Browsing by Author "Muller, E."
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Item Characterisation of urban environment and activity across space and time using street images and deep learning in Accra(Scientific reports, 2022) Nathvani, R.; Clark, S.N.; Muller, E.; Alli, A.S.; Bennett, J.E.; Nimo, J.; Moses, J.B.; Baah, S.; Metzler, A.B.; Brauer, M.; Suel, E.; Hughes, A.F.; Agyemang, E.; Owusu, G.; Agyei‑Mensah, S.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.Item Do Poverty and Wealth Look the Same The World Over? A Comparative Study of 12 Cities From Five High-Income Countries Using Street Images(EPJ Data Science, 2023) Suel, E.; Muller, E.; et al.Urbanization and inequalities are two of the major policy themes of our time, intersecting in large cities where social and economic inequalities are particularly pronounced. Large scale street-level images are a source of city-wide visual information and allow for comparative analyses of multiple cities. Computer vision methods based on deep learning applied to street images have been shown to successfully measure inequalities in socioeconomic and environmental features, yet existing work has been within specific geographies and have not looked at how visual environments compare across different cities and countries. In this study, we aim to apply existing methods to understand whether, and to what extent, poor and wealthy groups live in visually similar neighborhoods across cities and countries. We present novel insights on similarity of neighborhoods using street-level images and deep learning methods. We analyzed 7.2 million images from 12 cities in five high-income countries, home to more than 85 million people: Auckland (New Zealand), Sydney (Australia), Toronto and Vancouver (Canada), Atlanta, Boston, Chicago, Los Angeles, New York, San Francisco, and Washington D.C. (United States of America), and London (United Kingdom). Visual features associated with neighborhood disadvantage are more distinct and unique to each city than those associated with affluence. For example, from what is visible from street images, high density poor neighborhoods located near the city center (e.g., in London) are visually distinct from poor suburban neighborhoods characterized by lower density and lower accessibility (e.g., in Atlanta). This suggests that differences between two cities is also driven by historical factors, policies, and local geography. Our results also have implications for image-based measures of inequality in cities especially when trained on data from cities that are visually distinct from target cities. We showed that these are more prone to errors for disadvantaged areas especially when transferring across cities, suggesting more attention needs to be paid to improving methods for capturing heterogeneity in poor environment across cities around the world.Item Molecular variability analysis of five new complete cacao swollen shoot virus genomic sequences(Archives of Virology, 2005-02) Muller, E.; Sackey, S.Cacao swollen shoot virus (CSSV), a member of the family Caulimoviridae, genus Badnavirus occurs in all the main cacao-growing areas of West Africa. We amplified, cloned and sequenced complete genomes of five new isolates, two originating from Togo and three originating from Ghana. The genome of these five newly sequenced isolates all contain the five putative open reading frames I, II, III, X and Y described for the first sequenced CSSV isolate, Agou1 originating from Togo. Their genomes have been aligned with the genome of Agou1. The nucleotide and amino acid sequence identities between isolates have been calculated and a phylogenetic analysis has been made including other pararetroviruses. Maximum nucleotide sequence variability between complete genomes of CSSV isolates was 29.4%. Geographical differentiation between isolates appears more important than differentiation between mild and severe isolates. ORF X differs greatly in size and sequence between the Togolese isolates Nyongbo2 and Agou1, and the four other isolates, its functional role is therefore clearly questionable. © Springer-Verlag 2004.