SATELLITE AND ARTIFICIAL INTELLIGENCE IN MAPPING MULTIDIMENSIONAL POVERTY IN AFRICA

Context and background Multidimensional Poverty (MP) considers poverty in multiple dimensions of deprivations such as health, education, energy, the standard of living and access to basic services. MP remains a major challenge in Africa, with a large proportion of the population living in MP. According to United Nations Development Programme (UNDP), Africa has shown the highest Multidimensional Poverty Index (MPI) having over 40% of its population living in MP. Goal and Objectives: This paper is a review, aimed at assessing the potential of the integration of satellite and Artificial Intelligence (AI) in mapping MP, with a specific focus on Africa. Methodology: Based on the reviews of past studies, the combination of satellite data such as nighttime light, daytime satellite imagery and high-resolution settlement data in combination with techniques such as field surveys, statistical correlation models (transfer learning) and AI (deep learning) has been applied in mapping MP. Results: The findings from studies show that the combination of satellite data and AI has the capability of providing more accurate and granular MP maps, compared to the traditional approach. Again, this paper explains the concept of MP with a specific focus on Africa and presents a map depicting the current MPI in African countries. Finally, pitfalls especially in the accuracy, granularity and frequency of MP data were identified. Consequently, the satellite and AI approaches are recommended for more accurate, frequent, cost-effective and granular data, required in mapping poverty and design of interventions that effectively address the needs of the vulnerable populations in Africa.

Satellite Earth observation to support sustainable rural development

Traditional survey and census data are not sufficient for measuring poverty and progress towards achieving the Sustainable Development Goals (SDGs). Satellite Earth Observation (EO) is a novel data source that has considerable potential to augment data for sustainable rural development. To realise the full potential of EO data as a proxy for socioeconomic conditions, end-users – both expert and non-expert – must be able to make the right decisions about what data to use and how to use it. In this review, we present an outline of what needs to be done to operationalise, and increase confidence in, EO data for sustainable rural development and monitoring the socioeconomic targets of the SDGs. We find that most approaches developed so far operate at a single spatial scale, for a single point in time, and proxy only one socioeconomic metric. Moreover, research has been geographically focused across three main regions: West Africa, East Africa, and the Indian Subcontinent, which underscores a need to conduct research into the utility of EO for monitoring poverty across more regions, to identify transferable EO proxies and methods. A variety of data from different EO platforms have been integrated into such analyses, with Landsat and MODIS datasets proving to be the most utilised to-date. Meanwhile, there is an apparent underutilisation of fusion capabilities with disparate datasets, in terms of (i) other EO datasets such as RADAR data, and (ii) non-traditional datasets such as geospatial population layers. We identify five key areas requiring further development to encourage operational uptake of EO for proxying socioeconomic conditions and conclude by linking these with the technical and implementational challenges identified across the review to make explicit recommendations. This review contributes towards developing transparent data systems to assemble the high-quality data required to monitor socioeconomic conditions across rural spaces at fine temporal and spatial scales

Computational socioeconomics

Uncovering the structure of socioeconomic systems and timely estimation of socioeconomic status are significant for economic development. The understanding of socioeconomic processes provides foundations to quantify global economic development, to map regional industrial structure, and to infer individual socioeconomic status. In this review, we will make a brief manifesto about a new interdisciplinary research field named Computational Socioeconomics, followed by detailed introduction about data resources, computational tools, data-driven methods, theoretical models and novel applications at multiple resolutions, including the quantification of global economic inequality and complexity, the map of regional industrial structure and urban perception, the estimation of individual socioeconomic status and demographic, and the real-time monitoring of emergent events. This review, together with pioneering works we have highlighted, will draw increasing interdisciplinary attentions and induce a methodological shift in future socioeconomic studies

Biodiversity conservation and rural livelihoods : a comparative study of selected conservation approaches in Zimbabwe.

Doctor of Philosophy in Geography and Environmental Management. University of KwaZulu-Natal, Durban 2014.Historically, protected areas have operated as islands of biodiversity conservation in isolation from nearby communities. There is, however, a growing consensus that for protected areas to be more effective in conserving biodiversity, particularly in developing countries, they must incorporate the livelihood needs of poor local communities they often share boundaries with. This is because most of these communities historically pre-date the protected areas, have pre-existing rights to resources in them and have often been adversely affected by their designation. Successful protected area management thus depends on the collaboration, involvement and support of local communities. In this context, this study examines biodiversity conservation in Zimbabwe using two case studies, a private protected area (Malilangwe) and a community-conserved area (Mahenye) in terms of their livelihood impacts on local communities. The need to incorporate livelihoods goals into conservation areas in Zimbabwe has further been necessitated by the persistent failure of conventional post-independence rural development initiatives in the country. The study employed the mixed-methods approach in data collection and analysis involving both quantitative (questionnaire) and qualitative (interviews, group discussions and observation) techniques. Simple random sampling was used in selecting 150 households for questionnaire interviews from each of the two targeted communities adjacent to the conservation areas, while purposive and snowball sampling were employed in selecting key-informant interviewees. The Statistical Package for the Social Sciences (SPSS) was used in analysing quantitative data, while thematic analysis was used to analyse qualitative data. The study identifies various livelihood benefits and costs from the conservation areas to the local communities. There were some similarities and differences in the livelihood impacts of the protected areas. The main livelihood benefits from the conservation areas to the communities included the enhancement of income, health and education; in addition to improved environmental sustainability. Various hindrances to the flow of the livelihood benefits were also identified. Among the livelihood costs from the conservation areas to the local communities included, inter alia, loss of land and livelihoods, destruction of crops by wildlife, devouring of livestock by wildlife and human harassment by wildlife. Such costs were further exacerbated by lack of compensation from the conservation areas. The study recommends various measures for enhancing livelihood benefits from the conservation areas to the local communities which include, inter alia, compensation to communities for livelihood costs incurred from conservation, increased community involvement in conservation decision-making and a widening of the portfolio of livelihood-enhancing initiatives by the conservation areas. The main contribution of this study to the conservation-development discourse in Zimbabwe is that it has shown that, besides the much publicised communal areas management programme for indigenous resources (CAMPFIRE), other conservation approaches such as private protected areas can achieve similar, if not better, livelihood impacts on surrounding communities. The need for policy makers to promote other conservation approaches, besides CAMPFIRE, as alternative and equally effective vehicles for attaining rural development through conservation is thus apparent