Using Microsoft© Power BI© to visualise Rustenburg local municipality’s air quality data
dc.contributor.author | Wright, Caradee Yael | |
dc.contributor.author | Wernecke, Bianca | |
dc.date.accessioned | 2021-03-26T10:56:50Z | |
dc.date.available | 2021-03-26T10:56:50Z | |
dc.date.issued | 2020 | |
dc.description.abstract | Microsoft© Power BI© is a business analytics tool that visualises data in an accessible manner. It creates visual data reports quickly in a series of panels to give an overview of data while still offering access to more sophisticated visualisation methods too. While statistical tools, like R and MatLab, remain the ‘gold standard’ for analysing air quality data, simple methods to visualise data are also helpful. Here, we explored the use of Power BI Desktop© to visualise and interpret air quality data for the Rustenburg Local Municipality. Rustenburg is in the Waterberg-Bojanala Priority Area – the third air pollution priority area for air quality management. Ambient PM10 data for three towns, namely, Thlabane, Marikana and Boiketlong, were obtained for six years (2013-2018) in South Africa. Data underwent quality control before being imported into Power BI©. A four-panel dashboard was generated to show (in) compliance with the daily and annual average South African National Ambient Air Quality Standard for PM10, annual and average concentrations, frequency of exceedances and a summary of data availability by site. Generally, PM10 data quantity and quality were poor and where data were available, concentrations were high. This type of data visualisation tool can be applied with relative ease by Air Quality Officers and Environmental Health Practitioners for a snapshot overview of the air quality in their area of jurisdiction. The interactive dashboard is also useful for making graphics for policy documents and reports. | en_ZA |
dc.description.department | Geography, Geoinformatics and Meteorology | en_ZA |
dc.description.librarian | pm2021 | en_ZA |
dc.description.sponsorship | South African Medical Research Council and the National Research Foundation (South Africa). | en_ZA |
dc.description.uri | https://www.cleanairjournal.org.za | en_ZA |
dc.identifier.citation | Wright, C.Y. & Wernecke, B. 2020, 'Using microsoft© power BI© to visualise rustenburg local municipality's air quality data', Clean Air Journal, vol. 30, no. 1, pp. 1-5. | en_ZA |
dc.identifier.issn | 1017-1703 (print) | |
dc.identifier.issn | 2410-972X (online) | |
dc.identifier.other | 10.17159/caj/2020/30/1.7512 | |
dc.identifier.uri | http://hdl.handle.net/2263/79127 | |
dc.language.iso | en | en_ZA |
dc.publisher | National Association of Clean Air | en_ZA |
dc.rights | © 2020. The Author(s). Published under a Creative Commons Attribution Licence. | en_ZA |
dc.subject | Air pollution | en_ZA |
dc.subject | Environmental health | en_ZA |
dc.subject | South Africa (SA) | en_ZA |
dc.subject | Waterberg-Bojanala priority area | en_ZA |
dc.title | Using Microsoft© Power BI© to visualise Rustenburg local municipality’s air quality data | en_ZA |
dc.type | Article | en_ZA |