Statistics Insight
In this section, I demonstrate how I perform quantitative data analysis, visualize results and combine spatial insight using statistical methods and programming languages such as R and Python. These tools enable me to extract meaningful insights from complex datasets, create compelling visualizations, and support data-driven decision-making.
Analysing GHGs emission in the UK
Using the ggplot2 package, I created visualisations to explore GHG emission patterns in the UK, including a violin plot that showed significant differences across three emission sub-sectors through an ANOVA test.
I also ran two regression analyses:
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The first showed a strong, significant link between domestic electricity emissions and population density, suggesting urban areas have higher household emissions.
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The second looked at how population size impacts emissions in commercial vs. non-commercial sectors. The result indicates a similar influence across both.
Interactive Web Map of car crashes in South Yorkshire, England
An interactive map to explore car crash data across South Yorkshire, UK, spanning 2016 to 2020. The map helps identify when and where serious and fatal crashes occur—and under what specific conditions. It brings together open data, geographic insight, and storytelling.
Hightlight : Heatmaps, Interactive Markers
Tools : Python, Folium, Pandas, GeoPandas, Jupyter Notebooks