Classification of synoptic weather types using the self-organising map and its application to climate and air quality data visualisation

2013 ◽  
Vol 33 ◽  
pp. 52 ◽  
Author(s):  
Jiang ◽  
Dirks ◽  
Luo
Keyword(s):  
Air Quality ◽  
The Self ◽  
Quality Data ◽  
Data Visualisation ◽  
Weather Types ◽  
Synoptic Weather ◽  
Self Organising Map ◽  
Air Quality Data

AqVision: A Tool for Air Quality Data Visualisation and Pollution-Free Route Tracking for Smart City

2019 ◽  
Author(s):  
Abdur Rahim Mohammad Forkan ◽  
Geoff Kimm ◽  
Ahsan Morshed ◽  
Prem Prakash Jayaraman ◽  
Abhik Banerjee ◽  
...  
Keyword(s):  
Air Quality ◽  
Smart City ◽  
Quality Data ◽  
Data Visualisation ◽  
Air Quality Data

scholarly journals Missing data exploration in air quality data set using R-package data visualisation tools

2020 ◽  
Vol 9 (2) ◽  
pp. 755-763
Author(s):  
Shamihah Muhammad Ghazali ◽  
Norshahida Shaadan ◽  
Zainura Idrus
Keyword(s):  
Air Quality ◽  
Missing Data ◽  
Missing Values ◽  
Missing At Random ◽  
Data Exploration ◽  
Quality Data ◽  
Gap Size ◽  
Data Visualisation ◽  
Data Set ◽  
Air Quality Data

Missing values often occur in many data sets of various research areas. This has been recognized as data quality problem because missing values could affect the performance of analysis results. To overcome the problem, the incomplete data set need to be treated or replaced using imputation method. Thus, exploring missing values pattern must be conducted beforehand to determine a suitable method. This paper discusses on the application of data visualisation as a smart technique for missing data exploration aiming to increase understanding on missing data behaviour which include missing data mechanism (MCAR, MAR and MNAR), distribution pattern of missingness in terms of percentage as well as the gap size. This paper presents the application of several data visualisation tools from five R-packges such as visdat, VIM, ggplot2, Amelia and UpSetR for data missingness exploration.  For an illustration, based on an air quality data set in Malaysia, several graphics were produced and discussed to illustrate the contribution of the visualisation tools in providing input and the insight on the pattern of data missingness. Based on the results, it is shown that missing values in air quality data set of the chosen sites in Malaysia behave as missing at random (MAR) with small percentage of missingness  and do contain long gap size of  missingness.

scholarly journals Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018)

2021 ◽  
Vol 18 (3) ◽  
pp. 1333
Author(s):  
Ahmad R. Alsaber ◽  
Jiazhu Pan ◽  
Adeeba Al-Hurban 
Keyword(s):  
Air Quality ◽  
Missing Data ◽  
Random Forest ◽  
Missing Values ◽  
Imputation Method ◽  
Environmental Data ◽  
Environmental Research ◽  
Quality Data ◽  
Data Set ◽  
Air Quality Data

In environmental research, missing data are often a challenge for statistical modeling. This paper addressed some advanced techniques to deal with missing values in a data set measuring air quality using a multiple imputation (MI) approach. MCAR, MAR, and NMAR missing data techniques are applied to the data set. Five missing data levels are considered: 5%, 10%, 20%, 30%, and 40%. The imputation method used in this paper is an iterative imputation method, missForest, which is related to the random forest approach. Air quality data sets were gathered from five monitoring stations in Kuwait, aggregated to a daily basis. Logarithm transformation was carried out for all pollutant data, in order to normalize their distributions and to minimize skewness. We found high levels of missing values for NO2 (18.4%), CO (18.5%), PM10 (57.4%), SO2 (19.0%), and O3 (18.2%) data. Climatological data (i.e., air temperature, relative humidity, wind direction, and wind speed) were used as control variables for better estimation. The results show that the MAR technique had the lowest RMSE and MAE. We conclude that MI using the missForest approach has a high level of accuracy in estimating missing values. MissForest had the lowest imputation error (RMSE and MAE) among the other imputation methods and, thus, can be considered to be appropriate for analyzing air quality data.

aiRe - A web-based R application for simple, accessible and repeatable analysis of urban air quality data

2021 ◽  
Vol 138 ◽  
pp. 104976
Author(s):  
Juan José Díaz ◽  
Ivan Mura ◽  
Juan Felipe Franco ◽  
Raha Akhavan-Tabatabaei
Keyword(s):  
Air Quality ◽  
Urban Air Quality ◽  
Quality Data ◽  
Urban Air ◽  
Web Based ◽  
Air Quality Data
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