 2022. Spatial analysis of socio-economic factors and their relationship with the cases of COVID-19 in Pernambuco, Brazil. Trop Med Int Health 27:397–407. DOI: https://doi.org/10.1111/tmi.13731
Dokmanic I, Parhizkar R, Ranieri J, Vetterli M, 2015. Euclidean Distance Matrices: Essential theory, algorithms, and applications. IEEE Signal Process Mag 32:12–30. DOI: https://doi.org/10.1109/MSP.2015.2398954
Ei SU, Laohasiriwong W, Sornlorm K, 2023. Spatial autocorrelation and heterogenicity of demographic and healthcare factors in the five waves of COVID-19 epidemic in Thailand. Geospat Health 18:1183 DOI: https://doi.org/10.4081/gh.2023.1183
Getis A, Ord JK, 1992. The Analysis of Spatial Association by Use of Distance Statistics. Geogr Anal 24:189–206. DOI: https://doi.org/10.1111/j.1538-4632.1992.tb00261.x
Juliani IP, Nasution H, 2024. Spatial autocorrelation analysis using the moran and lisa index on the spread of malaria disease in North Sumatra Province. Jurnal Lebesgue: Jurnal Ilmiah Pendidikan Matematika, Matematika Dan Statistika 5;154–64. DOI: https://doi.org/10.46306/lb.v5i1.543
Kang D, Choi H, Kim JH, Choi J, 2020. Spatial epidemic dynamics of the COVID-19 outbreak in China. International J Infect Dis 94:96–102. DOI: https://doi.org/10.1016/j.ijid.2020.03.076
Li H, Calder CA, Cressie N, 2007. Beyond Moran’s I: Testing for spatial dependence based on the spatial autoregressive model. Geogr Anal 39:357–75. DOI: https://doi.org/10.1111/j.1538-4632.2007.00708.x
Malkauthekar MD, 2013. Analysis of Euclidean distance and Manhattan distance measure in face recognition. IET Conf Publ CP646:503–07. DOI: https://doi.org/10.1049/cp.2013.2636
Morais LR de A, Gomes GS da S, 2021. Applying spatio-temporal scan statistics and spatial autocorrelation statistics to identify COVID-19 clusters in the world-a vaccination strategy? Spatial Spatio-Temp Epidemiol 39:100461. DOI: https://doi.org/10.1016/j.sste.2021.100461
Moran P, 1950. Continuous Stochastic Phenomena. Biometrika 37:17–23. DOI: https://doi.org/10.1093/biomet/37.1-2.17
Phillips B, Anand M, Bauch CT, 2020. Spatial early warning signals of social and epidemiological tipping points in a coupled behaviour-disease network. Sci Rep 10:1–12. DOI: https://doi.org/10.1038/s41598-020-63849-0
Rahman MS, Pientong C, Zafar S, Ekalaksananan T, Paul RE, Haque U, Rocklöv J, Overgaard HJ, 2021. Mapping the spatial distribution of the dengue vector Aedes aegypti and predicting its abundance in northeastern Thailand using machine-learning approach. One Health 13:100358 DOI: https://doi.org/10.1016/j.onehlt.2021.100358
Saiful Bahri MA, Karbassi Z, Majid MR, Sabri S, Johar F, Ludin, AMN. 2014. Comparison of Spatial Autocorrelation Analysis Methods for Distribution Pattern of Diabetes Type II Patients in Iskandar Malaysia Neighbourhoods. In Int Conf Urban Reg Planning: Planning in Uncertain World, 9-11 May, 2014, Skudai, Johor. Malaysia. pp. 1–18.
Silalahi FES, Hidayat F, Dewi RS, Purwono N, Oktaviani N, 2020. GIS-based approaches on the accessibility of referral hospital using network analysis and the spatial distribution model of the spreading case of COVID-19 in Jakarta, Indonesia. BMC Health Serv Res 20:1053. DOI: https://doi.org/10.1186/s12913-020-05896-x
Suryowati K, Bekti RD, Faradila A, 2018. A comparison of weights matrices on computation of dengue spatial autocorrelation. IOP Conference Series: Materials Sci Engineer 335:012052 DOI: https://doi.org/10.1088/1757-899X/335/1/012052
Syetiawan A, Harimurti M, Prihanto Y, 2022. A spatiotemporal analysis of COVID-19 transmission in Jakarta, Indonesia for pandemic decision support. Geospat Health 17:85–97. DOI: https://doi.org/10.4081/gh.2022.1042
Thorndike RL, 1953. Who belongs in the family? Psychometrika 18:267-76 DOI: https://doi.org/10.1007/BF02289263
Vilinová K, Petrikovičová L, 2023. Spatial autocorrelation of COVID-19 in Slovakia. Trop Med Infect Dis 8:298. DOI: https://doi.org/10.3390/tropicalmed8060298
Wetchayont P, Waiyasusri K, 2021. Using Moran’S I for Detection and Monitoring of the Covid-19 Spreading Stage in Thailand During the Third Wave of the Pandemic. Geogr Environ Sustain 14:155–67. DOI: https://doi.org/10.24057/2071-9388-2021-090
Supporting Agencies
Ministry of Health MalaysiaHow to Cite
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
Copyright (c) 2025 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Iuria Betco, Ana Isabel Ribeiro, David S. Vale, Luis Encalada-Abarca, Cláudia M. Viana, Jorge Rocha, Sentiment analysis using a lexicon-based approach in Lisbon, Portugal , Geospatial Health: Vol. 20 No. 1 (2025)
- Mai Liu, Yin Zhang, Impact of climate change on dengue fever: a bibliometric analysis , Geospatial Health: Vol. 20 No. 1 (2025)
- Beatriz Martínez-Lòpez, Tsviatko Alexandrov, Lina Mur, Fernando Sánchez-Vizcaíno, José M. Sánchez-Vizcaíno, Evaluation of the spatial patterns and risk factors, including backyard pigs, for classical swine fever occurrence in Bulgaria using a Bayesian model , Geospatial Health: Vol. 8 No. 2 (2014)
- Vitomir Djokić, Ivana Klun, Vincenzo Musella, Laura Rinaldi, Giuseppe Cringoli, Smaragda Sotiraki, Olgica Djurković-Djaković, Spatial epidemiology of Toxoplasma gondii infection in goats in Serbia , Geospatial Health: Vol. 8 No. 2 (2014)
- Ruy Brayner Oliveira Filho, Karla Campos Malta, Vania Lucia Assis Santana, Mabel Hanna Vance Harrop, Danilo Tancler Stipp, Daniel Friguglietti Brandespim, Rinaldo Aparecido Mota, José Júnior Wilton Pinheiro, Spatial characterization of Leptospira spp. infection in equids from the Brejo Paraibano micro-region in Brazil , Geospatial Health: Vol. 8 No. 2 (2014)
- Jia-Cheng Zhang, Wen-Dong Liu, Qi Liang, Jian-Li Hu, Jessie Norris, Ying Wu, Chang-Jun Bao, Fen-Yang Tang, Peng Huang, Yang Zhao, Rong-Bin Yu, Ming-Hao Zhou, Hong-Bing Shen, Feng Chen, Zhi-Hang Peng, Spatial distribution and risk factors of influenza in Jiangsu province, China, based on geographical information system , Geospatial Health: Vol. 8 No. 2 (2014)
- Naoko Nihei, Osamu Komagata, Kan-ichiro Mochizuki, Mutsuo Kobayashi, Geospatial analysis of invasion of the Asian tiger mosquito Aedes albopictus: competition with Aedes japonicus japonicus in its northern limit area in Japan , Geospatial Health: Vol. 8 No. 2 (2014)
- Rebekah S. Huber, Namkug Kim, Carl E. Renshaw, Perry F. Renshaw, Douglas G. Kondo, Relationship between altitude and lithium in groundwater in the United States of America: results of a 1992-2003 study , Geospatial Health: Vol. 9 No. 1 (2014)
- Luis E. Escobar, Andrés Lira-Noriega, Gonzalo Medina-Vogel, A. Townsend Peterson, Potential for spread of the white-nose fungus (Pseudogymnoascus destructans) in the Americas: use of Maxent and NicheA to assure strict model transference , Geospatial Health: Vol. 9 No. 1 (2014)
- Wendelin Moser, Helena Greter, Christian Schindler, Fiona Allan, Bongo N. R. Ngandolo, Daugla D. Moto, Jürg Utzinger, Jakob Zinsstag, The spatial and seasonal distribution of Bulinus truncatus, Bulinus forskalii and Biomphalaria pfeifferi, the intermediate host snails of schistosomiasis, in N'Djamena, Chad , Geospatial Health: Vol. 9 No. 1 (2014)
You may also start an advanced similarity search for this article.
