Visualizing Patterns

Finding hidden clues in Pittsburgh

Yiwei Huang
Nov 7 · 7 min read

10/31 Introduction to data visualization

We’re introduced to the project prompt this week and briefly did one exercise on thinking about data regarding water quality.

2 major purposes for data visualization:

  • For us to better understand the hidden fact and pattern
  • To communicate outwardly with others about our findings.

Keep in mind in this project

  • use 3–5 types of data
  • 20–30 points (not over 50)
  • be remind of apples to apple comparison, cannot compare data sets that don’t have the same metrics
  • Static data (structural/ geographical/ neighborhoods /land use/ area/ demographic)
  • Dynamic data (Emergency/ Events changing)

Class brainstorm — Narratives around FOOD

11/07

Reading1: Chp 3. Representing Data

Topics Forming (abandoned):

Tips for question framing:

  • Note to mark data which is already there and data that is easy to find.
  • If you couldn’t find the data you need, would your topic fall-apart? If so, jump shift to more suitable questions.
  • What’s the order of information you want to go into?

Data Viz That interests me:

  • Show different combination through different criteria

11/12 Sorting and support the Narrative with data

  • apples to apples, can have subsets, try Separate Spreadsheets.
  • Linear (0, 1, 2, 3)
  • Categorical (cloudy/sunny)

Things to do before Thursday

  • Refine the questions
  • Determin which scales am I working on
  • Refine 3–5 buckets in each column(datasets)
  • What is the coordinate system that can be used as an anchor
  • create layers of information that we can find pattern and relations

11/14

What Types of Dataset have you collected?

  1. The active Alcohol License and Extended Permit dataset issued by PALCB (2019)
  • After Grouping the Licensees by Zipcode, (see the blue and light blue chunks in the middle pic) I counted the quantity of each license category in every zip code area. (see right pic)
  • I intend to use the data to depict the shape of the “infrastructure” for alcohol consumption in each zip code area.
“Shape“ of Zipcode 15201
  • Demographic (underage drinking)
  • Location/Neighbourhood

In-Class Exercise:

Research Question: What is the research question you aim to investigate through the data you collected?

11/21:

Q: What question are you exploring?

  • The number of alcohol-related licenses issued in each zip code
  • The number of alcohol-related death in the year 2018 in zipcode(both )
  • different types of alcohol-related crime
  • The number of alcohol-related licenses issued in each zip code
  • The number of alcohol-related death in the year 2018 in zipcode(both )
  • (Part of a whole)how do they form an industry? what is their relationship?
  • (Location) What is the alcohol license condition in every zip code area?
  • (Category/Linear)What is the quantity of each kind of license in every zip code area?
  • (Linear) What is the quantity of alcohol-related death in every zip code?
  • (location)8 people travel elsewhere after drinking and died.
  • (Linear/Catagory) What is the quantity and categories of alcohol-related crime in every zip code?
  • (Part of a whole)how do they form an industry? what is their relationship?
  • (Location) What is the alcohol license condition in every zip code area?
  • (Category/Linear)What is the quantity of each kind of license in every zip code area?
  • (Linear) What is the quantity of alcohol-related death in every zip code?
  • (location)What are the outliers(incident zipcode → decedent zip code)?
  • (Linear/Catagory) What is the quantity and categories of alcohol-related crime in every zip code?
  • Categorization around 7 is the maximum of perception
  • Temporal building (bring someone into the information)
  • pacing + simultaneity. show relationships between relative data
  • Narrative + indexical stories
  • Expectations + Perception (Norman, D., Thing that makes us smart, Appropriateness principles)
  • mimicking known behaviors (builds on what people know, but at the same time push the boundaries, bring something new)
  • discovery critical thinking (not include text in the beginning . /How do you engage the user to find the pattern?)
    Yiwei Huang

    Written by

    CMU MDes Candidate

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