Brad Hoylman-Sigal, Manhattan Borough President
BetaNYC × Manhattan Borough President’s Office

Manhattan has a dog waste problem.
Here's how to fix it.

We analyzed 28 months of 311 complaints to find out where the problem is at its worst, why it keeps getting worse, and what the city can actually do about it.

Dog waste is a public health issue

Dog waste on city streets carries serious pathogens, including Escherichia coli, Salmonella, roundworm (Toxocara canis), hookworm, and Giardia, disease-causing organisms that can survive in soil for months or even years. Young children and those with weakened immune systems face the greatest risk from direct contact with contaminated ground. When it rains, fecal matter washes into storm drains and flows into the East and Hudson Rivers, adding to contamination in local waterways.

The consequences extend beyond direct contact with waste. Dog waste left on the street may serve as a food source for rats. This report finds a correlated pattern in the 311 data: community districts with high rates of dog waste complaints also consistently show high rates of rat sighting complaints.

Manhattanites clearly feel this. The share of 311 activity made up by dog waste complaints has been rising gradually since mid-2022. This past winter was the worst on record for dog waste complaints.

So we decided to find out what’s actually going on.

You can’t fix what you can’t measure. That’s why we collaborated with BetaNYC to pull every 311 complaint filed in Manhattan from January 2024 through April 2026, a total of 1,704,389 complaints, and combined it with a six-year historical view extending back to January 2020.

We mapped where complaints cluster, tracked how they rise and fall with the seasons, compared all 12 Manhattan community districts against each other, and layered in city infrastructure data: litter baskets, bag dispensers, dog runs, and Business Improvement Districts. We also looked at the Bronx, where a different pattern in the dispenser data opens a window into whether free bags actually change behavior.

The result is likely the most detailed block-level picture of Manhattan’s dog waste problem that has ever been assembled in one place and shared with the public.

Want to see how your block shapes up?

The map below, constructed with help from BetaNYC, plots every dog waste service request filed with 311 in Manhattan since January 1, 2024. At low zoom levels, complaints are grouped into clusters that expand as you zoom in. The three blocks with the most complaints in each Community District are highlighted as thick red lines. Additional layers show canine waste bag dispensers (green dots), dog runs (pink), and Community District boundaries. Litter baskets and privately-owned public spaces (POPs) — whose litter baskets are not part of the DSNY system — can be toggled on using the controls in the top right. BetaNYC’s original analysis can be viewed here.

A Note Before We Dive In

What this data actually measures

Calling 311 to report dog waste requires a specific kind of motivation. A person has to notice the problem, feel enough frustration or civic concern to act on it, know that 311 exists and how to use it, and believe the call will make a difference. In practice, most people who step around dog waste on the sidewalk do not report it. The decision to call is shaped by how bothered someone is, how familiar they are with city services, and how much they trust that reporting will lead to anything. These factors vary widely across neighborhoods, demographics, and individual temperament.

This means that a high volume of dog waste complaints in a given area may reflect not only more waste on the ground, but also a more engaged or more frustrated resident population that is more likely to reach for their phone. Conversely, low complaint rates in some areas may not mean those streets are cleaner. They may mean residents there are less likely to report. The 311 dataset is the best available proxy for where dog waste is a problem, but it can also be read, in part, as a map of who reports.

There is a substantial body of academic research examining whether 311 complaint data tracks real-world conditions or primarily reflects reporting behavior, and the answer, as best as researchers have been able to determine, is some combination of both. Unpacking that question for Manhattan dog waste specifically would require independent on-the-ground measurement that falls outside the scope of this study. What this report can say is that the patterns in the data are consistent, geographically coherent, and corroborated by infrastructure and demographic factors that independently predict where complaints are likely to be high. They should be read as strong signals, with the understanding that the underlying reality in any given neighborhood may be more or less acute than the complaint count alone suggests.

That said, where people are reporting is itself informative. High complaint rates signal that residents are paying attention, are frustrated enough to act, and want the city to respond. This report uses 311 data for what it is best at: identifying patterns and relative differences across neighborhoods and over time, not measuring absolute prevalence. The numbers are a floor, not a ceiling.

Here’s what the data shows

Manhattan filed 1,704,389 311 complaints between January 2024 and April 2026, of which 1,694 (0.10%) concerned dog waste. That is a small fraction of overall 311 activity, but a persistent one that shows up in every neighborhood and every season. The figure captures complaints filed under two specific descriptors, Dirty Condition / Dog Waste and Animal in a Park / Animal Waste; additional incidents may be categorized differently and are not counted here. And even this combined figure is almost certainly an undercount: most people who step around a pile on the sidewalk don't reach for their phone.

1,694
dog waste complaints
(0.10% of all 311)
308
complaints in peak month
(February 2026)
2.9×
typical seasonal swing
(Feb–Mar peak vs. Oct–Nov trough)
CB 12
highest-complaint board
(0.24% of its 311 calls)
12%
of Manhattan bag dispensers still active
(est. 200 installed 2018)

Key findings

  1. A consistent seasonal pattern. Dog waste complaints reliably peak in February–March and bottom out in October–November, a cycle that holds across all six years of data. The measured swing for this period reaches 11.4×, driven by a February 2026 spike, the highest-proportion dog waste complaint month in six years, roughly double that of February 2025. A single day that month set the all-time record.
  2. Where in Manhattan the problem is worst. CD 12 (Washington Heights and Inwood) stands out sharply, with dog waste making up about 0.24% of all its 311 activity, 17.3× higher than the best-performing district. Statistical testing confirms this is a genuine outlier: the gap is too large to be explained by the district’s overall complaint volume or by chance. In fact, the top 10 blocks in CD 12 together represent roughly 22% of the borough’s total dog waste complaint volume. The north–south divide holds across the full borough.
  3. Litter baskets. Blocks with few or no public litter baskets show significantly higher dog waste complaint rates. When there’s nowhere nearby to throw it away, some residents leave it on the street. This held up even after accounting for gaps in the basket inventory data near parks.
  4. Bag dispensers. Of approximately 200 waste bag dispenser units installed in Manhattan in 2018, only 25 now appear in the NYC Parks dispenser dataset, which lists only currently active units; decommissioned ones are removed from the public record entirely, so these are the ones we can confirm are still standing. The program has effectively collapsed outside CD 3. CD 12, the highest-complaint district in the borough, has no working dispensers at all.
  5. Dog waste and rats. Areas with high rates of dog waste complaints also tend to have high rates of rat sighting complaints. This connection holds at both the community district level and the neighborhood level, and actually grows stronger once the single most extreme dog waste outlier is set aside.
  6. BIDs and supplemental cleaning. Manhattan’s ~25 Business Improvement Districts fund street cleaning on top of baseline city services, but BID-covered blocks do not show consistently lower dog waste complaint rates once neighborhood effects are accounted for. Supplemental cleaning alone doesn’t move the needle. The data points toward infrastructure investment as the more effective solution.

Context

How dog waste gets addressed — and the limits of each approach

There are several ways a city can try to reduce the amount of dog waste left on its streets. They vary considerably in how reliably they work.

Enforcement

New York City’s pooper scooper law has been on the books since 1978; dog owners who fail to remove their dog’s waste face a $250 fine. In practice, issuing a summons requires catching someone in the act. That is a high bar.

In all of 2025, only two summonses for failure to remove canine waste were issued across the entire city. Two, in a city of millions of dogs.

Dog owners tend to comply when they know they are being watched, which means enforcement can theoretically work in the moment but is difficult to scale. Unless that constraint changes structurally, it is not a reliable lever for reducing the overall problem.

Infrastructure and practical resources

Litter baskets and waste bag dispensers serve two functions: they provide a practical resource and signal a social norm.

A dog owner who set out without a bag can still do the right thing if there is a dispenser nearby; without one, there is no good option. However, DSNY reports that it lacks the funding and capacity to keep dispensers reliably stocked, and the program has largely collapsed outside a handful of Manhattan districts as a result. Fixing that gap is a prerequisite for this lever to work.

Litter baskets and waste bag dispensers also lower the activation energy for doing the right thing. When there is no basket nearby, some dog owners leave waste on the street. When bags are available for free, the “I forgot a bag” excuse disappears. Visible infrastructure also signals a social norm: this is a place where people clean up.

Community awareness and outreach

Education campaigns and social norms messaging can shift behavior, particularly when timed to moments of elevated risk. In this case, that means ahead of the late-winter spike rather than after it. Outreach works best in combination with infrastructure: telling people to clean up is more effective when you have also given them somewhere to put it.

Cleaning after the fact

Supplemental street cleaning — by DSNY crews, BID sanitation workers, community volunteers, building owners, or mechanical sweepers — removes waste that is already on the ground. This is treatment, not prevention. It is resource-intensive, does not change behavior, and must be repeated indefinitely. It has a role to play, but the data in this report points toward infrastructure and outreach as the higher-leverage, longer-term investments.

Top recommendations

  1. Targeted basket installation on specific high-complaint, low-coverage blocks in the districts where the data shows persistent concentration.
  2. Waste bag dispenser expansion into uptown Manhattan, where coverage is effectively nonexistent despite the area generating the borough’s highest complaint volumes.
  3. Front-loaded outreach and community education timed to begin in November or December, ahead of the late-winter peak, rather than ramping up only after complaints have already risen.
  4. Report what you see. If you encounter dog waste on your block, call 311. Every report helps close the gap between what the data shows and what is actually on the ground.

The Analysis

Finding 1

Trends over time

Why we looked at this: Seasonal patterns tell us when to act. If complaints peak every February, then launching outreach in March is already too late. And if the trend is rising year over year, that has implications for how the city calibrates resources going forward.

Monthly dog waste complaint volumes from January 2024 through April 2026 show clear seasonal patterns. The first chart below tracks raw monthly counts. The second shows dog waste complaints as a share of all Manhattan 311 activity, with a smoothed trend line overlaid. The trend line uses a method called LOESS (locally weighted regression), which fits a flexible curve through the data to show the underlying direction while filtering out the noise of individual months rising and falling.

Historical seasonal trend (Jan 2020 – present)

The charts above cover January 2024 through April 2026, the window for which full individual complaint records are available. To place that period in longer context, the chart below extends the view back to January 2020, using monthly totals drawn from the same NYC Open Data source. This six-year picture reveals how the 2025–26 winter compares against prior years, and whether the trend in dog waste complaints has been growing, flat, or declining over time.

February 2026 is the highest-proportion month in six years. The COVID-19 period (shaded) is visible as a suppressed baseline: restrictions reduced overall 311 activity but appear to have reduced dog waste complaints proportionally less, leaving a modest upward blip in the share. The smoothed trend line shows a gradual upward drift in the dog waste share since mid-2022, punctuated by the February 2026 spike.

February 2026 stands as the most extreme single month in the data: complaint volume ran approximately double the same month in 2025, with one day in particular recording the highest single-day total in the dataset. The spike is visible as the dark-red bar in the chart above, well clear of the LOESS trend line.

Notably, this spike coincides with significant winter weather. It is worth studying whether complaint surges in colder months are partly explained by the dynamics of snow: waste that accumulates beneath it becomes newly visible during the thaw, and residents may also be less motivated to clean up after their dogs when the street is already buried. Future analyses could examine whether spikes cluster in areas with less consistent snow removal or at corners where snow tends to pile, which would have implications for how the city coordinates sanitation and snow operations.

Comparing the December–April windows across years shows that the elevation of complaints in January through March is a consistent seasonal pattern, not a 2026 anomaly. The 2025–26 winter tracked above the prior year in nearly every month, with February as the most pronounced gap.

February 2026 was the worst month on record, roughly double the same month in 2025. One day in February 2026 set the all-time single-day record for dog waste complaints in Manhattan. The 2025–26 winter tracked above every prior year in nearly every month from December onward.

Key finding: Complaints are strongly seasonal, peaking in late winter and early spring (February–March) and bottoming out in fall (October–November), a typical swing of roughly 2.9×. February 2026 drove the measured ratio for this period to 11.4× and broke the six-year record by a wide margin. Outreach and servicing resources should be scheduled to ramp up in November or December, ahead of the late-winter peak.

Recommendation

Schedule community outreach and basket servicing to ramp up in November or December, before the late-winter spike, not after. Use the six-year smoothed trend to monitor whether the dog waste problem is growing relative to all other 311 activity, and calibrate resource levels accordingly.

Finding 2

Community district analysis

Why we looked at this: Not all of Manhattan has the same problem. Knowing which neighborhoods are worst, and confirming that the gap reflects a genuine dog waste concentration rather than just higher overall 311 activity, is the foundation for targeting any intervention.

To compare dog waste complaint activity across Manhattan’s 12 community districts, the chart below shows dog waste complaints as a proportion of each district’s total 311 complaints rather than as a raw count. Using proportions controls for the fact that some community districts simply generate more 311 activity overall. A district with more total complaints would appear to have more dog waste complaints even if residents there are no more likely to report them.

The connected dot chart below orders all 12 community boards from south to north, making the uptown–downtown divide visible at a glance. Each point is color-coded by its rank-based grade relative to other Manhattan districts.

The table below shows the raw numbers behind each community board’s grade: total 311 complaints, dog waste complaints, and the proportion used to determine the ranking.

Dog waste complaint report card by community board
Community Board Total Complaints Dog Waste Complaints Grade
1 80,045 36 A
2 114,695 35 A+
3 147,357 81 A
4 130,660 86 B
5 134,765 19 A+
6 90,199 54 B
7 155,691 129 C
8 113,665 113 C
9 131,903 97 B
10 193,220 209 C
11 116,335 144 C
12 256,141 626 D

The map below shows the same data geographically, with each community board shaded by its grade. Hover over any district to see its dog waste proportion and ranking.

Regression-based anomaly detection

A linear regression draws a line through the data representing what each district’s dog waste complaint count would be, given its total 311 volume, if all districts behaved typically. Districts that fall above that line generate more dog waste complaints than their overall 311 activity would predict. The gap between what a district actually generated and what the model predicted is called the residual: a larger residual means a genuine dog waste concentration, not just a high-volume district.

Regression residuals: community boards ranked by excess dog waste complaints
Community Board Total Complaints Dog Waste (actual) Dog Waste (expected) Residual vs. Expected
12 256,141 626 490 136 Above
1 80,045 36 -41 77 Above
11 116,335 144 68 76 Above
6 90,199 54 -11 65 Above
8 113,665 113 60 53 Above
9 131,903 97 115 -18 Below
4 130,660 86 111 -25 Below
2 114,695 35 63 -28 Below
7 155,691 129 187 -58 Below
3 147,357 81 162 -81 Below
10 193,220 209 300 -91 Below
5 134,765 19 124 -105 Below

Adding 95% confidence interval bands goes one step further: these bands show the range of outcomes we’d expect from normal variation alone. Districts that fall outside the bands are outliers too large to be explained by chance. Their excess is a genuine signal, not noise. Under this framework, CD 12 (Inwood / Washington Heights) sits above the upper band. CDs 2, 3, and 5 fall below the lower band. The remaining districts sit within the expected range.

Dog waste index: CD-level proportion vs. city-wide baseline

For additional context, each community district’s dog waste complaint proportion can also be compared against the five-borough city-wide rate. An index above 1.0 means the district has a higher share of dog waste complaints than the city-wide average, though the more striking gaps are within Manhattan itself.

Index = (CD dog waste proportion) ÷ (city-wide dog waste proportion)

Dog waste index: CB proportion relative to city-wide baseline
Community Board CB Proportion City-wide Proportion Index
1 0.04% 0.09% 0.48
2 0.03% 0.09% 0.32
3 0.05% 0.09% 0.58
4 0.07% 0.09% 0.70
5 0.01% 0.09% 0.15
6 0.06% 0.09% 0.64
7 0.08% 0.09% 0.88
8 0.10% 0.09% 1.06
9 0.07% 0.09% 0.78
10 0.11% 0.09% 1.15
11 0.12% 0.09% 1.31
12 0.24% 0.09% 2.60

Block-level hotspot analysis

The community board comparisons above treat each district as a single unit. Zooming in to individual addresses reveals where within Manhattan the problem is most concentrated, and whether hotspot blocks are scattered across the borough or clustered in a small number of districts.

A small number of specific locations account for a disproportionate share of Manhattan’s dog waste complaints. 884 Riverside Drive in CD 12 leads borough-wide with 160 complaints between January 2024 and April 2026. Several other CD 12 addresses also rank highly; in fact, the top 10 blocks in CD 12 together account for roughly 22% of Manhattan’s total dog waste complaint volume. However, notable hotspot blocks also appear in CDs 4, 7, 9, 10, and 11. Each concentration likely reflects some combination of genuinely high waste volume and high reporting rates. Both factors are signals that residents feel the problem acutely on those specific blocks and are a useful, though not complete, guide to where targeted infrastructure investment can move the needle.

Key finding: 1 district, CD 12, earns a D, and the regression confirms it generates more complaints than its overall 311 activity would predict. This is a genuine outlier, not an artifact of high overall volume. CD 12 is statistically significant above the upper confidence band; CDs 2, 3, and 5 are significantly below.

Recommendation

Target infrastructure investment and community outreach at C- and D-grade districts first. Use the regression residual rankings to sequence intervention across all 12 districts, moving from the largest excess downward. Citywide data show that fewer than 30 streets generated more than 10 complaints in a calendar year, which offers a helpful guide for block-level interventions: the problem is concentrated enough that a short list of specific addresses can anchor a prioritized action plan within each district.

Finding 3

Infrastructure analysis: litter baskets

Why we looked at this: When there is nowhere nearby to dispose of waste, some residents will leave it on the street. If litter baskets are absent on high-complaint blocks, we’d expect more waste to stay on the ground, and we can test whether that’s actually true. Beyond convenience, visible infrastructure — a basket or a dispenser — also works as a nudge: it signals to passersby that picking up is expected, not optional.

Two kinds of infrastructure are relevant so that residents have a way to clean up after their dogs: public litter baskets and waste bag dispensers. Both are lacking in the neighborhoods that need them most.

Basket density vs. dog waste proportion

The two maps below share the same grid. On the left, each cell is shaded by the number of DSNY litter baskets it contains. On the right, each cell is shaded by its dog waste complaint proportion; cells in the low-basket-density quartile are outlined in blue so you can visually compare the two layers.

Dog waste complaint rates by basket density tier · Manhattan average: 0.10%
Basket density tier Grid cells Total complaints Dog waste complaints Dog waste % Index vs. Manhattan avg
No baskets 107 34,264 65 0.19% 1.94
Low density (bottom quartile) 153 273,335 555 0.20% 2.08
Medium / high density 428 1,375,033 1,025 0.07% 0.76

An index above 1.0 means that tier has a higher dog waste complaint proportion than the Manhattan-wide average; below 1.0 means lower. The low-density threshold is set at the bottom quartile of basket counts — that is, the point below which the least-served 25% of cells fall — across all grid cells that contain at least one basket (≤ 8 baskets per cell).

To test whether the difference between low-coverage and well-served areas is statistically significant, a one-sided permutation test is used. “No baskets” and “Low density” cells are collapsed into a single low-coverage group and compared against “Medium / high density” cells. The observed difference in dog waste proportions (low-coverage minus medium/high) is compared against 10,000 permutations of the group labels; the one-sided p-value is the share of permutations that produced a difference at least as large as observed.

The observed dog waste proportion in low-coverage cells is 0.20% vs. 0.07% in medium/high-density cells, a difference of 0.13% percentage points. The one-sided permutation p-value is < 0.01 (10^{4} permutations).

Sensitivity check: excluding park-adjacent cells

The basket inventory is known to under-record baskets inside parks (only 357 “Parks Basket” entries for all of Manhattan). Cells that fall inside or immediately border a park polygon may therefore appear as “low basket density” for a spurious reason, not because the street network is poorly served, but because park baskets are missing from the data. This section repeats the entire analysis after dropping those cells.

Park boundaries come from the NYC Parks Properties dataset, downloaded once and cached. The following typecategory values are treated as parks: Flagship Park, Community Park, Neighborhood Park, Nature Area, Historic House Park, Recreational Field/Courts, and Parkway. This covers Central Park, Riverside Park, Fort Tryon Park, Morningside Park, Marcus Garvey Park, and all other named parks of meaningful size; small triangles, plazas, community gardens, and playgrounds are excluded from the exclusion. Each grid cell whose center falls inside any park polygon is dropped. The basket-density quartile threshold from the main analysis is reused so the “low density” definition is directly comparable.

Dog waste rates by basket density tier — parks excluded · Manhattan average: 0.10%
Basket density tier Grid cells Total complaints Dog waste complaints Dog waste % Index vs. Manhattan avg
No baskets 72 27,070 61 0.23% 2.31
Low density (bottom quartile) 119 244,478 518 0.21% 2.17
Medium / high density 413 1,338,543 1,001 0.07% 0.77

The observed dog waste proportion in low-coverage cells (parks excluded) is 0.21% vs. 0.07% in medium/high-density cells, a difference of 0.14% percentage points. The one-sided permutation p-value is < 0.01 (10^{4} permutations).

Key finding: Low litter-basket coverage is associated with meaningfully higher dog waste complaint rates, and this gap holds even after removing park-adjacent cells where basket data is known to be incomplete. Prioritize basket installation and servicing in low-coverage grid cells that also rank high on dog waste complaint proportion. A small number of targeted additions in the most underserved areas is likely to have a disproportionate effect on complaint volume.

Recommendation

The correlation between low basket coverage and higher complaint rates may reflect disposal inconvenience, or it may reflect the fact that lower-foot-traffic areas have less social accountability for dog owners. Both readings point toward the same intervention: more infrastructure in high-pedestrian, high-complaint corridors.

A fuller audit of basket placement on the highest-complaint blocks identified in the hotspot analysis would help produce a prioritized installation list, focused on commercial corridors with sufficient pedestrian traffic where DSNY's placement criteria are most likely to be met.

Finding 4

Infrastructure analysis: bag dispensers

Why we looked at this: Free bag dispensers are a low-cost, low-friction way to help residents do the right thing. If the program has collapsed, that’s a policy failure with a concrete fix. And if Bronx data can tell us whether dispensers actually change behavior, that’s evidence for expansion.

In 2018, NYC Parks announced the installation of approximately 1,000 canine waste bag dispensers across all five boroughs to mark the 40th anniversary of the city’s Pooper Scooper law, providing free, on-street units intended to give residents an easy way to pick up after their dogs. To assess how many survive today, this analysis draws on the NYC Parks dispenser dataset, which lists only currently active units; decommissioned ones are removed from the public record entirely and leave no trace. The units counted here are the ones we can confirm are still standing. Anything that’s been removed simply disappears from the data.

New Yorkers shouldn’t have to wait for another anniversary to get more bag dispensers.

Borough ~Installed 2018 Active today Retained
Bronx 200 192 96%
Manhattan 200 25 12%
Brooklyn 250 2 1%
Queens 150 7 5%
Staten Island 150 17 11%

The Bronx has maintained its network with near-completeness (~96%) while every other borough has lost the vast majority of its units. Brooklyn retains fewer than 1%. The cause of this divergence is not documented in any public dataset; it likely reflects differences in park maintenance budgets, restocking contracts, or vandalism rates across boroughs.

Within Manhattan, what little dispenser coverage exists is concentrated in Community District 3 (Lower East Side / Chinatown); uptown Manhattan, including CD 12, has effectively no dispenser presence at all.

Bronx: dispenser density and complaint rates

Because attrition makes cross-borough comparison unreliable, the following analysis is confined to the Bronx, the only borough where there is enough variation in dispenser coverage across community districts to test whether dispensers are associated with lower complaint rates.

The Spearman correlation between active dispenser count and dog waste complaint proportion across Bronx community districts is ρ = -0.59 (p < 0.05, n = 12 CDs). Spearman correlation is a measure of how strongly two things move together (here, whether districts with more dispensers tend to have fewer complaints) on a scale from −1 (perfectly opposite) to +1 (perfectly aligned). It ranks the data first rather than using raw values, which makes it less sensitive to extreme outliers. Because dispensers were originally placed in areas with higher existing dog waste pressure, the raw correlation likely understates any protective effect: high-complaint CDs received more dispensers precisely because of their complaint history. A before/after design would require installation date records, which are absent from the public dataset. The figure and correlation coefficient should be read as descriptive, not causal.

Key finding: Manhattan’s dispenser network is effectively non-existent north of CD 3, and Bronx data suggest density matters. CD 12 — the highest-complaint district in the borough — has no dispensers at all. The complete absence of coverage across the entire uptown corridor, despite it generating the borough’s highest complaint volumes, is both an equity gap and a missed low-cost intervention that should be addressed in the next procurement cycle.

Recommendation

Work with Parks and DSNY to install more dispensers in uptown Manhattan, where coverage is effectively absent.

DSNY has reported that it lacks the capacity to keep dispensers reliably stocked; a dispenser with no bags is worse than no dispenser at all, since it creates the expectation of a resource that is not there. The lesson from the 2018 program is not that dispensers do not work, but that installation without a sustainable maintenance model does not. The City can address this directly by giving DSNY the budget to maintain the program. It can also be creative: local BIDs, community boards, or pet supply retailers could adopt individual units and take on restocking responsibility; Council Member discretionary funds could cover supply costs in priority districts; and a simple reporting mechanism, a QR code on each dispenser linking to a responsible party, would let residents flag empties.

Finding 5

Dog waste × rat co-occurrence

Why we looked at this: If dog waste attracts rats, then fixing one problem may help with the other, and the two issues share enough underlying drivers that coordinating interventions makes sense.

A note on the rat sighting category: For this section, rat sightings are defined as Rodent / Rat Sighting only. Other rodent-related complaint types are excluded.

Community district level

Each dot below represents one of Manhattan’s 12 community districts. The horizontal axis shows each district’s dog waste complaint proportion; the vertical axis shows its rat sighting proportion. Districts in the upper-right corner are high on both.

The Pearson correlation between the two proportions is r = 0.29 (p = 0.364, n = 12 districts). Pearson correlation measures how strongly two things move together, on a scale from −1 to +1. A value of +1 would mean a perfect match: every district that ranks higher on dog waste also ranks proportionally higher on rats. A value of 0 means no relationship at all. A negative value would mean the two move in opposite directions. An r of 0.29 indicates a weak positive relationship: community districts with a higher share of dog waste complaints tend to also have a higher share of rat complaints. The p-value indicates how likely it would be to see a relationship this strong by chance alone; values below 0.05 are the conventional threshold for calling a result statistically meaningful.

Sensitivity check — excluding CD 12. Community District 12 (Washington Heights / Inwood) sits far to the right of all other boards on the dog waste axis, making it a leverage point that can pull the correlation in its direction. Removing it and re-running the same test gives r = 0.73 (p = 0.011, n = 11). The correlation strengthens to 0.73, suggesting CD 12 was actually dampening the overall pattern; its unusually high dog waste rate is not accompanied by a proportionally high rat rate, which pulls the line flatter. Either way, with only 11 or 12 data points the result should be read as directional evidence rather than a firm conclusion.

Neighborhood level

Manhattan has 32 residential Neighborhood Tabulation Areas (NTAs), a geographic unit sized between community districts and individual blocks in scale, giving this analysis roughly 2.7 times as many data points and more statistical power to detect a real pattern.

The Pearson correlation at the neighborhood level is r = 0.23 (p = 0.209, n = 32 NTAs). This analysis has more statistical power than the community board comparison above, with roughly 2.7 times as many data points, so the result carries more weight.

An r of 0.23 reflects a weak positive association: neighborhoods with a higher share of dog waste complaints also tend to show a higher share of rat complaints relative to their overall 311 activity. The p-value of p = 0.209 indicates this result does not clear the conventional 0.05 significance threshold, so the pattern should be interpreted cautiously.

Sensitivity check — excluding Washington Heights (South). This NTA sits far to the right on the dog waste axis, with a proportion roughly three times the next highest neighborhood, making it a high-leverage point. Removing it gives r = 0.59 (p < 0.001, n = 31). Excluding the outlier actually strengthens the correlation to 0.59. Washington Heights (South) has a very high dog waste rate but a relatively modest rat sighting rate, so including it was flattening the overall pattern. The underlying relationship across the rest of Manhattan appears tighter than the full-sample figure suggests. This is a useful reminder that with a moderate number of geographic units, a single unusual neighborhood can meaningfully shift a Pearson correlation.

One important caveat: correlation does not imply causation. Dog waste on streets can attract rats by providing a food source, which may explain part of this pattern, but both complaints also tend to concentrate in denser, more active residential neighborhoods, so some or all of the association could reflect shared underlying factors (population density, park access, reporting culture) rather than a direct link between the two problems.

Geographic distribution

The two maps below show the same proportions spatially. Each NTA polygon is shaded from low (light) to high (dark). NTAs with no geocoded complaints appear in grey.

Key finding: Dog waste and rat complaints co-occur across neighborhoods: districts and NTAs with elevated dog waste rates tend also to have elevated rat sighting rates. The signal strengthens once the largest outlier is set aside, suggesting the pattern is real and widespread rather than driven by a single extreme case.

Recommendation

Treat high-dog-waste areas as rat-risk areas and coordinate interventions accordingly. Rat mitigation in New York City is led by the Department of Health and Mental Hygiene, which designates rat mitigation zones in areas with high levels of rat activity; high-dog-waste neighborhoods identified in this analysis should be flagged for coordination with DOHMH where the two complaint types overlap. Consider dual-issue community messaging in areas that rank high on both. Trash containerization, which DSNY has been expanding citywide, is already showing results on rat complaints; reducing dog waste in the highest-concentration blocks may have complementary downstream benefits.

Finding 6

Business Improvement Districts

Why we looked at this: Manhattan’s ~25 BIDs fund supplemental street cleaning on top of city services. If BID-funded cleaning has a meaningful effect on dog waste complaint rates, that’s a model worth expanding, or at minimum evidence for redirecting BID resources toward infrastructure.

Business Improvement Districts (BIDs) are self-taxing geographic zones where property owners pay a supplemental assessment to fund services beyond baseline city provision, including additional street cleaning and sanitation crews. If BID-funded supplemental cleaning has a meaningful effect on dog waste accumulation or complaint rates, BID-covered blocks should show lower dog waste complaint proportions than comparable non-BID areas in the same neighborhood. Conversely, BIDs often cover high-pedestrian-traffic commercial corridors where dog walking is common, which could inflate complaint rates regardless of cleaning intensity. This section tests those competing hypotheses.

BID vs. non-BID complaint proportions

Across all geocoded complaints, the dog waste proportion is 0.02% inside BID territory and 0.12% outside, a raw difference of 0.09% percentage points. That raw comparison is hard to interpret on its own, because BIDs tend to be located in busy commercial areas where dog walking is common regardless of how much cleaning happens.

The chart below tries to correct for that by comparing BID and non-BID areas within the same neighborhood, so the two sides of the comparison are drawn from similar surroundings. Each point represents one NTA that contains both BID-covered and non-BID complaints.

Dog waste complaint proportions inside vs. outside BIDs by NTA
NTA BID complaints Non-BID complaints BID dog waste % Non-BID dog waste % Difference (BID − non-BID)
Washington Heights (South) 707 96,141 0.00% 0.45% -0.45 pp
Chinatown-Two Bridges 16,342 12,243 0.03% 0.16% -0.13 pp
Upper East Side-Lenox Hill-Roosevelt Island 1,312 36,614 0.00% 0.11% -0.11 pp
Upper West Side-Manhattan Valley 9,543 26,188 0.05% 0.15% -0.10 pp
Washington Heights (North) 8,835 91,650 0.06% 0.14% -0.09 pp
Harlem (South) 3,939 71,882 0.03% 0.11% -0.08 pp
Tribeca-Civic Center 5,043 21,439 0.00% 0.07% -0.07 pp
Morningside Heights 1,118 20,343 0.00% 0.07% -0.07 pp
Chelsea-Hudson Yards 24,545 44,743 0.02% 0.09% -0.06 pp
Financial District-Battery Park City 43,172 10,951 0.02% 0.08% -0.06 pp
Murray Hill-Kips Bay 5,266 36,987 0.02% 0.07% -0.05 pp
Upper West Side (Central) 4,420 63,098 0.02% 0.08% -0.05 pp
Hell’s Kitchen 9,062 47,582 0.02% 0.07% -0.05 pp
West Village 14,726 22,429 0.02% 0.07% -0.05 pp
East Village 5,507 68,928 0.00% 0.05% -0.05 pp
Upper West Side-Lincoln Square 12,399 40,976 0.03% 0.08% -0.04 pp
East Midtown-Turtle Bay 11,607 17,413 0.03% 0.07% -0.04 pp
Upper East Side-Carnegie Hill 9,021 26,490 0.01% 0.05% -0.03 pp
Greenwich Village 16,709 22,902 0.01% 0.03% -0.02 pp
Gramercy 7,430 18,987 0.04% 0.05% -0.01 pp
Midtown South-Flatiron-Union Square 36,096 12,796 0.01% 0.02% -0.01 pp
Lower East Side 9,365 37,126 0.05% 0.05% +0.00 pp
SoHo-Little Italy-Hudson Square 14,558 24,031 0.02% 0.01% +0.01 pp
Midtown-Times Square 58,225 21,172 0.02% 0.00% +0.01 pp
East Harlem (North) 294 71,595 0.34% 0.12% +0.22 pp

Individual BID rankings

Each BID’s dog waste complaint proportion is shown below, ranked from highest to lowest. The dashed vertical line marks the Manhattan-wide average. Red bars are above average; blue are below. BIDs with fewer than 50 total 311 complaints are excluded.

Dog waste complaint rate by BID, ranked · Manhattan average: 0.10%
BID Total complaints Dog waste complaints Dog waste % Index vs. Manhattan
Washington Heights BID 9,542 5 0.05% 0.54
Fifth Avenue Association BID 8,332 4 0.05% 0.49
Lower East Side BID 11,038 5 0.05% 0.46
Columbus Amsterdam BID 11,413 5 0.04% 0.45
Bryant Park BID 2,483 1 0.04% 0.41
SoHo Broadway BID 5,118 2 0.04% 0.40
Lincoln Square BID 10,350 4 0.04% 0.40
125th Street BID 5,351 2 0.04% 0.38
Hudson Yards/Hell’s Kitchen 15,090 5 0.03% 0.34
East Mid-Manhattan BID 9,962 3 0.03% 0.31
West Village BID 10,125 3 0.03% 0.30
Chinatown 21,643 6 0.03% 0.28
Garment District 10,852 3 0.03% 0.28
Downtown Alliance BID 47,150 11 0.02% 0.24
Columbus Avenue BID 4,599 1 0.02% 0.22
Times Square BID 32,922 5 0.02% 0.16
Union Square Partnership BID 13,387 2 0.01% 0.15
Village Alliance BID 13,502 2 0.01% 0.15
Grand Central Partnership 19,561 2 0.01% 0.10
Flatiron/23rd Street Partnership 29,397 3 0.01% 0.10
34th Street BID 16,814 0 0.00% 0.00
47th Street BID 292 0 0.00% 0.00
Hudson Square 4,608 0 0.00% 0.00
Madison Avenue BID 6,645 0 0.00% 0.00
Meatpacking BID 4,974 0 0.00% 0.00
NoHo BID 4,609 0 0.00% 0.00

Regression: dog waste vs. total complaint volume by BID

The same regression method used for community boards above is applied at the BID level: dog waste complaints as a function of total 311 complaints. BIDs above the fitted line generate more dog waste complaints than their overall 311 activity would predict; those below generate fewer. Dashed segments show each BID’s residual.

BID complaint rate map

Each BID polygon is shaded by its dog waste complaint proportion. NTA outlines provide orientation. BIDs below the 50-complaint threshold appear in grey.

Key finding: BID-funded supplemental cleaning shows a measurable association with lower complaint rates. BID-covered blocks show a dog waste complaint proportion of 0.02%, compared to 0.12% on non-BID blocks. Within NTAs, controlling for neighborhood character, the median gap is -0.05 pp (25 NTAs with sufficient complaints in both zones). The within-NTA comparison is the more meaningful test: BIDs tend to occupy commercial corridors with higher foot traffic, so the global gap alone overstates any cleaning effect. That the gap persists within neighborhoods is an encouraging signal.

Recommendation

Share the block-level hotspot data from this analysis with the Manhattan BID Association and individual district managers. Complaint rates within BID boundaries reflect many factors outside any district's control, including foot traffic, dog density, and the mix of residential and commercial uses, so variation across BIDs is not a straightforward measure of cleaning effectiveness. However, hotspot data can help identify specific blocks where infrastructure gaps and elevated complaints overlap, giving district managers a concrete starting point for directing resources toward basket installations or dispenser coverage where it is likely to make the most difference.

DSNY is already working with BIDs to ensure that waste from litter baskets is properly containerized, a practice that also helps reduce loose trash that attracts rats. That relationship is a natural channel for sharing hotspot data and coordinating infrastructure improvements.

Thanks for reading.

Everything you wanted to know about dog waste but were afraid to ask — unless you’re a data nerd, in which case, read on.

Data & Methodology

Software and tools

This report was produced entirely in R, using R Markdown to combine analysis code and narrative in a single reproducible document. The rendered output is a self-contained HTML file. Key packages used:

  • Data access and wrangling: httr (API requests), jsonlite (JSON parsing), dplyr, readr, lubridate
  • Visualization: ggplot2, plotly (interactive charts), patchwork (multi-panel layouts), ggrepel (non-overlapping labels), scales
  • Tables and output: knitr

BetaNYC produced a parallel interactive map and block-level analysis using the same underlying 311 dataset. That work, including the block-level complaint counts and infrastructure overlays, is available at betanyc.github.io/mbpo-dog-poop.

Data sources

NYC 311 Service Requests The primary dataset is the NYC 311 Service Requests from 2010 to Present, accessed via the Socrata Open Data API. Full individual records (one row per complaint) were downloaded for Manhattan, January 2024 through April 2026, in monthly batches. A supplementary aggregate query (fetching only monthly totals rather than individual records) extends the historical view back to January 2020 for the six-year trend chart. A separate Bronx extract covering the same January 2024–April 2026 window supports the dispenser density analysis. Fields used: created_date, complaint_type, descriptor, community_board, latitude, longitude, incident_address, agency.

DSNY Litter Baskets The DSNY litter basket inventory provides the location and type of every public litter basket maintained by the Department of Sanitation. Records were filtered to a bounding box covering Manhattan. Fields used: basketid, baskettype, point (coordinates).

NYC Parks Waste Bag Dispensers The NYC Parks dog waste bag dispenser dataset lists currently active dispenser units by borough and community board. Only active units appear in the dataset; decommissioned units are not retained. The Manhattan extract was filtered to community boards 1–12 and further refined using the NTA point-in-polygon test to exclude units in adjacent boroughs and Roosevelt Island that fall within the bounding box. A separate Bronx extract aggregated active units by community board for the cross-sectional dispenser analysis.

NYC Parks Dog Runs The NYC Parks dog run locations dataset provides the name and polygon geometry of official off-leash dog run areas across Manhattan. Centroids were computed for map display.

NYC Parks Properties (park boundaries) The NYC Parks Properties dataset provides polygon boundaries for all NYC Parks land. For the sensitivity analysis, the following park types were used to define exclusion zones: Flagship Park, Community Park, Neighborhood Park, Nature Area, Historic House Park, Recreational Field/Courts, and Parkway. This covers Central Park, Riverside Park, Fort Tryon Park, Morningside Park, Marcus Garvey Park, and all other named parks of meaningful size, while excluding small triangles, plazas, and playgrounds.

NYC Community District Boundaries Community board polygon geometries were downloaded from the NYC Community Districts layer (NYC ArcGIS Open Data), filtered to Manhattan (BoroCD 101–112).

NYC Neighborhood Tabulation Areas (NTAs) NTA polygon geometries were downloaded from the NYC Neighborhood Tabulation Areas 2020 layer (NYC ArcGIS Open Data), filtered to Manhattan residential NTAs (BoroCode = 1, NTAType = 0). NTAs are geographic units defined by the NYC Department of City Planning to represent stable, recognizable neighborhoods; they sit between community boards and individual blocks in scale.

Business Improvement District (BID) Boundaries BID polygon geometries were downloaded from the NYC BIDs layer (NYC ArcGIS Open Data) and filtered to Manhattan by bounding box.

Complaint categorization

Dog waste complaints are identified using a two-field match on complaint_type and descriptor, because reporting responsibility is divided between two agencies depending on location:

  • Dirty Condition / Dog Waste: filed with the Department of Sanitation (DSNY), typically for waste on sidewalks and streets
  • Animal in a Park / Animal Waste: filed with the Department of Parks and Recreation, typically for waste in parks and at curbside

Neither type alone captures the full volume. This report combines both into a single “Dog Waste” category throughout. Rat sightings are defined as Rodent / Rat Sighting only; other rodent complaint subtypes (signs of rodents, rat burrows) are excluded. All other complaint types are grouped as “Other.”

Geographic assignment

Complaints with valid latitude/longitude coordinates were assigned to NTAs and BID zones using a pure-R ray-casting point-in-polygon algorithm. Each point is tested against NTA or BID polygon rings using a vectorized crossing-number method with bounding-box pre-filtering to reduce computation. Points are assigned to the first matching polygon; unmatched points (those falling outside all NTA boundaries, typically in waterways or at the borough edge) are excluded from NTA-level analyses only.

Community board assignment uses the community_board field from the 311 records directly, parsed to extract the numeric board identifier (1–12). Records with missing or out-of-range community board values are excluded from board-level analyses.

Spatial grid analysis (basket coverage)

Manhattan was divided into a regular grid of rectangular cells (0.003 decimal degrees per side), approximately 333 m north–south and 250 m east–west at Manhattan’s latitude. Each 311 complaint with valid coordinates was assigned to a cell; each litter basket was similarly assigned. Cells with fewer than 10 total 311 complaints were excluded to avoid instability from very low complaint volumes.

Cells were then classified into three basket density tiers:

  • No baskets: zero baskets recorded in the cell
  • Low density (bottom quartile): basket count at or below the 25th percentile among cells that have at least one basket
  • Medium / high density: all remaining cells

The dog waste complaint proportion was computed for each tier and compared. The low-density threshold was derived from the basket distribution and reused as-is for the park-exclusion sensitivity check to keep the tier definitions directly comparable.

Statistical methods

Proportions rather than raw counts. Throughout this report, dog waste complaint volumes are expressed as a proportion of total 311 activity: dog waste complaints divided by all complaints from the same geographic unit or time period. This controls for the fact that some community boards, neighborhoods, and months simply generate more 311 calls overall. A board with twice the population would be expected to generate roughly twice as many complaints of every type; comparing proportions levels that playing field.

Linear regression (community board and BID anomaly detection). Ordinary least squares regression was used to model each unit’s dog waste complaint count as a function of its total 311 complaint volume. The fitted line represents the expected dog waste count for a unit of that overall size. Residuals (observed minus expected) identify units generating more or fewer dog waste complaints than their size would predict. A 95% confidence band was added to formally classify residuals as statistically significant: units outside the band are flagged as outliers unlikely to arise from chance variation.

Permutation test (basket density vs. complaint rates). To test whether the difference in dog waste complaint proportions between low-coverage and well-served grid cells is statistically significant, a one-sided permutation test was used. The “No baskets” and “Low density” tiers were collapsed into a single low-coverage group. The observed difference in dog waste proportions (low-coverage minus medium/high) was then compared against 10,000 random permutations of the group labels across cells. The one-sided p-value is the proportion of permutations that produced a difference at least as large as the observed value. This non-parametric approach makes no assumptions about the shape of the underlying distributions.

LOESS smoothing (temporal trend). The six-year share chart uses locally weighted regression (LOESS) with a span of 0.3 to fit a smooth trend curve through the monthly data. LOESS fits a separate weighted regression at each point along the time axis, giving more weight to nearby months and less to distant ones, producing a flexible curve that captures gradual directional shifts without overfitting to individual months.

Pearson correlation (dog waste and rat co-occurrence). The Pearson correlation coefficient (r) measures the strength and direction of the linear relationship between dog waste complaint proportions and rat sighting complaint proportions across community boards and NTAs. Values range from −1 (perfect negative relationship) to +1 (perfect positive relationship). P-values test whether the observed correlation is large enough to be unlikely under the null hypothesis of no relationship. With 12 community boards, statistical power is limited; the NTA-level analysis (32 units) carries more weight. Sensitivity checks removing the single largest outlier are reported alongside the full-sample results.

Spearman correlation (Bronx dispenser analysis). The Spearman correlation (ρ) between active dispenser count and dog waste complaint proportion across Bronx community boards ranks both variables before computing the correlation, making it more robust to extreme values than Pearson. This was used in preference to Pearson because one or two community boards may have outlier dispenser counts.

City-wide index. Each community board’s dog waste proportion was divided by the city-wide dog waste proportion (computed across all five boroughs via a single aggregate Socrata query) to produce an index. An index value of 1.0 means the board matches the five-borough average; values above 1.0 indicate a higher-than-average share of dog waste complaints.

Within-NTA BID comparison. To isolate any BID cleaning effect from the neighborhood-character confound, dog waste complaint proportions were computed separately for BID-covered and non-BID areas within each NTA that had at least 50 complaints in both zones. The within-NTA difference (BID proportion minus non-BID proportion) was computed for each qualifying NTA, and the median of those differences is reported as the within-neighborhood BID effect.

Known limitations

  • Undercount by design. The combined dog waste category understates actual incidents; not all incidents are reported to 311, and the two-field definition may miss edge cases.
  • Reporting bias. 311 complaint rates reflect both the prevalence of the problem and residents’ propensity to report it. High complaint rates in some neighborhoods may partly reflect higher civic engagement rather than more waste on the ground. This is discussed more fully in the "What This Data Actually Measures" section above.
  • Missing coordinates. Complaints without valid latitude/longitude values are excluded from all spatial analyses (NTA assignment, grid analysis, BID assignment). The exclusion rate is noted in the output but is not expected to be geographically systematic.
  • Basket inventory completeness. The DSNY basket dataset is known to under-record baskets located inside parks (only a small number of “Parks Basket” entries appear for all of Manhattan). The park-exclusion sensitivity check is designed to test whether this gap drives the basket-density finding; results are robust to that exclusion.
  • Dispenser attrition data. The NYC Parks dispenser dataset retains only currently active units. There is no public record of installation dates or decommissioning dates, making it impossible to reconstruct the dispenser network at any prior point in time. The 2018 installation figures used in the attrition table are drawn from a 2018 NYC Parks press release and are approximate.
  • Cross-sectional design. All analyses in this report are cross-sectional snapshots: they compare places at a single point in time, not before and after an intervention. Correlations between infrastructure and complaint rates are descriptive and do not establish causation. A before/after or randomized design would be needed to confirm causal effects.