Rapid urbanisation in Sub-Saharan Africa demands innovative land management strategies that promote sustainable, inclusive, and resilient urban development. This study investigates the potential of land readjustment (LR) to foster neighbourhood-scale social–ecological urban resilience (SEUR) through a case study of the Nunga LR project in Kigali, Rwanda. Grounded in the social–ecological system (SES) theory and operationalised through the social–ecological land readjustment model for resilient neighbourhoods, the research evaluates LR practices against an integrated benchmark framework combining LR aspects, neighbourhood design standards, and resilience attributes. The study uses secondary data, project shapefiles, and key informant interviews to assess how Rwanda’s emerging LR model contributes to resilient neighbourhood development. Findings demonstrate strong community mobilisation and adaptive governance capacity. However, critical resilience dimensions—including modularity, green infrastructure integration, land-use diversity, and adaptive feedback mechanisms—were only partially operationalised. Consequently, while LR improved spatial formalisation and basic infrastructure provision, it fell short of creating a truly resilient, multifunctional neighbourhood ecosystem. These findings highlight the need to reframe LR from a purely technical land management tool into a systemic resilience-building mechanism. Policy recommendations include mandating green/blue infrastructure in LR plans, establishing innovative financing mechanisms, institutionalising adaptive monitoring, strengthening affordability safeguards, and promoting multifunctional spatial layouts. The study contributes to urban resilience and land governance scholarship by offering a context-sensitive, empirically tested model for integrating SEUR principles into LR practice in rapidly urbanising African cities. Future research should pursue longitudinal analyses and dynamics modelling of land readjustment impacts to deepen understanding of urban resilience pathways in the Global South.

Malaria is a vector borne disease posing a severe health risk to the population of Sub Saharan Africa and particularly in the East African Rift-Valley Region. The fact that malaria is still killing hundreds of thousands of people annually is due to insufficient researchers about its causing factors and the inefficiency of its control measures. With three main objectives: (1) to identify and map potential anopheles mosquito habitat, (2) to determine and map malaria prevalence, and, (3) to assess the relationship between malaria prevalence and malaria risk factors; the aim of this study is to spatially model malaria risk factors in a primarily rural area situated in South Eastern Rwanda. The data used in this research were obtained through a combination of high-resolution ortho-photography obtained from Rwanda Natural Resources Authority (RNRA) and primary data collection. In addition to this, detailed malaria occurrence and socio-economic data were obtained from Rwanda Biomedical Center (RBC). Spatial clusters of malaria occurrence were subsequently determined using Getis and Ord spatial statistics. This cluster analysis showed that malaria distribution is characterized by zones with high malaria risk, so called hot spots, zones with moderate malaria risk known as not significant spots and zones of low malaria risk known as cold spots. The current research demonstrates that malaria prevalence varies from one household to another and from one administrative unit (village) to another. The relationship between malaria prevalence and malaria risk factors was assessed using a logistic regression model. Results clearly indicate that malaria infection increases with the proximity to irrigated farmland. It also increases with household size. It was also proven that lower housing quality (mud houses; unburnt brick walls, earth floor) is associated with higher risk of malaria infection. This research proves that the proximity to irrigated farmland, the household size and housing quality are the main malaria underlying factors in the study area. It therefore suggests that people should not only live far from irrigated farmlands which are considered as the main anopheles mosquito breeding sites but also the housing quality should be improved. Key words: Spatial modelling, Malaria risk, malaria causing factors, Ruhuha, Rwanda