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and forests which provide essential ecosystem services such as carbon sequestration and support for biodiversity have been replaced by built-up land cover.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.

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ABSTRACT. The gap between scientific knowledge and implementation in the fields of biodiversity conservation, environmental management, and climate change adaptation has resulted in many calls from practitioners and academics to provide practical solutions responding effectively to the risks and opportunities of global environmental change, e.g., Future Earth. We present a framework to guide the implementation of science-action partnerships based on a real-world case study of a partnership between a local municipality and an academic institution to bridge the science-action gap in the eThekwini Municipal Area, South Africa. This partnership aims to inform the implementation of sustainable land-use planning, biodiversity conservation, environmental management, and climate change adaptation practice and contributes to the development of human capacity in these areas of expertise. Using a transdisciplinary approach, implementation-driven research is being conducted to develop several decision-making products to better inform land-use planning and management. Lessons learned through this partnership are synthesized and presented as a framework of enabling actions operating at different levels, from the individual to the interorganizational. Enabling actions include putting in place enabling organizational preconditions, assembling a functional well-structured team, and actively building interpersonal and individual collaborative capacity. Lessons learned in the case study emphasize the importance of building collaborative capacity and social capital, and paying attention to the process of transdisciplinary research to achieve more tangible science, management, and policy objectives in science-action partnerships. By documenting and reflecting on the process, this case study provides conceptual and practical guidance on bridging the science-action gap through partnerships.

Plans for smart mobility through cycling are often hampered by lack of information on cycling patterns and trends, particularly in cities of the developing world such as Johannesburg. Similarly, traditional methods of data collection such as bicycle counts are often expensive, cover a limited spatial extent and not up-to-date. Consequently, the dataset presented in this paper illustrates the spatial and temporal coverage of cycling patterns and trends in Johannesburg for the year 2014 derived from the geolocation based mobile application Strava. To the best knowledge of the authors, there is little or no comprehensive dataset that describes cycling patterns in Johannesburg. Perhaps this dataset is a tool that will support evidence based transportation planning and smart mobility.