analysis tool to other diabetes studies that have been restricted to global models. A result like this is expected when regressing a strongly trended series like diabetes.
In conclusion the OLS with adjusted R square of 0.11 ( 11.0% variability) shows a lesser spatial variability and shows that all variables are significant , whereas the global GWR shows an adjusted R square of 0.64 ( 64% variability ) more variability with all variables significant, local GWR improved the model fit accounting for 100% variability with six significant variables ( obesity, urban population growth, Age dependency ratio, physician density, HDI and GNI). This shows that there is a relationship between these variables and diabetes in Africa , with consideration on the non-stationarity of the variables.
Figure 4.24. LISA Cluster map of diabetes prevalence in West Africa region
From the LISA map in Figure 4.24, within the West Africa region, countries such as Senegal, Cote D'Ivoire and Liberia (in the HL category) as well as Mali and Serial-Leone (in the LH category) are spatial outliers with negative spatial autocorrelation. Therefore, diabetes prevalence was significantly dispersed in the region. The region has the highest number of outliers.
Figure 4.25. LISA cluster map of diabetes in Central Africa region
The LISA map in Figure 4.25 shows that from the Central Africa region, countries such as Chad (in the HL category) as well as Congo DRC (in the LH category) are spatial outliers with negative spatial autocorrelation. Therefore, diabetes prevalence was significantly dispersed in the region.
Figure 4.26. LISA cluster map of diabetes in North Africa region
The LISA map in Figure 4.26 shows that from the North Africa region, Libya in the HL category, as well as Morocco in LH category are spatial outliers with negative spatial autocorrelation. Therefore, for diabetes in the North Region, two HH areas and two LL spots were detected, the largest HH areas being in Algeria and Sudan, while the two smaller hotspots were found in Egypt and South Sudan. In addition, diabetes prevalence was significantly dispersed in the region.
Figure 4.27. LISA cluster map of diabetes in East Africa region
The LISA map in Figure 4.27 shows that from the East Africa region, Eritrea and Tanzania (in the HL category), as well as Ethiopia and Uganda, are spatial outliers with negative spatial autocorrelation.
The region shows a non-significant association with diabetes. Diabetes prevalence was significantly clustered in the region.
Figure 4.28. LISA cluster map of diabetes in the Southern Africa region
The LISA map in Figure 4.28 shows that from the Southern Africa region, countries such as South Africa and Madagascar (in the HL category) and Lesotho (LH category) are spatial outliers with positive spatial autocorrelation. The region shows a significant association with diabetes. Diabetes prevalence was significantly clustered in the region. Other countries in this region are Angola, Mozambique, and Zambia in High-High categories with Botswana, Namibia and Zimbabwe in the with Low-Low categories.
The spatial clusters shown on the LISA cluster map only referred to the core of the cluster. The cluster is classified as such when the value at a location (either high or low) is more like its neighbours (as summarized by the weighted average of the neighbouring values and its spatial lag) than would be the case under spatial randomness.
In the North Region, two HH areas and two LL spots were detected, the largest HH areas being in Algeria and Sudan, while two smaller hotspots were found in Egypt and South Sudan.
4.4.1. Conclusion
The results of the LISA cluster analysis and scatter plots for the five regions show that the following countries have high diabetes prevalence alongside neighbouring countries also with high diabetes prevalence rates (they are denoted with red and in the high-high category (HH)): Benin, Guinea, Guinea Bissau, Cameroon, Equatorial Guinea, Rwanda, Angola, Mozambique, Zambia, Sudan, Algeria, Burundi.
Likewise, the countries which have high diabetes prevalence and have neighbouring countries with low diabetes prevalence rates (HL) are Cote D’Ivoire, Liberia, Senegal, Chad, Eritrea, Tanzania, Lesotho, Libya, and Swaziland. They are denoted with pink. The countries with low diabetes prevalence which have neighbouring countries also with low diabetes prevalence rates (in the low-low category (LL)) are denoted in royal blue and are Burkina Faso, Nigeria, Guinea Bissau, Central Africa Republic, Gabon, Somalia, Djibouti, Namibia, Botswana, and Zimbabwe.
Similarly, countries with low diabetes prevalence, but which have neighbouring countries with high diabetes prevalence rates (in the LH category) are denoted in light blue, and are South Africa, Mali, Sierra Leone, Congo republic, Congo DRC, Ethiopia, Uganda, and Madagascar.
From the overall analysis, we saw the importance of LISA in identifying the countries from the map which have contributed strongly to the overall trend of the positive autocorrelation in the GMI and Moran I. Countries with high diabetes surrounded by low diabetes countries are: from Southern Africa region are: South Africa and Madagascar, from East Africa region are: Eritrea and Tanzania, from North Africa region: Libya, from Central Africa region: Chad, from West Africa region are: Senegal, Cote d’Ivoire and Liberia. These countries need urgent action. Likewise, countries with low diabetes surrounded by high diabetes countries are: from Southern Africa region: Lesotho, from East Africa region: Ethiopia and Uganda, from North Africa region: Morocco, from central Africa region: Congo DRC and from West Africa region: Mali. These countries can be used as an example for their neighbours.
In conclusion the LISA map of the African continent has helped to identified (16) sixteen outlier countries such as South Africa, Mali, Serial-Leon, Congo DRC, Ethiopia, Uganda, Madagascar, Cote
‘Ivoire, Liberia, Senegal, Chad, Eritrea, Tanzania, Lesotho, Libya, Morocco. These countries are contributing strongly to the global diabetes prevalence in Africa based on the pattern of the prevalence observed from the analysis.
4.5. Results of Classical Statistics