COVID-19 has presented itself with an extreme impact on the resources of its epi-centres. In Uganda, there is uncertainty about what will happen especially in the main urban hub, the Greater Kampala Metropolitan Area (GKMA). Consequently, public health professionals have scrambled into resource-driven strategies and planning to tame the spread. This paper, therefore, deploys spatial modelling to contribute to an understanding of the spatial variation of COVID-19 vulnerability in the GKMA using the socioeconomic characteristics of the region. Based on expert opinion on the prevailing novel Coronavirus, spatially driven indicators were generated to assess vulnerability. Through an online survey and auxiliary datasets, these indicators were transformed, classified, and weighted based on the BBC vulnerability framework. These were spatially modelled to assess the vulnerability indices. The resultant continuous indices were aggregated, explicitly zoned, classified, and ranked based on parishes. The resultant spatial nature of vulnerability to COVID-19 in the GKMA sprawls out of major urban areas, diffuses into the peri-urban, and thins into the sparsely populated areas. The high levels of vulnerability (24.5% parishes) are concentrated in the major towns where there are many shopping malls, transactional offices, and transport hubs. Nearly half the total parishes in the GKMA (47.3%) were moderately vulnerable, these constituted mainly the parishes on the outskirts of the major towns while 28.2% had a low vulnerability.

The real estate sector in Uganda has been substantially impacted by the onset of COVID-19 in this country. Studies conducted worldwide have indicated that, pandemics affect property market activities differently. Additionally, the effect of pandemics on property market activity varies from one place to another. Studies conducted in Uganda, however, have not captured how the effect of COVID-19 on property market activities varies from one place to another. This study therefore explored the spatial variability of the effect of COVID-19 on property market activities in Kampala district, Uganda. The study took advantage of the spatial statistical analytical models advocated by GIS (Getis-Ord Gi*, OLS, GWPR) and a unique dataset of property transactions registered by the Ministry of Lands, Housing and Urban Development (MLHUD) during the outbreak of the deadly disease. Whereas the study observed high volumes of property transactions registered in the residential outskirts of the city, low volumes were observed in the Central Business District (CBD) and the low-income areas of the eastern and western parts of the district. On the other hand, the local model approach of GWPR exposed the substantial effects of COVID-19 on property market activities that varied from -39% to 10%. It was further established that COVID-19 generated negative effects in areas with low and high prices of land per acre, to the extent of increasing as the prices dropped or increased. On the contrary, a positive effect was realized in the residential outskirts of the city where prices of land per acre were moderate. Work from home, land parcel size as well as the composition of the population, proved to be the main drivers of the changes in property market transactions (activity). The findings of the study underpin the earlier postulations of various researchers that pandemics affect property market activity. However, the effects of the pandemics vary from one pandemic to another and from one place to another.

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Abstract

Under the second phase of the National Oil Palm Project, the Government of Uganda plans to extend the oil palm project to Northern Uganda. According to the Final Project Design Report (2017) of the National Oil Palm Project, and based on the rainfall, soil and temperature of the region, areas in Northern Uganda have already been mapped for the project. However, no detailed information on the degree of suitability of the areas has been provided. In this research, other parameters such as land cover, elevation and slope were identified through the literature review. Furthermore, on the basis of the reclassify tool in ArcMap 10.8, the data were then reclassified into four classes, namely, highly suitable (S1), moderately suitable (S2), marginally suitable (S3) and unsuitable (N.) With the aid of the Analytical Hierarchical Process (AHP), pairwise comparison matrices were constructed and the weight of each parameter was computed. The suitability map obtained from a weighted linear combination identified 38.18%, 35.54%, 21.41% and 4.87% of the land area as highly suitable, moderately suitable, marginally suitable and unsuitable, respectively. A geospatial assessment of the suitability of the land for oil palm growing was carried out. It was based on only the soil types, but excluded the chemical properties of the soil. Therefore, further research on the chemical properties of the soils at suitable sites should be carried out. In-depth research should be carried out While considering social and economic factors among the criteria to determine the willingness and financial capability of the people to venture into oil palm growing as a source of income, Oil Palm Uganda Limited should conduct in-depth research into this issue.