Burkina Faso's infrastructure : a continental perspective

Infrastructure contributed 1.3 percentage points to Burkina Faso's annual per capita gross domestic product (GDP) growth over the past decade, much of it due to improvements in information and communication technology (ICT). Raising the country's infrastructure endowment to that of the region's middle-income countries (MICs) could boost annual growth by more than 3 percentage points per capita. Burkina Faso has made significant progress developing its infrastructure in recent years, especially in the ICT sector. The country has also moved forward in the areas of road maintenance and water and sanitation, but still faces challenges in these sectors, as well as in the electricity sector. As of 2007, Burkina Faso faced an annual infrastructure funding gap of $165 million per year, or 4 percent of GDP. That gap could be cut in half by the adoption of more appropriate technologies to meet infrastructure targets in the transport and the water and sanitation sectors. Even if Burkina Faso were unable to increase infrastructure spending or otherwise close the infrastructure funding gap, simply by moving from a 10- to 18-year horizon the country could address its efficiency gap and meet the posited infrastructure targets.Transport Economics Policy&Planning,Infrastructure Economics,Town Water Supply and Sanitation,E-Business,Energy Production and Transportation

Asking Too Much: The Ninth Circuit’s Erroneous Review of Social Security Disability Determinations

Disability determinations made by the Social Security Administration’s administrative law judges are subject to judicial review by Article III courts. By statute, these courts apply the “substantial evidence” standard of review on appeal from the agency. The substantial evidence standard is a forgiving one that defers to the findings of the agency. But the Ninth Circuit Court of Appeals has modified this standard. It now reviews certain categories of SSA findings not only for substantial evidence, but for support by “clear and convincing reasons.” This heightened standard of review is facially at odds with the statutorily mandated substantial evidence standard. It also undercuts the principle of deference given to the initial factfinder by the substantial evidence standard of review

Characterisation of urban environment and activity across space and time using street images and deep learning in Accra

The urban environment influences human health, safety and wellbeing. Cities in Africa are growing faster than other regions but have limited data to guide urban planning and policies. Our aim was to use smart sensing and analytics to characterise the spatial patterns and temporal dynamics of features of the urban environment relevant for health, liveability, safety and sustainability. We collected a novel dataset of 2.1 million time-lapsed day and night images at 145 representative locations throughout the Metropolis of Accra, Ghana. We manually labelled a subset of 1,250 images for 20 contextually relevant objects and used transfer learning with data augmentation to retrain a convolutional neural network to detect them in the remaining images. We identified 23.5 million instances of these objects including 9.66 million instances of persons (41% of all objects), followed by cars (4.19 million, 18%), umbrellas (3.00 million, 13%), and informally operated minibuses known as tro tros (2.94 million, 13%). People, large vehicles and market-related objects were most common in the commercial core and densely populated informal neighbourhoods, while refuse and animals were most observed in the peripheries. The daily variability of objects was smallest in densely populated settlements and largest in the commercial centre. Our novel data and methodology shows that smart sensing and analytics can inform planning and policy decisions for making cities more liveable, equitable, sustainable and healthy

Characterisation of urban environment and activity across space and time using street images and deep learning in Accra

The urban environment influences human health, safety and wellbeing. Cities in Africa are growing faster than other regions but have limited data to guide urban planning and policies. Our aim was to use smart sensing and analytics to characterise the spatial patterns and temporal dynamics of features of the urban environment relevant for health, liveability, safety and sustainability. We collected a novel dataset of 2.1 million time-lapsed day and night images at 145 representative locations throughout the Metropolis of Accra, Ghana. We manually labelled a subset of 1,250 images for 20 contextually relevant objects and used transfer learning with data augmentation to retrain a convolutional neural network to detect them in the remaining images. We identified 23.5 million instances of these objects including 9.66 million instances of persons (41% of all objects), followed by cars (4.19 million, 18%), umbrellas (3.00 million, 13%), and informally operated minibuses known as tro tros (2.94 million, 13%). People, large vehicles and market-related objects were most common in the commercial core and densely populated informal neighbourhoods, while refuse and animals were most observed in the peripheries. The daily variability of objects was smallest in densely populated settlements and largest in the commercial centre. Our novel data and methodology shows that smart sensing and analytics can inform planning and policy decisions for making cities more liveable, equitable, sustainable and healthy