Matrix Completion With Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference

Inferring air quality from a limited number of observations is an essential task for monitoring and controlling air pollution. Existing inference methods typically use low spatial resolution data collected by fixed monitoring stations and infer the concentration of air pollutants using additional types of data, e.g., meteorological and traffic information. In this work, we focus on street-level air quality inference by utilizing data collected by mobile stations. We formulate air quality inference in this setting as a graph-based matrix completion problem and propose a novel variational model based on graph convolutional autoencoders. Our model captures effectively the spatio-temporal correlation of the measurements and does not depend on the availability of additional information apart from the street-network topology. Experiments on a real air quality dataset, collected with mobile stations, shows that the proposed model outperforms state-of-the-art approaches

A stigmergy-based analysis of city hotspots to discover trends and anomalies in urban transportation usage

A key aspect of a sustainable urban transportation system is the effectiveness of transportation policies. To be effective, a policy has to consider a broad range of elements, such as pollution emission, traffic flow, and human mobility. Due to the complexity and variability of these elements in the urban area, to produce effective policies remains a very challenging task. With the introduction of the smart city paradigm, a widely available amount of data can be generated in the urban spaces. Such data can be a fundamental source of knowledge to improve policies because they can reflect the sustainability issues underlying the city. In this context, we propose an approach to exploit urban positioning data based on stigmergy, a bio-inspired mechanism providing scalar and temporal aggregation of samples. By employing stigmergy, samples in proximity with each other are aggregated into a functional structure called trail. The trail summarizes relevant dynamics in data and allows matching them, providing a measure of their similarity. Moreover, this mechanism can be specialized to unfold specific dynamics. Specifically, we identify high-density urban areas (i.e hotspots), analyze their activity over time, and unfold anomalies. Moreover, by matching activity patterns, a continuous measure of the dissimilarity with respect to the typical activity pattern is provided. This measure can be used by policy makers to evaluate the effect of policies and change them dynamically. As a case study, we analyze taxi trip data gathered in Manhattan from 2013 to 2015.Comment: Preprin

Spatiotemporal Deep Learning 모델을 기반으로 한 도시 전역의 대기 오염 보간과 예측

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2019. 2. 차상균.대기 오염은 대도시에서 가장 큰 문제 중 하나이다. 많은 국가들은 주요 도시 주변에 대기 오염 모니터링 센터를 건설하여 대기 오염 물질을 수집하고 해당 지역의 시민들에게 대기 오염을 경고한다. 그러나 도시에서의 대기 오염은 균일하지 않으며 시공간 (spatiotemporal)적인 문제이다. 대기 오염은 위치 (공간적 특성)과 시각 (시간적 특성)에 따라 달라진다. 따라서, 도시 전체의 대기 오염 보간과 예측은 시민들이 시간과 공간에 대해 대기의 질을 파악하고, 나아가 건강에 대한 위협을 제거하기 위한 필요 조건이다. 대기 오염은 도시 전역의 여러 시공간적 요인에 의해 영향을 받는 것으로 알려져 있다. 그 중, 기상이 대기 오염에 가장 큰 영향을 주는 것으로 인식되고 있다. 그 외에, 교통량은 대기 오염의 주요 원인인 도로의 차량 밀도를 반영한다. 평균 주행 속도는 도시 대기 오염에 영향을 준다고 판단되는 교통 체증을 나타낸다. 마지막으로, 외부 대기 오염원은 도시 대기 오염 문제의 근원 중 하나라고 주장된다. 본 논문에서는 서울시의 대기 오염 데이터, 기상 데이터, 교통량, 평균 주행 속도와 같은 많은 시공간적 데이터와 서울의 대기 오염에 영향을 준다고 알려진 중국의 3개 지방(베이징, 상하이, 산동)의 대기 오염 데이터를 제시하였다. 대기 오염에 대한 최근의 연구에서는 특정 위치와 시간의 대기 오염 예측 모델을 구축하려고 시도해왔다. 그러나 대부분 연속되지 않은 위치에대한 대기 오염을 예측하거나 직접 만든 공간 및 시간적 특성을 사용하는 데 중점을 두었다. 최근 CNN (Convolutional Neural Network), RNN (Recurrent Neural Network) 및 LSTM (Long-Short Term Memory)과 같은 딥러닝 모델이 공간 및 시간 관련 문제에서 우수하다고 알려져있다. 본 논문에서는 CNN과 LSTM을 결합한 ConvLSTM (Convolutional Long-Short Term Memory) 모델을 제안하였으며, 이를 통해 데이터의 공간 및 시간적 특성을 효율적으로 처리하고 최근의 다른 연구 결과보다 뛰어난 성능을 달성하였다.Air pollution is one of the most concerns of big cities. Many countries in the world have constructed air quality monitoring stations around major cities to collect air pollutants and make the warning to urban citizens about the air pollution around them. However, air pollution is not uniform in the city, but it is a spatiotemporal problem. It changes by locations (spatial feature) and by time (temporal feature). Consequently, citywide air pollution interpolation and prediction is a requirement of urban people to know the air quality through time and spaces to eliminate the health risks. Moreover, air pollution is affected by many spatiotemporal factors throughout the whole city. Among them, meteorology is recognized to be one the most significant effects to air pollution. Besides that, traffic volume reflects the density of vehicles on roads which is the primary cause of air pollution. Average driving speed indicates the traffic congestion which also reasonably influences air pollution over the city. Finally, external air pollution sources from outside areas are claimed to be the reason contributing to a city's air pollution problem. In this thesis, we present many spatiotemporal datasets collected over Seoul city, Korea such as air pollution data, meteorological data, traffic volume, average driving speed, and air pollution of 3 China areas like Beijing, Shanghai, Shandong, which are known to have the effect to Seoul's air pollution. Recent research in air pollution has tried to build models to predict air pollution by locations and in the future time. Nonetheless, they mostly focused on predicting air pollution in discrete locations or used hand-crafted spatial and temporal features. Recently, Deep learning models such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long-Short Term Memory (LSTM) are known to be superior in spatial and temporal relating problems. In this thesis, we propose the usage of Convolutional Long-Short Term Memory (ConvLSTM) model, a combination of CNN and LSTM, which efficiently manipulates the spatial and temporal features of the data and outperforms other recent research.1 INTRODUCTION 1 1.1 Air pollution description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Citywide Air pollution Interpolation and Prediction . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Spatiotemporal datasets introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.4 Thesis contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8 2 RELATED WORK 11 2.1 Spatiotemporal Air pollution interpolation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2 Machine Learning/Neural Networks based Air pollution prediction models . . . .12 2.3 Spatiotemporal Deep Learning models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3 Spatiotemporal Deep Learning Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16 3.1 CNN and LSTM models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16 3.2 ConvLSTM model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.3 Air Pollution Interpolation and Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4 EXPERIMENTS AND EVALUATIONS 29 4.1 Baselines description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.2 Experiments and Evaluations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4.2.1 Air pollution Interpolation: experiments and evaluations . . . . . . . . . . . . . . . . . 34 4.2.2 Air pollution Forecasting: experiments and evaluations . . . . . . . . . . . . . . . . . . 41 5 CONCLUSIONS AND FUTURE WORK 45 5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 5.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46Maste

Quantifying the health burden misclassification from the use of different PM2.5 exposure tier models: A case study of London

Exposure to PM2.5 has been associated with increased mortality in urban areas. Hence, reducing the uncertainty in human exposure assessments is essential for more accurate health burden estimates. Here we quantify the misclassification that occurs when using different exposure approaches to predict the mortality burden of a population using London as a case study. We develop a framework for quantifying the misclassification of the total mortality burden attributable to exposure to fine particulate matter (PM2.5) in four major microenvironments (MEs) (dwellings, aboveground transportation, London Underground (LU) and outdoors)in the Greater London Area (GLA), in 2017. We demonstrate that differences exist between five different exposure Tier-models with incrementally increasing complexity, moving from static to more dynamic approaches. BenMap-CE, the open source software developed by the U.S. Environmental Protection Agency, is used as a tool to achieve spatial distribution of the ambient concentration by interpolating the monitoring data to the unmonitored areas and ultimately estimate the change in mortality on a fine resolution. Our results showed that using the outdoor concentration as a surrogate for the total population exposure but ignoring the different exposure concentration that occurs indoors and the time spent in transit, would lead to a misclassification of 1,174 predicted mortalities in GLA. Indoor exposure to PM2.5 is the largest contributor to total population exposure, accounting for 80% of total mortality, followed by the London Underground which contributes 15%, albeit the average percentage of time spent there by Londoners is only 0.4%. We generally confirmed that increasing the complexity and incorporating important microenvironments, such as the highly polluted LU, could significantly reduce the misclassification in health burden assessments