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Influence of socioeconomic drivers on land use and cover changes using Remote sensing and GIS in Dar es Salaam, Tanzania
  • Olipa Simon,
  • James Lyimo,
  • Nestory Yamungu
Olipa Simon
University of Dar es Salaam Institute of Resource Assessment

Corresponding Author:[email protected]

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James Lyimo
University of Dar es Salaam Institute of Resource Assessment
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Nestory Yamungu
University of Dar es Salaam
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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 R2 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.