Abstract
ABSTRACT Urbanization is one of the most impactful human activities across the world today affecting the quality of urban life and its sustainable development. Urbanization in Africa is occurring at an unprecedented rate and it threatens the attainment of Sustainable Development Goals (SDGs). Urban sprawl has resulted in unsustainable urban development patterns from social, environmental, and economic perspectives. This study is among the first examples of research in Africa to combine remote sensing data with social media data to determine urban sprawl from 2011 to 2017 in Morogoro urban municipality, Tanzania. Random Forest (RF) method was applied to accomplish imagery classification and location-based social media (Twitter usage) data were obtained through a Twitter Application Programming Interface (API). Morogoro urban municipality was classified into built-up, vegetation, agriculture, and water land cover classes while the classification results were validated by the generation of 480 random points. Using the Kernel function, the study measured the location of Twitter users within a 1 km buffer from the center of the city. The results indicate that, expansion of the city (built-up land use), which is primarily driven by population expansion, has negative impacts on ecosystem services because pristine grasslands and forests which provide essential ecosystem services such as carbon sequestration and support for biodiversity have been replaced by built-up land cover. In addition, social media usage data suggest that there is the concentration of Twitter usage within the city center while Twitter usage declines away from the city center with significant spatial and numerical increase in Twitter usage in the study area. The outcome of the study suggests that the combination of remote sensing, social sensing, and population data were useful as a proxy/inference for interpreting urban sprawl and status of access to urban services and infrastructure in Morogoro, and Africa city where data for urban planning is often unavailable, inaccurate, or stale.
Highlights
Urban sprawl is one of the major outcomes of trans formations resulting from population agglomeration in urban centers (Cobbinah and Darkwah 2016; Mosammam et al 2017; Xu et al 2019a)
This study focused on identifying the extent and pattern of urban sprawl in Morogoro urban munici pality using remote sensing data from the Landsat platform, social sensing data from the Twitter plat form and census population data within the study area with a temporal span of 2011 to 2017
The results show that the built-up area of the Morogoro urban municipality expanded during the study period as the sprawl corresponded with increase in population of the Morogoro urban municipality between 2012 and 2017
Summary
Urban sprawl is one of the major outcomes of trans formations resulting from population agglomeration in urban centers (Cobbinah and Darkwah 2016; Mosammam et al 2017; Xu et al 2019a). Growth in urban populations worldwide is considered as the factor directly responsible for the unprece dented rate of urban sprawl being recorded majorly in cities within the global south. As the population of an urban center increases, its need for infrastructures such as transportation, water, sewage and facilities such as housing, commerce, health, schools, and recreation increases, most often resulting in the phe nomenon known as urban sprawl (Fenta et al 2017; Sumari et al 2017; Tanveer et al 2019; Ujoh, Igbawua, and Ogidi Paul 2019). The com bination of remote sensing and Global Positioning
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