Sidewalk Crowding in the Age of COVID-19

Using Data to Inform the Reopening of New York City

The novel coronavirus has fundamentally changed how we inhabit urban space. Sidewalks have become hosts to a variety of new activities: seating for restaurants and cafes, spaces for queuing to enter buildings, and retail pick-ups/drop-offs, among others. In addition to continuing to provide space for pedestrian circulation and other pre-COVID19 uses, municipal governments must now make room for these curbside newcomers and do so in accordance with social-distancing and other public safety protocols.

NYC outdoor seating regulations for restaurants. [Source: New York Times]

In a city like New York, which is already known for dense sidewalks, safely adapting to new sidewalk conditions raises pressing questions. Where is there not enough sidewalk space to accommodate these new activities? In a city as large as NYC, how can the city and community groups logically and comprehensively plan and design for a safe reopening through the pedestrianization of streets, sidewalk expansions, and other novel adaptations of public space? Enter data!

Sidewalk Density Web Tool [Source: KPFui]

We created a set of web tools that helps everyone understand sidewalk population density in order to aid NYC decision makers in the immense challenge of reopening the city in an effective, safe, and equitable way. These tools leverage detailed datasets and simulate new, COVID-era sidewalk uses. They aim to empower the city and community groups to make decisions to reduce the risk of sidewalk crowding in New York City more rapidly and with a higher degree of confidence.

The suite is comprised of two primary tools — a City-Wide tool that highlights areas where high levels of sidewalk crowding are likely and a Neighborhood tool that uses routing analysis to locate specific street segments that are within high instances of pedestrian, movement, queuing, and business activity.

In the city-wide analysis, constituent datasets are weighted on the fly and can be done so to model different real-world scenarios. For instance, we can simulate Phase 2 (displayed here) reopening by reducing the weights for pre-COVID job population, office buildings, etc. and increasing the weights for residential population and restaurants. [Source: KPFui]

City-Wide Tool

The City-Wide Tool (https://sidewalk.kpfui.dev/) determines which areas of New York City are most likely to have high levels of sidewalk crowding. The tool aggregates a variety of constituent datasets — ranging from resident and job population figures, business locations, and building information — into a composite score of square feet of sidewalk per pedestrian.

The different constituent datasets that were incorporated into the analysis include resident and job population figures, business locations, and building use and size, among others. [Source: KPFui]

Given the fluid and complex nature of cities, the varying priorities of different NYC agencies and community groups, as well as the unpredictable nature of the novel coronavirus, creating a model that could be adjusted and calibrated in real time was essential. The utility of the dynamic web map we created is clear when simulating different scenarios — weekday and weekend peak hours, for instance.

Maps of weekday (left) and weekend (right) peak-hour sidewalk stress make clear the difference in where people are out on NYC sidewalks (and for what reasons). Notice the concentration of sidewalk stress in Midtown Manhattan and other business districts during weekday peak-hours and the more dispersed distribution of sidewalk stress throughout NYC with significantly more people out in the outer boroughs (closer to their homes). Our web-based dynamic model allows users to adjust weights of the different component data to quickly and intuitively simulate various conditions. [Source: KPFui]

Sidewalks become crowded for many different reasons — hordes of shoppers, people heading home, or a lack of sidewalk space, for example. Clustering the most stressed grid cells allowed us to better understand the factors that were most important in sidewalk crowding in our analysis. The clustering algorithm groups areas of the city together based on similar reasons for crowding: Midtown and FiDi score high in offices and working population, the Upper East Side and Flatbush experience crowding because of large residential populations, etc.

When clustering based on restaurant and retail density, four typologies appear. Each of these typologies is characterized by distinct restaurant and retail conditions, which exist across the city. [Source: KPFui]

Neighborhood Tool

From the city-wide tool, we identify neighborhoods with high sidewalk crowding and zoom in for detailed study of pedestrian density along sidewalks at different times of day relative to the effective width of each sidewalk (https://neighborhoods.kpfui.dev/). It does so by simulating pedestrian traffic to local destinations — transit, businesses, etc. — and include both moving and standing pedestrians. Some wide sidewalks might still not have enough space given business and pedestrian flow, whereas some narrow sidewalks might be so lightly trafficked that they do not experience much stress. This tool is a resource for identifying hotspots within the neighborhood where large number of pedestrians congregate and the demand for more sidewalk space is high.

The simulation shows the approximate amount of pedestrian queuing and walking to these businesses. This analysis was generated using urbano.io, a mobility modeling tool developed by the Environmental Systems Lab at Cornell University. It is seriously cool — check it out. [Source: KPFui]

Notes from under the hood..

(An assortment of interesting tidbits from the analysis)

Incorporating Sidewalk Area Given that many regulations aimed at mitigating the spread of COVID-19 are explicitly tied to spatial thresholds/indicators, knowing the amount of sidewalk space per pedestrian throughout the city is a powerful resource for decision makers and is thus what this suite of tools seeks to quantify. Given the variation in sidewalk space throughout New York City, plotting the constituent and composite data relative to sidewalk area produced unexpected and informative findings.

The map of residential population (left) looks as we would expect, with large concentrations of population in Manhattan and select locations in the outer boroughs. However, when we account for sidewalk area, many areas with strictly high population become less relevant, and acute hotspots throughout the city become visible. [Source: KPFui]

Establishing ratios and rules of thumb for these calculations proved to be a challenging aspect of the project. However, having a resultant figure that could be easily understood in a spatial sense (square feet of sidewalk per pedestrian) allowed us to adjust these calculations to compensate for values that were much too small (pre-COVID results of 2 square feet of sidewalk per person, for instance). [Source: KPFui]

Sidewalk Stress in the Outer Boroughs Outside of obvious hot spots in mid and lower Manhattan, the analysis in a Phase 2 simulation reveals distinct hotspots in the outer boroughs. These include Co-op City and parts of Tremont in the Bronx, Jackson Heights and Flushing in Queens, and Flatbush and Sunset Park in Brooklyn.

Co-op city (web map on the left and satellite imagery on the right) in the Bronx emerges as a distinct hot spot in the simulated Phase 2 analysis. [Source: KPFui and Google Earth]

Office Workers on the Sidewalk The clustering mode can be used to quickly identify neighborhoods that share similar levels of sidewalk stress. If the tool for 100% occupancy for workers in retail, restaurants, and office businesses and 50% occupancy for residents and the results are clustered — Midtown Manhattan shares similar characteristics with Flushing, Queens. Both areas are simulated to have a high levels of worker-generated sidewalk stress. Perhaps solutions implemented in Midtown could also improve conditions in Flushing.

Areas in yellow also have the lowest number of residents and highest number of office workers affecting sidewalk stress. [Source: KPFui]

Neighborhood Pedestrian Pattern The neighborhood analysis simulates the travel demands of three busiest times of day — morning rush hour, lunch time, and evening rush hour. Taking the aggregated pedestrian travel demand from Google Places data, the analysis model simulates pedestrians commuting or visiting businesses per sidewalk. The tool further breaks down the category of pedestrians on each street to understand what activities contributes to the most sidewalk stress. In Jackson heights during lunch time, we see the high pedestrian traffic to restaurants along commercial streets, attributing to its sidewalk stress. This information helps inform decisions about phased reopening for small businesses and strategies for locating outdoor dining space.

Neighborhood Analysis of Jackson Heights, Queens showing street pedestrian traffic at 7:00PM on a typical Saturday night. [Source: KPFui}

Neighborhood Analysis showing sidewalk area per person in Jackson Heights, Queens, identifying streets that experience higher pedestrian traffic in relation to the sidewalk area available. Highlighted sidewalks indicate instances where less than 30 sqft of sidewalk is available per person — the minimum distance recommended for social distancing by the CDC. [Source: KPFui]

Sidewalk Width determines Stress Street activity alone does not indicate sidewalk stress. A neighborhood with large sidewalks could support commercial activities while providing enough distance for pedestrians to social distance safely. Similarly, while residential sidewalks tend to have lower pedestrian traffic, neighborhoods with narrow sidewalks would still exhibit sidewalk stress as the area per pedestrian is low.

Pedestrian traffic along Fulton Street in BedStuy is simulated to be far greater than Macon Street where an Open Street has currently be established. [Source: KPFui]

Leveraging Open Streets As businesses adapt to new COVID-19 operation strategies, businesses have transformed the sidewalk as extensions of their stores through measures such as designating queuing lines along store perimeters or setting up outdoor seating. This information provides insight to better inform urban interventions led by the community or by city agencies to expand pedestrian zones on commercial streets, or to create better synergy with the current Open Streets initiative.

Special thanks to Assistant Professor Timur Dogan and PhD student Yang Yang from the Environmental Systems Lab at Cornell University for their mobility modeling tool urbano.io and their help customizing it for generating the analysis data for the the neighborhoods web map.