• Sean Keenan

Using Data-wrapper

Last week, I analyzed and refined multiple sets of data. With this data jotted down, I can now attempt to use and visualize some of this data. To do this, I’ll be using the site data-wrapper, specifically the three different map features.

Choropleth Map

For this first map, I choose to make a choropleth map using the unemployment data I found last week. As choropleth maps don’t work very well when using absolute data, I decided to use the unemployment net change over the past 12 months. When making the physical map itself, I tried to make sure the colors could be differentiated fairly easily, as well as made sense with what our usual perception for these maps are. That means I choose a lighter peach color for a low percentage change and a darker blue color for a high percentage change. This makes it easy to tell at a glance what states have had a relatively low change in unemployment (the midwest) and which had a larger change (the northeast). I also made it possible to hover over the map itself to see the exact percentage if desired.

Symbol Map

For the second map, I wanted to try out the symbol map function. My data from last week didn’t provide any easy options for this, so I decided to use data from the Department of Health in regards to how COVID-19 is affecting Pennsylvania, specifically the number of cases in each county. The website provided a detailed document of each county's probable and confirmed cases, though it was difficult to copy over directly into datawrapper, so I ended up having to re-type the data into an excel sheet before I copied it over. For the physical map itself, I choose the symbol’s to be red circles that grow depending on the number of cases in that county. I choose red as it sticks out and is the color most would assume in regards to a topic such as this. Similar to the previous map, you can hover over any of the circle’s to see both the county as well as the number of cases.

Locator Map

For the final map, I choose to make a locator map. For the data used in the locator map, I choose to use the top ten video game related stores in Pennsylvania, according to Yelp. There’s a heavy amount of stores that sell video games in some format, so I wanted to try to narrow it down in some way. A small struggle I had though is that unlike the other map types, I couldn’t find a feature that allowed you to highlight a location and see more about it. This means you can’t figure out the exact address of the location without it cluttering the map and obscuring the title. To combat that I choose to just name the store itself and use the map as a way to see how far apart each store is from each other.

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© 2020 by Sean Keenan.