1 of 34

Small and Medium-Size Cities and Health Equity Dataset

A data story on investigating the environmental and healthy lifestyle resources selected cities.

2 of 34

Introduction

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?

Would you have liked to live there? What do you think people from that city would have thought about living in your city?

Introduction for all Grade Bands

3 of 34

Introduction

In this investigation, 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 data set 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.

Introduction for all Grade Bands

4 of 34

Time to look at the data!

5 of 34

Data Question - Notice

What do you notice about the data? Start by saying, “I notice that…”

To be filled
To be filled
To be filled
To be filled
To be filled
To be filled

Learning Goal: For all Grade Bands

6 of 34

Data Question - Wonder

What do you wonder about the data? Start by saying, “I wonder if…”, “I wonder why…”, or “I wonder how…”

To be filled
To be filled
To be filled
To be filled
To be filled
To be filled

Learning Goal: For all Grade Bands

7 of 34

Data Question

What are the city types in each of the regions?
What do you notice about the population in the cities in each region?
What city types are found in each region?
Does park access vary for city types?
Does the walkability score vary for city types?
Do center cities tend to have lower walkability scores than suburban cities?

All Grade Bands

8 of 34

Data Question

Is there a relationship between air pollution and walkability?�
Does deaths due to cardiovascular disease vary for city types, regions, access to healthy food, or walkability score?

Grade Bands II & III

9 of 34

Data Collection

Which notice and wonder statements can we answer?

Insert your notice and wonder statements

Learning Goal: For all Grade Bands

10 of 34

Data Collection

Which notice and wonder statements can we not answer?

Insert your notice and wonder statements

Learning Goal: For all Grade Bands

11 of 34

Data Collection

What we can answer:

What is the average unemployment rate for each city type?�
What is the average physical inactivity score for each city type?

Grade Band I

12 of 34

Data Collection

What we cannot answer:

What is causing certain population trends for each city type?
Why do some city types have higher or lower park access, walkability score, or air pollution?

Grade Band I

13 of 34

Data Collection

What we can answer:

Which region or city type has higher home ownership?
Is there a relationship between life expectancy and walkability, air pollution, or limited access to healthy food?

Grade Band II

14 of 34

Data Collection

What we cannot answer:

What other factors could contribute to higher cardiovascular deaths for certain cities?
What makes a city have a higher walkability score or lower air pollution?

Grade Band II

15 of 34

Data Collection

What we can answer:

How does the average travel time to work vary for different races?
How does the average unemployment rate vary for different races?

Grade Band III

16 of 34

Data Collection

What we cannot answer:

Do you think people consistently characterize their race in the data?
The unemployment rate only captures people who are not working and are looking for work; it does not include people who have decided not to look for work at all. How might this affect your interpretation of unemployment rate data?

Grade Band III

17 of 34

Data Analysis

Data are often “messy,” such as missing values and in the wrong units (e.g., feet vs. inches).

Time to look at a “messy” version of the data and identify what parts of the data are messy!

Learning Goal: For all Grade Bands

18 of 34

Data Analysis

Insert the selected visualization.

Grade Bands I and II

19 of 34

Data Analysis

Time to analyze the data! Follow these steps:

Sort, filter, and aggregate the data.
Report how you altered the data.
Compare states above and below the national average.

Grade Band III

30 of 34

Data Equity - Who is represented �in the data?

Are you represented in the data?
Are your family members represented in the data?
Are your friends represented in the data?
Are people in your community represented in the data?
Do the data reflect your experiences?

For all Grade Bands

31 of 34

Data Ethics - How should we �report the data?

Suppose we published our data story and recommendations.

What would the title be?
What information, key concepts, and takeaways would be included?

For all Grade Bands

32 of 34

Data Ethics - How should we �report the data?

Now, to evaluate our answers from the previous questions.

What conclusions would someone make from the title alone?
Does the content of the data story match the title?
Does the data story credit who collected, analyzed, and/or visualized the data?
Would it be important to know the answer to the previous question? Why or why not?

For all Grade Bands

33 of 34

Data Privacy - Are we telling the �right data story?

Suppose the data were reported for different cities. Would the answer to the data question change? Why or why not?
Suppose the data information about residents of the cities. What new data questions would you have?

For all Grade Bands

34 of 34

Data Privacy - Are we telling the �right data story?

If your information was part of the data that now has resident information, would you be more comfortable with the data being reported at the county level, city level, or neighborhood level? Why or why not?
At what geographic level would answer the data question while protecting your personal information?

For all Grade Bands