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Data-driven Decision-making

An article presenting data-driven decisions as a decision-making method.
© Luleå University of Technology

In this article, we present data-driven decisions as a method of decision-making.

Food example

Food availability and food quality are important aspects that are influenced by climate change in several ways. Extreme weather events as a direct consequence of climate change and high temperatures are mainly dangerous for crop production if they happen when the plants are flowering. In order to understand this phenomenon, we look at datasets!

Crop Production and Climate Change is a free dataset provided by Kaggle (the link is available at the end of this article), which explores the relationship between crop production and climate change over time.

That dataset provides data on crop yields, harvested areas, and production quantities for wheat, maize, rice, and soybeans. The data is presented in tonnes per hectare. Even though the portal requires you to create an account and log in, the dataset is publicly available. That dataset can, among other things, be used to examine the effects of different crops on climate change and compare yields between different climates. Note that the dataset that we have provided to you is a relatively light version, so it will be easier for you to use it.

Now, in case we want to make food-related decisions with regard to crop yields, we need to get insights from the data, hence our decision is data-driven. Accordingly, we explore the dataset using Orange. If you are unable to perform the exercise, please follow along the process below as best you can.

  • Open Orange on your computer
  • Download the excel sheet. You can access the dataset here.
  • In the process area, we need to connect five widgets: CSV File Import, Data Table, Scatter Plot (2), and k-means as follows:
  • Direct the CSV file import to the location where the file MOOC DSCC EX1 final.CSV is in your computer, after you have downloaded it.
  • Double-click on the Scatter plot. Configure the parameters in the left pane
  • Double-click on the K-means and then on the scatter Plot (1) to see the below figure:

scatter plot two

From the File menu, save this process file as “Task6”.

Share your thoughts

Before moving on, please take the time to reflect on these questions.

  • What does the first scatter plot tell us?
  • Does the second scatter plot confirm the first?
  • How can the two be combined to support our food related decisions?

Write your answer in the comments below and share your insights with your fellow learners.

© Luleå University of Technology
This article is from the free online

Data Science for Climate Change

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