Data Processing

Let's talk Data

I will be using my main data from Plastic Waste Inputs from Land to Water by Jambek et al. and supporting data from Geyer et al. and The World Bank. The main dataset has collection of waste management by country metrics, I used this dataset for my main visualization, and used the supporting datasets in supporting visualization.

The dataset covers per country, on range of topics, but what we will be focus on is the plastic waste management. This data set has 193 rows and 19 columns which was 47kb. There was a large range of column types that I can choose for my d3 implementations. It has categorical (economic status), countries for location, numerical(amount of plastic used, percent mismanaged) and time (year of reference).

The initial dataset didn't need to do much processing other than making separate csv files for separate graphs. For example, I created a csv of just country names and plastic waste weight. I did need to add other datasets to create certain graphs. I used continents to create my treemap and gdp for my scatter plot, so I found a dataset that had country with continents and gdp at The World Bank. I was able to combine them using the country codes provided in both dataset, for example china would be chn for its country code.