17 research outputs found

    Implications for Tracking SDG Indicator Metrics with Gridded Population Data

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    Achieving the seventeen United Nations Sustainable Development Goals (SDGs) requires accurate, consistent, and accessible population data. Yet many low- and middle-income countries lack reliable or recent census data at the sufficiently fine spatial scales needed to monitor SDG progress. While the increasing abundance of Earth observation-derived gridded population products provides analysis-ready population estimates, end users lack clear use criteria to track SDGs indicators. In fact, recent comparisons of gridded population products identify wide variation across gridded population products. Here we present three case studies to illuminate how gridded population datasets compare in measuring and monitoring SDGs to advance the “fitness for use” guidance. Our focus is on SDG 11.5, which aims to reduce the number of people impacted by disasters. We use five gridded population datasets to measure and map hazard exposure for three case studies: the 2015 earthquake in Nepal; Cyclone Idai in Mozambique, Malawi, and Zimbabwe (MMZ) in 2019; and flash flood susceptibility in Ecuador. First, we map and quantify geographic patterns of agreement/disagreement across gridded population products for Nepal, MMZ, and Ecuador, including delineating urban and rural populations estimates. Second, we quantify the populations exposed to each hazard. Across hazards and geographic contexts, there were marked differences in population estimates across the gridded population datasets. As such, it is key that researchers, practitioners, and end users utilize multiple gridded population datasets—an ensemble approach—to capture uncertainty and/or provide range estimates when using gridded population products to track SDG indicators. To this end, we made available code and globally comprehensive datasets that allows for the intercomparison of gridded population products

    Adaptive Governance and Market Heterogeneity: An Institutional Analysis of an Urban Food System in Sub-Saharan Africa

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    African cities face immense challenges over the coming decades. As countries urbanize, African cities must maintain service provision for rapidly increasing populations, yet with limited resources. In particular, urban food systems must be able to cope with regional food shortages and catalyze (or at least enable) the distribution of food from diverse sources in order to ensure that the cost of food remains affordable for all of the segments of a city’s population. Food systems in most African cities are composed of wholesale sellers, formal markets, street vendors, shops, and increasingly large-scale international stores, creating an evolving landscape of food sources. At the same time, urban population growth can result in rapid changes in urban structure with new peri-urban development and transitions in socioeconomic status within existing areas. Governance plays an important role in the creation and coordination of formal and informal actors across different types of food providers. At the municipal level, new markets must be approved to keep pace with urban expansion. Within residential areas, market management committees must work to maintain traditional markets in the context of increasing competition from large-scale grocers and small-scale street vendors. We use household and market-level data that was collected in Lusaka, Zambia, to conduct an institutional analysis of residential areas to examine the interplay between households, public markets, and street vendors. Analysis of the city’s food system identifies a complex network of relationships featuring formal and informal governance arrangements, which may affect food system functionality

    CHC-CMIP6

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    Variability in urban population distributions across Africa

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    Africa is projected to add one billion urban residents by 2050. Yet developing sustainable solutions to tackle the host of challenges posed by rapid urban population growth is stymied by a lack municipality-level population data across the continent. To fill this gap, we intersect volunteered urban settlement data from OpenStreetMap with five synthetic gridded population datasets to estimate the how Africa's urban population is distributed among over 4750 individual urban settlements across Africa. We assess how urban settlement distributions changed from 2000 to 2015 within and between countries and across moisture zones. To this end, we construct urban settlement Lorenz curves to calculate change in Gini coefficients and test the degree to which Africa's urban settlements distributions fit power law distributions exhibited by Zipf's law. Our results reveal that 77%-85% of urban settlements in Africa have fewer than 100 000 people and that at least 50% of Africa's urban population live in urban settlements with fewer than 1 million residents. Across almost all African countries, the distribution of urban population shifted towards larger cities between 2000 and 2015. However, in arid regions, our results indicate that small- and medium-sized urban settlements are absorbing a greater share of urban population growth compared to large urban settlements. While our urban population estimates vary across gridded population datasets and differ from United Nations estimates, this is the first paper to measure urban population across Africa using a consistent methodology to identify urban settlement populations. Unlike UN urban population data for Africa, our results can readily be incorporated with geolocated environmental, public health, and economic data to support efforts to monitor United Nations Sustainable Development Goals related to urban sustainability, poverty reduction, and food security across Africa's ever-growing urban settlements.National Science Foundation [SES-1360463, BCS-1115009, BCS-1026776]Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

    High resolution climate change observations and projections for the evaluation of heat-related extremes

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    Abstract The Climate Hazards Center Coupled Model Intercomparison Project Phase 6 climate projection dataset (CHC-CMIP6) was developed to support the analysis of climate-related hazards, including extreme humid heat and drought conditions, over the recent past and in the near-future. Global daily high resolution (0.05°) grids of the Climate Hazards InfraRed Temperature with Stations temperature product, the Climate Hazards InfraRed Precipitation with Stations precipitation product, and ERA5-derived relative humidity form the basis of the 1983–2016 historical record, from which daily Vapor Pressure Deficits (VPD) and maximum Wet Bulb Globe Temperatures (WBGTmax) were derived. Large CMIP6 ensembles from the Shared Socioeconomic Pathway 2-4.5 and SSP 5-8.5 scenarios were then used to develop high resolution daily 2030 and 2050 ‘delta’ fields. These deltas were used to perturb the historical observations, thereby generating 0.05° 2030 and 2050 projections of daily precipitation, temperature, relative humidity, and derived VPD and WBGTmax. Finally, monthly counts of frequency of extremes for each variable were derived for each time period

    Measures and Determinants of Urban Food Security: Evidence from Accra, Ghana

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    The urban population in Africa south of the Sahara (SSA) is expected to expand rapidly from 376 million people in 2015 to more than 1.25 billion people by 2050. Measuring and ensuring food security among urban households will become an increasingly pertinent task for development researchers and practitioners. In this paper we characterize food security among a sample of low- and middle-income residents of Accra, Ghana, using 2017 survey data. We find that households tend to purchase food from traditional markets, local stalls and kiosks, and street hawkers, and rarely from modern supermarkets. We characterize food security using three established metrics: the Household Food Insecurity Access Scale (HFIAS); the Household Food Insecurity Access Prevalence (HFIAP); and the Food Consumption Score (FCS). We then estimate the determinants of food security using general linear models. The food security metrics are not strongly correlated. For example, according to HFIAP, as many as 70 percent of households sampled are food insecure, but only 2 percent fall below acceptable thresholds measured by FCS. Model results show that household education, assets, and dwelling characteristics are significantly associated with food security according to HFIAS and HFIAP, but not with FCS. The poor correlation and weak model agreement between the dietary recall metric, FCS, and the experience-based metrics, HFIAS and HFIAP, call for closer attention to measurement of urban food security. Given Africa’s urban future, our findings highlight the need for an urban-oriented comprehensive approach to the food security of urban households.Non-PRIFPRI1; GSSP; CRP2; 3 Building Inclusive and Efficient Markets, Trade Systems, and Food Industry; 4 Transforming Agricultural and Rural EconomiesPIM; DSGDCGIAR Research Program on Policies, Institutions, and Markets (PIM
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