The operational integration of Earth observation (EO) into the analysis of rural poverty and broader dynamics of human wellbeing is in its early stages. There is considerable scope for novel applications given the current proliferation of technological and computational capabilities. To develop this research agenda, it is necessary to synthesise scholarly contributions to the field in order to disseminate findings and stimulate debate, while catalysing uptake and development of methodologies. We conducted a systematic review of the scientific literature that investigates the novel applications of satellite EO for monitoring socioeconomic conditions and poverty in rural spaces of the Global South. We consider the challenges and opportunities for achieving evidence-based policymaking at finer temporal and spatial scales than is currently practised when measuring socioeconomic conditions. We investigate these challenges and the opportunities for integrating EO into monitoring poverty and human wellbeing in the context of sustainable rural development. Overall evidence suggests that the extensive spatial coverage and accessibility of data at different resolutions, paired with near real-time observations and a five-decade temporal legacy of satellite EO primes these data products for monitoring rural wellbeing. Our findings indicate a requirement to develop EO approaches for monitoring poverty dimensions across multiple spatial and temporal scales. Further requirements include testing the performance of methodologies in different social-ecological systems, to interrogate the performance of EO metrics when predicting different measures of rural poverty and wellbeing, and to operationalise the integration of disparate datasets.
Sars-CoV-2 is spread through contact between people and an understanding of where people are in contact with each other is necessary to prevent its spread. In this paper, the residential building density of Bulawayo was considered a proxy for high density of people. OpenStreetMap (OSM) building data was downloaded and converted from polygon to point data for use in the analysis. World View 2 data was used to visually map those areas where data was missing in OSM. More automated methods were attempted using eCognition however the short turnaround time of the project limited the success of this approach and work in this regard in ongoing. Land use attribute data was joined to the building shape file in order to select only those building which were designated as residential in nature. The residential building density was calculated per hectare and a hot spot analysis of the residential building density determined statistically significant clusters of high density residential buildings. The high density areas are mostly located in the west of the City, where new settlements are being created to accommodate new arrivals to the city. The East is typified by low density housing, largely a legacy of the City’s colonial past. A series of maps which could be printed on A3 paper were produced for the City. The maps displayed both the results of the hot spot analysis and land use and these have been made available to City officials to help in planning their response to the COVID-19 pandemic.
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