Data Visualization Project 1 by Rohan RamnarainMapping New York’s Barks: Tracking NYC Dog-Barking Complaints (2010–2025) Here is our research question for this project and visualization: How did dog-barking noise complaints filed with NYC’s 311 service (specifically those labeled “Noise, Barking Dog (NR5)”) evolve between 2010 and 2025 in the five boroughs and were there noticeable shifts before, during, and after the height of the COVID-19 pandemic? The data originate from the NYC 311 Service Requests dataset, encompassing 2010 to 2025. I filtered specifically for complaints whose Descriptor field is “Noise, Barking Dog (NR5),” ensuring only barking‐dog incidents are included. Key variables include: the “Created Date”, which is the timestamp of each complaint (year, month, day, and time), the common “Descriptor (Noise, Barking Dog (NR5))” which is our only complaint being studied, and the “Borough”, “Latitude”, and “Longitude” for each of these complaints. One limitation is that 311 data capture reported noise disturbances only and this of course masks the true incidence of barking, which might be higher, especially when you consider that against the backdrop of the COVID-19 pandemic, which could have added another layer of obfuscation and skewed the data in either direction, but most likely upwards – i.e, people spending more time at home may have noticed barking more often. But I digress… let’s take a look at these visualizations to get a feel for the data and the underlying trends that may help us answer our research question. Graduated Symbol Map (2010–2025) This map uses circles of varying size to represent the volume of barking-dog complaints within each area over the entire 2010–2025 span. Large circles indicate more complaints and the smaller circles indicate fewer complaints – but note that space and population density must be considered when looking at bubble sizes. The key thing that jumps out at you on first glance is how Brooklyn and Queens host numerous large circles, suggesting consistently high complaint volumes in certain neighborhoods. Manhattan shows many medium-sized circles in a smaller physical area, which shows how dense housing can amplify noise disturbances. Choropleth Map This choropleth map shades neighborhoods by the total count of “Noise, Barking Dog (NR5)” complaints from 2010–2025 with darker shades corresponding to higher complaint frequencies, and areas in deep blue or green shades being shown as hotspots for barking complaints. This helps pinpoint neighborhoods with persistent or widespread issues, potentially guiding targeted noise policy or outreach efforts. Everything is summed for that particular zipcode – that is how this map differs from the graduated symbol aside from aesthetics. This map is my ideal map for analyzing the dataset because I can spot my own zipcode and the zipcodes of my family members who may or may not own dogs and therefore may be the ideal audience for this particular visualization. The soft colors are also a plus in immediately identifying the areas of extreme complaint amounts. Street Map with Colored Clusters Here, each complaint is plotted as a distinct dot, color-coded by borough, which is a more granular visualization of exactly where—and how densely—barking complaints occur and density is the key to this visualization because the dense patches of orange in Brooklyn and teal in Queens reveal high concentrations of dog-barking complaints. Manhattan (red) is more compact but still dotted extensively, reflecting high population density and possibly greater sensitivity to noise as we saw in the first visualization. This has the added benefit of including commonly known and lived on streets, parks, and landmarks throughout the city. Out of all the visualizations, this one is perhaps the most inviting and the most interactive – I actually went and spotted the complaints right on the street outside my house, and the audience can do the same – this is also the most immediately related visualization. Heat Map on Street Map This final map applies a heat layer over the city, highlighting areas of the highest complaint density in darker or more intense “hot zones.”, a way to focus on the intense regions down to the local level in neighborhoods like Williamsburg, Greenpoint, and western Queens – these mirror the clusters we saw in previous maps. The heat map on the street level perhaps underscores how closely packed housing correlates with higher complaint density. As we have seen, most of these “Noise, Barking Dog (NR5)” complaints are concentrated in Brooklyn and Queens, with certain Manhattan neighborhoods also showing high densities and the Bronx and Staten Island being the boroughs with the least complaints. The next steps would be normalization of each zipcode and each region against the population density of the area, as well as the number of parks nearby, and the distance to the nearest park for each complaint, as a way to test that as a correlating variable. We also need a time-series based way of looking at this, perhaps through animations through the years in the dataset – 2010 to 2025. The final purpose of this data visualization would be to motivate politicians and local community boards to address the dog complaint […] “Data Visualization Project 1 by Rohan Ramnarain”