Mário Cardoso, A. Cavalheiro, Alexandre Borges, A. F. Duarte, A. Soares, M. Pereira, N. Nunes, L. Azevedo, Arlindo L. Oliveira
Europe was hit hard by the COVID-19 pandemic and Portugal was severely affected, having suffered three waves in the first twelve months. Approximately between January 19th and February 5th 2021 Portugal was the country in the world with the largest incidence rate, with 14-day incidence rates per 100,000 inhabitants in excess of 1,000. Despite its importance, accurate prediction of the geospatial evolution of COVID-19 remains a challenge, since existing analytical methods fail to capture the complex dynamics that result from the contagion within a region and the spreading of the infection from infected neighboring regions. We use a previously developed methodology and official municipality level data from the Portuguese Directorate-General for Health (DGS), relative to the first twelve months of the pandemic, to compute an estimate of the incidence rate in each location of mainland Portugal. The resulting sequence of incidence rate maps was then used as a gold standard to test the effectiveness of different approaches in the prediction of the spatial-temporal evolution of the incidence rate. Four different methods were tested: a simple cell level autoregressive moving average (ARMA) model, a cell level vector autoregressive (VAR) model, a municipality-by-municipality compartmental SIRD model followed by direct block sequential simulation, and a new convolutional sequence-to-sequence neural network model based on the STConvS2S architecture. We conclude that the modified convolutional sequence-to-sequence neural network is the best performing method in this task, when compared with the ARMA, VAR, and SIRD models, as well as with the baseline ConvLSTM model.
{"title":"Modeling the Geospatial Evolution of COVID-19 using Spatio-temporal Convolutional Sequence-to-sequence Neural Networks","authors":"Mário Cardoso, A. Cavalheiro, Alexandre Borges, A. F. Duarte, A. Soares, M. Pereira, N. Nunes, L. Azevedo, Arlindo L. Oliveira","doi":"10.1145/3550272","DOIUrl":"https://doi.org/10.1145/3550272","url":null,"abstract":"Europe was hit hard by the COVID-19 pandemic and Portugal was severely affected, having suffered three waves in the first twelve months. Approximately between January 19th and February 5th 2021 Portugal was the country in the world with the largest incidence rate, with 14-day incidence rates per 100,000 inhabitants in excess of 1,000. Despite its importance, accurate prediction of the geospatial evolution of COVID-19 remains a challenge, since existing analytical methods fail to capture the complex dynamics that result from the contagion within a region and the spreading of the infection from infected neighboring regions. We use a previously developed methodology and official municipality level data from the Portuguese Directorate-General for Health (DGS), relative to the first twelve months of the pandemic, to compute an estimate of the incidence rate in each location of mainland Portugal. The resulting sequence of incidence rate maps was then used as a gold standard to test the effectiveness of different approaches in the prediction of the spatial-temporal evolution of the incidence rate. Four different methods were tested: a simple cell level autoregressive moving average (ARMA) model, a cell level vector autoregressive (VAR) model, a municipality-by-municipality compartmental SIRD model followed by direct block sequential simulation, and a new convolutional sequence-to-sequence neural network model based on the STConvS2S architecture. We conclude that the modified convolutional sequence-to-sequence neural network is the best performing method in this task, when compared with the ARMA, VAR, and SIRD models, as well as with the baseline ConvLSTM model.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2021-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48171362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T. S. Jepsen, Christian S. Jensen, Thomas D. Nielsen
Travel time and speed estimation are part of many intelligent transportation applications. Existing estimation approaches rely on either function fitting or data aggregation and represent different tradeoffs between generalizability and accuracy. Function-fitting approaches learn functions that map feature vectors of, e.g., routes to travel time or speed estimates, which enables generalization to unseen routes. However, mapping functions are imperfect and offer poor accuracy in practice. Aggregation-based approaches instead form estimates by aggregating historical data, e.g., traversal data for routes. This enables very high accuracy given sufficient data. However, they rely on simplistic heuristics when insufficient data is available, yielding poor generalizability. We present a Unifying approach to Travel time and speed Estimation (UniTE) that combines function-fitting and aggregation-based approaches into a unified framework that aims to achieve the generalizability of function-fitting approaches and the accuracy of aggregation-based approaches when data is available. We demonstrate empirically that an instance of UniTE can improve the accuracies of travel speed and travel time estimation by 40–64% and 3–23%, respectively, compared to using only function fitting or data aggregation.
{"title":"UniTE—The Best of Both Worlds: Unifying Function-fitting and Aggregation-based Approaches to Travel Time and Travel Speed Estimation","authors":"T. S. Jepsen, Christian S. Jensen, Thomas D. Nielsen","doi":"10.1145/3517335","DOIUrl":"https://doi.org/10.1145/3517335","url":null,"abstract":"Travel time and speed estimation are part of many intelligent transportation applications. Existing estimation approaches rely on either function fitting or data aggregation and represent different tradeoffs between generalizability and accuracy. Function-fitting approaches learn functions that map feature vectors of, e.g., routes to travel time or speed estimates, which enables generalization to unseen routes. However, mapping functions are imperfect and offer poor accuracy in practice. Aggregation-based approaches instead form estimates by aggregating historical data, e.g., traversal data for routes. This enables very high accuracy given sufficient data. However, they rely on simplistic heuristics when insufficient data is available, yielding poor generalizability. We present a Unifying approach to Travel time and speed Estimation (UniTE) that combines function-fitting and aggregation-based approaches into a unified framework that aims to achieve the generalizability of function-fitting approaches and the accuracy of aggregation-based approaches when data is available. We demonstrate empirically that an instance of UniTE can improve the accuracies of travel speed and travel time estimation by 40–64% and 3–23%, respectively, compared to using only function fitting or data aggregation.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2021-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45927316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accuracy and interpretability are two essential properties for a crime prediction model. Accurate prediction of future crime occurrences along with the reason behind a prediction would allow us to plan the crime prevention steps accordingly. The key challenge in developing the model is to capture the non-linear and dynamic spatial dependency and temporal patterns of a specific crime category, while keeping the underlying structure of the model interpretable. In this article, we develop AIST, an Attention-based Interpretable Spatio Temporal Network for crime prediction. AIST models the dynamic spatio-temporal correlations for a crime category based on past crime occurrences, external features (e.g., traffic flow and point of interest information) and recurring trends of crime. Extensive experiments show that AIST outperforms the state-of-the-art techniques in terms of accuracy (e.g., AIST shows a decrease of 4.1% on mean average error and 7.45% on mean square error for the Chicago 2019 crime dataset) and interpretability.1
{"title":"AIST: An Interpretable Attention-Based Deep Learning Model for Crime Prediction","authors":"Yeasir Rayhan, T. Hashem","doi":"10.1145/3582274","DOIUrl":"https://doi.org/10.1145/3582274","url":null,"abstract":"Accuracy and interpretability are two essential properties for a crime prediction model. Accurate prediction of future crime occurrences along with the reason behind a prediction would allow us to plan the crime prevention steps accordingly. The key challenge in developing the model is to capture the non-linear and dynamic spatial dependency and temporal patterns of a specific crime category, while keeping the underlying structure of the model interpretable. In this article, we develop AIST, an Attention-based Interpretable Spatio Temporal Network for crime prediction. AIST models the dynamic spatio-temporal correlations for a crime category based on past crime occurrences, external features (e.g., traffic flow and point of interest information) and recurring trends of crime. Extensive experiments show that AIST outperforms the state-of-the-art techniques in terms of accuracy (e.g., AIST shows a decrease of 4.1% on mean average error and 7.45% on mean square error for the Chicago 2019 crime dataset) and interpretability.1","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44988912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent advances in big spatial data acquisition and deep learning allow novel algorithms that were not possible several years ago. We introduce a novel inverse procedural modeling algorithm for urb...
{"title":"Automatic Deep Inference of Procedural Cities from Global-scale Spatial Data","authors":"ZhangXiaowei, ShehataAly, BenešBedřich, AliagaDaniel","doi":"10.1145/3423422","DOIUrl":"https://doi.org/10.1145/3423422","url":null,"abstract":"Recent advances in big spatial data acquisition and deep learning allow novel algorithms that were not possible several years ago. We introduce a novel inverse procedural modeling algorithm for urb...","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83887413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article explores the use of deep learning to choose an appropriate spatial partitioning technique for big data. The exponential increase in the volumes of spatial datasets resulted in the deve...
本文探讨了如何利用深度学习为大数据选择合适的空间划分技术。空间数据集数量的指数级增长导致了…
{"title":"Using Deep Learning for Big Spatial Data Partitioning","authors":"VuTin, BelussiAlberto, MiglioriniSara, EldwayAhmed","doi":"10.1145/3402126","DOIUrl":"https://doi.org/10.1145/3402126","url":null,"abstract":"This article explores the use of deep learning to choose an appropriate spatial partitioning technique for big data. The exponential increase in the volumes of spatial datasets resulted in the deve...","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84638537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lars Lorch, Heiner Kremer, W. Trouleau, Stratis Tsirtsis, Aron Szanto, B. Scholkopf, M. Gomez-Rodriguez
Multiple lines of evidence strongly suggest that infection hotspots, where a single individual infects many others, play a key role in the transmission dynamics of COVID-19. However, most of the existing epidemiological models fail to capture this aspect by neither representing the sites visited by individuals explicitly nor characterizing disease transmission as a function of individual mobility patterns. In this work, we introduce a temporal point process modeling framework that specifically represents visits to the sites where individuals get in contact and infect each other. Under our model, the number of infections caused by an infectious individual naturally emerges to be overdispersed. Using an efficient sampling algorithm, we demonstrate how to estimate the transmission rate of infectious individuals at the sites they visit and in their households using Bayesian optimization (BO) and longitudinal case data. Simulations using fine-grained and publicly available demographic data and site locations from Bern, Switzerland showcase the flexibility of our framework. To facilitate research and analyses of other cities and regions, we release an open-source implementation of our framework.
{"title":"Quantifying the Effects of Contact Tracing, Testing, and Containment Measures in the Presence of Infection Hotspots","authors":"Lars Lorch, Heiner Kremer, W. Trouleau, Stratis Tsirtsis, Aron Szanto, B. Scholkopf, M. Gomez-Rodriguez","doi":"10.1145/3530774","DOIUrl":"https://doi.org/10.1145/3530774","url":null,"abstract":"Multiple lines of evidence strongly suggest that infection hotspots, where a single individual infects many others, play a key role in the transmission dynamics of COVID-19. However, most of the existing epidemiological models fail to capture this aspect by neither representing the sites visited by individuals explicitly nor characterizing disease transmission as a function of individual mobility patterns. In this work, we introduce a temporal point process modeling framework that specifically represents visits to the sites where individuals get in contact and infect each other. Under our model, the number of infections caused by an infectious individual naturally emerges to be overdispersed. Using an efficient sampling algorithm, we demonstrate how to estimate the transmission rate of infectious individuals at the sites they visit and in their households using Bayesian optimization (BO) and longitudinal case data. Simulations using fine-grained and publicly available demographic data and site locations from Bern, Switzerland showcase the flexibility of our framework. To facilitate research and analyses of other cities and regions, we release an open-source implementation of our framework.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42107437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：