dmalary/DVIA-ex_assign-4

DVIA assign 4

Education and Salary difference btw men and women

1. npm install
2. node fileName.js
3. open local server to index:html

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Reqs & output

Data + reqs

https://canvas.newschool.edu/courses/1525001/assignments/8211916

https://data.census.gov/cedsci/

Output:

https://datawrapper.dwcdn.net/G1zyw/1/

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Project Approach

Answer project questions as you go along

1. Audience questions + sketch
2. Check + prep data (acquire, parse, filter)
3. Mine/explore/sketch/iterate several simple graphics, re: stories (same or diff?), pick one
4. Code initial draft (pseudo first?)
5. Refine/declutter/clean/annotate/clarify (color, labels, titles, legend, summary, etc)
6. Interactivity (more than tooltips, eg: scrolling)
7. Share

===================== 1

• Why are we doing this?

  • What are questions that you want to explore with this visualization?

• What are you hoping to achieve?

  • What will I be looking at(title)?

• Who are we targeting?

  • How is the end product going to be used?
  • How are we publishing?

• Sketch summary:

  • sketch
  • Questions
  • Data set
  • Inspirations
  • Data vis method
  • Current problems

===================== 2

• What data do we have available?
  • Which quantitative dataset is used?

    https://measuringu.com/qual-methods/ https://www.questionpro.com/blog/qualitative-research-methods/

  • What are the properties of the data set?
  • How many data points
  • What's the quality of the data?
  • Which other existing materials should we take into account?
  • Which constraints do we have?
• explore
  • w/ R

    https://www.r-graph-gallery.com/all-graphs.html https://cran.r-project.org/web/packages/tidyjson/vignettes/visualizing-json.html

  • w/ RawGraphs

    https://rawgraphs.io/ https://github.com/densitydesign/raw/

  • w/ DataWrapper

    https://app.datawrapper.de/create/map

• clean

  https://csvjson.com/csv2json https://www.npmjs.com/package/csvtojson

===================== 3-6

• Which visualization method is used and why?

  https://www.data-to-viz.com/ https://www.d3-graph-gallery.com/ https://www.d3indepth.com/ https://observablehq.com/@d3/ https://observablehq.com/@d3/gallery https://python-graph-gallery.com/ https://www.r-graph-gallery.com/ https://bl.ocks.org/ https://bost.ocks.org/mike/ https://www.axismaps.com/projects http://datamaps.github.io/ | https://github.com/markmarkoh/datamaps/blob/master/README.md#getting-started https://kepler.gl/ https://plotly.com/ | https://github.com/plotly/plotly.js

• What does the visualization enable?
• Is this a static visualization? Is it interactive?
• color: Is it intentional and intuitive? data decodable by audience?

  https://colorhunt.co/ https://d3-legend.susielu.com/ https://colorbrewer2.org/

  • legends, annotations
• clear text hierarchy?
• link to raw data?

===================== 7

• Who else is doing something similar?
• Abstract/summary/about, re: methodolgy? (1-2 pars)
  • What were your considerations?
  • What tools did you explore?
  • What challenges did you run into?
  • How did you iterate?

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Concept & build

• Questions
  • education difference btw men and women?
    • calc difference in edu of men vs women, for bachelor+
    • at or above poverty line
    • can we show additional data? eg toggle button for states where female salary > male salary
  • Unemployment rate by county/state?
  • Insured rates?
  • Diabetes and obesity prevalence/other health stats?
  • Election results by race/age/etc?
  • Utilities data?
• Goal
  • visualize salary difference between men and women, for the same education group (bachelor+), and same socio-economic group (> poverty)
  • this visual would be useful for policy makers, students, researchers

• Data source:
  • https://data.census.gov/cedsci/

• We'll be using a choropleth map visual.

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Project refs

https://github.com/madhat5/DVIA-ex_assign-2 https://www.d3-graph-gallery.com/graph/bubble_tooltip.html https://www.d3-graph-gallery.com/graph/interactivity_tooltip.html https://www.d3-graph-gallery.com/graph/line_select.html https://observablehq.com/@d3/bubble-map https://bost.ocks.org/mike/bubble-map/ https://www.d3indepth.com/scales/ https://d3-legend.susielu.com/ http://statchatva.org/data-visualizations/

Notes

• Methodology influences:
  • Ben Fry, Amanda Cox, Alberto Cairo, Mike Bostock