Mapping and predicting subsidence from spatio-temporal regression models of groundwater-drawdown and subsidence observations

2020 ◽  
Vol 28 (8) ◽  
pp. 2865-2876
Author(s):  
Muhammad Zeeshan Ali ◽  
Hone-Jay Chu ◽  
Thomas J. Burbey
Keyword(s):  
Regression Models ◽  
Groundwater Drawdown ◽  
Spatio Temporal

Additive-Multiplicative Regression Models for Spatio-Temporal Epidemics

2009 ◽  
Vol 51 (6) ◽  
pp. 961-978 ◽  
Author(s):  
Michael Höhle
Keyword(s):  
Regression Models ◽  
Spatio Temporal

Enhancing Regression Models with Spatio-temporal Indicator Additions

2013 ◽  
pp. 433-444 ◽  
Author(s):  
Annalisa Appice ◽  
Sonja Pravilovic ◽  
Donato Malerba ◽  
Antonietta Lanza
Keyword(s):  
Regression Models ◽  
Spatio Temporal

scholarly journals Prevalence of Occupation Associated with Increased Mobility During COVID-19 Pandemic

2020 ◽  
Author(s):  
Enbal Shacham ◽  
Stephen Scroggins ◽  
Matthew Ellis
Keyword(s):  
Regression Models ◽  
Spatial Regression ◽  
Food Service ◽  
Service Workers ◽  
Census Tracts ◽  
Service Business ◽  
Mobility Data ◽  
Spatio Temporal ◽  
Food Service Workers ◽  
Essential Food

AbstractPurposeIdentifying geographic-level prevalence of occupations associated with mobility during local stay-at-home pandemic mandate.MethodsA spatio-temporal ecological framework was applied to determine census-tracts that had significantly higher rates of occupations likely to be deemed essential: food-service, business and finance, healthcare support, and maintenance. Real-time mobility data was used to determine the average daily percent of residents not leaving their place of residence. Spatial regression models were constructed for each occupation proportion among census-tracts within a large urban area.ResultsAfter adjusting for demographics, results indicate census-tracts with higher proportion of food-service workers, healthcare support employees, and office administration staff are likely to have increased mobility.ConclusionsIncreased mobility among communities is likely to exacerbate COVID-19 mitigation efforts. This increase in mobility was also found associated with specific demographics suggesting it may be occurring among underserved and vulnerable populations. We find that prevalence of essential employment presents itself as a candidate for driving inequity in morbidity and mortality of COVID-19.

scholarly journals Kernel averaged predictors for spatio-temporal regression models

2012 ◽  
Vol 2 ◽  
pp. 15-32 ◽  
Author(s):  
Matthew J. Heaton ◽  
Alan E. Gelfand
Keyword(s):  
Regression Models ◽  
Spatio Temporal

scholarly journals Social determinants and spatio-temporal variation of Ischemic Heart Disease in Manitoba

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Justin Dyck ◽  
Robert Tate ◽  
Julia Uhanova ◽  
Mahmoud Torabi
Keyword(s):  
Heart Disease ◽  
Ischemic Heart Disease ◽  
Social Determinants ◽  
Poisson Regression ◽  
Regression Models ◽  
Temporal Trend ◽  
Indigenous Population ◽  
Temporal Trends ◽  
Socioeconomic Factor ◽  
Spatio Temporal

Abstract Introduction The aim was to study any spatial and/or temporal patterns of ischemic heart disease (IHD) prevalence and measure the effects of selected social determinants on these spatial and space-time patterns. Methods Data were obtained from the Population Research Data Repository housed at the Manitoba Centre for Health Policy to identify persons who were diagnosed with IHD between 1995 and 2018. These persons were geocoded to 96 geographic regions of Manitoba. An area-level socioeconomic factor index (SEFI-2) and the proportion of the population who was Indigenous were calculated for each geographic region using the 2016 Canadian Census data. Associations between these factors and IHD prevalence were measured using Bayesian spatial Poisson regression models. Temporal trends and spatio-temporal trends were measured using Bayesian spatio-temporal Poisson regression models. Results Univariable models showed a significant association with increased regional Indigenous population proportion associated with a higher prevalence of IHD (RR: 0.07, 95% CredInt: (0.05, 0.10)) and for SEFI-2 (RR: 0.17, 95% CredInt: (0.11, 0.23)). Using a multivariable model, after accounting for the proportion of the population that was Indigenous, there was no evidence of an association between IHD prevalence and area-level socioeconomic factor. Spatio-temporal models showed no significant overall temporal trend in IHD prevalence, but there were significant spatially varying temporal trends within the 96 regions. Conclusions Association between Indigenous population proportion and IHD is consistent with previous research. No significant overall temporal trend was measured. However, regions with significantly increasing trends and significantly decreasing trends in IHD prevalence were identified.

Development of Spatio-Temporal Land Use Regression Models for Gaseous Pollutants in London, UK, within the Steam Project

2018 ◽  
Vol 2018 (1) ◽  
Author(s):  
Konstantina Dimakopoulou ◽  
Sean David Beevers ◽  
Evangelia Samoli ◽  
Benjamin Barratt ◽  
Antonis Analitis ◽  
...  
Keyword(s):  
Land Use ◽  
Regression Models ◽  
Gaseous Pollutants ◽  
Land Use Regression ◽  
Spatio Temporal

scholarly journals Prediction of Air Pollution in Smart Cities Using Machine Learning Techniques

2021 ◽  
Vol 9 (12) ◽  
pp. 273-277
Author(s):  
Mrs. Gowri G
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Predictive Model ◽  
Regression Models ◽  
Smart Cities ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Temporal Data ◽  
Spatio Temporal

Abstract: Air-pollution is one of the main threats for developed societies. According to the World Health Organization (WHO), pollution is the main cause of deaths among children aged under five years. Smart cities are called to play a decisive role to increase such pollution in real-time. The increase in air pollution due to fossil fuel consumption as well as its ill effects on the climate has made air pollution forecasting an important research area in today’s times. Deployment of the Internet of things (IoT) based sensors has considerably changed the dynamics of predicting air quality. prediction of spatio-temporal data has been one of the major challenges in creating a good predictive model. There are many different approaches which have been used to create an accurate predictive model. Primitive predictive machine learning algorithms like simple linear regression have failed to produce accurate results primarily due to lack of computing power but also due to lack of optimization techniques. A recent development in deep learning as well as improvements in computing resources has increased the accuracy of predicting time series data. However, with large spatio-temporal data sets spanning over years. Employing regression models on the entire data can cause per date predictions to be corrupted. In this work, we look at dealing with pre-processing the times series. However, pre-processing involves a similarity measure, we explore the use of Dynamic Time Warping (DTW). K-means is then used to classify the spatio-temporal pollution data over a period of 16 years from 2000 to 2016. Here Mean Absolute error (MAE) and Root Mean Square Error (RMSE) have been used as evaluation criteria for the comparison of regression models. Keywords: Spatio-temporal data, Primitive predictive machine learning algorithms, regression models

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