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SENSEABLE CITY LABThe 15-minute City Quantified Using Human Mobility Data∗
Timur Abbiasov*1 , Cate Heine*1 , Sadegh Sabouri*1 , Arianna Salazar-Miranda*1 ,Paolo Santi1,2 , Edward Glaeser*3 , and Carlo Ratti1,4
1

Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
2
3

Instituto di Informatica e Telematica del CNR, Pisa, Italy

Department of Economics, Harvard University, Cambridge, MA, USA
4

Politecnico di Milano, Italy
January 2024

Abstract
Amid rising congestion and transport emissions, policymakers are embracing the “15-minutecity” model, which envisions neighborhoods where basic needs can be met within a short walkfrom home. Prior research primarily examined amenity access without examining its relationshipto behavior. We introduce a measure of local trip behavior using GPS data from 40 million US
mobile devices, defining 15-minute usage as the proportion of consumption-related trips madewithin a 15-minute walk from home. Our findings show that the median resident makes only
14% of daily consumption trips locally. Differences in access to local amenities can explain
84% and 74% of the variation in 15-minute usage across and within urban areas, respectively.
Historical data from New York zoning policies suggest a causal relationship between local accessand 15-minute usage. However, we find a trade-off: increased local usage correlates with higher
experienced segregation for low-income residents, signaling potential socio-economic challenges
in achieving local living.

Keywords: 15-minute city, mobility, sustainability, land use, walkability∗

These authors contributed equally and are listed in alphabetical order.

ariana@mit.edu.

Corresponding Author:

1

Introduction

Urban areas account for over 60% of global greenhouse gas emissions, and the proportionof the global population residing in cities is projected to reach 68% by 2050 ([1, 2]). The
call to establish sustainable, inclusive and affordable cities is therefore crucial ([3]). Central
to this effort is the restructuring of cities, with a particular emphasis on human mobility.
The transportation sector—the second-largest energy consumer worldwide and the leading
contributor to US greenhouse gas emissions—requires significant policy intervention ([4, 5]).
As a result, urban policies have transitioned away from automobile-focused designs,
favouring instead urban models that emphasize proximity and encourage active transportation. The “15-minute city” concept, which proposes that all daily essentials should be accessible within a short walk from home, exemplifies this shift ([6, 7]). This model aims not
only to minimize transportation emissions but also to foster community life by situating
day-to-day activities within close reach of residential areas. Nevertheless, the practicality of
executing such a model and the social and environmental repercussions of restructuring cities
for walkability remain largely unexplored. Amid these uncertainties, current propositions forthe “15-minute city” model lack definitive indicators to evaluate the influence of proximity
to amenities on travel behaviour, thus calling into question the model’s feasibility ([8]).
Existing research that explores the ability of residential neighbourhoods to meet essential
needs within a walkable distance typically focuses on the accessibility of essential amenities, such as grocery stores and restaurants ([9–13]). This approach, however, presumes
a predictable response in residents’ daily travels to changes in proximity to amenities, anassumption that may not always hold true. Although studies do provide evidence of a correlation between accessibility and travel behaviour ([14–16]), these insights are often limited to
specific contexts, failing to capture a comprehensive understanding of localized living acrossthe United States. Additionally, these studies tend not to address potential drawbacks ofthe 15-minute city model, including the risk of heightened socio-economic segregation as apossible consequence of an intensified focus on localized living ([17]).
One promising avenue to explore these issues lies in using large-scale mobile GPS data.
This approach can yield unique insights into human mobility patterns beyond the reach ofsmaller case studies or survey data ([18, 19]). This approach has significant implications,especially for informing climate policy ([20–22]).
In this paper, we present a comprehensive analysis of local living conditions across over
400 urban areas in the United States. Using GPS data that record visits to points of interest(POIs) by more than 40 million mobile phone users, we document a strong relationship1between urban residents’ ability to access local urban amenities and their usage of thoseamenities. Our results suggest a causal relationship and point to the potential benefits ofless restrictive zoning rules. Policies promoting mixed-use development could increase accessto diverse amenities in residential areas, reduce travel distances and allow for more efficient
trip-chaining. This, in turn, may alleviate travel-associated emissions and encourage moresustainable transportation modes.
Increasing local usage could also present a social trade-off: given the state of residential
segregation by race and income in the United States, would encouraging local living lead to
fewer encounters between socio-economic groups? To explore the potential impact of shorter
trips on the diversity of encounters during daily travels, we build on recent literature thatquantifies the diversity of social interactions in day-to-day activities ([23]). We found that in
low-income areas with higher local amenity usage, socio-economic isolation is more prevalent.
Our findings highlight how mobility data can deepen our understanding of human behaviour and its broader policy implications. We reveal a trade-off between the social andenvironmental goals of urban policy interventions and offer insights into the conditions underwhich local living policies might reduce transportation emissions or increase social isolation.

2
2.1

Results

Documenting local trip behavior in the US

We used large-scale mobile phone data that trace daily trips from approximately 40 million
smartphone users in the 420 most populated urban areas in the United States. The data
were provided by SafeGraph and contain information on trips to 5.3 million POIs, 3.9 millionof which are located within our sample urban areas. Further discussion on the quality andrepresentativeness of these data is available in the Methods section.
The dataset provides each POI’s geographical coordinates (latitude and longitude), classifies it according to the North American Industry Classification System (NAICS) code andrecords the number of visits originating from each home census block group in the UnitedStates. Our dataset covers 13.6 billion POI visits throughout 2019, our study period. Our
primary analysis was conducted at the neighbourhood level, identified by census block groups,
with additional analyses performed at the urban area level.
To evaluate trip patterns within a 15-minute walking distance, we obtained 15-minutewalking isochrones based on the pedestrian street network, originating from each blockgroup’s population-weighted centroid, using the Openrouteservice API. To construct ourmeasure of 15-minute usage, we concentrated on amenities encompassing essential functions2

in line with the 15-minute city model ([7]). The essential amenities in our analysis include
restaurants, schools, parks, health care, drugstores, arts and cultural institutions, grocery
stores, services and religious organizations.
We categorized individual POIs into these nine categories using their NAICS codes. The
respective codes are listed in Supplementary Table S.1. These categories make up 30% of the
POIs in our dataset and represent 35% of all trips.
We defined our 15-minute usage measure as the proportion of trips originating in eachneighbourhood that were aimed explicitly towards these essential amenities and occurred
within a 15-minute walk from the neighbourhood’s population-weighted centroid. Emphasizing these essential functions allowed us to assess the degree to which urban environments
provide convenient access to daily necessities, a core principle of the 15-minute city concept.
To assess how the use of local amenities is related to their accessibility, we computed anaccess measure. This measure describes the number of essential amenities within a 15-minute
walk from a given home census block group. Similar to our usage measure, we concentrated
on the previously mentioned nine categories of amenities. For each category, we tallied the
POIs within a 15-minute walking distance from the census block group and assigned eachneighbourhood a score, reflecting the percentile rank of the neighbourhood with respect tothat category. By averaging these scores across all categories and weighting them by thenational average trip share per category, we derived an index between 0 and 100 representing
local access.
Supplementary Table S.2 shows the correlations between our primary and alternative
usage metrics, including the share of trips within 10, 20 and 25 minutes and the median trip
distance. Although the 15-minute mark may appear arbitrary, our findings remain consistent
when using different time frames or similar measures. Supplementary Table S.3 shows thecorrelation between our primary and unweighted access metrics, where all categories areweighted equally. These alternative measures are highly correlated across neighbourhoods.
Figure 1 illustrates our methodology for constructing both the access (left) and 15-minuteusage measures (right), using a neighbourhood in Chicago as an example. The access measure,indicated by the dashed green boundary, represents the availability of amenities within a 15minute walk from the neighbourhood’s population-weighted centroid. The 15-minute usage
measure is determined by the proportion of trips taken by residents of that block group to
destinations inside the walking radius instead of outside of it.
Figure 2a plots the cumulative distribution of our measure of 15-minute usage across all
US neighbourhoods. Residents of the median US neighbourhood take only 14% of their trips
to basic amenities within a 15-minute walking radius. Residents in 84% of US neighbourhoods

3

aAccess within 15m

Services
SchoolRestaurant
Religious
Park
HealthGrocery
Arts & Culture

1

2
2
2
1

4
6

b

12

% trips within 15m

Services
SchoolRestaurant
Religious
Park
HealthGrocery
Drugstore
Arts & Culture

Figure 1: Example of measuring access and usage within a 15-minute walk with
SafeGraph data. a) POIs within and outside of the 15-minute walkshed from the centroidof the neighbourhood and count of POIs within the 15-minute walkshed by type of POI in
the upper-right corner. b) Trips to amenities within and outside the 15-minute walkshed
and share of trips within the 15-minute walkshed by type of POI in the upper-right corner.
Basemap reproduced from Mapbox under CC BY 3.0.
make less than half of their trips within a 15-minute walking radius.
Figure 2b plots 15-minute usage by the median resident across US urban areas, while
Figure 2c highlights the top 15 urban areas. A clear regional divide emerges, with urban
residents in the Northeast making approximately 34% of their trips within a 15-minute walking distance, whereas those in the South make just 18% or fewer of their trips within that
distance. This regional discrepancy aligns with findings from the latest National Household
Travel Survey, which indicates that southerners tend to depend more on personal vehicles for
local trips ([24]). This observation is also consistent with [25], who noted a lack of walkable
and transit-accessible neighborhoods in several southern cities.

4

CDF (% pop.)

a

1.00

b

0.75

Urban Areas in United States

0.50
0.25
0.00
0

25

50

75

100

Usage (% trips)
ChicagocChampaign

New York, NY
Bridgeport, CT
State College, PA
San Francisco, CA
Los Angeles, CA
Boulder, COChampaign, IL
Chicago, IL
Seaside, CA
San Jose, CA
Corvallis, OR
Burlington, VT
Santa Barbara, CA
Philadelphia, PA
Trenton, NJ

New York
Philadelphia

San Francisco
Murfreesboro
Los Angeles

Population (m)15m Usage (% trips)
0

0

10

20

30

40

10 12 14 16 18 20 25 30 40 50

1.1 4.6 11.5 22.7

Average Usage (% trips)

Figure 2: Local trips in the United States. a) The cumulative distribution of 15-minuteusage for US neighbourhoods. CDF, cumulative distribution function. b) 15-minute usage
for all urban areas in the United States. Basemap reproduced from Mapbox under CC BY
3.0. c) 15-minute usage for those urban areas that have the highest proportions of local trips.
Figure 3 plots 15-minute usage by median income, binned into deciles within each urban
area. As representative examples, New York, Detroit and Atlanta are highlighted to showcase
high, medium and low levels of local usage, respectively. Across all urban areas, 15-minuteusage decreases sharply with income. While residents in the bottom income decile make on
average 41% of their trips within a 15-minute radius, those in the top income decile makeonly 11% of their trips locally.

2.2

The role of access in local trips

Advocates for 15-minute cities emphasize the provision of accessible amenities, expecting
this to increase the frequency of local trips. We thus examined the relationship between
local access to amenities and 15-minute usage. In Figure 4a, we plot 15-minute usage against
our access measure, using population-weighted averages aggregated to the urban area level.
The 15-minute access measure explains 84% of the variation in 15-minute usage across urban
areas. Figure 4b demonstrates the relationship between 15-minute usage and access acrossneighbourhoods. Even within urban areas, 15-minute usage rises sharply with access. As we

515-minute Usage (% trips)

80

60

40
New York
20
DetroitAtlanta

0
1

2

3

4

5
6
7
Income deciles

8

9

10

Figure 3: Local trips by income levels. 15-minute usage by neighbourhood income
deciles for all urban areas, including New York (n = 13,908 block groups), Detroit (n =3,359 block groups) and Atlanta (n = 2,338 block groups). The error bands represent 95%
confidence intervals.
transition from neighbourhoods with the lowest local access to those with the highest, thelevel of 15-minute usage increases as well—the bottom 10% of neighborhoods with respect toaccess have a usage of close to 0%, while the top 10% have an average usage of 58%. These
differences in access explain 74% of the variation in local usage across neighbourhoods withina city.
Table 1 presents regression results where 15-minute usage is regressed against accessmeasures at the neighbourhood level (columns 1-3) and the urban area level (columns 4-6).
Column 1 indicates that a one-percentile increase in access corresponds to a 0.77-percentagepoint increase in 15-minute usage (P < 0.001). The results remain largely unchanged when
we factor in neighbourhood sociodemographics (column 2), public transit frequency andcommute lengths (column 3).
Column 2 also shows that among the demographic variables, median neighbourhood income is negatively correlated with 15-minute usage (β = −4.62, P < 0.001), confirming awell-established finding from the National Household Travel Survey ([26]). Conversely, population density is positively and significantly associated with 15-minute usage (β = 0.48,
p = .051). In column 3, we controlled for transit frequency and average commute length6aMidwest

Northeast

South

b

West

80
New York

R2 = 0.74

40

15−minute Usage

15−minute Usage (% trips)

R 2 = 0.84

San Francisco
Philadelphia

30

Reading

Los Angeles
SeattleCleveland

BostonDetroit20
Phoenix

PortlandMiami

Avondale

10

60

40

20

Atlanta

0

0
20

40

60

25

15−minute Access (# amenities, ranked)

50

75

100

15−minute AccessFigure 4: Access and local trips. a) The relationship between 15-minute usage andaccess at the urban area level. b) The relationship between 15-minute usage and accessacross neighbourhoods within urban areas. Block groups in six representative urban areas
are emphasized using colours that correspond to the cities indicated in (a). Each of the six
urban areas in (b) also features a fitted non-parametric spline regression, denoted by thesame colour as the city. The R2 value in a is derived from a regression analysis of 15-minuteusage on 15-minute access at the urban area level (n = 420 urban areas). The R2 value inb is derived from a regression analysis at the census block group level (n = 150, 159 blockgroups), incorporating urban area fixed effects.
to ensure that the observed relationship between access and usage is not confounded by thetransit availability in neighbourhoods and the geographical proximity between individuals’
homes and workplaces. We found that both variables are positively and significantly correlated with 15-minute usage (P = 0.005 and P < 0.001, respectively). Columns 4-6 confirm arobust link between 15-minute access and 15-minute usage across urban areas.
Across all specifications, access has considerable explanatory power. In columns 1-3, weestimate that access explains between 69.1% and 74.8% of the variation in 15-minute usage
across neighbourhoods. In columns 4-6, we estimate that access explains between 68.7% to83.6% of the variation in 15-minute usage across cities. To validate the robustness of ourresults, we repeated our baseline analyses using all POIs from the SafeGraph dataset, notlimiting them to our selected essential categories. This expanded analysis produced similar
results, lending further support to the robustness of our method (see Supplementary Table
S.4 for the details).

7

Table 1: Relationship between 15-minute usage and access across neighborhoods (census
block groups) and urban areas
Dependent Variable: 15-minute usage (percentage of trips)
Across CBGs

15-minute AccessAcross Urban Areas

(1)

(2)

(3)

(4)

(5)

(6)

0.769∗∗∗

0.710∗∗∗

0.715∗∗∗

0.601∗∗∗

0.509∗∗∗

0.493∗∗∗

p < 0.001

p < 0.001

p < 0.001

p < 0.001

p < 0.001

p < 0.001

0.451∗

0.183

1.950∗∗∗

2.124∗∗∗

p = 0.075

p = 0.378

p = 0.001

p < 0.001−4.615∗∗∗−4.238∗∗∗−2.429∗∗∗−3.328∗∗∗p < 0.001

p < 0.001

p < 0.001

p < 0.001

0.009∗−0.0003

0.042∗∗∗

0.018

p = 0.094

p = 0.948

p = 0.002

p = 0.132−0.003−0.0060.001−0.005p = 0.554

p = 0.238

p = 0.906

p = 0.587

Pop Density Log

Median Income Log% Education: BA +% WhiteTransit Frequency (per sq. mi)

Average Commute (mi)0.001∗∗∗

0.003∗∗∗

p = 0.005

p = 0.003

0.107∗∗∗−0.094∗∗∗p < 0.001

p < 0.001

Urban Area FE

✓

✓

✓

Observations

150,159

145,862

145,859

420

420

420

R2

0.738

0.753

0.756

0.836

0.845

0.875

Partial R2

0.748

0.691

0.696

0.836

0.708

0.687

Notes.— The table reports OLS estimates of the relationship between 15-minute usage and access. Columns 1 to 4 report
estimates at the neighborhood level (controlling for urban area fixed effects) and columns 5 to 8 report estimates at theurban area level. The covariates included in columns 2 to 4 and 6 to 8 come from the American Community Survey and
the National Transit Map from the Bureau of Transportation Statistics. The partial r-squared for access is computed asβ×cov(15-minute usage,15-minute access), where β is the regression coefficient for 15-minute access. These partial r-squaredvar(15-minute usage)

estimates might still represent upper bounds, as our regressions do not control for more detailed measures of the built
environment. The coefficients with *** are significant at the 1% confidence level; with ** are significant at the 5%
confidence level; and with * are significant at the 10% confidence level. Included P-values correspond to a two-sided
t-test for each estimate, using robust standard errors clustered at the county level.

8

To investigate how public transit accessibility impacts our main findings, we conducted an
additional robustness check. In particular, we replicated our baseline results for a subsample
comprising neighbourhoods with above-median transit accessibility. Columns 1 and 3 in
Supplementary Table S.5 correspond to columns 3 and 6 in Table 1. The resulting estimatesindicate that the influence of access on usage within this subsample is comparable to thatfound in our original baseline analysis. A detailed description of the public transit accessibility
variable can be found in the Methods section.

2.3

Towards the causal effect of access on 15-minute usage

The strong positive correlation between local access to amenities and local usage described in
the previous section supports the hypothesis that promoting mixed-use development within
residential areas could decrease average trip durations. However, the strength of this correlation may not necessarily indicate a causal relationship in which improved access encourages
more local trips. For instance, the regression may capture how increased local amenity use
attracts businesses, leading to improved access rather than vice versa. Moreover, areas with
abundant local amenities might appeal to those who would favour shorter trips regardless,even if the accessibility does not directly encourage the average person to opt for shorter
trips.
To address the first concern, we used spatial variation in the zoning code implemented in
New York City in 1961 ([27, 28]). The 1961 zoning resolution, for the first time, establishedcity-wide floor-to-area ratio (FAR) regulations, dividing New York City into 2,141 zoning
districts, each allowing different maximum densities for residential, commercial and otheruses. A map of the 378 commercial zoning districts is provided in Supplementary Figure S.1.
Our focus lies on the average maximum commercial FAR permitted in each neighbourhood,which we used to create exogenous variation in present-day access to amenities and services.
This source of variation should address the first but not the second issue mentioned
earlier, provided that the variation in business density triggered by the 1961 code does notreflect any preference of present-day residents to attract businesses. The 1961 zoning plan in
New York City was primarily informed by previous built-up densities and broad geographic
definitions. Therefore, following Shertzer et al. [29], we included controls for population
and built-up density, proximity to the city centre, employment composition, poverty and theshare of non-white population based on data from the 1960 Census.
Using the 1961 zoning code as a basis for identification does not resolve the issue of people
who prefer shorter trips selecting neighbourhoods with ample local access. Even if the retailcharacteristics of a neighbourhood were entirely random, residents would continue to choose
9neighbourhoods that meet their needs. As a result, we view our instrumental variable (IV)estimates as a progression towards understanding causality rather than as conclusive evidenceof the impact of access on usage.
Figure 5a reports the first-stage relationship between the average FAR allowed by the 1961
zoning resolution and our measure of access across neighbourhoods in New York City. Thereis a strong first-stage association, as indicated by an F statistic of 187 (see Table 2 column2), suggesting that neighbourhoods with fewer FAR restrictions in neighbouring commercial
districts see higher contemporary access levels to amenities within a 15-minute walking distance. Figure 5b reports the reduced-form relationship between historical FAR permissions
and 15-minute usage. Similarly, residents of neighbourhoods with less historical restrictions
on commercial areas tend to take short trips more frequently: residents of the highest-FAR
districts take 75% of their trips locally, while residents of the lowest-FAR districts take onlyb
15−minute Access (2019)

a
100

90

80

70

First stage F−stat = 186.54
60
0

5

10

15−minute Usage (% trips) (2019)about 50% of their trips locally.
100

75

50

25

0
0

Average FAR in commercial districts (1961 zoning)

5

10

Average FAR in commercial districts (1961 zoning)

Figure 5: First-stage and reduced-form relationships between historical zoning
regulations and current access to local services and 15-minute usage for New
York neighborhoods. a) The first-stage relation between historical zoning and current
access. b) The reduced-form relation between historical zoning and current 15-minute usage.
Each dot represents a census block group in New York City in New York City (n = 5, 582
block groups), and each black line corresponds to a log-linear regression model of the form
yi = log(x) + ϵi , where ϵ is a noise term. Supplementary Table S.9 summarizes the regression
results from these specifications.
Table 2 reports our IV estimates for New York City. Column 1 reports ordinary leastsquares (OLS) estimates for the relationship between 15-minute usage and access. The estimated 0.95 coefficient (P < 0.001) is slightly higher than the coefficients estimated in Table
10

1, columns 1-2, which can potentially be explained by the narrower range of access in New
York City than in our universe of urban areas.
Table 2: OLS and IV estimates of the relationship between 15-minute usage and accessacross neighborhoods (census block groups) in New York City
Dependent variable:
15-minute Usage (percentage of trips)
OLS

15-minute AccessPopulation Density (Log)Median Income (Log)IV estimates

(1)

(2)

(3)

(4)

0.951∗∗∗

1.249∗∗∗

1.176∗∗∗

1.228∗∗∗

p < 0.001

p < 0.001

p < 0.001

p < 0.001

4.261∗∗∗

3.933∗∗∗

3.973∗∗∗

p < 0.001

p < 0.001

p < 0.001−4.241∗∗∗−4.115∗∗∗−4.238∗∗∗p < 0.001

p < 0.001

p < 0.001✓✓✓

IV: Log Average Commercial FAR in 1961 Resolution
Distance to City Center

✓

✓

✓

✓

1960 Population and Housing Density

✓

✓

✓

✓

1960 Workers by Industry

✓

✓

✓

✓

1960 Poor and Non-white Families✓

First stage F-statNA

186.54180.5

164.34

Observations

5,205

5,582

5,205

5,205

R2

0.502

0.420

0.497

0.494

Notes.— The table reports OLS and IV estimates of the relationship between 15-minute usage and access for neighborhoods in New York City. Column 1 reports OLS estimates of the relationship between 15-minute usage and accessfor New York City, controlling for distance to the city center and a host of socio-economic controls for 1960, including
population and housing density and the share of workers by industry. Columns 2-4 report the IV estimates. Columns 2
and 3 also control for distance to the city center, population and housing density and the share of workers by industry.
Column 4 includes additional controls for the share of poor and non-white families in 1960. The covariates includedin these models come from [28] and [27]. The coefficients with *** are significant at the 1% confidence level; with **are significant at the 5% confidence level; and with * are significant at the 10% confidence level. Included P-values
correspond to a two-sided t-test for each estimate, using robust standard errors.

Columns 2-4 provide our IV estimates, each controlling for different covariates. Column 4,
our preferred specification, implies that a one-percentile increase in access is associated witha 1.23-percentage-point increase in local usage. This estimate aligns with the national OLS
estimates and exceeds its New York City OLS counterpart. This discrepancy might indicate

11attenuation in the OLS estimates due to measurement error. Given that we have only asingle snapshot of data capturing available amenities in 2019, our access measurement may
not perfectly reflect the dynamics of open and closed amenities throughout the year. Hence,the primary independent variable might be measured with error. If so, our OLS estimateswould be biased towards zero.
To address the issue of sorting, we conducted an additional robustness check by focusing
on a specific subset of census tracts in New York City. This subset consists of the top
quartile of census tracts in New York City with respect to the share of residents who wereraised in the area as children and continued to reside there as of 2015. By examining thissample, we can reduce the potential for self-selection from residents choosing locations basedon current amenity access. The IV estimates corresponding to this subsample are illustrated
in Supplementary Table S.6, and they closely match the magnitude of those obtained for thefull sample, suggesting that our findings are not likely driven by residential self-selection.
Our results indicate that restrictions on land use, particularly those curtailing commercial
development in residential areas, could contribute to extended travel distances and increased
carbon use. These results echo the conclusions drawn by [30], who found that residential landuse restrictions appear to increase America’s carbon footprint by constraining the growth ofenvironmentally friendly metropolitan areas, such as San Francisco and San Diego, while
simultaneously encouraging new household formation in regions with high carbon footprints,
such as Atlanta and Houston. The impact of local land-use regulations on global carbon
emissions could be a critical factor to consider in any forthcoming cost-benefit analysis ofsuch rules.

2.4

Trade-off between local living and segregation

While it might seem inherently beneficial from an environmental standpoint to limit intercity travel, reducing travel across neighborhoods could exacerbate the social isolation ofdisadvantaged communities ([17, 31]). Historical racial segregation within American cities hasoften been perpetuated by land-use controls and restrictive covenants, which have hindered
the migration of Black people into predominantly white areas. A policy encouraging more
localized travel could inadvertently limit opportunities for interaction and exchange acrossdifferent racial and income groups, potentially further diminishing opportunities for economic
mobility ([32, 33]).
To analyze this, we created a metric of experienced income segregation for each neighborhood within each city. This measure reflects the extent to which residents are exposed
to individuals from diverse income groups in their daily travels. Each user in our dataset12

is assigned the median income of their neighborhood, and we calculated the mean income
disparity between residents of a given neighbourhood and those they encountered at the same
POIs over the duration of our study period. Although our data do not specifically captureinterpersonal interactions (as individuals may visit the same grocery store at different timesof day or may not interact despite visiting simultaneously), it does capture co-location or
exposure, which has been demonstrated as a reliable proxy for interactions ([34]). This information serves as an indicator of the income diversity—or lack thereof—experienced byresidents of different neighbourhoods in their daily encounters. Our methodology is guided
by [34] and is explained in greater detail in the Methods section.

70
Low income
Low−moderate
Moderate−high60High income

80

70
Low income
Low−moderate
Moderate−high60High income

Experienced segregation

80c
Experienced segregationb
Experienced segregation

a

90

80

70

60

Trips < 15 minutes away
Trips > 15 minutes away

1 2 3 4 5 6 7 8 9 10

1 2 3 4 5 6 7 8 9 10

1 2 3 4 5 6 7 8 9 10

Access decile

Usage decile

Income decile

Figure 6: Local trip behavior and experienced segregation. a) Experienced segregation against 15-minute access for neighbourhoods in different income quartiles. b) Experienced segregation against 15-minute usage for neighbourhoods in different income quartiles.
c) Experienced segregation during short (<15 minutes) and long trips (>15 minutes) for
residents of neighbourhoods in different income deciles. The box plots show the means andthe 10th, 25th, 75th and 90th percentiles. In a), b), n = 41, 998, 37,676, 34,299 and 31,890
census block groups for income quartiles 1, 2, 3 and 4, respectively. For c) trips taken lessthan 15 minutes from home, n = 124, 656 block groups. For c) trips taken further than 15
minutes from home, n = 145, 855 block groups. (Note that residents of 21,119 census blockgroups take no trips within 15 minutes in our dataset).
Figure 6a,b plots our experienced segregation index against the 15-minute access andusage measures across neighbourhoods in varying income quartiles. The figure indicates anegative association between 15-minute usage and experienced segregation for residents ofupper-middle and high-income neighbourhoods. In fact, as local travel increases, residents
13

of high-income neighbourhoods experience more social mixing. This trend is reversed forresidents of low-income neighbourhoods, where experienced segregation rises 5.4% (from 71.4
to 75.2) as we transition from neighbourhoods with the lowest local usage to the highest.
Supplementary Table S.7 shows the results of a regression analysis that formalizes this relationship: a one-unit increase in usage is associated with a 0.071-unit increase in the physicalsegregation index (column 3). The rise in experienced segregation with usage is more than
twice as sharp in the 15 urban areas with the highest usage, where a one-unit increase inusage is associated with a 0.164-unit increase in the physical segregation index (column 1).
The positive association between the percentage of 15-minute trips and our segregationindex for residents of less affluent neighbourhoods indicates that these individuals are more
likely to interact with a socio-economically diverse group during longer trips than during
shorter ones. This is depicted in Figure 6c, where segregation experienced during long andshort trips is plotted separately for residents of neighbourhoods across different income deciles.
For residents of low-income neighbourhoods, long-distance travel results in a 16.7% reduction
in experienced segregation. Residents of high-income neighbourhoods, in contrast, see only
an 8.2% decrease in experienced segregation when they travel further from home. This
discrepancy underscores the importance of the balance between local living and segregation,
especially for lower-income communities.
The regression analysis in Supplementary Table S.7 shows that the differences in experienced segregation for residents of less affluent neighbourhoods, as documented in Figure 6,
are statistically significant (P < 0.001). Consistent with the findings in Figure 6, we estimate
a positive association between local trips and experienced segregation for residents of neighbourhoods in the lowest income quartile and a negative association between local trips andexperienced segregation in the highest income quartile. These differences remain robust evenwhen controlling for urban area fixed effects, and the results are comparable if we regress ourmeasure of experienced segregation on access to local amenities instead of 15-minute usage,
as seen in columns 2 and 4.
When we repeated the calculation assigning visitors’ incomes drawn from the census blockgroup’s income distribution instead of relying on the average block group incomes, we foundsimilar results, including a 0.57 Pearson correlation coefficient between this measure and ourprimary segregation measure. Our main analysis is centred on encounters specifically within
our designated POIs. However, when we broaden our scope to include the entire universeof POIs, there is a strong correlation (Pearson correlation coefficient, 0.981), suggesting thatthe selection of a specific POI set does not influence our findings.

14

3

Discussion

Our paper presents a large-scale evaluation of local living patterns across US urban areas,
exploring their potential social and environmental impacts. We leveraged a large dataset ofmobile GPS traces to examine the use of everyday amenities within walkable distance fromhome.
We developed an index of local living—defined as the proportion of trips made within a
15-minute walk from home—at the neighbourhood level across more than 400 US urban areas,
termed the “15-minute usage” index. We anticipate that this index will be a crucial starting
point for future environmentally oriented policy interventions. Our index reveals that US
urban areas currently fall short of the 15-minute ideal: the median US city resident makes
only 14% of daily trips to basic amenities locally. However, substantial regional and socioeconomic disparities exist within the United States—for instance, lower-income residents andthose living in the northeastern United States make a higher proportion of trips to basic
amenities locally, suggesting that these areas might be more receptive to local living policies
and interventions.
We found a strong correlation between the accessibility of amenities within a 15-minute
walk from home and the usage of these amenities, providing preliminary evidence to support
the 15-minute city model’s underlying premise: local living is determined by access to local
amenities. Furthermore, we provide evidence indicating a causal relationship by leveraging
variation in access created by the 1961 New York City zoning ordinance across New York
City neighbourhoods. Our findings reveal that areas with more permissive commercial zoning
in 1961 have a larger proportion of local trips today, suggesting that local zoning regulations
shape the level of access to nearby amenities and that more flexible local zoning could be anatural policy lever for those advocates interested in increasing local trips.
In the final section of the paper, we investigated a potential downside to promoting local
living: we examine whether urban residents who stay closer to home are less likely to engage
with a diverse group in their daily lives, thus intensifying socio-economic segregation. We
found that more localized trips and greater access to local amenities are associated withhigher levels of experienced segregation only for low-income residents. This is probably
because opportunities for higher-income social interactions in most cities are located awayfrom low-income neighbourhoods.
The findings presented in this paper enhance our understanding of urban travel patterns and highlight the importance of mobility data for addressing environmental and social
sustainability in cities. Collectively, our results suggest that the “15-minute city” planning15

strategies could significantly reduce trip lengths if they successfully enhance access. However,our dataset also allows us to identify that such policies could inadvertently increase the social
isolation of the less affluent. This implies that to gain both environmental and social benefits
from the 15-minute approach, policies promoting local living in US cities should be pairedwith dedicated efforts to create opportunities for social mixing, such as subsidized housing
or mandating public spaces catering to diverse groups.
This trade-off would be challenging to uncover using smaller-scale travel surveys or transportation models and underscores the value of using large-scale behavioural data in understanding how people use cities. However, from the planner’s perspective, implementing thefindings presents certain challenges. Planning policies, such as zoning, land-use regulations
and business permits, often do not impact access directly. Moreover, their implementation
can face local barriers, particularly factors like NIMBYism (Not in My Back Yard). Nevertheless, our work provides a straightforward empirical framework that could be employed forpolicy evaluation of urban interventions promoting sustainable mobility and shorter trips.
There are some limitations and potential future extensions worth mentioning. The 15minute city concept is centred on the idea that everyone should be able to reach their essentialsocial functions within a short, sustainable commute. We did not explicitly include workplaces
as one of these essential functions in our analysis; however, since some POIs cater to both
work and consumption, our dataset may include some work trips. Also, the dataset usedin the analysis does not allow us to infer each person’s mode of travel, as it is aggregatedat the neighbourhood level, and thus we can only measure the potential for trips withinwalking distance without conclusively determining whether people are actually walking tomeet their daily needs. In addition, we focused exclusively on a measure of experienced
segregation based on average block group income. Future work could consider segregation by
race and ethnicity or take into account within-block group income distributions so as not to
obscure within-block group diversity. Analysing dwell time could also help add nuance to thesegregation analysis: incorporating the length of visits to different POIs would lend itself to amore realistic understanding of exposure between socio-economic groups. Finally, our analysis
in New York points towards a causal relationship between amenity access and local usage,
but we cannot perfectly account for the potential sorting of people into neighbourhoods.
Moreover, the direct applicability of these findings to other US regions may be limited due tovarying zoning constraints and density demands. These results are thus particularly relevant
to larger urban environments with similar zoning influences. Additional studies are necessary
to explore these dynamics in less dense or differently regulated areas.

16

4
4.1

Methods

Measuring 15-minute usage and accessTo conduct the analysis within a 15-minute walking radius, we extracted 15-minute walking isochrones using the Openrouteservice API. Openrouteservice is an open-source routing
platform that leverages crowdsourced street network data from OpenStreetMap. Using the‘foot-walking’ profile, we generated walking isochrones that specifically rely on the pedestrian
street network. The platform presumes a pedestrian travel speed of 5 km h−1 .
The completeness of OpenStreetMap’s street networks, especially in densely populated
urban areas and within the United States, has been demonstrated ([35]). In fact, when
considering pedestrian pathways, it may surpass the US Census TIGER/LINE data in termsof completeness ([36]). However, it is noteworthy that the completeness tends to decline inless urbanized areas, which may result in a higher probability of errors in the isochrones insmaller cities or the outer, less urbanized areas of large cities.
To calculate our usage index for a given block group, we first restricted our POIs to thosewithin our selected POI categories and located within the block group’s urban area. We thencalculated the total number of trips taken by residents of that block group to all POIs within
the block group’s 15-minute walkshed and divided it by the total number of local trips taken
by residents of that block group. If the residents of a given block group took no trips within
our data, they were given a usage index value of 0.
To aggregate our usage index to the urban area level, we took a population-weighted
average as follows:P
usageA ={a∈A} usagea ∗ populationa

populationA,

where A represents an urban area and {a ∈ A} represent the block groups contained
within it.
To calculate our access index for a given block group, we overlaid our walksheds with
the POI locations in our dataset, identifying the number of amenities in each category thatare reachable within a 15-minute walk of each home block group. We then converted eachamenity total by category into a percentile across all census block groups, giving a value
between 0 and 100 for every block group for each category. We then constructed weightsof relative importance for each category by calculating the total percentage of trips to thatcategory in our dataset. The reasoning here is that access to places that people use more
frequently is more important to local living than access to rarely visited places. Finally, we

17combined all of this into an access index for block group a as follows:
accessa =X

access percentilea,c ∗ weightc , where{c∈categories}

X

weightc = 1{c∈categories}

Our index, while highly correlated with raw counts of POIs within each walkshed (Spearman R, 0.963), assigns appropriate weight to amenity categories with fewer establishments
yet high visitation. For instance, our dataset shows that an average block group contains
22.96 restaurants and 2.63 schools. If we assessed access purely on total counts, restaurants
would overwhelmingly dominate the index. Yet, for individuals using them, schools form an
integral part of daily life, often visited daily. Therefore, the presence of an additional school
within a 15-minute walk radius is probably more impactful than an extra restaurant. Our
access calculation methodology acknowledges this significance.
Similar to the usage index, to aggregate our access index to the urban area level, we took
a population-weighted average as follows:P
accessA =4.2{a∈A} accessa ∗ populationa

populationA

Data source description

Safegraph data The data used in this study were sourced by SafeGraph, a company thatcollects anonymous location data from around 10% of smartphones in the United States.
Previous studies have addressed the representativeness of this dataset, showing that overall,
it is relatively well balanced across demographics and space when aggregating across large
geographic units. For instance, [37] document a strong correlation between the number ofdevices and population across different levels of geographic aggregation (r-squared = 0.97 forcounties, r-squared = 0.47 for tracts, and r-squared = 0.36 for block groups). An internalreport by Safegraph reports similar correlations ([38]), which point to a high level of representativeness of the Safegraph data across geographies. [37] also document small samplingbiases across states and counties with different gender, age, and income composition. Thesestatistics suggest that the Safegraph data has reasonable coverage across regions and demographic groups. The high coverage presumably reflects the high penetration of smartphonedevices across all population segments in the United States ([39]).
SafeGraph primarily collects anonymized location data from numerous smartphone applications where the users have explicitly granted location-sharing permissions. Thus, it may
18

not fully represent individuals who do not regularly use smartphones or those who opt not
to share their location data.
To create the measure of 15-minute usage, we focused on amenities that represent essential functions associated with the 15-minute city model. Supplementary Table S.1 shows thefocus categories and their associated six-digit NAICS codes. To evaluate the completenessof the POIs contained in the SafeGraph data, we examined whether the over four million
POIs included in the dataset represent all the local opportunities available in a neighbourhood. Supplementary Table S.8 reports the relationship between the number of SafeGraphestablishments in our sample and the corresponding number of businesses in the 2019 Census
County Business Patterns data. Column 1 corresponds to a pooled OLS regression of census
establishment counts on SafeGraph establishment counts, and column 2 presents the resultsof the same model with two-digit NAICS code fixed effects added. The results suggest that,on average, the relationship between the two measures is almost one-to-one and statisticallysignificant at P < .01.
Socioeconomic data For the empirical analysis, we used data from the American Community Survey 5-Year Estimates by census block group for 2015-2019. We gathered socioeconomic data, including population, land area (used to compute densities), income, andeducation. For each neighborhood, we use median household income as estimated by the2019 American Community Survey 5-Year Estimates as our measure of neighborhood income. We assign income quartiles and deciles to each block group on the basis of their
population-weighted percentile ranking within their respective urban areas.
The variable representing the average commute length was calculated using the 2019
Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, also
referred to as LODES. This dataset offers an aggregate count of jobs linked to a home census
block and a work census block. We computed the average commute distance by determining
the mean Haversine distance, which was then weighted according to the total jobs for each
census block group.
The public transit frequency variable was obtained from the Environmental Protection
Agency. This measure was calculated by dividing aggregate transit service frequency by total
land acreage and converting it to units per square mile.
Historical data The additional data used in the IV estimation for New York City was
obtained from two sources. First, we combined information on the FAR levels assigned to eachtype of zoning district from the 1961 Zoning Handbook (https://www1.nyc.gov/assets/planning/download/pdf/about/city-planning-history/zoning_handbook_1961.pdf) withthe map of 1961 zoning districts provided by the Department of City Planning to obtain the19spatial distribution of restrictions established by the 1961 Zoning Resolution. Second, weused the 1960 Census data at the census tract level to control for various neighbourhood
characteristics observed pre-1961. FAR restrictions at the census block group level are computed by taking the area-weighted average of 1961 FAR levels assigned to commercial districts
that overlap with the 15-minute walkshed around each Census block group. Similarly, historical controls at the CBG level are computed by taking the area-weighted averages of thecorresponding variables in the Census by overlapping the 15-minute walkshed around each
census block group with the 1961 Census tracts.
For the robustness check addressing the self-selection of residents, we used demographic
data obtained from the Opportunity Atlas ([40]).
County Business Patterns data To produce Supplementary Table S.8, we obtained
counts of business establishments by industry at the county level from the 2019 Census
County Business Patterns data (CBP). This dataset uses 2017 NAICS codes for industry
classification, from which we extracted the broader 2-digit industry labels, defined by the
Census.

4.3

Physical Segregation Index

For a given individual of income ki located in POI L, we calculated the integration or diversity of interactions that they experienced in that POI as a weighted average socio-economic
difference between them and every other SafeGraph user located in that POI as follows:P
experienced integrationki ,L =kj pkj ,L · ski →kjP

kj pkj ,L,ski →kj = |ri − rj |where pkj ,L is the number of people of income kj who visit L and rj is the income rank ofindividuals of income kj . We then aggregate up to the home block group level, describing
the experienced integration of individuals from block group j as:
P
L∈POIs experienced segregationkj ,L · pj,LP
experienced integrationj =,L∈POIs pj,Lwhere pj,L is the number of people from j who visit L.
Finally, we defined:
experienced segregationj = 1 − experienced integrationj
We then scale the measure to range between 0 and 100, where 100 corresponds to the highest
level of experienced segregation and 0 to the lowest.
20References
1. United Nations.

Generating power, 2012.

URL https://www.un.org/en/climatechange/

climate-solutions/cities-pollution. Accessed January 19, 2023.
2. United Nations. World population prospects, 2019. Department of Economic and Social Affairs.
3. David Griggs, Mark Stafford-Smith, Owen Gaffney, Johan Rockström, Marcus C Öhman, Priya
Shyamsundar, Will Steffen, Gisbert Glaser, Norichika Kanie, and Ian Noble. Sustainable development goals for people and planet. Nature, 495(7441):305–307, 2013.
4. U.S. Energy Information Administration. International energy outlook 2021 (ieo2021), 2021. URL
https://www.eia.gov/outlooks/ieo/pdf/IEO2021_ReleasePresentation.pdf.
5. U.S.
house

Environmental
gas

Protection

emissions,

Agency.

2020.

Fast
URL

facts

on

transportation

green-

https://www.epa.gov/greenvehicles/

fast-facts-transportation-greenhouse-gas-emissions. Accessed January 19, 2023.
6. Zaheer Allam, Simon Elias Bibri, Didier Chabaud, and Carlos Moreno. The ‘15-Minute City’concept can shape a net-zero urban future. Humanities and Social Sciences Communications, 9(1):1–5, 2022. doi: 10.1057/s41599-022-01145-0.
7. Carlos Moreno, Zaheer Allam, Didier Chabaud, Catherine Gall, and Florent Pratlong. Introducing
the “15-minute city”: Sustainability, resilience and place identity in future post-pandemic cities.
Smart Cities, 4(1):93–111, 2021. ISSN 26246511. doi: 10.3390/smartcities4010006.
8. Alain Bertaud. Last utopia: The 15-minute city, 2022. URL https://urbanreforminstitute.
org/wp-content/uploads/2022/02/15mincity-bertaud.pdf. Accessed January 19, 2023.
9. Eduardo Graells-Garrido, Feliu Serra-Burriel, Francisco Rowe, Fernando M Cucchietti, and Patricio Reyes. A city of cities: Measuring how 15-minutes urban accessibility shapes human mobility
in barcelona. PloS one, 16(5):e0250080, 2021.
10. Alessia Calafiore, Richard Dunning, Alex Nurse, and Alex Singleton. The 20-minute city: An
equity analysis of liverpool city region. Transportation Research Part D: Transport and Environment, 102:103111, 2022.
11. Ana Luisa Gaxiola-Beltrán, Jorge Narezo-Balzaretti, Mauricio Adolfo Ramı́rez-Moreno, Blas LuisPérez-Henrı́quez, Ricardo Ambrocio Ramı́rez-Mendoza, Daniel Krajzewicz, and Jorge de-JesúsLozoya-Santos. Assessing urban accessibility in monterrey, mexico: A transferable approach toevaluate access to main destinations at the metropolitan and local levels. Applied Sciences, 11(16):7519, 2021.
12. Denise Capasso Da Silva, David A King, and Shea Lemar. Accessibility in practice: 20-minutecity as a sustainability planning goal. Sustainability, 12(1):129, 2019.

21

13. Elias Willberg, Christoph Fink, and Tuuli Toivonen. The 15-minute city for all?–measuring
individual and temporal variations in walking accessibility. Journal of Transport Geography, 106:103521, 2023.
14. Darren Scott and Mark Horner. Examining the role of urban form in shaping people’s accessibility
to opportunities: An exploratory spatial data analysis. Journal of Transport and Land Use, 1(2),Nov. 2008. doi: 10.5198/jtlu.v1i2.25. URL https://www.jtlu.org/index.php/jtlu/article/
view/25.
15. Daniel G Chatman. Residential choice, the built environment, and nonwork travel: evidence using
new data and methods. Environment and planning A, 41(5):1072–1089, 2009.
16. V Dimitra Pyrialakou, Konstantina Gkritza, and Jon D Fricker. Accessibility, mobility, andrealized travel behavior: Assessing transport disadvantage from a policy perspective. Journal oftransport geography, 51:252–269, 2016.
17. EdwardofGlaeser.

opportunity

nomics,

pages

The

15-minute

city

is

LSE

COVID-19.

foreveryone

—

1–4,

2021.

URL

a

dead

end—cities

must

be

placesLondonSchoolofEco-https://blogs.lse.ac.uk/covid19/2021/05/28/

the-15-minute-city-is-a-dead-end-cities-must-be-places-of-opportunity-for-everyone/.
18. Moritz UG Kraemer, Adam Sadilek, Qian Zhang, Nahema A Marchal, Gaurav Tuli, Emily L
Cohn, Yulin Hswen, T Alex Perkins, David L Smith, Robert C Reiner, et al. Mapping global
variation in human mobility. Nature Human Behaviour, 4(8):800–810, 2020.
19. Laura Alessandretti, Piotr Sapiezynski, Vedran Sekara, Sune Lehmann, and Andrea Baronchelli.
Evidence for a conserved quantity in human mobility. Nature human behaviour, 2(7):485–491,
2018.
20. Mirjam A Jenny and Cornelia Betsch. Large-scale behavioural data are key to climate policy.
Nature Human Behaviour, 6(11):1444–1447, 2022.
21. Zachary A Needell, James McNerney, Michael T Chang, and Jessika E Trancik. Potential for
widespread electrification of personal vehicle travel in the united states. Nature Energy, 1(9):1–7,
2016.
22. Matteo Böhm, Mirco Nanni, and Luca Pappalardo. Gross polluters and vehicle emissions reduction. Nature Sustainability, pages 1–9, 2022.
23. Susan Athey, Billy Ferguson, Matthew Gentzkow, and Tobias Schmidt. Estimating experienced
racial segregation in us cities using large-scale gps data. Proceedings of the National Academy of
Sciences, 118(46):e2026160118, 2021. doi: 10.1073/pnas.2026160118. URL https://www.pnas.
org/doi/abs/10.1073/pnas.2026160118.

2224. Federal Highway Administration. National household travel survey, us department of transportation, washington, dc., 2017. URL https://nhts.ornl.gov.
25. Bradley Bereitschaft. Do socially vulnerable urban populations have access to walkable, transitaccessible neighborhoods? a nationwide analysis of large us metropolitan areas. Urban Science, 7(1):6, 2023.
26. John Pucher and John L. Renne. Socioeconomics of urban travel: Evidence from the 2001 NHTS.
Transportation Quarterly, 57(3):49–77, 2003. ISSN 02789434.
27. Petr Martynov. Welfare effects of zoning: Density restrictions and heterogeneous spillovers. Working paper, University of California, Berkeley, 2022.
28. Tate Twinam. Danger zone: Land use and the geography of neighborhood crime. Journal of Urban
Economics, 100:104–119, 2017. ISSN 0094-1190. doi: https://doi.org/10.1016/j.jue.2017.05.006.
URL https://www.sciencedirect.com/science/article/pii/S009411901730044X.
29. Allison Shertzer, Tate Twinam, and Randall P. Walsh. Zoning and the economic geographyof cities. Journal of Urban Economics, 105:20–39, 2018. ISSN 0094-1190. doi: https://doi.
org/10.1016/j.jue.2018.01.006. URL https://www.sciencedirect.com/science/article/pii/
S0094119018300056.
30. Edward L. Glaeser and Matthew E. Kahn.

The greenness of cities: Carbon dioxide emis-

sions and urban development. Journal of Urban Economics, 67(3):404–418, 2010. ISSN 00941190. doi: https://doi.org/10.1016/j.jue.2009.11.006. URL https://www.sciencedirect.com/
science/article/pii/S0094119009001028.
31. Pierre Moreno, Carlos; Veltz. The “15-minute city”: the way forward or an ideological mirage?,Préparer la transition mobilitaire, 2022.
32. John F Kain. Housing Segregation, Negro Employment, and Metropolitan Decentralization. The
Quarterly Journal of Economics, 82(2):175–197, nov 1968. ISSN 00335533, 15314650. doi: 10.
2307/1885893. URL http://www.jstor.org/stable/1885893.
33. Raj Chetty, Matthew O Jackson, Theresa Kuchler, Johannes Stroebel, Nathaniel Hendren,
Robert B Fluegge, Sara Gong, Federico Gonzalez, Armelle Grondin, Matthew Jacob, et al. Social
capital i: measurement and associations with economic mobility. Nature, pages 1–14, 2022.
34. Yang Xu, Alexander Belyi, Paolo Santi, and Carlo Ratti. Quantifying segregation in an integrated
urban physical-social space. Journal of the Royal Society Interface, 16(160):20190536, 2019.
35. Christopher Barrington-Leigh and Adam Millard-Ball. The world’s user-generated road map ismore than 80% complete. PloS one, 12(8):e0180698, 2017.

23

36. Dennis Zielstra, Hartwig H Hochmair, and Pascal Neis. Assessing the effect of data imports on thecompleteness of openstreetmap–au nited s tates case study. Transactions in GIS, 17(3):315–334,
2013.
37. Zhenlong Li, Huan Ning, Fengrui Jing, and M Naser Lessani. Understanding the bias of mobile
location data across spatial scales and over time: a comprehensive analysis of SafeGraph datain the United States. (March), 2023. URL https://www.researchgate.net/publication/
369087412_Understanding_the_bias_of_mobile_location_data_across_spatial_scales_
and_over_time_a_comprehensive_analysis_of_SafeGraph_data_in_the_United_States.
38. Ryan Fox Squire. What about bias in the SafeGraph dataset?, 2019.
39. Petroc Taylor. Smartphone penetration rate as share of the population in the United States from2010 to 2021. Technical report, Statista, 2023. URL https://www.statista.com/statistics/
201183/forecast-of-smartphone-penetration-in-the-us/.
40. Raj Chetty, John N Friedman, Nathaniel Hendren, Maggie R Jones, and Sonya R Porter. The
opportunity atlas: Mapping the childhood roots of social mobility. Working Paper 25147, National
Bureau of Economic Research, October 2018. URL http://www.nber.org/papers/w25147.

24

5

Supplementary Information
Table S.1:

Essential Function Categories and Associated NAICS Code

Category

6-Digit NAICS CodeHealthcare

621111, 621112, 621410, 621492, 621493, 621498, 622110 . .

Drugstores

446110 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Schools

611110 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Religious Organizations

813110 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Restaurants

722511, 722513, 722514, 722515 . . . . . . . . . . . . . . . . . . . . . . . . . . .

Groceries

445110, 445120. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Parks712190 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Cultural Institutions

711190, 711310, 712110 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Services

491110, 522110, 812111, 812112, 812113, 812320 . . . . . . . . . . .

Table S.2: Correlation of various measures of local living with 15-minute usage (percentageof trips)
Alternate measure

Neighborhood level correlation

Neighborhood level mean

Urban area level correlation

Urban area level mean

(1)

(2)

(3)

(4)

15 min. usage

1.000

0.239

1.000

0.158

10 min. usage

0.839

0.175

0.927

0.108

20 min. usage

0.903

0.284

0.960

0.190

25 min. usage

0.867

0.337

0.945

0.235

-0.7282.718-0.5993.552

150,159

150,159

420

420

Med. trip dist.
(km)
Observations

Notes.— The table shows neighborhood-level correlations and means for different measures of local usage. Column
1 provides the correlation between our baseline usage measure and alternative measures defined by the share of trips
within 10, 20, and 25 minutes, as well as the median trip length. Column 2 reports (unweighted) means for thesealternative variables. Columns 3 and 4 aggregate the data to urban areas and report urban area-level correlations and(unweighted) means.

25

Table S.3: Correlation between different local access measures
Counts of Amenities within a 15-minute walk (CBG-level)
EqualWeighted
AccessAll
categories

Healthcare

Parks

Restaurants

Schools

Services

Groceries

Drugstore

Religious

Arts and
Culture

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

0.982

0.984

0.785

0.681

0.969

0.785

0.914

0.858

0.731

0.775

0.379

Correlation withbaseline access index

Notes.— The table shows neighborhood-level Spearman correlations between different measures of local access to services
and amenities. Column 1 provides the correlation between our baseline access index and an alternative measure of access,where the scores associated with the number of amenities in each category are added with equal weights. Columns 2–11
provide correlations between our baseline measure and counts of the number of each type of amenity within 15-minutewalksheds.

26

Table S.4: Relationship between 15-minute usage and access across neighborhoods (census
block groups) and Urban areas: Using all POI destinations
Dependent Variable: 15-minute Usage (% of trips)
Across CBGs

15-minute AccessAcross Urban Areas

(1)

(2)

(3)

(4)

(5)

(6)

0.621∗∗∗

0.546∗∗∗

0.553∗∗∗

0.414∗∗∗

0.303∗∗∗

0.311∗∗∗

p < 0.001

p < 0.001

p < 0.001

p < 0.001

p < 0.001

p < 0.001

0.825∗∗∗

0.365

2.781∗∗∗

2.427∗∗∗

p = 0.009

p = 0.138

p < 0.001

p < 0.001−5.966∗∗∗−5.512∗∗∗−4.480∗∗∗−5.256∗∗∗p < 0.001

p < 0.001

p < 0.001

p < 0.001

0.038∗∗∗

0.025∗∗∗

0.062∗∗∗

0.036∗∗∗

p < 0.001

p = 0.001

p < 0.001

p = 0.008−0.017∗∗∗−0.021∗∗∗

0.012

0.005

p = 0.002

p < 0.001

p = 0.254

p = 0.617

Pop Density Log

Median Income Log% Education: BA +% WhiteTransit Frequency (per sq. mi)

Average Commute (mi)0.001∗∗∗

0.005∗∗∗

p = 0.003

p < 0.001

0.118∗∗∗−0.098∗∗∗p < 0.001

p < 0.001

Urban Area FE

✓

✓

✓

Observations

150,159

145,862

145,859

420

420

420

R2

0.715

0.741

0.748

0.699

0.731

0.786

Partial R2

0.712

0.626

0.634

0.699

0.512

0.525

Notes.— The table reports OLS estimates of the relationship between 15-minute usage and access. It uses the specifications from Table 1, but redefines the outcome variable to include trips to all POI destinations (as opposed toonly consumption trips). Columns 1 to 3 report estimates at the neighborhood level (controlling for urban area fixed
effects) and columns 4 to 6 report estimates at the urban area level. The partial R-squared for access is computed
as β ×cov(15-minute usage,15-minute access), where β is the regression coefficient for 15-minute access.
var(15-minute usage)

The coefficientswith *** are significant at the 1% confidence level; with ** are significant at the 5% confidence level; and with * aresignificant at the 10% confidence level. Included P-values correspond to a two-sided t-test for each estimate, using
robust standard errors clustered at the county level.

27

Table S.5: Relationship between 15-minute usage and access across neighborhoods (census
block groups) and Urban areas: Role of transit access

Dependent Variable: 15-minute Usage (% of trips)
Across CBGs

15-minute Access

Pop Density Log

Median Income Log% Education: BA +% WhiteTransit Frequency (per sq. mi)

Average Commute (mi)Full sample

Across Urban Areas

(1)

(2)

(3)

(4)

0.715∗∗∗

0.742∗∗∗

0.493∗∗∗

0.460∗∗∗

p < 0.001

p < 0.001

p < 0.001

p < 0.001

0.183

2.419∗∗∗

2.124∗∗∗

3.090∗∗

p = 0.378

p < 0.001

p < 0.001

p = 0.017−4.238∗∗∗−5.882∗∗∗−3.328∗∗∗−3.298∗∗p < 0.001

p < 0.001

p < 0.001

p = 0.017−0.0003−0.0020.018

0.054∗∗

p = 0.948

p = 0.867

p = 0.132

p = 0.014−0.0060.009−0.0050.025

p = 0.238

p = 0.376

p = 0.587

p = 0.180

0.001∗∗∗

0.0003∗∗0.003∗∗∗

0.003∗∗

p = 0.005

p = 0.022

p = 0.003

p = 0.049

0.107∗∗∗

0.104∗∗∗−0.094∗∗∗−0.066∗∗∗p < 0.001

p < 0.001

p < 0.001

p = 0.005✓✓

Only transit freq. above median✓✓

Urban Area FE✓✓

Observations

145,859

44,492

420

129

R2

0.756

0.625

0.875

0.819

Notes.— The table reports OLS estimates of the relationship between 15-minute usage and access for areas withhigh transit access. Columns 1 and 3 report estimates from specifications identical to columns 3 and 6 in Table 1,
respectively. Columns 2 and 4 restricts the estimation sample to CBGs and Urban Areas with above-average transit
frequency per square mile (obtained from EPA). The coefficients with *** are significant at the 1% confidence level;with ** are significant at the 5% confidence level; and with * are significant at the 10% confidence level. Included
P-values correspond to a two-sided t-test for each estimate, using robust standard errors clustered at the county level.

28

Table S.6: OLS and IV estimates for New York City: subsample of low-mobility neighborhoods (proxied by a high share of children that live and grew up in the same tract)
Dependent variable:
Usage (% of trips within a 15m walk)
OLS

Access to amenities within a 15m walk

Population Density (Log)Median Income (Log)IV estimates

(1)

(2)

(3)

(4)

0.835∗∗∗

1.131∗∗∗

1.088∗∗∗

1.207∗∗∗

p < 0.001

p < 0.001

p < 0.001

p < 0.001

3.776∗∗∗

3.371∗∗∗

3.325∗∗∗

p < 0.001

p < 0.001

p < 0.001−4.490∗∗∗−4.310∗∗∗−4.378∗∗∗p < 0.001

p < 0.001

p < 0.001

IV: Log Average Commercial FAR in 1961 Resolution

✓

✓

✓

Distance to City Center

✓

✓

✓

✓

1960 Population and Housing Density

✓

✓

✓

✓

1960 Workers by Industry

✓

✓

✓

✓

1960 Poor and Non-white Families✓

First stage F-stat

NA

71.14

67.12

53.01

Observations

1,326

1,385

1,326

1,326

R2

0.563

0.497

0.556

0.552

Notes.— This table reports OLS and IV estimates of the relationship between 15-minute usage and access for New York
City. We use the specifications from Table 2 and restrict the estimation sample to low-mobility neighborhoods, definedas those in the top quartile according to the share of children who grew up and still live in the same Census tract by
2015. These data come from the Opportunity Atlas (Chetty et al. 2018). Column 1 reports OLS estimates controllingfor distance to the city center and a host of socio-economic controls for 1960, including population and housing density
and the share of workers by industry. Columns 2-4 report IV estimates. Columns 2 and 3 control for distance to thecity center, population and housing density and the share of workers by industry. Column 4 includes additional controls
for the share of poor and non-white families in 1960. The coefficients with *** are significant at the 1% confidence
level; with ** are significant at the 5% confidence level; and with * are significant at the 10% confidence level. Included
P-values correspond to a two-sided t-test for each estimate, using robust standard errors.

29

Table S.7: Relationship between experienced segregation and local trips for neighborhoodsof different income levels.

Dependent variable:
Physical Segregation Index (PSI)
Main 15 urban areas(1)
Usage × Low income
Usage × Moderate-low income
Usage × Moderate-high income
Usage × High income(2)

Full sample(3)0.164∗∗∗

0.071∗∗∗

p < 0.001

p < 0.001

0.041∗∗

0.007

p = 0.013

p = 0.318-0.069∗∗∗-0.056∗∗∗p < 0.001

p < 0.001-0.103∗∗∗-0.060∗∗∗p < 0.001
Access × Low income
Access × Moderate-low income
Access × Moderate-high income
Access × High income(4)

p < 0.001
0.182∗∗∗

0.041∗∗∗

p < 0.001

p = 0.003

0.043∗∗∗-0.009p = 0.006

p = 0.131-0.084∗∗∗-0.069∗∗∗p < 0.001

p < 0.001-0.114∗∗∗-0.070∗∗∗p < 0.001

p < 0.001

Urban area fixed effects

✓

✓

✓

✓

R-squared

0.17

0.20

0.24

0.24

Observations

34739

34739

145855145855

Notes.— The table reports OLS estimates of the relationship between experienced segregation and 15-minute usage(columns 1 and 3) and access (columns 2 and 4). We provide estimates for neighborhoods in the 15 cities with thehighest levels of 15-minute usage and for all neighborhoods in our sample. The table reports estimates interacted with
dummies for low income neighborhoods (bottom quartile of income in urban area), moderate-low income neighborhoods(second quartile), moderate-high income neighborhoods (third quartile), and high income neighborhoods (top quartile).
Robust standard errors are computed with clustering at the urban area level. The coefficients with *** are significant
at the 1% confidence level; with ** are significant at the 5% confidence level; and with * are significant at the 10%
confidence level. Included P-values correspond to a two-sided t-test for each estimate, using robust standard errors.

30

Table S.8: Relationship between business counts in Safegraph and Census Data

All County x Industry Pairs
Establishments Count from Census (CBP)
(1)

(2)

0.925∗∗∗

0.991∗∗∗

p < 0.001

p < 0.001

No

Yes

Observations

11,391

11,391

R2

0.521

0.591

Establishments Count from SafegraphTwo-Digit NAICS Code FE

Notes.— This table reports estimates from a regression of the number of businesses in a county (from the 2019 Census
County Business Patterns data, CBP) and the number of Safegraph establishments in the same two-digit NAICS category
and county. Each observation corresponds to a county and two-digit industry cell. Column (1) reports estimates from apooled OLS regression of Census establishment counts on Safegraph establishment counts across these cells, and column(2) presents the results of estimating the same model with two-digit NAICS code fixed effects added. The coefficientswith *** are significant at the 1% confidence level; with ** are significant at the 5% confidence level; and with * aresignificant at the 10% confidence level. Included P-values correspond to a two-sided t-test for each estimate, using
robust standard errors clustered at the county level.

31

Table S.9: First-stage and reduced-form estimates of 1961 zoning regulation on current
access and usage.
Dependent variable:
AccessLog Average Commercial FAR

Usage (% trips)

(1)

(2)

(3)

(4)

6.522∗∗∗

4.460∗∗∗

7.187∗∗∗

5.569∗∗∗

p < 0.001

p < 0.001

p < 0.001

p < 0.001

Controls:
Distance to City Center✓✓

1960 Population and Housing Density✓✓

1960 Workers by Industry✓✓

Observations

5,582

5,582

5,582

5,582

R2

0.475

0.696

0.224

0.329

Notes.— The table reports first-stage (columns 1 and 2) and reduced-form (columns 3 and 4) estimates of the relationshipbetween 1961 zoning and access and usage for neighborhoods in New York City. Columns 2 and 4 control for distance tothe city center and a host of socio-economic controls for 1960, including population and housing density and the shareof workers by industry. The covariates included in these models come from Twinam (2017) and Martynov (2022). The
coefficients with *** are significant at the 1% confidence level; with ** are significant at the 5% confidence level; andwith * are significant at the 10% confidence level. Included P-values correspond to a two-sided t-test for each estimate,using robust standard errors.

32

1961 NYC Zoning Resolution (Commercial Districts)
FAR
0.5
1
2
3.4
4
5
6
10
15

Figure S.1: Historical zoning. Maximum floor-to-area ratio in commercial uses established according to the 1961 New York City Zoning Resolution.

33