1 of 59

Interactive Workshop: Supporting Teachers in Spatial Data Investigations of Issues of Food Access

Travis Weiland

University of North Carolina Charlotte

2 of 59

What do you notice? What do you wonder?

3 of 59

If you are interested in going deeper

4 of 59

Main Goal: Think about Incorporating Spatial Data Investigations into Mathematics

5 of 59

Overview

  • Why Spatial Data?
  • What is Spatial Data?
  • Connections to Mathematics Curriculum
  • Research Project Co-Designing with Teachers
  • Playing with Data
  • Findings
  • Question

​

6 of 59

Acknowledgement

This material is based upon work supported by the National Science Foundation under DRK-12 Grant #2143816 and #2517085. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the view of the National Science Foundation.

​

We could not do this work without the support of many classroom teachers you volunteer to spend their time working with us.

7 of 59

Research Team

Anita Sundrani, PhD

6-12 Math Manager

Chicago Public Schools,

Office of Teaching and Learning

Mandy Delavari�Doctoral Student Mathematics Education

University of Houston

Laura Shelton, PhD

Assistant Professor, STEM Education

Marist University

​

​

8 of 59

Why Spatial Data Investigations?

9 of 59

10 of 59

Place and Space Matter

  • People organize many things based on location and space
  • Many phenomenon need to be considered spatially to uncover patterns and trends
  • Natural phenomenon are spatially located and oriented
  • Space matters and spatial data helps capture that information
  • Data investigations allow for an approach for engaging students in considering real world issues that often include spatial components

11 of 59

What is Spatial Data?

12 of 59

Spatial Data

Spatial datasets contain data about geographic entities or features defined using coordinate and projection information that reference locations on Earth.

13 of 59

Types of Spatial Data

  • Vector Data: Represents discrete geographic features like cities, roads, or property boundaries.
    • Points: Single, distinct locations (e.g., a specific well or a store).
    • Lines: Represent connected linear features (e.g., rivers or roads).
    • Polygons: Bounded areas (e.g., a city's boundary, a lake, or a national park).
  • Raster Data: Represents continuous data, often used for things like elevation, temperature, or land cover.
    • Uses a grid of equal-sized cells or pixels to represent geographic data.
    • Can also represent discrete data, such as a land cover map where each cell has a specific category. 

14 of 59

There are many ways to visualize spatial data

✔️

✔️

15 of 59

Choropleth Map

​

Uses colors or shading to show different levels of a particular measurement in different geographic areas.

​

16 of 59

Dot Density Map

Uses a dot or another symbol to show the presence of a feature or phenomenon.

17 of 59

Types of data visualization

Interested in Learning About Different Types of Data Visualizations?

https://datavizproject.com

https://datavizcatalogue.com

​

18 of 59

Mathematics Curriculum

19 of 59

Statistics & Data Reasoning

20 of 59

Declarations or Guiding Principles

  1. Data science is contextual and interdisciplinary
  2. Data science is an investigative process.
  3. Data science understandings and experiences are for everyone.
  4. Data science educators must develop and practice ethical uses of data.

https://www.nctm.org/Standards-and-Positions/Position-Statements/Data-Science/

21 of 59

  • All students should have the opportunity to take four years of high school mathematics, and data science content should be available to all students in order to complete their high school mathematics graduation requirement. 
  • A high school data science course merits mathematics credit if it includes substantive student work with essential concepts, including those from Functions, Quantitative Literacy, Visualizing and Summarizing Data, Statistical Inference, and Probability (NCTM, 2018). 
  • A high school data science course merits mathematics credit if it includes substantive student work with skills students develop from their understanding of the essential concepts.
  • A high school data science course merits mathematics credit if it includes substantive student work with habits of mind in mathematics and statistics.
  • Students should have access to mathematical action technology within and out of school to support their mathematical and statistical work in any high school mathematics course they choose to take.
  • A high school data science course involves significant content knowledge and skills. A data science course is a valuable resource for students in learning how to appreciate and understand the world around them.

22 of 59

23 of 59

24 of 59

Guidance Documents

25 of 59

Data Education

Mathematics Education

Statistics Education

Data Science Education

Data Education

26 of 59

Supporting Teachers in Spatial Data Investigations of Issues of Food Access

27 of 59

28 of 59

29 of 59

Learning Activities Sequence

Learning Goals: To deepen teachers' understanding of local food insecurity issues and how to investigate relevant issues using data.

  1. Guest Speaker from local food access & advocacy nonprofit - overview of food insecurity and food access issues through local data and relevant stories
  2. We presented a CODAP workspace we had designed for teachers to use in a data investigation
    1. Included a map of spatial data layers showing the locations of grocery stores,
    2. overlaid on census tract data from USDA food access atlas
  3. Teachers were given time to explore the data and formulate investigation questions
  4. Teachers engaged in investigating the dataset based on their questions

30 of 59

31 of 59

Food Access Activities Conjecture Map

Embodiment

Project Outcomes

Teachers develop a critical statistical literacy for doing statistics

Teachers develop a critical statistical literacy for teaching statistics

Teachers translate learning from PLC to practice in classroom

Building and maintaining a PLC

Design Principles

Data investigations of relevant issues are at the core of authentic practice of critical statistical literacy

Authentic practice involves engaging in ongoing cycles of reflection and action

Real data and appropriate technology tools must be a part of the design of data investigations for authentic practice

Pedagogy is modeled and made explicit

Community building is an explicit aspect of the design

Understanding of content and context are both valued in the development of central ideas in data investigations

The design is transparent and explicitly communicated to participants/teachers

The relevance of topics is in the eyes of the beholder and should be considered at different levels (I.e. international, national, community, local, home) in relation to dialectic tensions (difference---representation; certain---uncertain; signal---noise).

Thursday Morning: Introduce Food Desert Context - Carl's talk

Thursday Afternoon: Formulate Problems, writing stats Qs

Thursday Afternoon: Explore food desert data

Thursday Afternoon:  Investigate & connect food desert data

Friday Morning: Connecting Food Desert to Teaching

Learning Experience Outcomes

Developing knowledge of context around relevant issues (ie – food access)

Formulate questions to drive a data investigation.

Using CODAP to visualize and explore data

Connecting statistical concepts to teach standards

Create a data investigation activity to explore a relevant issue.

Thursday Morning: KLEWS Chart discussion

Friday Morning: CODAP Guided Practice

Friday Afternoon: End of Day Debrief

32 of 59

Source: https://nihcm.org/publications/the-current-state-of-food-insecurity-in-america

33 of 59

34 of 59

35 of 59

36 of 59

USDA's Food Access Data�

USDA's data and methodology for identifying geographic areas that may have limited food access have evolved since the 2008 farm bill. Although the 2014 farm bill (P.L. 113-79, §7517) repealed the 2008 farm bill provision (§7527), USDA continues to develop and report such data. Current USDA estimates for 2019 are available in its Food Access Research Atlas data. USDA data are for populations within census tracts, which are statistical subdivisions of a county, with a population size between 1,200 and 8,000 people or an average of 4,000 people. Criteria for low-income and low-access census tracts shown in Figure 1 reflect

  • low-income (LI): poverty rate of 20% or greater, or median family income at or below 80% of the statewide or metropolitan area median family income; and
  • low-access (LA): a low-income tract with at least 500 people or 33% of the tract's population living more than 1 mile (urban areas) or more than 10 miles (rural areas) from the nearest supermarket or grocery store. (USDA LA data are also available assuming different measures of distance, ranging 0.5 miles to 20 miles.)

37 of 59

Census Tract Definition

Census tracts are small, relatively permanent statistical subdivisions of a county.

Uniquely numbered with a numeric code

Interested to know more?

​

CensusTracts Overview

Explore Census Data

38 of 59

Consider This

  1. What do you notice?
  2. What do you wonder?
  3. What is a claim you could make from this data?
  4. How does this make you feel?

39 of 59

Food Insecurity Data

40 of 59

Other Data Tools for Related Issues

41 of 59

Playing with the Data

42 of 59

Explore Fulton County

  • Here is a link to a dataset for Fulton County, the county we are currently in.
  • I’ll do a quick run through of CODAP and how to use it.
  • If you are familiar feel free to play.

43 of 59

Consider Data

Here are some questions to help you think about the data.

  • What are the cases/observational units?
  • What variables are present in this data set?
  • How do you think they were measured?
  • What questions do you have about the data?
  • What questions would you like to investigate with the data?
  • What is the structure of your data?
  • Do you need to create, modify, or filter any of the attributes/variables?

44 of 59

45 of 59

What makes a good investigation question?

The variable(s) of interest is/are clear and available

    • Report what variables are present in the question
    • Are those variables in the dataset? 

The population of interest is clear

    • What is the population in the question?

The intent is clear

    • Is it clear what the intent of the question is? What is the question getting at? 

Variability is present/expected in the data

    • Is there more than one possible outcome for the variables being investigated?

The question can be answered with the data

    • Is it possible to answer the question as it is intended with the data we have available to us? 

The question is one that is worth investigating, that is interesting, and has a purpose

    • Why is this question worth investigating? So what? 

The question allows for analysis to be made of a whole group 

    • Is the question focused on overall trends and patterns?
    • Does the question just ask for a descriptive statistic? If yes it is not a statistical question. 

(modified from: Arnold & Pfannkuch, 2019)

46 of 59

Formulate Questions and Explore

  • Try to formulate a question
  • What would you need to explore in the data to answer it?
  • Do you have the data you need to answer the question?
  • How could you use this with your students

47 of 59

Putting the Pieces Together

To help guide your process of constructing an investigation for your students consider our flowchart with linked investigation briefs.

Let’s take some time to explore this resource linked here.

You may also want to consider how the standards questions and type of data connect

Here is another tool to help

48 of 59

Explore & Visualize Data

49 of 59

Explore & Visualize Data

50 of 59

Consider Models

51 of 59

Consider Models

52 of 59

Creating Your Own Workspace

53 of 59

Findings and Questions

54 of 59

Findings

Finding: Relevant data investigations (e.g., food insecurity) can motivate teachers to consider actions to improve their communities.

​

Themes:

  • Awareness & Understanding of the social issue (Reading the world)
  • Responsibility & Empathy - showing emotional responses (Praxis)
  • Classroom Action & Integration (Writing the world)

​

55 of 59

Building awareness around the social issue

Design Principles: 

  • Data Investigations of Relevant Issues
  • Content & Context are Equally Important
  • Real Data & Appropriate Technology

Embodiments:

  • A guest speaker’s grassroots perspective
  • Collaborative learning activities - CODAP spatial datasets: Teachers analyzed local data with and from the community

“The emphasis is on talking to people in the community who know about issues and not just to people in charge who are separated from the issue.” �- Sarah

  • Teachers valued local, community-sourced data and began seeing grassroots involvement as key to change.

56 of 59

Developing Social and Emotional Responsibility

Design Principle:

  • Context & Content are Equally Important

Embodiment:

  • Discussions with guest speaker and their community through storytelling around the students experiencing food insecurity 

“There are students that are without food access, and we should be sensitive about how that affects their ability to learn.” - Nancy 

“We need to advocate more for students who are displaying signs of hunger.” - Leona

�

57 of 59

Planning Action through Teaching

Design Principles: 

  • Data Investigations of Relevant Issues
  • Ongoing Cycles of Reflection and Action

Embodiments:

  • Providing opportunities for teachers to explore food insecurity using spatial data in CODAP
  • Allowing teachers to recognize the potential ways to incorporate real-world problems into their teaching

“I plan to use this information in lessons I teach my students.” - Anna 

“I think we are learning useful and meaningful statistics that can translate well into the classroom. Not just for the purpose of teaching math/statistics but to help generate a more informed generation that will one day be the ones making these very decisions.” - Nancy

�

58 of 59

Thank You

  • Please reach out if you have questions or comment
  • We are also happy to provide support for teachers and districts around teaching statistics and data science concepts and practices
  • We will have all our resources up on our website that is going live in a couple weeks www.criticalstatisticalliteracy.org

59 of 59

Examples of Relevant Curriculum