Exploring Rural Shrink Smart Through Guided Discovery Dashboards

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<br><br>
# Exploring Rural Shrink Smart Through Guided Discovery Dashboards
## SDSS 2022
### June 8, 2022
#### Denise Bradford
#### Department of Statistics, University of Nebraska - Lincoln 
##### <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:black;overflow:visible;position:relative;"><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg> [denise.bradford@huskers.unl.edu](denise.bradford@huskers.unl.edu) 

##### <svg aria-hidden="true" role="img" viewBox="0 0 496 512" style="height:1em;width:0.97em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:black;overflow:visible;position:relative;"><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg> [drbradford12](https://github.com/drbradford12)

???
Thank you for inviting me for a visit today and for attending my talk.

---
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# Outline

<br>
.large.emph[
+ Introduction to Rural Shrink Smart Project with Iowa State University and Open-Source Data Challenges

+ Motivation - Why Use Dashboards?

+ Guiding Design Principles & Dashboard Design Workflow

+ Dashboard Philosophy - PCPs and the Novice User
  + Using Guided Discovery Learning (GDL)

+ Dashboard Design Workflow 
  + Feedback Loop (Active and Passive Data Collections)

+ Future Work
]

???
In this talk today, I will provide a background of the Rural Shrink Smart Project. I will then provide a framework to way using a dashboard can be helpful to these population discovery of gaining insights from other towns. I will continue to discuss the usefulness of parallel coordinate plots on the naive user. I will conclude with future work.

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.center[
# Project Introduction and Open-source Data Description
]

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# Rural Shrink Smart Project
.center[
<img src="images/Shrink_Smart_Logo.png" width="35%"/>
]

+ <span style="color:darkred"> Project Question:</span> How do some cities manage to maintain quality of life for residents amid shrinking populations?
+ Can this be taught? 
  
.medium.emph[
This material is based upon work supported by the National Science Foundation under Grant No. 1952007. We would also like to thank our collaborators at Iowa State, the Iowa League of Cities, and the various Iowa government organizations which publish their data at (https://data.iowa.gov).
]

---
# Basic Idea

+ Assemble publicly available data sets
    + Available over lots of different geographic measurements    
    Lat/Long, Zip code (5 or 9 digit, changes over time), Address, City, Census units, School District, County
    + May not be complete for small towns    
    (ACS estimates aren't useful in these areas - not enough people surveyed)

+ Assess usability, interpretability and adaptability to new statistical methodology.

+ .emph.cerulean[The Rural Shrink Smart EDA dashboard incorporates two major components]: 
    + Data Visualization Interface component with Guided Discovery Learning (GDL) principles
    + Exploratory Data Analysis component utilizing statistical graphical elements, such as maps and Parallel Coordinate Plots (PCPs)

---
# Challenges

+ Quality of Life is a subjective assessment
  + Iowa Small Town poll has measured subjective QOL by decade for one small town in each of the 99 counties in Iowa (same town each year)
  + Survey response rates keep decreasing over time

+ There are objective measures of things that contribute to QOL available in public datasets

+ School ratings
  + Town budgets for services
  + Access to medical care, shopping, etc.
  + Transportation trends
  + Demographic shifts
  + City level cooperative agreements with other government entities and NGOs

---
# Challenges

+ There are objective measures of things that contribute to QOL available in public datasets

+ <span style="color:darkred">Project Question:</span> How do we leverage <span style="color:blue">data science</span>  and <span style="color:blue">exploratory data methods</span> to inform town leaders to reduce the shrinkage and maintain reliability?
    + How does an interactive dashboard of open-source data integrate into overall decision-making?
    + Can we focus on estimating only things that objective measurements don't capture?
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.center[
# Motivation - Why Use Dashboards?
]
---
  
# Identifying Key Metrics
<b><u>Audience</u></b>

.medium.emph[
+ As small rural towns, traditionally, do not have access:
     + Data scientists 
     + Ability to easily leverage data collected locally to support decisions

Our research team will provide communities with data about services in small town Iowa in order to assist with developing strategies to improve quality of life for their residents amid shrinking populations 
]
--
<b><u>Visualization Goals</u></b>

.medium.emph[
+ Create a central place for the excess of data to be conveyed in a single comprehensive visual.
    + We will utilize Parallel Coordinate Plot (PCPs)
+ Create a map feature that will allow for town leaders to understand the distance of essential QoL components (e.g. Fire Dept, Schools, etc.)
+ Add an overview of town general basics related to the seven QoL factors.
]
--
<b><u>Engage People</u></b>

.medium.emph[
+ We will assess our visualizations to determine which strategies for user interface and interactive graphics design are most useful to empower town leaders to make discoveries in publicly available data assembled with a focus on items that impact rural quality of life.
]

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# Visualization

+ Create a town-focused dashboard that lets leaders see how their town compares to other similar towns (geographically and as measured by the unsupervised statistical analysis)

+ Goal is to help towns see what strategies for maintaining QOL work and don't

+ Emphasis on things towns are empowered to change
    + Can't focus on e.g. agricultural policy
    + City budgets, partnerships with outside entities, services that matter and don't
--

+ How do we best design charts/graphs/UI to make people feel empowered and help them explore the data with an open mind?
    + How do we incorporate statistical analyses into this? Much of the data will provide qualitative differences but not statistically significant differences.
---
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.center[
# Guiding Design Principles and Dashboard Design Workflow
]    
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# Guiding Design Principles

Our initial set of dashboard design principles are:

+ The town leaders are the focus audience (central focus of the app)
    + Local Map (Local Essential Services)
    + Vital Statistics

+ Facilitate comparisons with other towns in order to allow the user to explore other potential solutions

+ Present the user with peer comparisons in order to widen the scope of exploration beyond the initial set of obvious peers in the local region.
    + PCP to compare the towns with varying similar features (e.g. economy, population, rurality or user input)

+ Allow for more detailed data and feature requests to improve the dashboard design over time.

---

# Dashboard Design Workflow

+ Compile a single data set
    + rows: Each town
    + columns: Each variable with appropriate spatial aggregation    
    e.g. distance to service instead of lat/long

+ Unsupervised Analysis
    + Which towns are most similar based only on the public data?
 
+ Dashboard Layout
    + Which layout will allow the user to engage the most without overwhelming their curiosity?
   
+ Dashboard Feedback
    + What elements on the dashboard are useful, confusing, misleading or incorrect from town leaders?
    
---
# Dashboard Design Workflow

## Unsupervised Analysis
<br>

- Unsupervised clustering methods were used to determine data quality

<br>
-  During the process, we found that schools, fire departments, post offices and hospitals were missing from our open-source data

<br>
-  Resulted in finding other open-source data that were used for sanity checks along with adding information of missing data
    
???

We found that in towns that the project leaders understood were off. For example, we found that many of the schools that were known to be in the towns were not in the dataset that we were using. Along with the fact that we have the leaflet feature, we could see that schools were in particular parts of towns that we did not see.