16 results found
Jimenez MP, Suel E, Rifas-Shiman SL, et al., 2022, Street-view greenspace exposure and objective sleep characteristics among children, ENVIRONMENTAL RESEARCH, Vol: 214, ISSN: 0013-9351
Suel E, Sorek-Hamer M, Moise I, et al., 2022, What you see is what you breathe? Estimating air pollution spatial variation using street level imagery, Remote Sensing, Vol: 14, ISSN: 2072-4292
High spatial resolution information on urban air pollution levels is unavailable in many areas globally, partially due to high input data needs of existing estimation approaches. Here we introduce a computer vision method to estimate annual means for air pollution levels from street level images. We used annual mean estimates of NO2 and PM2.5 concentrations from locally calibrated models as labels from London, New York, and Vancouver to allow for compilation of a sufficiently large dataset (~250k images for each city). Our experimental setup is designed to quantify intra and intercity transferability of image-based model estimates. Performances were high and comparable to traditional land-use regression (LUR) and dispersion models when training and testing on images from the same city (R2 values between 0.51 and 0.95 when validated on data from ground monitoring stations). Like LUR models, transferability of models between cities in different geographies is more difficult. Specifically, transferability between the three cities i.e., London, New York, and Vancouver, which have similar pollution source profiles were moderately successful (R2 values between zero and 0.67). Comparatively, performances when transferring models trained on these cities with very different source profiles i.e., Accra in Ghana and Hong Kong were lower (R2 between zero and 0.21) suggesting the need for local calibration with local calibration using additional measurement data from cities that share similar source profiles.
Yadav N, Sorek-Hamer M, Pohle MV, et al., 2022, Deep Transfer Learning on Satellite Imagery Improves Air Quality Estimates in Developing Nations, Publisher: ArXiv
Urban air pollution is a public health challenge in low- and middle-incomecountries (LMICs). However, LMICs lack adequate air quality (AQ) monitoringinfrastructure. A persistent challenge has been our inability to estimate AQaccurately in LMIC cities, which hinders emergency preparedness and riskmitigation. Deep learning-based models that map satellite imagery to AQ can bebuilt for high-income countries (HICs) with adequate ground data. Here wedemonstrate that a scalable approach that adapts deep transfer learning onsatellite imagery for AQ can extract meaningful estimates and insights in LMICcities based on spatiotemporal patterns learned in HIC cities. The approach isdemonstrated for Accra in Ghana, Africa, with AQ patterns learned from two UScities, specifically Los Angeles and New York.
Sorek-Hamer M, Von Pohle M, Sahasrabhojanee A, et al., 2022, A deep learning approach for meter-scale air quality estimation in urban environments using very high-spatial-resolution satellite imagery, Atmosphere, Vol: 13, Pages: 1-16, ISSN: 2073-4433
High-spatial-resolution air quality (AQ) mapping is important for identifying pollution sources to facilitate local action. Some of the most populated cities in the world are not equipped with the infrastructure required to monitor AQ levels on the ground and must rely on other sources, such as satellite derived estimates, to monitor AQ. Current satellite-data-based models provide AQ mapping on a kilometer scale at best. In this study, we focus on producing hundred-meter-scale AQ maps for urban environments in developed cities. We examined the feasibility of an image-based object-detection analysis approach using very high-spatial-resolution (2.5 m) commercial satellite imagery. We fed the satellite imagery to a deep neural network (DNN) to learn the association between visual urban features and air pollutants. The developed model, which solely uses satellite imagery, was tested and evaluated using both ground monitoring observations and land-use regression modeled PM2.5 and NO2 concentrations over London, Vancouver (BC), Los Angeles, and New York City. The results demonstrate a low error with a total RMSE < 2 µg/m3 and highlight the contribution of specific urban features, such as green areas and roads, to continuous hundred-meter-scale AQ estimations. This approach offers promise for scaling to global applications in developed and developing urban environments. Further analysis on domain transferability will enable application of a parsimonious model based merely on satellite images to create hundred-meter-scale AQ maps in developing cities, where current and historical ground data are limited.
Suel E, Bhatt S, Brauer M, et al., 2021, Multimodal deep learning from satellite and street-level imagery for measuring income, overcrowding, and environmental deprivation in urban areas, Remote Sensing of Environment: an interdisciplinary journal, Vol: 257, ISSN: 0034-4257
Data collected at large scale and low cost (e.g. satellite and street level imagery) have the potential to substantially improve resolution, spatial coverage, and temporal frequency of measurement of urban inequalities. Multiple types of data from different sources are often available for a given geographic area. Yet, most studies utilize a single type of input data when making measurements due to methodological difficulties in their joint use. We propose two deep learning-based methods for jointly utilizing satellite and street level imagery for measuring urban inequalities. We use London as a case study for three selected outputs, each measured in decile classes: income, overcrowding, and environmental deprivation. We compare the performances of our proposed multimodal models to corresponding unimodal ones using mean absolute error (MAE). First, satellite tiles are appended to street level imagery to enhance predictions at locations where street images are available leading to improvements in accuracy by 20, 10, and 9% in units of decile classes for income, overcrowding, and living environment. The second approach, novel to the best of our knowledge, uses a U-Net architecture to make predictions for all grid cells in a city at high spatial resolution (e.g. for 3 m × 3 m pixels in London in our experiments). It can utilize city wide availability of satellite images as well as more sparse information from street-level images where they are available leading to improvements in accuracy by 6, 10, and 11%. We also show examples of prediction maps from both approaches to visually highlight performance differences.
Suel E, Sorek-Hamer M, Moise I, et al., 2020, Predicting air pollution spatial variation with street-level imagery, Machine Learning in Public Health (MLPH) Workshop, 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Publisher: NeurIPS
Danesh Yazdi M, Kuang Z, Dimakopoulou K, et al., 2020, Predicting fine particulate matter (PM2.5) in the Greater London area: an ensemble approach using machine learning methods, Remote Sensing, Vol: 12, Pages: 1-18, ISSN: 2072-4292
Estimating air pollution exposure has long been a challenge for environmental health researchers. Technological advances and novel machine learning methods have allowed us to increase the geographic range and accuracy of exposure models, making them a valuable tool in conducting health studies and identifying hotspots of pollution. Here, we have created a prediction model for daily PM2.5 levels in the Greater London area from 1st January 2005 to 31st December 2013 using an ensemble machine learning approach incorporating satellite aerosol optical depth (AOD), land use, and meteorological data. The predictions were made on a 1 km × 1 km scale over 3960 grid cells. The ensemble included predictions from three different machine learners: a random forest (RF), a gradient boosting machine (GBM), and a k-nearest neighbor (KNN) approach. Our ensemble model performed very well, with a ten-fold cross-validated R2 of 0.828. Of the three machine learners, the random forest outperformed the GBM and KNN. Our model was particularly adept at predicting day-to-day changes in PM2.5 levels with an out-of-sample temporal R2 of 0.882. However, its ability to predict spatial variability was weaker, with a R2 of 0.396. We believe this to be due to the smaller spatial variation in pollutant levels in this area.
Suel E, Polak J, Bennett J, et al., 2019, Measuring social, environmental and health inequalities using deep learning and street imagery, Scientific Reports, Vol: 9, ISSN: 2045-2322
Cities are home to an increasing majority of the world’s population. Currently, it is difficult to track social, economic, environmental and health outcomes in cities with high spatial and temporal resolution, needed to evaluate policies regarding urban inequalities. We applied a deep learning approach to street images for measuring spatial distributions of income, education, unemployment, housing, living environment, health and crime. Our model predicts different outcomes directly from raw images without extracting intermediate user-defined features. To evaluate the performance of the approach, we first trained neural networks on a subset of images from London using ground truth data at high spatial resolution from official statistics. We then compared how trained networks separated the best-off from worst-off deciles for different outcomes in images not used in training. The best performance was achieved for quality of the living environment and mean income. Allocation was least successful for crime and self-reported health (but not objectively measured health). We also evaluated how networks trained in London predict outcomes three other major cities in the UK: Birmingham, Manchester, and Leeds. The transferability analysis showed that networks trained in London, fine-tuned with only 1% of images in other cities, achieved performances similar to ones from trained on data from target cities themselves. Our findings demonstrate that street imagery has the potential complement traditional survey-based and administrative data sources for high-resolution urban surveillance to measure inequalities and monitor the impacts of policies that aim to address them.
Suel E, Polak J, 2018, Incorporating online shopping into travel demand modelling: challenges, progress, and opportunities, Transport Reviews, Vol: 38, Pages: 576-601, ISSN: 0144-1647
There is a large body of literature, spanning multiple disciplines, concerned with the relationship between traditional (physical) shopping and associated travel behaviour. However, despite the recent rapid growth of digital retailing and online shopping, the impact on travel behaviour remain poorly understood. Although the issue of the substitution and complementarity between conventional and virtual retail channels has been extensively explored, few attempts have been made to extend this work so as to incorporate virtual retail channels into modelling frameworks that can link shopping and mobility decisions. Here, we review the existing literature base with a focus on most relevant dimensions for personal mobility. How online activity can be incorporated into operational transport demand models and benefits of such effort are discussed. Existing frameworks of shopping demand are flexible and can, in principle, be extended to incorporate virtual shopping and the associated additional complexities. However, there are significant challenges associated with lack of standard ontologies for crucial concepts and insufficiencies in traditional data collection methods. Also, supply-side questions facing businesses and policy-makers are changing as retailing goes through a digital transformation. Opportunities and priorities need to be defined for future research directions for an assessment of existing tools and frameworks.
Suel E, Daina N, Polak JW, 2017, A hazard-based approach to modelling the effects of online shopping on intershopping duration, Transportation, ISSN: 1572-9435
Despite growing prevalence of online shopping, its impacts on mobility are poorlyunderstood. This partially results from the lack of sufficiently detailed data. In thispaper we address this gap using consumer panel data, a new dataset for this context.We analyse one year long longitudinal grocery shopping purchase data from Londonshoppers to investigate the effects of online shopping on overall shopping activity pat-terns and personal trips. We characterise the temporal structure of shopping demandby means of the duration between shopping episodes using hazard-based durationmodels. These models have been used to study inter-shopping spells for traditionalshopping in the literature, however effects of online shopping were not considered.Here, we differentiate between shopping events and shopping trips. The former refersto all types of shopping activity including both online and in-store, while the latteris restricted to physical shopping trips. Separate models were estimated for each andresults suggest potential substitution effects between online and in-store in the contextof grocery shopping. We find that having shopped online since the last shopping tripsignificantly reduces the likelihood of a physical shopping trip. We do not observethe same effect for inter-event durations. Hence, shopping online does not have a sig-nificant effect on overall shopping activity frequency, yet affects shopping trip rates.This is a key finding and suggests potential substitution between online shopping andphysical trips to the store. Additional insights on which factors, including basket sizeand demographics, affect inter-shopping durations are also drawn.
Suel E, Polak J, 2017, Development of joint models for channel, store, and travel mode choice: grocery shopping in London, Transportation Research Part A - Policy and Practice, Vol: 99, Pages: 147-162, ISSN: 0965-8564
The nature of shopping activity is changing in response to innovation in retailing and the growth in online channels. There is a growing interest from transport researchers, policy makers, marketing and retail businesses in understanding the implications of this change. However, existing tools and techniques developed for analysing behaviour in traditional retail environments do not adequately represent emerging complexities resulting from digital innovation. In this paper, we advance existing destination and mode choice models by incorporating online channels in a unified framework. This is a critical extension to existing transport literature on destination choice which largely ignores online activity. Specifically, we develop discrete choice models using elemental store (including both online and in-store) alternatives for joint choice of channel, store, and travel mode. We demonstrate the use of a widely-accepted consumer panel dataset with minor modifications, for the first time in transport research, together with API based data mining tools that offer great potential for enrichment.The analysis focuses on grocery shopping and uses consumer data collected from two selected boroughs in London; results from multinomial logit and nested logit estimations are reported. The extension presented here provides the tools to quantify the effects of increased online shopping on traditional store formats and travel patterns. Our results showed virtual alternatives currently offer an attractive substitute among early adopters for large basket shopping mostly for high income groups. This might suggest a significant reduction in shopping trips to hypermarkets often associated with large basket shopping potentially leading to store closures. Online deliveries mostly draw from driving trips and less so from walking and public transport trips. The present study also confirmed previous findings related to smaller stores and longer travel distances being associated with declining
Suel E, Polak J, 2016, Hypothesis testing in discrete choice models: It’s more complicated than you think, International Choice Modelling Conference
Suel, Daina, Polak, 2016, A hazard-based approach to modelling effects of online shopping on intershopping duration
Suel E, Tolouei R, Le Vine S, et al., 2015, Quantifying Effects of Residential Property Markets and International Migration on Trip Rates in Britain, 95th Annual Meeting of the Transportation Research Board
Suel E, Le Vine S, Polak J, 2015, Empirical Application of Expenditure Diary Instrument to Quantify Relationships Between In-Store and Online Grocery Shopping Case Study of Greater London, TRANSPORTATION RESEARCH RECORD, Pages: 45-54, ISSN: 0361-1981
Suel E, Keirstead J, Polak J, 2014, Connecting Research with Cities: Mapping the UK's research landscape on urban systems and technologies, Connecting Research with Cities: Mapping the UK's research landscape on urban systems and technologies
The aim of this research is to provide the Future Cities Catapult with an overview of the activity of theUK academic sector in technology areas relevant to the development of integrated city systems.The specific objectives are:• Identifying emerging technologies and research areas that relate to integrated citysystems and which are likely to have an impact over a 5-10 year time horizon.• Documenting the nature of the research funding streams available and the fundingrequirements associated with different technologies and research areas.• Describing how innovation and translation processes operate within the UKacademic sector including the strengths and weaknesses of these arrangements.These results provide insights into potential collaboration opportunities with UK academia, inparticular, by highlighting key innovation opportunities and means of reducing existing barriers to thesuccessful commercialisation of relevant academic research.
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