PROJECTData4Kids: Virtually Teaching Kids about Data Science

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  • City Health Equity

    Can you recall a time when you visited someone else in another city? Maybe you visited another family member, or a friend. What was different about that city compared to yours? Was it larger? Smaller? Was it easier or harder to walk around? Did people live in apartments? Houses?

    In this Data Story, you will imagine you are helping a family decide where in the United States to live and consider what makes a city “livable.” We will use a dataset that includes information about different US cities, including: where they are located, how their population is changing, and what access they offer to parks, healthy food, breathable air, and other factors.

    City Health Equity - Data Cards

    This is an additional/alternative teaching approach for the City Health Equity Data Story. Here, the teacher can hand out index cards (or card stock) and drawing utensils to students. Have the students pick a city from the data and make a data card with various data points from the data set. We have provided a couple of examples in the slides and downloadable materials. 


    An eviction is when a landlord forces people to leave a home they are renting. Almost a million families are evicted each year in the United States. Using data from courts that process evictions, we can look at the eviction rate, which is how often evictions happen in each state. We can also look at eviction rates over time.

    Food Insecurity

    Across the United States, millions of families and children who do not have enough to eat. Using data from different sources, we can look at how many people receive benefits from two major government programs: the Supplemental Nutrition Assistance Program and the National School Lunch Program. We can also look at how many people receive benefits from those programs compared with rates of poverty and obesity, as well as characteristics of each state like age and race.

    Imagery Analysis

    Have you ever looked at a picture and wondered what type of information you could get from it? Often pictures contain more information than we can initially see when observing them. From colors to objects, there’s no shortage of information we can gather when looking at a photo. In this Data Story, we look at some photos and see if we can identify anything that stands out.

    National Parks

    Across the United States, over 400 parks of different types and sizes are considered "national" and are managed by the U.S. National Park Service. (There are many more state and local parks, too; maybe you get to spend time at some of those.) We will look at these various "national parks," including sites with "National Park" in their name, and their host geographies, and explore the distribution of these treasures.

    You can also check out the data via the interactive ArcGIS map.

    Life Expectancy and Income

    There is a huge difference in people’s life expectancy in countries around the world. No high-income countries have short life expectancy, and no low-income countries have long life expectancy. Still, there are large differences in life expectancy between countries on the same income level, depending on how the money is distributed and how it is used.

    In this Data Story, we look at income and life expectancy in countries across the world. We will use a dataset that includes information about the number of people living in different countries, as well as the average incomes and life expectancies in those countries.