Any given environment, whether wild or urban, involves a vast array of factors that are highly interdependent. Urban environments tend to implicate humans at the forefront of both influences and outcomes, whereas the complexity of non-human environments is often neglected and misunderstood.

I have used three unrelated datasets to create visualizations and tell the story of a given habitat. The first dataset that I used was from a study tracking the change in insect populations within control and thinned forest areas of a specific forest reserve of New Mexico. The measurements were plentiful and the data included many columns for things like insect length and each level of taxonomic classifications. I found the amount overwhelming in that I didn’t know what to focus on.

The story that became immediately clear is that thinning and burning the forest area greatly increased insect counts while the untouched overgrown forest continued to see a decline in populations. This provided a clear picture of the fact that treating the forest had a positive impact on insect life and from there, I found some other ways in which it promoted wellness and biodiversity. The thinned areas ultimately showed higher insect populations, a new species introduced, and longer lives.

I initially had some random graphs and charts and most of them didn’t contribute much to the overall takeaway. The most helpful piece of feedback I got regarded the structure of these charts and that a comparison presentation would be effective, as well as providing some visual indicators of the kinds of landscapes and insects the study is discussing.

The second dataset that I used focused on the ecology of an urban environment that I’m very familiar with– NYC. I had heard about the Heat Vulnerability Index (HVI) measurement before but didn’t know what it could tell us. The HVI of a given location refers to residents’ level of risk of dying during or after extreme heat. The other datasets that I used were the 2015 tree census for the city, which maps out every single tree in residential areas within the city, and average income data for each zip code. Since the HVI data was measured by zip code and that was the main focus, the tree census and income data would need to follow suit.

I initially was only using the tree data as a means to investigate a correlation between HVI and tree coverage. Tree coverage is also a factor already included in the calculation of HVI so the comparison more so tracked the weight of that influence. The maps I produced from that data alone were not very effective and although it showed some vague connections between lack of tree coverage and increased HVI, I knew a large part of the story was missing. Feedback on these maps made it clear that I needed more data and it should probably be data regarding socioeconomic status.

I found and included average income data for each zip code of NYC. Income is also a factor already calculated into HVI measurements so now, I had two different factors being compared to the ultimate accumulative measurement which allowed me to illustrate more of the picture. The second map is interactive and this dashboard can be accessed through this link. The correlation of income to HVI seems much stronger than anything else, and is a worthwhile highlight of the fact that income/wealth can shape entire communities and that includes all of these things like tree presence, access to healthcare, access to air conditioning, accompanying health struggles and more.

The third dataset that I found interesting and chose to visualize was a food web of the habitants of the Alvarado Lagoon System, which is located in the Southwest area of the Gulf of Mexico on the coast of Mexico. The system includes various channels, lagoons, and wetlands and is a significant feeding and reproductive area for the fish and crustacean populations. The data consists of 30 fish, crustaceans, and organisms and so each habitant is its own node. The edges indicate the presence of a given target node in the source node’s diet.

I first created some networks that didn’t have any organizational structures but highlighted, in two different iterations, the major prey and the major predators of the area. I used size and color to mark these differences but the connections between the species remained tangled and unfocused. I found that the idea I’m most interested in, throughout all of the projects this semester, are the factors in an environment that are holding up everything else; those overlooked components that could cause the collapse of an entire ecosystem.

A balanced ecosystem keeps itself and all of its populations in check through the presence of both food source and food eater but I wanted to highlight the major sources for the area as many of them are microscopic or types of seagrass, both of which have their health widely disregarded by humans and human behavior.

Feedback on my networks made it clear that I needed to better utilize color and size and find a better visual organizational for the data. My second iteration was this circle network, which utilized color to group together highly-connected groups, and I still used size to highlight the biggest food sources.

The lines are still relatively useless though in showing where the bulk of connections are. The most helpful tip was to try out using a Sankey diagram, which I found to be most effective because of the point-point structure which flowed well with the predator-prey story I was trying to illustrate.

Each line represents the presence and frequency of a habitant on the right in a habitant on the left’s diet. This diagram finally had the effect that I was trying to see all along and organized the key players by prevalence, which subsequently highlights the major food sources that uphold most of this particular ecosystem.

My focus on key influences in a given interdependent system has the underlying goal of diagnosing complications. A human habitat that is failing, which here could be represented by a high HVI rating, has deeper factors that we can pinpoint in order to invoke a cascade of improvement. The wellness of an ecosystem like the Alvarado Lagoon System, with its status as a wetland of international importance, is our responsibility to uphold and not disturb by way of runoff, farmland expansion, or pollution. In problems where we could slap on temporary bandaids, we’ll see far greater returns by getting to the bottom of the relationships at hand and reforming from the root.