Did you know that 1 in 5 New Yorkers is over the age of 60? That’s over 1.8 million people, a number that’s growing year by year.

As NYC’s older adult population increases, so does the need for services that support them—things like elder care, transportation, meals, and mental health resources. But here’s the big question: Where do older adults live, and do they have access to the services they need?

To answer this, I explored multiple datasets to map:

1. Where older adults live in NYC.
2. The types and locations of aging-related services.
3. Whether these services align with neighborhoods where older adults reside.

Dataset and Variables

To answer my research question, I used three key datasets:

1. 2020 NYC Census Data
   • Source: The Department of City Planning (DCP)
   • Collection Details: Data collected through the 2020 Census provides demographic details for neighborhoods across New York City.
   • Variables Used:
     • Population by age: Aggregated population data for individuals aged 60 to 85+ in each neighborhood.
     • Households Headed by Individuals Aged 65+ Living Alone: An indicator of potential social isolation among older adults.
2. Older Adult Contract Provider Data
   • Source: NYC Department for the Aging (DFTA) via NYC Open Data.
   • Collection Details: Data on aging-related services provided under contracts managed by the DFTA, distributed across NYC.
   • Variables Used:
     • Longitude & Latitude: Geographic locations of services.
     • Service types: Categories of services, like Older Adult Centers or mental health support.
     • Program Name: Names of the service providers.
3. 2020 Neighborhood Tabulation Areas (NTAs) Shapefile
   • Source: The Department of City Planning (DCP)
   • Collection Details: NTAs are medium-sized geographic areas created by combining census tracts.
   • Variables Used:
     • NTA Name: Names of the Neighborhood Tabulation Areas.

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1. Where Do NYC’s Older Adults Live?

To start, I mapped NYC’s older adult population. The visualization highlights:

• Households headed by individuals aged 65+ living alone (orange circles).
• Higher percentages of adults aged 60 and over (darker shading).

Out of 272 neighborhoods, 12 neighborhoods have older adults making up over 30% of the population. Nearly half of NYC’s neighborhoods have populations where older adults account for over 20%.

Take East Midtown-Turtle Bay, for example:

• Older adults make up 30.3% of the population—about 14,000 residents.
• Over 5,143 households are headed by individuals aged 65+ living alone.

This raised an important question: Do neighborhoods with high numbers of older adults living alone have enough services to support them?

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2. Mapping Aging Services Across NYC Neighborhoods

Next, I mapped the distribution of aging services across the city. The map shows:

• Colored dots representing Service types. You can filter it by clicking service at the legand.
• Darker blue areas for neighborhoods with more services.

The concentration of resources doesn’t always align with the population distribution.

• In East Midtown-Turtle Bay, where older adults make up over 30% of the population, there is only one service.
• In contrast, Williamsburg, with less than 10% older adults, has two services, revealing a clear geographic imbalance.

I also noticed that Older Adult Centers dominate the map, making me wonder if certain types of services are more available than others.

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3. What Types of Services Are Available?

To answer that, I created a tree map showing the breakdown of aging services by type.

Older Adult Centers dominate, with 312 services across the city. But services like Elder Abuse & Crime Support (8 total) and Geriatric Mental Health Care (6 total) are underrepresented.

This gap suggests that while general support services are widespread, specialized services for the most vulnerable are lacking.

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4. Comparing Resources Across NYC’s Boroughs

To explore borough-level disparities, I compared the total number of services relative to the older adult population. This pie chart breaks down service counts by borough. Hover over any section to see detailed population figures, including the total count and percentage of older adults in that borough.

Brooklyn has the most services (141 total), but its large population means there’s 1 resource per 3,207 older adults.

Staten Island has the fewest services—just 18 total—resulting in 1 resource for every 4,271 older adults.

These findings raise questions about borough-specific challenges. For instance, are transportation barriers limiting access to services in Staten Island?

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5. Do Resources Align with Population Needs?

Finally, I mapped neighborhoods by the percentage of older adults and overlaid pie charts to show the numbers and types of services available.

In neighborhoods like Canarsie, older adults make up 22.6% of the population but have just one service.

Interestingly, many services are located in areas with higher numbers of households headed by individuals aged 65+ living alone, suggesting a focus on neighborhoods with greater isolation risks. Still, gaps remain in areas like Canarsie, where needs might be unmet.

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Key Takeaways

This analysis revealed some clear patterns—and some critical gaps:

1. Where older adults live
   Nearly half of NYC’s neighborhoods have populations where older adults make up more than 20%.
   
2. Resource imbalance
   Aging services are concentrated in certain areas, leaving neighborhoods like East Midtown-Turtle Bay and Canarsie underserved.
   
3. Service gaps
   General services dominate, while specialized services remain limited.
   
4. Borough disparities
   Staten Island faces the greatest challenges in resource availability.

What’s Missing and Where to Go Next?

While this analysis offers valuable insights, it’s just the beginning. There are still unanswered questions and areas to explore further.

1. Limitations and Biases
   The data tells us where services are located and what they offer, but it doesn’t show their quality or capacity. A single center might be overwhelmed, while another is underutilized.
   
   Access isn’t just about proximity—income, transportation, and awareness all play a big role. And since the datasets are from 2020, they don’t reflect recent changes, like new services or shifts in population needs.
   
2. Future Directions
   To build on this work:
   • Survey older adults to understand their real-life challenges and what’s missing.
   • Analyze service usage to find out which areas are struggling to meet demand.
   • Incorporate socioeconomic data to reveal barriers like income or transportation.
   • Update data regularly to keep up with NYC’s evolving needs and population trends.

These steps could help create a more accurate and actionable roadmap for supporting NYC’s older adults.

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NYC’s older adult population is growing, and ensuring equitable access to services is more critical than ever. This analysis reveals key areas for improvement, including increasing specialized services, addressing borough-level disparities, and prioritizing underserved neighborhoods.

Curious about your neighborhood? Explore the full visualization to see where older adults live and what resources are available nearby:

Over the past few weeks, I’ve been grappling with sleep disruptions, staying up late despite my attempts to stick to a regular schedule. Inspired by a TED-Ed video, “Can You Change Your Sleep Schedule?,” I began to wonder if my irregular sleep patterns could be influenced by a lack of sunlight. The video highlighted the importance of the circadian system, our body’s internal clock, which relies on sunlight to regulate sleep and wakefulness. Research suggests that daily exposure to natural light for at least 20-30 minutes can be particularly effective in supporting sleep quality.

This prompted me to take a closer look at my own habits and investigate whether deliberate sun breaks throughout the day could improve my sleep. I hoped these daily moments of natural light would help me establish a more consistent sleep pattern and reduce nighttime disruptions. My goal with this small project was to discover if something as simple as sun exposure could positively impact my overall rest.

Research Question: How does outdoor sun exposure during the day influence sleep duration and quality at night?

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Data Collection Method & Variables

To investigate this question,I collected data on sun exposure and sleep patterns over a two-week period. Each entry in my dataset represents either a sun break taken during the day or a sleep session logged from the previous night. I manually tracked my sun breaks, while my sleep metrics—like total sleep time and disruptions—were recorded with a sleep tracking device.

• Sun Break Data: I logged the duration of each sun break, which body parts were exposed (e.g., face, arms), the intensity of sunlight (how warm or energizing it felt), weather conditions, and the UV index. I also tracked environmental sounds, such as traffic noise, airplane sounds, car honking, and sirens. This gave me a fuller picture of my exposure to both natural and urban elements during each sun break.
• Sleep Data: I used my sleep tracking device to capture total sleep duration and awake time (minutes spent awake during the night), which served as a proxy for sleep quality. Together, these metrics allowed me to examine whether there was any connection between sun exposure during the day and my sleep patterns at night.

After collecting data for a week, I realized that analyzing sounds would require a melted dataset. Originally, each type of sound had its own column (e.g., bus, airplane, honking, sirens), making it difficult to compare across sound types. By melting the data into two columns—Sound Type and Sound Count—I was able to treat each sound as a single variable. This restructuring made it easier to compare the frequency of different sound types in Tableau, allowing me to explore whether certain sounds were more common during sun breaks and how they might affect my overall experience.

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A Three-Week Sleep Pattern Overview

The first chart I created tracked my sleep duration across three weeks, with key events annotated. This view provided a broad sense of how my sleep fluctuated over time and allowed me to see if specific factors, like the start of sun breaks or life disruptions, had any notable effects.

• Observations: My sleep duration ranged from around 4 hours to over 8 hours on different nights. Key events included the start of my sun breaks, an interruption when I picked up family from the airport at 2 a.m., and the day I began new medication.

This initial analysis hinted at a potential benefit from regular sun exposure, though it was clear that many factors influenced my sleep, making it difficult to isolate the effects of sun breaks alone in this short timeframe.

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Comparing Sleep Quality Before and After Sun Breaks

To better understand the impact of my sun breaks, I compared my average sleep duration and disturbances before and after starting these daily outdoor breaks.

• Observations: The average sleep duration increased slightly, from 6.5 hours before sun breaks to 6.7 hours after. But there was also a increase in time spent awake during sleep. However, I also noticed a small increase in the time spent awake during sleep, suggesting that while sun breaks might support sleep duration, other factors like evening routines still impacted sleep quality.

This section reinforced the potential of natural light exposure to support better sleep, but it also highlighted the influence of many external factors on my overall sleep quality.

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Exploring UV Intensity and Sleep Disturbances

To see if sunlight intensity had any effect on sleep quality, I looked at the relationship between UV index and awake time during sleep.

• Observations: Days with a higher UV index generally corresponded with fewer disturbances (i.e., less awake time during sleep). I also tended to take longer sun breaks on these high-UV days, likely because they felt warmer and more inviting.

This insight was intriguing but subtle, indicating a need for further study if I want to validate the connection between sunlight intensity and sleep disturbances.

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Fun Fact: A Look at Environmental Sounds

During my sun breaks, I also tracked the types of sounds around me. Listening to traffic noise, airplane sounds, honking, and sirens added a unique layer to each break. Tracking sounds helped me stay present and engaged with my surroundings instead of scrolling through my phone. While the sounds themselves didn’t appear to directly impact my sleep, noticing them brought a mindfulness element to my sun breaks, making these outdoor moments more intentional and refreshing.

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Conclusion

Tracking my sun exposure and sleep patterns over two weeks provided some interesting insights, but it also highlighted the complexity of trying to influence sleep quality through a single lifestyle adjustment.

1. Sun Breaks Might Help Stabilize Sleep: Regular exposure to sunlight showed a small positive effect on my sleep duration and disturbances, though more time and data would be needed to draw definitive conclusions.
2. Intensity Matters: Higher UV days seemed particularly beneficial, suggesting that the quality of sunlight (not just the quantity) may play a role in sleep quality.
3. Mindful Breaks Add Value: Tracking environmental sounds helped me stay present during sun breaks, making these outdoor moments more intentional and refreshing. While not directly impacting sleep, this awareness seemed to contribute positively to my overall routine.

Reflection

With just a few weeks of data, it’s hard to say if sun breaks alone improved my sleep. The small changes hint at some benefits, but many factors—like stress, diet, and routine—affect sleep quality. This project showed me how small, intentional habits can add up. Even if effects are gradual, paying attention to daily patterns can reveal valuable insights for long-term well-being. This experiment was a great opportunity to reflect on my habits, and I plan to keep tracking these elements to see if stronger patterns emerge over time.

New York City has a long legal history in the fight against rats, with efforts to control the rodent population stretching back over a century, as highlighted in the city’s legal history of rat control. The city has implemented various measures, including new regulations from Mayor Eric Adams and DSNY Commissioner Jessica Tisch, such as a trashcontainerization strategy and even appointing a “rat czar”. But how big is the problem, when are New Yorkers most likely to encounter these unwelcome guests, and where are rats reported the most?

Thanks to NYC’s open data, we can analyze 311 rodent complaint data to uncover trends and patterns. In this post, I’ll use visualizations to explore when and where New Yorkers report rat activity, helping us better understand this ongoing issue.

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1. Rodent Complaints Over Time: Analyzing Trends by Year

To start, let’s take a look at how rodent complaints have trended over the years. Using NYC’s 311 call data, I tracked the number of rodent-related complaints from 2010 to 2023. The visualization below shows a significant upward trend, with rodent complaints steadily increasing over time.

In 2023, we see the highest number of complaints on record—over 49,000 calls, nearly double the numbers from 2010. Interestingly, we see a temporary dip in complaints during 2020, likely linked to the COVID-19 lockdown when reduced human activity in the city during the pandemic may have affected both rodent activity and reporting behaviors. As residents returned to normal routines post-pandemic, complaints spiked once again.

Exploring Trends by Borough

By grouping the data by borough, we can uncover how the rodent problem varies across different parts of the city. The second visualization shows the trend of rodent complaints by borough, highlighting which areas have experienced the most significant increases in recent years.

Looking at rodent complaints by borough, Brooklyn stands out with the highest and sharpest increase in complaints over the past decade. By 2023, Brooklyn recorded nearly 18,000 complaints—more than doubling its numbers from 2010. Manhattan also shows a significant rise, though at a slightly slower rate, while Queens has steadily climbed, particularly after 2021. Interestingly, the Bronx experienced a noticeable decline in complaints since 2021, suggesting some improvement in rodent control efforts there. In contrast, Staten Island has consistently reported the fewest rodent complaints, with little fluctuation over the years. This variation between boroughs highlights the localized nature of NYC’s rodent problem, potentially influenced by differing sanitation practices and urban density.

Revealing Seasonal Patterns

Finally, changing the time bucket to a monthly view reveals seasonal trends. The third visualization highlights how rodent complaints peak during the summer months and dip during the winter. Warmer weather typically increases rodent activity, leading to more sightings and complaints, while colder temperatures tend to reduce rodent movement.

By adjusting the time scale and grouping the data by different factors, we can reveal new insights—such as the impact of seasonality and geographic variation—on the rodent problem in NYC.

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2. Where Do Rodent Complaints Occur?

Understanding where rodent complaints are made is just as important as understanding when they are made. Grouping the data by location type reveals valuable insights into where New Yorkers are most likely to encounter rodent issues. The treemap below breaks down 311 calls based on the location of the sightings.

As the treemap shows, a significant 73% of rodent complaints originate from residential areas. This does not necessarily indicate that rodents are more common in residential spaces, but rather suggests a potential reporting bias. Homeowners and renters are more likely to report rodents because the presence of pests has a direct and immediate impact on their living conditions. In contrast, people may be less inclined to report rodents in public spaces, such as the subway or parks, where they do not feel personally responsible for taking action.

The next largest categories of complaints come from food-related spaces, including restaurants and grocery stores, followed by non-residential areas, such as offices and commercial buildings. Complaints from vacant spaces and construction sites also make up a notable portion of the total, pointing to the potential for rodents to thrive in these less maintained environments.

Breaking down this data further by borough reveals additional patterns:

As the bar chart shows, Brooklyn leads with the highest number of rodent complaints across all location types, particularly in residential and vacant spaces. This could indicate both a higher rodent population in Brooklyn and a greater tendency of residents to report sightings.

In Manhattan, the majority of complaints also stem from residential areas, but the borough stands out for a larger proportion of reports from non-residential and food-related spaces, reflecting the commercial and dense urban nature of the area.

Meanwhile, the Bronx shows a particularly high percentage of complaints from residential areas, reinforcing the trend that homeowners and renters are more likely to report rodent activity.

Queens and Staten Island show fewer complaints overall, but a similar pattern emerges: most reports are made in residential areas, with some activity in food-related spaces and construction sites.

This distribution highlights the need for targeted rodent control efforts, particularly in residential neighborhoods and food establishments. It also raises important questions about the management of waste and hygiene in public and commercial spaces across different boroughs.

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3. When Do New Yorkers Report Rodent Complaints the Most?

After examining when and where rodent complaints occur across the city, I wanted to uncover more detailed insights by focusing specifically on residential areas and narrowing the scope to a recent one-year period from October 2023 to September 2024. This approach helps reveal more precise patterns that indicate how frequently residents encounter and report rodents.

To achieve this, I grouped the data by time of day and month, adjusting the time bucket to better understand when New Yorkers are most likely to report rodent activity in their homes. The heatmap below shows the density of rodent-related 311 calls across each hour and month within this focused time period.

Several patterns emerge:

• Time of Day: The majority of complaints are made between 9 AM and 3 PM, with a clear peak around 9 AM to 12 PM. This suggests that rodent sightings are often reported during daylight hours, possibly as people go about their morning routines and work commutes.
• Month: Complaints are highest during the warmer months, particularly from June through September, possibly due to increased rodent activity as food sources become more available.

This grouped data provides a clearer picture of when New Yorkers are most likely to encounter rodents and make a report. Now, let’s dive deeper by examining the same data for individual boroughs to see how these trends vary.

Manhattan

In Manhattan’s residential areas, rodent complaints peak at different times, especially during the summer months of May and July, with reports spreading from 9 AM to 9 PM. 

Brooklyn

Brooklyn, the borough with the most reported rodent sightings, shows a pattern similar to the overall city-wide trend, with complaints peaking between 9 AM and 10 PM during the warmer months of the year. 

Bronx

The Bronx has a broader seasonal spread, with rodent complaints peaking from February to October, focused mainly in the morning hours between 9 AM and 11 AM. 

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Conclusion

What We Learned

From the analysis of NYC’s 311 rodent complaint data, several important patterns and trends emerge.

1. Steady Increase in Rodent Complaints Over Time: The overall number of complaints has grown significantly since 2010, peaking in 2023 with over 49,000 calls, nearly doubling the complaints from a decade earlier. The temporary dip in 2020 during the COVID-19 lockdown suggests that reduced human activity might have influenced both rodent behavior and reporting patterns. However, complaints spiked again as the city returned to its normal pace.
2. Borough-Specific Trends: Brooklyn stands out as the borough with the most rodent complaints, especially in residential areas and vacant spaces. Manhattan shows a larger proportion of complaints in commercial and food-related spaces, which reflects the borough’s dense urban environment. Interestingly, the Bronx has seen a decline in complaints since 2021, while Queens and Staten Island report fewer overall complaints but still follow the trend of higher activity in residential areas.
3. Seasonal Peaks and Time of Day: Rodent complaints peak during the summer months (June to September), with significantly lower activity in the colder winter months. The majority of reports are made between 9 AM and 3 PM, suggesting that residents encounter rodents during their daily routines, particularly in the morning.
4. Residential Areas Dominate Reports: Across all boroughs, 73% of complaints come from residential areas, indicating a potential reporting bias. Homeowners and renters are more likely to report rodents due to the direct impact on their living conditions, while sightings in public spaces may be underreported.

Limitations of the Data

While the 311 rodent complaint data provides valuable insights, there are a few key limitations to consider:

1. Reporting Bias: The dataset reflects where complaints are reported, not necessarily where rodents are most active. Residential areas dominate the reports, but this does not mean that rodents are only found in these locations. Public spaces, like subways and parks, may have high rodent activity but fewer reports due to a lack of personal responsibility to file a complaint.
2. Lack of Population Data: The data does not provide information on the actual rodent population. Complaints are a reflection of human sightings and reports, but without population data, we cannot accurately measure the scale of the rodent problem in each area.
3. Varying Levels of Public Awareness: The number of complaints could also be influenced by differing levels of awareness and access to 311 services across neighborhoods. Some residents may be more proactive in reporting, while others may be unaware or indifferent to the reporting process.
4. Effect of New Policies: While the city has recently implemented new rodent control measures, such as the trash containerization strategy, it is still too early to determine the full effect of these policies on rodent activity and complaint numbers. A longer-term analysis would be needed to assess whether these initiatives have made a measurable impact.

By understanding these limitations, we can better interpret the trends revealed by the data and recognize areas where further study or additional data may be necessary to fully grasp the extent of the rodent problem in New York City.

Next Steps

The next step for my analysis is to continue monitoring the impact of new policies and refine the data by zooming into ZIP code-level trends. This will help me gain a more localized understanding of rodent activity and assess how recent initiatives are affecting different neighborhoods.

Here’s how I plan to approach this:

• Track complaint trends by ZIP code to identify where rodent activity is most concentrated and where improvements are happening.
• Compare pre- and post-policy data to evaluate the effectiveness of new measures like trash containerization.
• Examine ZIP codes with consistent declines in complaints to uncover successful interventions or environmental factors.
• Incorporate additional data points like neighborhood density and socioeconomic factors to better understand the drivers of rodent activity.

By focusing on these steps, I’ll be able to refine my understanding of where rodent control efforts are most needed and where progress is being made.