Imperial College London

DrRickyNathvani

Faculty of MedicineSchool of Public Health

Early Career Research Fellow
 
 
 
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Contact

 

r.nathvani

 
 
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Location

 

UREN.1118Sir Michael Uren HubWhite City Campus

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Summary

 

Publications

Publication Type
Year
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6 results found

Metzler AB, Nathvani R, Sharmanska V, Bai W, Muller E, Moulds S, Agyei-Asabere C, Adjei-Boadih D, Kyere-Gyeabour E, Tetteh JD, Owusu G, Agyei-Mensah S, Baumgartner J, Robinson BE, Arku RE, Ezzati Met al., 2023, Phenotyping urban built and natural environments with high-resolution satellite images and unsupervised deep learning, Science of the Total Environment, Vol: 893, Pages: 1-14, ISSN: 0048-9697

Cities in the developing world are expanding rapidly, and undergoing changes to their roads, buildings, vegetation, and other land use characteristics. Timely data are needed to ensure that urban change enhances health, wellbeing and sustainability. We present and evaluate a novel unsupervised deep clustering method to classify and characterise the complex and multidimensional built and natural environments of cities into interpretable clusters using high-resolution satellite images. We applied our approach to a high-resolution (0.3 m/pixel) satellite image of Accra, Ghana, one of the fastest growing cities in sub-Saharan Africa, and contextualised the results with demographic and environmental data that were not used for clustering. We show that clusters obtained solely from images capture distinct interpretable phenotypes of the urban natural (vegetation and water) and built (building count, size, density, and orientation; length and arrangement of roads) environment, and population, either as a unique defining characteristic (e.g., bodies of water or dense vegetation) or in combination (e.g., buildings surrounded by vegetation or sparsely populated areas intermixed with roads). Clusters that were based on a single defining characteristic were robust to the spatial scale of analysis and the choice of cluster number, whereas those based on a combination of characteristics changed based on scale and number of clusters. The results demonstrate that satellite data and unsupervised deep learning provide a cost-effective, interpretable and scalable approach for real-time tracking of sustainable urban development, especially where traditional environmental and demographic data are limited and infrequent.

Journal article

Suel E, Muller E, Bennett J, Blakely T, Doyle Y, Lynch J, Mackenbach J, Middel A, Mizdrak A, Nathvani R, Brauer M, Ezzati Met al., 2023, 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, Vol: 12, Pages: 1-14, ISSN: 2193-1127

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 disad

Journal article

Bennett J, Rashid T, Zolfaghari A, Doyle Y, Suel E, Pearson-Stuttard J, Davies B, Fecht D, Muller ES, Nathvani RS, Sportiche N, Daby H, Johnson E, Li G, Flaxman S, Toledano M, Asaria M, Ezzati Met al., 2023, Changes in life expectancy and house prices in London from 2002 to 2019: Hyper-resolution spatiotemporal analysis of death registration and real estate data, The Lancet Regional Health Europe, Vol: 27, Pages: 1-13, ISSN: 2666-7762

Background:London has outperformed smaller towns and rural areas in terms of life expectancy increase. Our aim was to investigate life expectancy change at very-small-area level, and its relationship with house prices and their change.Methods:We performed a hyper-resolution spatiotemporal analysis from 2002 to 2019 for 4835 London Lower-layer Super Output Areas (LSOAs). We used population and death counts in a Bayesian hierarchical model to estimate age- and sex-specific death rates for each LSOA, converted to life expectancy at birth using life table methods. We used data from the Land Registry via the real estate website Rightmove (www.rightmove.co.uk), with information on property size, type and land tenure in a hierarchical model to estimate house prices at LSOA level. We used linear regressions to summarise how much life expectancy changed in relation to the combination of house prices in 2002 and their change from 2002 to 2019. We calculated the correlation between change in price and change in sociodemographic characteristics of the resident population of LSOAs and population turnover.Findings:In 134 (2.8%) of London's LSOAs for women and 32 (0.7%) for men, life expectancy may have declined from 2002 to 2019, with a posterior probability of a decline >80% in 41 (0.8%, women) and 14 (0.3%, men) LSOAs. The life expectancy increase in other LSOAs ranged from <2 years in 537 (11.1%) LSOAs for women and 214 (4.4%) for men to >10 years in 220 (4.6%) for women and 211 (4.4%) for men. The 2.5th-97.5th-percentile life expectancy difference across LSOAs increased from 11.1 (10.7–11.5) years in 2002 to 19.1 (18.4–19.7) years for women in 2019, and from 11.6 (11.3–12.0) years to 17.2 (16.7–17.8) years for men. In the 20% (men) and 30% (women) of LSOAs where house prices had been lowest in 2002, mainly in east and outer west London, life expectancy increased only in proportion to the rise in house prices. In contrast, in the 30% (men) and

Journal article

Nathvani R, Clark S, Muller E, Alli A, Bennett J, Nimo J, Moses J, Baah S, Metzler A, Brauer M, Suel E, Hughes A, Rashid T, Gemmel E, Moulds S, Baumgartner J, Toledano M, Agyemang E, Owusu G, Agyei-Mensah S, Arku R, Ezzati Met al., 2022, Characterisation of urban environment and activity across space and time using street images and deep learning in Accra, Scientific Reports, Vol: 12, ISSN: 2045-2322

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.

Journal article

Clark S, Alli A, Nathvani R, Hughes A, Ezzati M, Brauer M, Toledano M, Baumgartner J, Bennett J, Nimo J, Bedford Moses J, Baah S, Agyei-Mensah S, Owusu G, Croft B, Arku Ret al., 2021, Space-time characterization of community noise and sound sources in Accra, Ghana, Scientific Reports, Vol: 11, Pages: 1-14, ISSN: 2045-2322

Urban noise pollution is an emerging public health concern in growing cities in sub-Saharan Africa (SSA), but the sound environment in SSA cities is understudied. We leveraged a large-scale measurement campaign to characterize the spatial and temporal patterns of measured sound levels and sound sources in Accra, Ghana. We measured sound levels and recorded audio clips at 146 representative locations, involving 7-days (136 locations) and 1-year measurements between 2019 and 2020. We calculated metrics of noise levels and intermittency and analyzed audio recordings using a pre-trained neural network to identify sources. Commercial, business, and industrial areas and areas near major roads had the highest median daily sound levels (LAeq24hr: 69 dBA and 72 dBA) and the lowest percentage of intermittent sound; the vice-versa was found for peri urban areas. Road-transport sounds dominated the overall sound environment but mixtures of other sound sources, including animals, human speech, and outdoor music, dominated in various locations and at different times. Environmental noise levels in Accra exceeded both international and national health-based guidelines. Detailed information on the acoustical environmental quality (including sound levels and types) in Accra may guide environmental policy formulation and evaluation to improve the health of urban residents.

Journal article

Clark S, Alli AS, Brauer M, Ezzati M, Baumgartner J, Toledano M, Hughes A, Nimo J, Moses J, Terkpertey S, Vallarino J, Agyei-Mensah S, Agyemang E, Nathvani R, Muller E, Bennett J, Wang J, Beddows A, Kelly F, Barratt B, Beevers S, Arku Ret al., 2020, High-resolution spatiotemporal measurement of air and environmental noise pollution in sub-Saharan African cities: Pathways to Equitable Health Cities Study protocol for Accra, Ghana, BMJ Open, Vol: 10, ISSN: 2044-6055

Introduction: Air and noise pollution are emerging environmental health hazards in African cities, with potentially complex spatial and temporal patterns. Limited local data is a barrier to the formulation and evaluation of policies to reduce air and noise pollution. Methods and analysis: We designed a year-long measurement campaign to characterize air and noise pollution and their sources at high-resolution within the Greater Accra Metropolitan Area, Ghana. Our design utilizes a combination of fixed (year-long, n = 10) and rotating (week-long, n = ~130) sites, selected to represent a range of land uses and source influences (e.g. background, road-traffic, commercial, industrial, and residential areas, and various neighbourhood socioeconomic classes). We will collect data on fine particulate matter (PM2.5), nitrogen oxides (NOx), weather variables, sound (noise level and audio) along with street-level time-lapse images. We deploy low-cost, low-power, lightweight monitoring devices that are robust, socially unobtrusive, and able to function in the Sub-Saharan African (SSA) climate. We will use state-of-the-art methods, including spatial statistics, deep/machine learning, and processed-based emissions modelling, to capture highly resolved temporal and spatial variations in pollution levels across Accra and to identify their potential sources. This protocol can serve as a prototype for other SSA cities. Ethics and dissemination: This environmental study was deemed exempt from full ethics review at Imperial College London and the University of Massachusetts Amherst; it was approved by the University of Ghana Ethics Committee. This protocol is designed to be implementable in SSA cities to map environmental pollution to inform urban planning decisions to reduce health harming exposures to air and noise pollution. It will be disseminated through local stakeholder engagement (public and private sectors), peer-reviewed publications, contribution to policy documents, media, a

Journal article

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