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.

For many developing countries such as Uganda, precise gravimetric geoid determination is hindered by the low quantity and quality of the terrestrial gravity data. With only one gravity data point per 65 km2, gravimetric geoid determination in Uganda appears an impossible task. However, recent advances in geoid modelling techniques coupled with the gravity-field anomalies from the Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) satellite mission have opened new avenues for geoid determination especially for areas with sparse terrestrial gravity. The present study therefore investigates the computation of a gravimetric geoid model overUganda (UGG2014) using the Least Squares Modification of Stokes formula with additive corrections. UGG2014 was derived from sparse terrestrial gravity data from the International Gravimetric Bureau, the 3 arc second SRTM ver4.1 Digital Elevation Model from CGIAR-CSI and the GOCE-only global geopotential model GO_CONS_GCF_2_TIM_R5. To compensate for the missing gravity data in the target area, we used the surface gravity anomalies extracted from the World Gravity Map 2012. Using 10 Global Navigation Satellite System (GNSS)/levelling data points distributed over Uganda, the RMS fit of the gravimetric geoid model before and after a 4-parameter fit is 11 cm and 7 cm respectively. These results show that UGG2014 agrees considerably better with GNSS/levelling than any other recent regional/ global gravimetric geoid model. The results also emphasize the significant contribution of the GOCE satellite mission to the gravity field recovery, especially for areas with very limited terrestrial gravity data.With an RMS of 7 cm, UGG2014 is a significant step forward in the modelling of a “1-cm geoid” over Uganda despite the poor quality and quantity of the terrestrial gravity data used for its computation.

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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