Day 106 of 180 Days of Data Viz Learning #jfdi

I’m doing some form of data visualization learning for 180 days because I need to #JFDI.

See post explaining how and why I’m doing this.

Code Learning:

Knight Journalism Intermediate D3 for Data Visualization 

Lecture Week Three Quiz

Three Takeaways:

  • Geographic data will likely start with an ESRI Shapefile
  • Northern hemisphere geography is overrepresented; severe distortion near poles in Mercator projection
  • Cardinal directions (NSEW) are preserved (as up/down/right/left) in Mercator projection

Discussion Question Answer:

  • With web-based mapping tools becoming common and ever-more accessible, the field of cartography is undergoing massive changes and an influx of amateur mapmakers.

    What considerations should non-cartographers (or anyone) keep in mind to create responsible, honest maps? (Consider sharing an example to illustrate your point.)

    Remembering that many maps are not without biases is always important to consider.  Eg. what is a country, how are continents sized, famously demonstrated in the Mercator versus Peter example in the West Wing.

    In a less political sense as well, overlaying data on maps can pose it’s own challenges to amateur cartographers, such as choosing the right visual encodings to not mislead on the data or respect privacy.

Learn JS Summarizing Data and Iterating

Three Takeaways:

  • To use d3.min and d3.max at same time, use d3.extent()
    • eg. var landExtent – d3.extent(data, function(d) { return d.land_area;});
  • Summary stats include d3.mean, d3.mean, and d3.deviation
    • eg var landSD = d3.deviation(data, function(d){ return d.land_area;});
  • Iterating.  You can process data using forEach() that performs a specified action for each element in array in ascending order, without using a loop function.
    • data.forEach(function(d) { count += 1; });
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