Space‐time autoregressive estimation and prediction with missing data based on Kalman filtering

2020 ◽  
Vol 31 (7) ◽  
Author(s):  
Leonardo Padilla ◽  
Bernado Lagos‐Álvarez ◽  
Jorge Mateu ◽  
Emilio Porcu
Keyword(s):  
Missing Data ◽  
Kalman Filtering ◽  
Space Time ◽  
Estimation And Prediction

scholarly journals Concurrent Spatiotemporal Daily Land Use Regression Modeling and Missing Data Imputation of Fine Particulate Matter Using Distributed Space Time Expectation Maximization

2018 ◽  
Author(s):  
Seyed Mahmood Taghavi-Shahri ◽  
Alessandro Fassò ◽  
Behzad Mahaki ◽  
Heresh Amini
Keyword(s):  
Land Use ◽  
Particulate Matter ◽  
Missing Data ◽  
Exposure Assessment ◽  
Expectation Maximization ◽  
Space Time ◽  
Data Imputation ◽  
Land Use Regression ◽  
Missing Data Imputation

AbstractGraphical AbstractLand use regression (LUR) has been widely applied in epidemiologic research for exposure assessment. In this study, for the first time, we aimed to develop a spatiotemporal LUR model using Distributed Space Time Expectation Maximization (D-STEM). This spatiotemporal LUR model examined with daily particulate matter ≤ 2.5 μm (PM2.5) within the megacity of Tehran, capital of Iran. Moreover, D-STEM missing data imputation was compared with mean substitution in each monitoring station, as it is equivalent to ignoring of missing data, which is common in LUR studies that employ regulatory monitoring stations’ data. The amount of missing data was 28% of the total number of observations, in Tehran in 2015. The annual mean of PM2.5 concentrations was 33 μg/m3. Spatiotemporal R-squared of the D-STEM final daily LUR model was 78%, and leave-one-out cross-validation (LOOCV) R-squared was 66%. Spatial R-squared and LOOCV R-squared were 89% and 72%, respectively. Temporal R-squared and LOOCV R-squared were 99.5% and 99.3%, respectively. Mean absolute error decreased 26% in imputation of missing data by using the D-STEM final LUR model instead of mean substitution. This study reveals competence of the D-STEM software in spatiotemporal missing data imputation, estimation of temporal trend, and mapping of small scale (20 × 20 meters) within-city spatial variations, in the LUR context. The estimated PM2.5 concentrations maps could be used in future studies on short- and/or long-term health effects. Overall, we suggest using D-STEM capabilities in increasing LUR studies that employ data of regulatory network monitoring stations.Highlights-First Land Use Regression using D-STEM, a recently introduced statistical software-Assess D-STEM in spatiotemporal modeling, mapping, and missing data imputation-Estimate high resolution (20×20 m) daily maps for exposure assessment in a megacity-Provide both short- and long-term exposure assessment for epidemiological studies

A Kalman filter method for estimation and prediction of space–time data with an autoregressive structure

2019 ◽  
Vol 203 ◽  
pp. 117-130 ◽  
Author(s):  
Bernardo Lagos-Álvarez ◽  
Leonardo Padilla ◽  
Jorge Mateu ◽  
Guillermo Ferreira
Keyword(s):  
Kalman Filter ◽  
Space Time ◽  
Filter Method ◽  
Time Data ◽  
Estimation And Prediction ◽  
Kalman Filter Method
Sign in / Sign up

Export Citation Format

Share Document