As someone who thinks visually, I really enjoyed the using these tools and the concomitant challenges they threw up. They reemphasized some of issues about my data sets that I have long known, as well as outlining potentially creative means to overcome these limitations. First about my data set: as a series of numbers and descriptions about dredging, collated from both the United States and Canada, data on the quantity of dredged material constantly shifts across units depending on the agency and/or country reporting the data. In specific, the shifts are from the imperial system and the metric system. Data also shifts from describing specific dredging sites to being extremely general. Thus, mapping such data is challenging. Furthermore, I would have liked to map the physical sites of dredging and collate those with the volumes. However, as I realized, since the maps available at the Rumsey Collection (and through all other sources) were made at different times, they also have slight differences in projections. In addition, given that most of the area surrounding Detroit was rural and Detroit itself was a small town, when Georectifying the maps, it was difficult to find about five consistent points that I could use to create maps that showed the same areas across time. I wanted to begin with the earliest map available in the collection—one from 1764—but that map was extremely difficult to georectify because it does not enough points to create a legible map. Creating a timeline brought up some interesting issues. Some of the dredging data has temporal limits i.e. it shows when dredging was conducted. However, that is not consistent throughout the data set. Furthermore, there are times when entire years are lumped together in the reporting on dredging volumes. In trying to create a timeline, I was not sure how to factor for the non granular nature of my data set.  

I found creating a storymap really fruitful. It helped me think through the limitations of my data. I realized that if I could find photos of dredging, I could easily create a story map that showed sites as well as volumes with the additional possibility of embedding videos and such. Using Tableau was a great learning curve. For years that data was amenable i.e. annual volumes of dredged volumes. But the issue was not having a data set that was legible throughout. I faced similar issues with trying to use Mapbox and Kelper. 

Going forward, I need to go back to my data set and find more sources and hopefully find more data that can be standardized. I also need to rethink my categories of standardization in order to get most out of the data. I need to pay closer attention to the kinds of maps I can realistically create especially since I still want to create map layers that will allow users to see the river for itself in a specific time period but have other maps overlaid. 

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