I’ve spent months on parking lot digitization, weeks on land cover classification, and now I’m finally getting into data visualization. Thankfully, this does not mean I have two weeks to write my report, hold my exit seminar, and make all of the final maps required for the Long Island Solar Roadmap Project. I’m going to continue my work here at The Nature Conservancy through the end of the month of August, during which time I’ll be able to continue to refine data, carry out analysis, and make output maps.

I’ve got some thoughts that go so far beyond the scope of this project and even my EOI coursework that I went and started writing those down elsewhere. Those thoughts definitely have an impact on how I think about this project and my coursework and my career, but my reflections on how this project is going cannot contain them without both of us getting lost. I’ll be posting part 2 of these reflections in a couple days.

For me, this is perhaps the most difficult part of this project: trying to figure out how to communicate results. Solving technical problems and refining methodology can certainly be frustrating at times, but it doesn’t quite compare to the unsettlingly open-ended nature of finding effective ways to visualize results. After working with datasets for so long, it’s incredibly difficult to pull myself out of my work and try to see what I’m doing with an outsider’s perspective, imagining someone approaching my maps and visualizations with little or no context for what is being communicated.

This is also when it becomes very difficult to manage my time effectively. It can be so tempting to continue to toggle between slightly different visualizations or just continue to make more and more maps. Today I spent a couple hours going down the rabbit hole of figuring out how to display arterial roads labelled with highway numbers displayed within their respective highway type (Interstate, US Highway, etc.). It can get so out of control, and yet I don’t think I’ll be able to really feel confident about my cartographic skills until I’ve spent a lot of time on complicated, niche problems like this. A lot of cartographic work involves digging into a labyrinth of cartographic tools and inscrutable design settings. Efficiently gaining knowledge of how to do this kind of stuff requires looking at innumerable map design tutorials. The key to being able to do a lot of this kind of work is spending a ton of time attempting to do a diverse array of tasks for a multitude of purposes. I’m only beginning to appreciate after nearly three years of GIS experience just how important familiarity with software and knowledge of the manner of all of the available tools is when it comes to being a good cartographer. Mastery of cartography clearly cannot be achieved merely by mastery of geospatial analytical knowledge, but by learning to identify and prioritize the elements required to visualize information and hitting a balance of those elements. Even as I’ve tried to apply that wisdom which is offered by all good sources of cartographic knowledge, I am still early on in the process of developing a cartographic intuition of how to make my maps better.

I really enjoyed having the opportunity to find areas suitable for ground-mounted solar installations using remote sensing and supervised image classification. That experienced forced me to rigorously interrogate and carefully evaluate my new heuristics for how to go about classifying land cover. But I ended up having to make a lot of judgements that required making intuitive assessments of which of the wide variety of possible methodology avenues available to me was the best. Knowing which was the best really just required me to lean on my breadth of experiences utilizing a diverse array of methodologies that are suited to slightly different tasks and different kinds of input data.

In the end, I’ve gained an appreciation for the responsibility of experts in any field and in my own careen in particular to make judgements of when the buck stops. Gaining feedback is valuable when trying to understand what is valuable to stakeholders and the people I’m carrying out analysis for. I of course don’t want to be spending time producing results that aren’t valuable, but when making judgements of geospatial methodology, I need to be able to interpret the needs of stakeholders, make a decision, and be able to defend those decisions.