Though my initial research questions regarding the pervasiveness of rodent activity in New York City were broader in scope than my familiarity with Tableau allowed me to successfully answer, through further familiarizing myself with the data and the software, I nonetheless arrived at a more general inquiry that I’ve attempted to respond to through the three visualizations found on my Tableau dashboard. While the fundamental question that my work responds to is, “How did rodent activity in New York City change during the COVID-19 pandemic?,” the visualizations that accompany my simple map also work to illuminate data relevant to questions regarding the types of rodent complaints being reported to 311 (conditions attracting rodents is a different issue entirely from a singular mouse sighting) as well the physical locations (3+ family apartment buildings vs. businesses, for example) most impacted by rodent activity. With anti-rodent garbage collection plans making headlines last fall primarily as a result of Mayor Eric Adams’ “war on rats,” the data visualized through my project provides a rudimentary example of how 311 data might be employed in the allocation of preventative resources in situations such as combating rodent infestations of 3+ family apartment buildings or curbing the disproportionate rodent reports coming from the Bronx as opposed to boroughs such as Queens. At its core, such a visualization works to simultaneously locate hubs of rodent activity as well as to highlight areas in which either applied resources or daily practices of residents are working to curb such a widespread issue, allowing audiences ranging from city officials to incoming transplants to make determinations about their approach to life in the city or the ways in which it can be improved.
My primary (but perhaps least effective) visualization is the spatialization of rodent complaints between 2020 and 2023 found at the top of my dashboard. Due to the sheer volume of rodent reports made each year, this map, when unfiltered to include the three+ year window I’m evaluating, appears as multi-colored television static. However, when one filters the map to represent a single year, the visualization provides a better idea of the way in which rodent reports have developed from year to year (for example, if one switches quickly between the 2020 and 2021 filter, the increase in rodent reports following the initial COVID-19 shutdown is clearly seen). My “Descriptions of Rodent Complaints in Each Borough of New York City between 2020 – 2023” visualization attempts to fill in the gaps that were left by the map. By allowing the user to simultaneously view the descriptions of the rodent complaints (mouse sighting, rat sighting, etc.), the borough they have occurred in, and the degree to which they’ve been reported each year, this visualization offers a “full view” of the matter at hand. I feel that this is my most effective visualization, as it gives a somewhat comprehensive view of the predominant issue (rat sightings), the foremost boroughs impacted (Manhattan and the Bronx), as well as the changes in reportage over time. Lastly, my location type visualization aims to provide insight into the infrastructure most vulnerable to rodent infestation from year-to-year (though it could be argued that establishments such as restaurants have less of an incentive to report rodent sightings so this data isn’t 100% reliable). I particularly found it interesting that rodent-sightings in 3+ family apartment buildings dropped significantly during the pandemic despite our increased time at home and the increased possibility for spotting critters that this would entail.
To be transparent, my selection process for these visualizations came with an element of trial and error. While my initial objective was always to produce a map as the key feature of this project, it was only upon developing it that I came to realize the limitations of Tableau and of my unfamiliarity with the software’s full capabilities. Upon recognizing the unaddressed components of the data that I felt needed representation, I explored the possible visualizations until I found that which I felt “answered” the question posed through my inquiry. Though this is far from a faultless portrayal of the rodent issue in New York City, I feel as if I’ve at least somewhat scratched the surface of what it means to address such matters through data visualization and cross my fingers that the visualizations I’ve produced, though somewhat busy and burdened to communicate a great deal of information in a small rectangular space, are clear, comprehensible, and effective.
In the future, I’d like to explore the ways in which such a project can work to navigate the occasionally vague nature of data and ideally avoid operating on assumptions. For example, I recognize that the impulse and incentive to report rodent sightings for people living in apartment buildings is different than for the coffee shop owner who found droppings in their storage room. While positive conclusions could be drawn from this that might be used to pressure landlords into redesigning their buildings’ waste removal systems to be more rodent-resistant, this deduction still operates on assumptions that ignore the biases and insufficiencies of the data being employed. Going forward, I aim to narrow my scope from the outset and approach questions that have more concrete answers that can decisively be drawn from the dataset.
Link to “Rats!” – Visualizations of Rodent Activity in New York City from 2020 to 2023
