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Visualizing and analyzing EV-PHEV data of Washington state Using Tableau | Realcode4you

Introduction

EVs and PHEVs are rapidly gaining mainstream success as people are becoming more environmentally conscious and at the same time want to get the maximum efficiency. In the past few years, there is a boom in consumption of EVs and PHEVs in the USA and thus I’ve selected to visualize the dataset of EV and PHEV registration in the state of Washington. The aim of this visualization and analysis is to find out which zipcodes had how many numbers of EVs and PHEVS and then we’ll investigate which make and model were registered the most and then look at the yearly trend. The visualizations will be linked with each other and changes according to the measure selected, it will be thoroughly interactive dashboard.


Analysis

EV PHEV count by zipcodes

This map tells the viewer about the number of EVs and PHEVs in various zip codes of Washington. When hovered each node on the map shows the zip code in large font and shows the number of PHEVs and EVs at a zip code. I believe this is a good way to showcase the information from the data as the viewer gets a complete view of the types of vehicles at every zip. Maps aren’t always the best way to represent data but when it comes to showcasing such a large geographical data, maps can be helpful without overwhelming the user. The colours in this visualization have been selected after taking into consideration people with any kind of colour blindness. This map follows Tufte’s guidelines by being clear and thorough, the data is not being misrepresented in the map and it has graphical integrity.


Top 10 EV PHEV registration by Make:


This bar graph tells the viewer about the top 10 make of EV-PHEV registration in Washington. When the pointer is moved on the bar it pops open a window that tells the viewer about the vehicle type, the name of the maker and the number of cars registered that belong to that make. This is quite a straightforward bar graph and is simple to read and comprehend. The graph is visually appealing, and the text-to-graphics ratio is appropriate, in my opinion. The graph does a good job of telling the viewer about the information that we found in the data. It adheres to Tufte’s guidelines by having a clear motive and design and it also has high ink to data ratio. Furthermore, both the axes are labelled properly, and the colour palette is appropriate for people with any kind of colour blindness.


Top EV PHEV models


This bar chart depicts the top EV and PHEV models. This is a clear and simple bar graph that is readable and understandable. In my opinion, the chart is graphically pleasing but at the same time tells the user what it wants to convey. Both the Y and X axes have been properly labelled, and the scale of the X-axis is proportional, with equal intervals of 5000. This bar graph does an excellent job of conveying to the viewer about the EV-PHEV models that are registered the highest. The graph does not have chart junk in it. When the pointer is hovered on the bars it pops open a window that tells the viewer about the vehicle type, the model’s name and the number of cars registered.


EV PHEV yearly growth:

Just a single glance at the graph and we can tell that this visualization depicts the yearly trend in EV and PHEV registration in Washington. The Y-axis has been labelled correctly with the number of vehicles registered. The scale of the graph is quite good. This graph has an excellent data-to-ink ratio. Both axes are labelled with enough information to determine the type of data represented by each axis. When the pointer is hovered on the lines it pops open a window that tells the viewer about the vehicle type, the year and the number of cars registered in that year. This graph to has been optimized for colour blind people and no matter what kind of colour blindness they have, they will be able to see it correctly.


EV PHEV market share percentage:

In this pie chart, the overall market share of EVs and PHEVs is visualized. Many say that pie charts shouldn’t be used to visualize but I think that pie charts are an excellent way to visualize 2-3 elements. One of the most important things about pie charts is that they should always begin clockwise, and this pie chart sure does that. It also has a high ink to data ratio and is clear and organized. When the pointer is hovered on the lines it pops open a window that tells the viewer about the vehicle type, the number of cars registered and the market share of the vehicle type in percentage.


References

Akyıldırım, N. E. and Zanders, S. 2020. The 12 Rules of Data Visualisation. ESTIEM. Available at: https://medium.com/estiem/the-12-rules-of-data-visualization-79994abb74aa [Accessed on: 18 April 2022]


Emery, A. K. 2015. When Pie Charts Are Okay (Seriously): Guidelines for Using Pie and Donut Charts. Available at: https://depictdatastudio.com/when-pie-charts-are-okay-seriously-guidelines-for-using-pie-an d-donut-charts/ [Accessed on: 18 April 2022]


Evergreen, S. and Emery, A. K. 2016. The Data Visualisation Checklist. Available at: https://stephanieevergreen.com/updated-data-visualization-checklist/ [Accessed on: 18 April 2022]


Frost, A. 2021. Start a Pie Chart at 12 ‘0 Clock and go Clockwise. Available at: https://www.addtwodigital.com/add-twoblog/2021/2/15/rule-5-start-your-pie-chart-at-12-oclo ck-and-go-clockwise . [Accessed on: 19 April 2022]


Safegraph.com. 12 Methods for Visualizing Geospatial Data on a Map. Available at: https://www.safegraph.com/guides/visualizing-geospatial-data [Accessed on: 19 April 2022]


Skelton, C. 2015. Rule #1: Pie Charts are Evil. Available at: http://www.chadskelton.com/2015/12/in-defence-of-datavisualization-rules.html?m=1 [Accessed on: 19 April 2022


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