No abstract

Urban resilience is one of the prominent themes in urban development discourse. Its importance resonates with the quest to balance rapid urbanization and adaptation to climate change and variability, which raises the need for building adaptive capacities within urban system. Despite the rapid urbanization in Mlandizi, Tanzania, the town is planned on a piecemeal basis that lacks a holistic view of the urban system. While the integration of urban resilience into the urban development planning process is effective for building adaptive capacities, piecemeal planning raises questions about its effectiveness to integrate the tenets of resilience for addressing a wider range of climate risks, shocks and stresses. This paper ascertained the extent to which piecemeal planning integrated urban resilience into the planning process in Mlandizi small-town. Mixed-research methods were used involving geospatial mapping, in-depth interviews and field observation. Land use/cover change analysis and mapping of urban development were conducted in piecemeal planned areas. Susceptibility to flooding was assessed in the Ruvu river floodplain through an overlay of houses on the Digital Elevation Model. In-depth interviews and field observations were conducted to ascertain the urban resilience outcomes of piecemeal planning. Results suggest that informal urbanization constitutes 90% of the housing development in Mlandizi. There is also rapid land use change and conversion of the natural landscape to man-made land uses, which results in diminishing green spaces. The results further indicate that the piecemeal planning process ignored consultation of stakeholders and strategic environmental assessment. As a result, it failed to provide an appropriate policy for integrating urban resilience with spatial planning. This paper argues for the adoption of comprehensive planning that integrates resilience in urban planning processes, and builds capacity for addressing a wide range of shocks, including the impacts of climate change.

Understanding human interactions demands modelling human-environment interactions. This study uses remote sensing and machine learning to evaluate land use and land cover (LULC) changes over 27 years in Dar es Salaam Metropolitan City, Tanzania, and the spatially varying relationships between LULC changes and socioeconomic driving factors. LULC change values and factors are retrieved from data points generated by regular sampling methods. The geographically weighted regression (GWR) model is then employed to analyse the relationships between LULC changes and the identified factors. The analysis of LULC changes reveals a dynamic transformation in land cover between 1995 and 2022, characterised by a notable 14.9% increase in built-up areas and a corresponding decline of 14.6% in bushland. 65.8% of the land cover experiences gains and losses, while 34.2% remains relatively stable over the 27 years. The GWR model surpasses the OLS model, achieving an R value of 0.73, signifying a strong association between LULC changes and the identified socioeconomic factors, explaining 73% of the LULC variation. Additionally, the influences of these factors, including the signs, significances, and coefficient values, exhibit considerable variations across different LULC change types. Notably, population density and proximity to the city centre significantly contribute to LULC changes, whereas the impact of gross domestic product and distance to roads is comparatively lesser. Moreover, poverty does not significantly drive LULC changes. This study’s findings suggest that urbanisation and urban sprawl, as indicated by population density and distance from the city centre, significantly influence land cover changes in the study area.

Land use land cover (LULC) changes affect the planet's energy balance and region's climate. Land Surface Temperature (LST) is a vital indicator of this change. Studies in Dar es Salaam Metropolitan City have investigated LST and its relationships with building heights and densities, urban heat islands, spectral indices, and urban morphological determinants. The present study used cross-sectional profiles, chord diagrams, and simple linear regression models to examine the influence of LULC changes on the LST in Dar es Salaam Metropolitan City (DMC). LST was extracted from Landsat 5 TM and 8 OLI/TIRS images for 1995, 2009, and 2017. LULC was identified via the supervised random forest classification algorithm. Between 1995 and 2017, built-up areas rose by 8%, vegetation fell 7%, and bare soil 3%. As a result, the average LST rose by 3 °C. Built-up areas had the highest temperatures (24–26.5 °C), followed by bare soil (22–25.5 °C). The lowest temperatures (21–25 °C) were on vegetation and water. Built-up area positively correlated with LST, while vegetation, water bodies, and bare soil negatively correlated. The study results can assist local authorities in enforcing urban planning regulations, raising public awareness, and guiding policymakers in creating sustainable planning and management strategies for the future. Keywords: Dar es Salaam, Land use land cover, simple linear regression model, land surface temperature, chord diagrams