ACM Transactions on Spatial Algorithms and Systems最新文献_第8页

Modeling the Geospatial Evolution of COVID-19 using Spatio-temporal Convolutional Sequence-to-sequence Neural Networks 基于时空卷积序列到序列神经网络的新冠肺炎地理空间演化模型

IF 1.9

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2021-05-06 DOI: 10.1145/3550272

Mário Cardoso, A. Cavalheiro, Alexandre Borges, A. F. Duarte, A. Soares, M. Pereira, N. Nunes, L. Azevedo, Arlindo L. Oliveira

{"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}

引用次数: 3

UniTE—The Best of Both Worlds: Unifying Function-fitting and Aggregation-based Approaches to Travel Time and Travel Speed Estimation UniTE——两全其美：统一基于函数拟合和聚合的行程时间和行程速度估计方法

IF 1.9

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2021-04-27 DOI: 10.1145/3517335

T. S. Jepsen, Christian S. Jensen, Thomas D. Nielsen

{"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}

引用次数: 2

AIST: An Interpretable Attention-Based Deep Learning Model for Crime Prediction 一种可解释的基于注意的深度学习犯罪预测模型

IF 1.9

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2020-12-16 DOI: 10.1145/3582274

Yeasir Rayhan, T. Hashem

引用次数: 7

Automatic Deep Inference of Procedural Cities from Global-scale Spatial Data 基于全球尺度空间数据的程序性城市自动深度推理

IF 1.9

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2020-10-27 DOI: 10.1145/3423422

ZhangXiaowei, ShehataAly, BenešBedřich, AliagaDaniel

引用次数: 3

Using Deep Learning for Big Spatial Data Partitioning 深度学习在大空间数据分区中的应用

IF 1.9

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2020-08-12 DOI: 10.1145/3402126

VuTin, BelussiAlberto, MiglioriniSara, EldwayAhmed

引用次数: 9

Quantifying the Effects of Contact Tracing, Testing, and Containment Measures in the Presence of Infection Hotspots 在感染热点地区量化接触者追踪、检测和遏制措施的效果

IF 1.9

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2020-04-15 DOI: 10.1145/3530774

Lars Lorch, Heiner Kremer, W. Trouleau, Stratis Tsirtsis, Aron Szanto, B. Scholkopf, M. Gomez-Rodriguez

{"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}

引用次数: 28