Food insecurity occurs when people do not have consistent access to affordable, nutritious food. We can make a real impact and educate others by visualizing our insights and predictions that have the most power to do social good. This pattern walks you through how to do just that, with IBM Watson Studio, pandas, PixieDust, and Watson Analytics.
Often in data science we do a great deal of work to glean insights that have an impact on society or a subset of it, and yet we end up not communicating our findings or communicating them ineffectively to non-data science audiences. That’s where visualizations have power. We can make a real impact and educate others by visualizing our insights and predictions that have the most power to do social good. We can bring awareness and maybe even change to important issues. This pattern walks you through how to do just that, with IBM Watson Studio, pandas, PixieDust, and Watson Analytics.
This pattern focuses on food insecurity throughout the US. Food insecurity occurs when people do not have consistent access to affordable, nutritious food. When access to proper nutrition or education about proper nutrition is unavailable, people go hungry or eat food that is low in nutrition. This is a more and more relevant problem for the United States as obesity and diabetes instances rise and two out of three adult Americans are considered obese, one third of American minors are considered obese, nearly ten percent of Americans have diabetes, and nearly fifty percent of the African American population have heart disease. Cardiovascular disease is the leading global cause of death, accounting for 17.3 million deaths per year, and rising. Native American populations more often than not do not have grocery stores on their reservation … and all of these trends are on the rise. The problem lies not only in low access to fresh produce, but food culture, low education on healthy eating, as well as racial and income inequality. We will use open government data related to low access, diet-related diseases, race, poverty, geography, and other factors in this pattern.
After going through this pattern, you will be able to:
- Use Watson Studio.
- Remove NaNs and 0s from a pandas dataframe.
- Visualize correlations and other findings using matplotlib, bokeh, seaborn, and PixieDust.
- Download your pandas dataframe from DSX.
- Upload your data into Watson Analytics.
- Use Watson Analytics to generate visualizations and share them with others.
This pattern was created for data scientists and data lovers who are interested in social justice issues and/or those who are new to DSX and Watson Analytics. This will guide the user through the power of visualizations, how to select them, and how to share them.
- Open Watson Studio and create a notebook.
- Download the data in Watson Studio and explore it.
- Load Pixie Dust and use for visualizations.
- Download the dataframe as a .csv file from Watson Studio.
- Upload the .csv file to Watson Analytics and visualize.
Find the detailed steps for this pattern in the README. This code pattern consists of two primary activities:
- Run a Jupyter notebook in the IBM Watson Studio.
- Analyze the data in Watson Analytics.