Background: Poverty and food insecurity are intrinsically linked as poor households often lack the resources required to access sufficient nutritious food to live an active and healthy life. Consumption and expenditure surveys are typically used to identify poor versus nonpoor households but are detailed and costly. Measures of wealth based on asset ownership and housing characteristics can be generated from lighter, less costly surveys. Objective: To examine whether indices based on asset ownership and housing characteristics (stock) complement household consumption (flow) when used to analyze inequalities in food security outcomes. Methods: Comprehensive data from Nepal, Malawi, Tanzania, Uganda, and Madagascar are used to examine correlations and overlaps in classification between indices of household wealth and consumption per capita. Inequality in food security indicators representing quantity, quality, and vulnerability is examined across wealth and consumption per capita quintiles. Results: Wealth indices are correlated with consumption per capita, with coefficients between 0.5 and 0.6. The prevalence of food insecurity decreases from poorer to wealthier quintiles for all variables and for all food security measures in all countries. Energy deficiency varies much more across consumption quintiles than wealth index quintiles. Interestingly, inequalities in the share of consumption of food are more pronounced across the wealth index quintiles than per capita consumption. Conclusion: Although wealth indices and consumption per capita are related and both are drivers of food security, they cannot be used interchangeably for food security analysis. Each inequality measure is important for describing different aspects of food security.
Natural disasters affect hundreds of millions of people worldwide every year. Emergency response efforts depend upon the availability of timely information, such as information concerning the movements of affected populations. The analysis of aggregated and anonymized Call Detail Records (CDR) captured from the mobile phone infrastructure provides new possibilities to characterize human behavior during critical events. In this work, we investigate the viability of using CDR data combined with other sources of information to characterize the floods that occurred in Tabasco, Mexico in 2009. An impact map has been reconstructed using Landsat-7 images to identify the floods. Within this frame, the underlying communication activity signals in the CDR data have been analyzed and compared against rainfall levels extracted from data of the NASA-TRMM project. The variations in the number of active phones connected to each cell tower reveal abnormal activity patterns in the most affected locations during and after the floods that could be used as signatures of the floods - both in terms of infrastructure impact assessment and population information awareness. The representativeness of the analysis has been assessed using census data and civil protection records. While a more extensive validation is required, these early results suggest high potential in using cell tower activity information to improve early warning and emergency management mechanisms.Comment: Submitted to IEEE Global Humanitarian Technologies Conference (GHTC) 201
The International Food Policy Research Institute (IFPRI), established in 1975, provides evidence-based policy solutions to sustainably end hunger and malnutrition and reduce poverty. The Institute conducts research, communicates results, optimizes partnerships, and builds capacity to ensure sustainable food production, promote healthy food systems, improve markets and trade, transform agriculture, build resilience, and strengthen institutions and governance. Gender is considered in all of the Institute's work. IFPRI collaborates with partners around the world, including development implementers, public institutions, the private sector, and farmers' organizations, to ensure that local, national, regional, and global food policies are based on evidence. IFPRI is a member of the CGIAR Consortium.
Genetic algorithm is a population based an adaptive search and optimizations techniques and genetic mimic the natural evolution process. The Genetic operators include selection, crossover and mutation. The aim to present this paper is it gives comparative selection strategies for solving an optimization problem in genetic algorithm and evaluates their performance.
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