Ezzati, M. et al. Cities for global health. BMJ 363, k3794 (2018).
Glazener, A. et al. Fourteen pathways between urban transportation and health: A conceptual model and literature review. J. Transp. Health 21, 101070 (2021).
Sowatey, E. et al. Spaces of resilience, ingenuity, and entrepreneurship in informal work in Ghana. Int. Plan. Stud. 23, 327–339 (2018).
Beek, J. & Thiel, A. Orders of trade: regulating Accra’s Makola market. J. Leg. Plur. Unoff. Law 49, 34–53 (2017).
Solomon-Ayeh, B. E., King, R. S. & Decardi-Nelson, I. Street Vending and the Use of Urban Public Space in Kumasi, Ghana. (2011).
Brown, A., Lyons, M. & Dankoco, I. Street traders and the emerging spaces for urban voice and citizenship in African cities. Urban Stud. https://doi.org/10.1177/0042098009351187 (2010).
Karley, N. Flooding and physical planning in urban areas in West Africa: Situational analysis of Accra, Ghana. Theor. Empir. Res. Urban Manag. 4, 25–41 (2009).
Honingh, D. et al. Urban river water level increase through plastic waste accumulation at a rack structure. Front. Earth Sci. 8, 1 (2020).
Douglas, I. et al. Unjust waters: Climate change, flooding and the urban poor in Africa. Environ. Urban. 20, 187–205 (2008).
Moulds, S., Buytaert, W., Templeton, M. R. & Kanu, I. Modeling the impacts of urban flood risk management on social inequality. Water Resour. Res. 57, e2020WR029024 (2021).
Grimes, J. E. et al. The roles of water, sanitation and hygiene in reducing schistosomiasis: a review. Parasit. Vectors 8, 156 (2015).
Johnson, S. A. M. et al. Myiasis in dogs in the Greater Accra Region of Ghana. Vector-Borne Zoonotic Dis. 16, 54–57 (2016).
United Nations, Department of Economic and Social Affairs, & Population Division. World urbanization prospects: the 2018 revision. (2019).
ARUP and Cities Alliance. Future Proofing Cities Metropolitan Cities in Ghana. (2016).
Daramola, A. & Ibem, E. O. Urban environmental problems in Nigeria: implications for sustainable development. J. Sustain. Dev. Afr. 12, 124–145 (2010).
Lall, S. V., Henderson, J. V. & Venables, A. J. Africa’s Cities : Opening Doors to the World. (World Bank, 2017).
Randall, S. et al. UN Census “Households” and Local Interpretations in Africa Since Independence. SAGE Open 5, 2158244015589353 (2015).
Randall, S. & Coast, E. Poverty in African households: The Limits of Survey and Census Representations. J. Dev. Stud. 51, 162–177 (2015).
Soomro, K., Bhutta, M. N. M., Khan, Z. & Tahir, M. A. Smart city big data analytics: An advanced review. WIREs Data Min. Knowl. Discov. 9, e1319 (2019).
Joubert, A., Murawski, M. & Bick, M. Measuring the big data readiness of developing countries—Index development and its application to Africa. Inf. Syst. Front. https://doi.org/10.1007/s10796-021-10109-9 (2021).
Kwan, M.-P. Algorithmic geographies: Big data, algorithmic uncertainty, and the production of geographic knowledge. Ann. Am. Assoc. Geogr. 106, 274–282 (2016).
Yang, D., Qu, B. & Cudre-Mauroux, P. Location-centric social media analytics: Challenges and opportunities for smart cities. IEEE Intell. Syst. 36, 3–10 (2021).
Yang, J., Hauff, C., Houben, G.-J. & Bolivar, C. T. Diversity in Urban Social Media Analytics. in Web Engineering (eds. Bozzon, A., Cudre-Maroux, P. & Pautasso, C.) 335–353 (Springer International Publishing, 2016). https://doi.org/10.1007/978-3-319-38791-8_19.
GSM Association. The Mobile Economy Sub-Saharan Africa. (2021).
Batran, M., Mejia, M. G., Kanasugi, H., Sekimoto, Y. & Shibasaki, R. Inferencing human spatiotemporal mobility in Greater Maputo via mobile phone big data mining. ISPRS Int. J. Geo-Inf. 7, 259 (2018).
Kung, K. S., Greco, K., Sobolevsky, S. & Ratti, C. Exploring universal patterns in human home-work commuting from mobile phone data. PLoS ONE 9, e96180 (2014).
Wesolowski, A., O’Meara, W. P., Eagle, N., Tatem, A. J. & Buckee, C. O. Evaluating spatial interaction models for regional mobility in sub-Saharan Africa. PLOS Comput. Biol. 11, e1004267 (2015).
Jay, J. et al. Neighbourhood income and physical distancing during the COVID-19 pandemic in the United States. Nat. Hum. Behav. 4, 1294–1302 (2020).
Shi, W., Zhang, A., Zhou, X. & Zhang, M. Challenges and prospects of uncertainties in spatial big data analytics. Ann. Am. Assoc. Geogr. 108, 1513–1520 (2018).
Blumenstock, J., Cadamuro, G. & On, R. Predicting poverty and wealth from mobile phone metadata. Science 350, 1073–1076 (2015).
Blumenstock, J. Don’t forget people in the use of big data for development. Nature 561, 170–172 (2018).
Arku, R. E. et al. Personal particulate matter exposures and locations of students in four neighborhoods in Accra, Ghana. J. Expo. Sci. Environ. Epidemiol. 25, 557–566 (2015).
Dionisio, K. L. et al. Within-neighborhood patterns and sources of particle pollution: Mobile monitoring and geographic information system analysis in four communities in Accra. Ghana. Environ. Health Perspect. 118, 607–613 (2010).
Samadi, Z., Yunus, R. M., Omar, D. & Bakri, A. F. Experiencing urban through on-street activity. Procedia – Soc. Behav. Sci. 170, 653–658 (2015).
Glaeser, E. L., Kominers, S. D., Luca, M. & Naik, N. Big data and big cities: The promises and limitations of improved measures of urban life. Econ. Inq. 56, 114–137 (2018).
Goel, R. et al. Estimating city-level travel patterns using street imagery: A case study of using Google Street View in Britain. PLoS ONE 13, e0196521 (2018).
Ibrahim, M. R., Haworth, J. & Cheng, T. Understanding cities with machine eyes: A review of deep computer vision in urban analytics. Cities 96, 102481–102481 (2020).
Weichenthal, S., Hatzopoulou, M. & Brauer, M. A picture tells a thousand…exposures: Opportunities and challenges of deep learning image analyses in exposure science and environmental epidemiology. Environ. Int. 122, 3–10 (2019).
Biljecki, F. & Ito, K. Street view imagery in urban analytics and GIS: A review. Landsc. Urban Plan. 215, 104217 (2021).
Rzotkiewicz, A., Pearson, A. L., Dougherty, B. V., Shortridge, A. & Wilson, N. Systematic review of the use of Google Street View in health research: Major themes, strengths, weaknesses and possibilities for future research. Health Place 52, 240–246 (2018).
Suel, E., Polak, J. W., Bennett, J. E. & Ezzati, M. Measuring social, environmental and health inequalities using deep learning and street imagery. Sci. Rep. 9, 6229 (2019).
Time to discover new places in Africa. Ghana, Senegal and Uganda now on Street View! Official Google Africa Blog. https://africa.googleblog.com/2017/02/time-to-discover-new-places-in-africa.html.
Krylov, V. A., Kenny, E. & Dahyot, R. Automatic discovery and geotagging of objects from street view imagery. Remote Sens. 10, 661 (2018).
Zhao, Z.-Q., Zheng, P., Xu, S.-T. & Wu, X. Object Detection With Deep Learning: A Review. IEEE Trans. Neural Netw. Learn. Syst. 30, 3212–3232 (2019).
Yin, L., Cheng, Q., Wang, Z. & Shao, Z. ‘Big data’ for pedestrian volume: Exploring the use of Google Street View images for pedestrian counts. Appl. Geogr. 63, 337–345 (2015).
Liu, J., Zhang, S., Wang, S. & Metaxas, D. Multispectral Deep Neural Networks for Pedestrian Detection. in Procedings of the British Machine Vision Conference 2016 73.1–73.13 (British Machine Vision Association, 2016). doi:https://doi.org/10.5244/C.30.73.
Rahman, M. M., Sainju, A. M., Yan, D. & Jiang, Z. Mapping Road Safety Barriers Across Street View Image Sequences: A Hybrid Object Detection and Recurrent Model. in Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery 47–50 (Association for Computing Machinery, 2021).
Fan, Q., Brown, L. & Smith, J. A closer look at Faster R-CNN for vehicle detection. in 2016 IEEE Intelligent Vehicles Symposium (IV) 124–129 (2016). https://doi.org/10.1109/IVS.2016.7535375.
Campbell, A., Both, A. & Sun, Q. (Chayn). Detecting and mapping traffic signs from Google Street View images using deep learning and GIS. Comput. Environ. Urban Syst. 77, 101350 (2019).
DeVries, T., Misra, I. & Wang, C. Does Object Recognition Work for Everyone? Proc. IEEECVF Conf. Comput. Vis. Pattern Recognit. CVPR Workshop 52–59.
Ghana Statistical Service. Greater Accra Population. (2020).
World Bank. Rising through Cities in Ghana : Ghana Urbanization Review Overview Report. (2015).
Clark, S. N. et al. Small area variations and factors associated with blood pressure and body-mass index in adult women in Accra, Ghana: Bayesian spatial analysis of a representative population survey and census data. PLOS Med. 18, e1003850 (2021).
Bixby, H. et al. Quantifying within-city inequalities in child mortality across neighbourhoods in Accra, Ghana: a Bayesian spatial analysis. BMJ Open 12, e054030 (2022).
Musah, B. I., Peng, L. & Xu, Y. Urban Congestion and Pollution: A Quest for Cogent Solutions for Accra City. IOP Conf. Ser. Earth Environ. Sci. 435, 012026 (2020).
Birago, D., Opoku Mensah, S. & Sharma, S. Level of service delivery of public transport and mode choice in Accra, Ghana. Transp. Res. Part F Traffic Psychol. Behav. 46, 284–300 (2017).
Clark, S. N. et al. 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 10, 1 (2020).
Gough, K. V. Continuity and adaptability of home-based enterprises: A longitudinal study from Accra, Ghana. Int. Dev. Plan. Rev. 32, 45–70 (2010).
Rooney, M. S. et al. Spatial and temporal patterns of particulate matter sources and pollution in four communities in Accra, Ghana. Sci. Total Environ. 435–436, 107–114 (2012).
Asante, L. A. & Mills, R. O. Exploring the Socio-Economic Impact of COVID-19 Pandemic in Marketplaces in Urban Ghana. Afr. Spectr. 55, 170–181 (2020).
Zhou, Z. et al. Chemical composition and sources of particle pollution in affluent and poor neighborhoods of Accra, Ghana. Environ. Res. Lett. 8, 044025 (2013).
Senadza, B., Never, B., Kuhn, S. & Asante, F. A. Profile and determinants of the middle classes in Ghana: Energy use and sustainable consumption. J. Sustain. Dev. 13, p11 (2020).
Urban Age Programme. Cities and Social Equity – Reports. https://urbanage.lsecities.net/reports/cities-and-social-equity#3-three-perspectives-on-inequality (2009).
Clark, S. N. et al. Space-time characterization of community noise and sound sources in Accra, Ghana. Sci. Rep. 11, 11113 (2021).
Alli, A. S. et al. Spatial-temporal patterns of ambient fine particulate matter (PM2.5) and black carbon (BC) pollution in Accra. Environ. Res. Lett. 16, 074013 (2021).
Forehead, H. & Huynh, N. Review of modelling air pollution from traffic at street-level – The state of the science. Environ. Pollut. 241, 775–786 (2018).
Sharma, A., Bodhe, G. L. & Schimak, G. Development of a traffic noise prediction model for an urban environment. Noise Health 16, 63 (2014).
Tang, U. W. & Wang, Z. S. Influences of urban forms on traffic-induced noise and air pollution: Results from a modelling system. Environ. Model. Softw. 22, 1750–1764 (2007).
Ganji, A., Minet, L., Weichenthal, S. & Hatzopoulou, M. Predicting traffic-related air pollution using feature extraction from built environment images. Environ. Sci. Technol. 54, 10688–10699 (2020).
Hong, K. Y., Pinheiro, P. O. & Weichenthal, S. Predicting outdoor ultrafine particle number concentrations, particle size, and noise using street-level images and audio data. Environ. Int. 144, 106044 (2020).
Qi, M. & Hankey, S. Using street view imagery to predict street-level particulate air pollution. Environ. Sci. Technol. 55, 2695–2704 (2021).
Suel, E. et al. What you see is what you breathe? Estimating air pollution spatial variation using street-level imagery. Rem. Sens. 14, 3429 (2022).
Yoada, R. M., Chirawurah, D. & Adongo, P. B. Domestic waste disposal practice and perceptions of private sector waste management in urban Accra. BMC Public Health 14, 697 (2014).
Owusu, G., Agyei-Mensah, S. & Lund, R. Slums of hope and slums of despair: Mobility and livelihoods in Nima, Accra. Nor. Geogr. Tidsskr. – Nor. J. Geogr. 62, 180–190 (2008).
Ezeh, A. et al. The history, geography, and sociology of slums and the health problems of people who live in slums. The Lancet 389, 547–558 (2017).
Turley, R., Saith, R., Bhan, N., Rehfuess, E. & Carter, B. Slum upgrading strategies involving physical environment and infrastructure interventions and their effects on health and socio-economic outcomes. Coch. Database Syst. Rev. https://doi.org/10.1002/14651858.CD010067.pub2 (2013).
Agyemang, E. The bus rapid transit system in the Greater Accra Metropolitan Area, Ghana: Looking back to look forward. Nor. Geogr. Tidsskr. – Nor. J. Geogr. 69, 28–37 (2015).
Citi FM. Aayalolo buses to ply Adenta-Accra route—Minister. Citi 97.3 FM – Relevant Radio. Always https://citifmonline.com/2017/03/aayalolo-buses-to-ply-adenta-accra-route-minister/ (2017).
Ministry of Transport Greater Accra Regional Coordinating Council. Transportation Master Plan: Greater Accra Region (Final Report). (2016).
Peppa, M. V. et al. Towards an end-to-end framework of CCTV-based urban traffic volume detection and prediction. Sensors 21, 629 (2021).
Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V. & Minbaleev, A. Traffic flow estimation with data from a video surveillance camera. J. Big Data 6, 73 (2019).
Palinwinde Jacobs, D. Activate CCTV cameras installed in Accra to curb crime—Okoe Vanderpuije. Citinewsroom – Comprehensive News in Ghana (2021).
Jili, B. Africa: Regulate surveillance technologies and personal data. Nature 607, 445–448 (2022).
Ouyang, W., Wang, X., Zhang, C. & Yang, X. Factors in Finetuning Deep Model for Object Detection With Long-Tail Distribution. in 864–873 (2016).
Bochkovskiy, A., Wang, C.-Y. & Liao, H.-Y. M. YOLOv4: Optimal Speed and Accuracy of Object Detection. https://github.com/AlexeyAB/darknet. (2020).
Tan, M., Pang, R. & Le, Q. V. EfficientDet: Scalable and Efficient Object Detection. in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 10778–10787 (2020). doi:https://doi.org/10.1109/CVPR42600.2020.01079.
World Bank Group. 2014 Land Cover Classification of Accra, Ghana. https://datacatalog.worldbank.org/search/dataset/0039825/c–2014-Land-Cover-Classification-of-Accra–Ghana (2014).
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
Pan, S. J. & Yang, Q. A Survey on Transfer Learning. IEEE Trans. Knowl. Data Eng. 22, 1345–1359 (2010).
Tan, C. et al. A Survey on Deep Transfer Learning. in Artificial Neural Networks and Machine Learning—ICANN 2018 (eds. Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L. & Maglogiannis, I.) 270–279 (Springer International Publishing, 2018). https://doi.org/10.1007/978-3-030-01424-7_27.
Yosinski, J., Clune, J., Bengio, Y. & Lipson, H. How transferable are features in deep neural networks? in Proceedings of the 27th International Conference on Neural Information Processing Systems – Volume 2 3320–3328 (MIT Press, 2014).
Ren, S., He, K., Girshick, R. & Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137–1149 (2017).
Huang, J. et al. Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors. in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 3296–3297 (IEEE, 2017). https://doi.org/10.1109/CVPR.2017.351.
Lin, T.-Y. et al. Microsoft COCO: Common Objects in Context. in Computer Vision—ECCV 2014 (eds. Fleet, D., Pajdla, T., Schiele, B. & Tuytelaars, T.) 740–755 (Springer International Publishing, 2014). https://doi.org/10.1007/978-3-319-10602-1_48.
Kuznetsova, A. et al. The open images dataset V4. Int. J. Comput. Vis. 128, 1956–1981 (2020).
Shorten, C. & Khoshgoftaar, T. M. A survey on Image Data Augmentation for Deep Learning. J. Big Data 6, 60 (2019).
Zoph, B. et al. Learning Data Augmentation Strategies for Object Detection. in Computer Vision—ECCV 2020 (eds. Vedaldi, A., Bischof, H., Brox, T. & Frahm, J.-M.) vol. 12372 566–583 (Springer International Publishing, 2020).
U.S. Geological Survey. Landsat-8 imagery. (2020).
Cape Town’s caracals have metallic pollutants in their blood — an environmental red flag
Schoolyard in Rouyn-Noranda, Que., was protected in arsenic dust: Natural environment Ministry report
Surroundings Canada opens Fisheries Act investigation into Kearl tailings releases