Space-time accessibility supports participation in after-work leisure activities

Jorge Gil; Laura Alessandretti; Rafael H. M. Pereira; Silvia De Sojo Caso; Yuan Liao

arxiv: 2510.10307 · v3 · pith:TF7WWHBAnew · submitted 2025-10-11 · 💻 cs.SI · cs.CE

Space-time accessibility supports participation in after-work leisure activities

Yuan Liao , Rafael H. M. Pereira , Jorge Gil , Silvia De Sojo Caso , Laura Alessandretti This is my paper

Pith reviewed 2026-05-21 21:32 UTC · model grok-4.3

classification 💻 cs.SI cs.CE

keywords space-time accessibilityleisure participationGPS datastructural equation modelingurban mobilitycapability approachParis regionafter-work activities

0 comments

The pith

Space-time accessibility between home and work increases after-work leisure participation

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper applies a space-time accessibility metric to measure feasible leisure opportunities that fit within daily home-to-work routines and time budgets. Analysis of high-resolution GPS traces from 2,415 working residents in the Paris region shows that people mostly choose leisure destinations inside the sets defined by this metric. Structural equation modeling finds a net positive effect of the metric on leisure participation, driven by a direct pathway that outweighs a smaller indirect reduction linked to shorter travel times. The work also links active travel and education to higher participation while noting constraints from local poverty and caregiving duties.

Core claim

Space-time accessibility (STA), rooted in the capability approach, captures feasible leisure opportunity sets between home and work given time budgets, individual transport modes, and urban infrastructure. GPS data confirm that most observed leisure locations lie within these STA-defined sets. Structural equation modeling shows STA exerts a significant positive total effect on leisure participation (β = 0.14, p < .001), driven by a significant direct effect (β = 0.18, p < .001) that is only modestly offset by an indirect pathway through reduced travel time (β = -0.04, p < .01).

What carries the argument

Space-time accessibility (STA) metric that quantifies reachable leisure opportunities between home and work under realistic daily time and transport constraints

If this is right

Most chosen leisure locations fall inside the STA opportunity sets
STA produces a net positive effect on diversity of leisure locations visited and activity duration
Active mode use and higher education directly increase leisure participation
Local poverty and caregiving responsibilities directly reduce leisure participation

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Policies that improve transport links and options along typical work-home corridors could expand real leisure choices for workers
The STA approach could be tested on other post-work activities such as exercise or errands to check whether the same pattern holds
Urban design that widens feasible option sets during common time windows may support work-life balance more effectively than measures focused only on average travel time reduction

Load-bearing premise

The space-time accessibility metric accurately captures the leisure opportunity sets that individuals actually consider and can reach

What would settle it

A large share of leisure visits falling outside the calculated STA sets, or a replication finding no net positive effect of STA on participation measures, would undermine the central claim

Figures

Figures reproduced from arXiv: 2510.10307 by Jorge Gil, Laura Alessandretti, Rafael H. M. Pereira, Silvia De Sojo Caso, Yuan Liao.

**Figure 1.** Figure 1: Accessibility as a human capability. Conceptual framework illustrating how we conceptualize space–time accessibility (STA) as a human capability, adapted from Luz and Portugal [2022], Luz et al. [2022]. The diagram shows how factors influencing activity participation interact with capability sets. 3 Materials This study draws on a combination of large-scale mobility traces, contextual geographic data, and … view at source ↗

**Figure 2.** Figure 2: The Paris region and its public transit lines. Grey lines represent IRIS boundaries, and black lines depict roads and motorways. The right panel provides a zoomed-in view of central Paris, where green points mark home and work locations. 3.2 Trip records We use a dataset collected via the Enquête Mobilité par GPS (EMG 2023) initiative and made available through participating in the NetMob 2025 Data Challen… view at source ↗

**Figure 3.** Figure 3: Space–Time Accessibility (STA). The light-green region represents the Potential Path Area (PPA), i.e., all locations reachable given the individual’s time budget and travel constraints between home and work. Opportunities within this area (dark green) are accessible, while those outside it (light green) are not. The direct home–work path is shown in bold orange, and a feasible work–activity–home path is sh… view at source ↗

**Figure 4.** Figure 4: Space–Time Accessibility (STA) set vs. visited leisure locations. One example individual. Home and work icons show where the person lives and works. Hexagons are at resolution 8, ∼0.74 km2 . for two reasons. First, not all locations labeled as leisure in the GPS data correspond to entries in the leisure POI dataset extracted from Overture Maps. Second, the GPS coordinates have been preprocessed by the data… view at source ↗

**Figure 5.** Figure 5: Pathway structure. This directed acyclic graph shows the hypothesized and empirically validated pathways. Exposure=STA value, Outcome=Activity participation, Black arrows=Causal paths, Brown arrows=Biasing paths. 5.2). Finally, we present the modeling outcomes (Section 5.3), detailing the quantified relationships and how different components link to space–time accessibility and shape leisure location diver… view at source ↗

**Figure 6.** Figure 6: presents a spatial overview of space–time accessibility value Ai (log-transformed), total travel time, and diversity of visited leisure locations across the study region, highlighting the geographic variations. Figure 6a shows the proportion of inhabitants with STA > 0, indicating the share of individuals who have feasible opportunities beyond their fixed home and work locations. Higher values are concentr… view at source ↗

**Figure 7.** Figure 7: Space–time accessibility value Ai and mobility and activity behaviors across transport modes. a, Total travel time (top) and leisure location diversity (bottom), comparing those with Ai = 0 (grey) and Ai > 0 (green). b, Total travel time as a function of Ai for car and public transit users (Ai > 0). c, Leisure location diversity (Ai > 0) by mode. Error bars indicate bootstrap median estimation errors. 5.2 … view at source ↗

**Figure 8.** Figure 8: Selectivity in spatial choice behavior across individuals. a, Distribution of individuals by the share of visited locations outside their modeled feasible set (STAi), with bar color indicating the proportion of car users in each bin. Bins include lower boundaries. b, Standardized effect size di plotted against the empirical p-value from the mean rank test, on a log scale. The vertical line marks the p = 0.… view at source ↗

**Figure 9.** Figure 9: Structural equation model of factors shaping leisure activity participation. Standardized path coefficients (p < 0.05) are shown along the arrows, with positive effects in teal and negative effects in orange. Individual attributes (left) influence transport mode choices and space–time accessibility (center), which in turn shape travel behavior and leisure activity participation (right). 5.3.1 Transport mod… view at source ↗

read the original abstract

Understanding how accessibility shapes participation in leisure activities is central to promoting inclusive and vibrant urban life. Conventional accessibility measures often focus on potential access from fixed home locations, overlooking the constraints and opportunities embedded in daily routines. In this study, we apply a space-time accessibility (STA) metric rooted in the capability approach, capturing feasible leisure opportunities between home and work given a certain time budget, individual transport modes, and urban infrastructure. Using high-resolution GPS data from 2,415 working residents in the Paris region, we assess how STA influences leisure participation during weekdays, measured as the diversity of leisure locations visited and activity duration. Observed destination choices confirm that most individuals select leisure locations within their STA-defined opportunity sets, validating the metric as a proxy for capability sets. Structural equation modeling shows that STA exerts a significant positive total effect on leisure participation ($\beta = 0.14$, $p < .001$), driven by a significant direct effect ($\beta = 0.18$, $p < .001$) that is only modestly offset by an indirect pathway through reduced travel time ($\beta = -0.04$, $p < .01$). Individual attributes also directly shape participation: active mode use and higher education promote leisure engagement, while local poverty and caregiving responsibilities constrain it. These findings highlight the value of person-centered, capability-informed accessibility metrics for understanding inequalities in urban mobility and informing transport planning strategies that expand real freedoms to participate in social life across diverse population groups.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper links space-time accessibility to leisure participation with GPS data but the supporting validation is only partial.

read the letter

This paper finds that space-time accessibility has a positive total effect on after-work leisure participation, with a beta of 0.14 in their structural equation model on Paris data. They use high-resolution GPS traces from 2,415 working residents to define feasible leisure spots between home and work, factoring in time budgets and individual transport modes. The model separates a direct positive path from a small negative indirect one through travel time, and it also flags roles for education, active modes, and caregiving. They add a check that most observed destinations sit inside the modeled opportunity sets. That step is a reasonable start for validating the metric as a proxy for capability sets. The work does a solid job moving accessibility analysis beyond fixed home locations and into actual daily schedules with real movement data. Applying the framework to leisure diversity and duration rather than just work trips is a useful extension. The softer spot is the validation itself. Showing that chosen locations are feasible confirms the sets are not too narrow, but it supplies little evidence that these are the sets people actually weigh or that unmodeled factors like cost, safety, or tighter personal constraints do not shrink the effective choice set. Because the participation measures come from the same visited locations, the check overlaps with the outcome and is not fully independent. The abstract also gives almost no detail on SEM specification, variable construction, or GPS processing, so robustness is hard to judge from what is here. This kind of paper is for urban mobility researchers and planners who care about person-centered, schedule-aware measures and equity in social participation. The dataset and question are concrete enough to deserve referee time, even if the methods will need more expansion and scrutiny in review. I would send it out for peer review.

Referee Report

2 major / 1 minor

Summary. The manuscript examines the link between space-time accessibility (STA) to leisure opportunities—defined via time budgets, individual transport modes, and urban infrastructure between home and work—and after-work leisure participation (measured as diversity of visited locations and activity duration). Using GPS traces from 2,415 working residents in the Paris region, it validates the STA metric by confirming that observed destinations largely fall inside the modeled opportunity sets and applies structural equation modeling to report a positive total effect of STA on participation (β = 0.14, p < .001), driven by a direct effect (β = 0.18) modestly offset by an indirect path through travel time (β = -0.04).

Significance. If the central interpretation holds, the work advances capability-based accessibility research by providing person-centered, routine-aware metrics that better capture real freedoms to participate in leisure than home-based measures. The use of high-resolution GPS data to both construct STA and measure outcomes, together with explicit decomposition into direct and indirect SEM paths, supplies a concrete empirical test of the capability approach with implications for transport planning aimed at reducing leisure-access inequalities.

major comments (2)

[Abstract] Abstract (validation paragraph): the claim that observed GPS destinations 'confirm' the STA metric as a proxy for capability sets only shows that chosen locations satisfy the modeled constraints; it supplies no evidence that the modeled sets match the opportunity sets individuals actually weigh or that unmodeled factors (cost, safety, social norms, tighter personal budgets) do not shrink effective choice sets below the STA boundary. Because the participation outcome is itself derived from the same visited locations, the check is not independent of the dependent variable and therefore does not rule out omitted-variable explanations for the reported direct effect (β = 0.18).
[Abstract and Methods] Abstract and Methods (SEM reporting): specific total, direct, and indirect effects with p-values are presented, yet no information is given on model specification (e.g., latent variables, error covariances, identification strategy), exact construction of the STA and participation variables, GPS processing steps (map-matching, time-budget allocation), or robustness checks (alternative specifications, subsample analyses). Without these details the reported β coefficients cannot be evaluated for bias or sensitivity.

minor comments (1)

[Abstract] Abstract: the phrase 'space-time accessibility (STA) metric rooted in the capability approach' is introduced without a one-sentence definition or citation to foundational references, which would help readers outside the subfield.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed comments, which help clarify the scope and limitations of our validation approach and methodological transparency. We address each major comment below and commit to revisions that strengthen the manuscript without altering its core claims.

read point-by-point responses

Referee: [Abstract] Abstract (validation paragraph): the claim that observed GPS destinations 'confirm' the STA metric as a proxy for capability sets only shows that chosen locations satisfy the modeled constraints; it supplies no evidence that the modeled sets match the opportunity sets individuals actually weigh or that unmodeled factors (cost, safety, social norms, tighter personal budgets) do not shrink effective choice sets below the STA boundary. Because the participation outcome is itself derived from the same visited locations, the check is not independent of the dependent variable and therefore does not rule out omitted-variable explanations for the reported direct effect (β = 0.18).

Authors: We agree that the observed-destination check primarily verifies consistency (chosen locations lie inside the modeled STA sets) rather than proving that the STA sets exactly match the full opportunity sets individuals weigh or that unmodeled factors such as cost, safety, or social norms do not further constrain choices. We also acknowledge the partial dependence between the validation and the participation measures, both derived from GPS traces, which limits its ability to fully rule out omitted-variable bias for the direct effect. This validation remains a useful basic consistency test but is not a comprehensive confirmation of the capability-set interpretation. We will revise the abstract and add an explicit limitations subsection in the Discussion to state these caveats and outline how future extensions could incorporate additional constraints such as cost or safety into the STA model. revision: yes
Referee: [Abstract and Methods] Abstract and Methods (SEM reporting): specific total, direct, and indirect effects with p-values are presented, yet no information is given on model specification (e.g., latent variables, error covariances, identification strategy), exact construction of the STA and participation variables, GPS processing steps (map-matching, time-budget allocation), or robustness checks (alternative specifications, subsample analyses). Without these details the reported β coefficients cannot be evaluated for bias or sensitivity.

Authors: The manuscript's Methods section already describes the STA construction (time-budgeted reachable leisure opportunities given mode and infrastructure), participation variables (location diversity and duration from GPS), and the SEM structure with direct and indirect paths. However, we accept that additional detail on identification strategy, possible error covariances, precise GPS processing steps, and robustness checks would improve evaluability. We will expand the Methods section with these elements—including explicit identification constraints, map-matching and time-allocation procedures, and new subsections reporting alternative specifications (different time budgets) and subsample analyses (e.g., by education or caregiving status). We will also add a brief robustness summary and, space permitting, reference key methodological features in the abstract. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation remains self-contained

full rationale

The paper constructs the STA metric from time budgets, individual modes, and infrastructure data, then validates that observed GPS leisure destinations fall inside the modeled sets as a consistency check. Participation is measured separately as diversity and duration of visited locations from the same GPS traces. Structural equation modeling then estimates the total effect (β = 0.14) and direct/indirect paths from variation across the 2,415 individuals. These coefficients are not forced by definition, by the validation step, or by any self-citation chain; the validation only confirms the metric is not overly narrow and supplies no algebraic identity that determines the reported betas. The analysis is therefore self-contained against external empirical benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim depends on fitted SEM parameters from the GPS dataset and standard statistical modeling assumptions rather than new theoretical entities or derivations.

free parameters (1)

SEM path coefficients (total, direct, indirect effects)
Estimated from the structural equation model fitted to the observed GPS trajectories, leisure participation measures, and individual attributes.

axioms (1)

domain assumption Linear relationships and standard SEM assumptions (e.g., no omitted variable bias, multivariate normality for inference)
Invoked to interpret the reported beta coefficients and p-values as direct and indirect effects.

pith-pipeline@v0.9.0 · 5803 in / 1228 out tokens · 55349 ms · 2026-05-21T21:32:19.862373+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

17 extracted references · 17 canonical work pages

[1]

doi:10.1007/BF00986754

ISSN 1573-7837. doi:10.1007/BF00986754. Gregorio Luz and Licinio Portugal. Understanding transport-related social exclusion through the lens of capabilities approach.Transport Reviews, 42(4):503–525,

work page doi:10.1007/bf00986754
[2]

Maarten Kroesen and Bert Van Wee

doi:10.1016/S0191-2615(01)00046-7. Maarten Kroesen and Bert Van Wee. Understanding how accessibility influences health via active travel: Results from a structural equation model.Journal of Transport Geography, 102:103379,

work page doi:10.1016/s0191-2615(01)00046-7
[3]

Siqi Song, Mi Diao, and Chen-Chieh Feng

doi:10.1016/j.jtrangeo.2022.103379. Siqi Song, Mi Diao, and Chen-Chieh Feng. Individual transport emissions and the built environment: A struc- tural equation modelling approach.Transportation Research Part A: Policy and Practice, 92:206–219,

work page doi:10.1016/j.jtrangeo.2022.103379 2022
[4]

Yingheng Zhang, Haojie Li, and Gang Ren

doi:10.1016/j.tra.2016.08.005. Yingheng Zhang, Haojie Li, and Gang Ren. Ex-post evaluation of transport interventions with causal mediation analysis. Transportation, 52:93–126,

work page doi:10.1016/j.tra.2016.08.005 2016
[5]

Mohammad Azmoodeh, Farshidreza Haghighi, and Hamid Motieyan

doi:10.1007/s11116-023-10413-0. Mohammad Azmoodeh, Farshidreza Haghighi, and Hamid Motieyan. The capability approach and social equity in transport: Understanding factors affecting capabilities of urban residents, using structural equation modeling. Transport Policy, 142:137–151,

work page doi:10.1007/s11116-023-10413-0
[6]

Linna Li, Jiayuan Cai, and Wenfeng Chen

doi:10.1016/j.tranpol.2023.08.010. Linna Li, Jiayuan Cai, and Wenfeng Chen. How does transport development contribute to rural income in china? evidence from county-level analysis using structural equation model.Travel Behaviour and Society, 34:100708,

work page doi:10.1016/j.tranpol.2023.08.010 2023
[7]

Job van Eldijk, Jorge Gil, and Lars Marcus

doi:10.1016/j.tbs.2023.100708. Job van Eldijk, Jorge Gil, and Lars Marcus. Disentangling barrier effects of transport infrastructure: synthesising research for the practice of impact assessment.European transport research review, 14(1):1,

work page doi:10.1016/j.tbs.2023.100708 2023
[8]

Populations légales des régions en 2020 – recensement de la population

INSEE. Populations légales des régions en 2020 – recensement de la population. https://www.insee.fr/fr/ statistiques/6683011?sommaire=6683037,

work page arXiv 2020
[9]

La Grande Conversation

Accessed: 2025-08-19. La Grande Conversation. Fewer parisians but more greater parisians: Den- sity in the Île-de-france. https://www.lagrandeconversation.com/en/society/ fewer-parisians-but-more-greater-parisians-density-in-the-ile-de-france ,

work page 2025
[10]

Île-de-France Mobilités

Accessed: 2025-08-18. Île-de-France Mobilités. Reference Guide for Mobility-as-a-Service (MaaS). PDF document on the PRIM platform, February

work page 2025
[11]

Biao Yin and Fabien Leurent

Online at: https://prim.iledefrance-mobilites.fr/content/files/2023/02/IDFM_ Reference-guide-for-Mobility-as-a-Service_english.pdf, accessed 2025-08-19. Biao Yin and Fabien Leurent. What are the multimodal patterns of individual mobility at the day level in the paris region? a two-stage data-driven approach based on the 2018 household travel survey.Transp...

work page 2023
[12]

Uber Technologies, Inc

URL https://arxiv.org/ abs/2506.05903. Uber Technologies, Inc. H3: A hexagonal hierarchical spatial index. https://h3geo.org/,

work page arXiv
[13]

Institut national de la statistique et des études économiques (INSEE)

Accessed: 2025-08-18. Institut national de la statistique et des études économiques (INSEE). Revenus, pauvreté et niveau de vie en 2021 (iris). Web page,

work page 2025
[14]

Overture Maps Foundation

URLhttps://www.insee.fr/fr/statistiques/8229323. Overture Maps Foundation. Overture maps api.https://overturemaps.org/,

work page arXiv
[15]

Mei-Po Kwan

Accessed: 2025-08-18. Mei-Po Kwan. Gender and individual access to urban opportunities: a study using space–time measures.The Professional Geographer, 51(2):210–227,

work page 2025
[16]

URLhttps://doi.org/10.32866/001c.21262

doi:10.32866/001c.21262. URLhttps://doi.org/10.32866/001c.21262. Nick Huntington-Klein.The effect: An introduction to research design and causality. Chapman and Hall/CRC,

work page doi:10.32866/001c.21262
[17]

Author contributions Y .L

Acknowledgements This research is funded by the Swedish Research Council (Project Number 2022-06215). Author contributions Y .L. conceptualized the study. All authors designed the methods. Y .L. processed the data and the model. All authors wrote the manuscript. Competing interests The authors declare that there are no conflicts of interest. Additional in...

work page 2022

[1] [1]

doi:10.1007/BF00986754

ISSN 1573-7837. doi:10.1007/BF00986754. Gregorio Luz and Licinio Portugal. Understanding transport-related social exclusion through the lens of capabilities approach.Transport Reviews, 42(4):503–525,

work page doi:10.1007/bf00986754

[2] [2]

Maarten Kroesen and Bert Van Wee

doi:10.1016/S0191-2615(01)00046-7. Maarten Kroesen and Bert Van Wee. Understanding how accessibility influences health via active travel: Results from a structural equation model.Journal of Transport Geography, 102:103379,

work page doi:10.1016/s0191-2615(01)00046-7

[3] [3]

Siqi Song, Mi Diao, and Chen-Chieh Feng

doi:10.1016/j.jtrangeo.2022.103379. Siqi Song, Mi Diao, and Chen-Chieh Feng. Individual transport emissions and the built environment: A struc- tural equation modelling approach.Transportation Research Part A: Policy and Practice, 92:206–219,

work page doi:10.1016/j.jtrangeo.2022.103379 2022

[4] [4]

Yingheng Zhang, Haojie Li, and Gang Ren

doi:10.1016/j.tra.2016.08.005. Yingheng Zhang, Haojie Li, and Gang Ren. Ex-post evaluation of transport interventions with causal mediation analysis. Transportation, 52:93–126,

work page doi:10.1016/j.tra.2016.08.005 2016

[5] [5]

Mohammad Azmoodeh, Farshidreza Haghighi, and Hamid Motieyan

doi:10.1007/s11116-023-10413-0. Mohammad Azmoodeh, Farshidreza Haghighi, and Hamid Motieyan. The capability approach and social equity in transport: Understanding factors affecting capabilities of urban residents, using structural equation modeling. Transport Policy, 142:137–151,

work page doi:10.1007/s11116-023-10413-0

[6] [6]

Linna Li, Jiayuan Cai, and Wenfeng Chen

doi:10.1016/j.tranpol.2023.08.010. Linna Li, Jiayuan Cai, and Wenfeng Chen. How does transport development contribute to rural income in china? evidence from county-level analysis using structural equation model.Travel Behaviour and Society, 34:100708,

work page doi:10.1016/j.tranpol.2023.08.010 2023

[7] [7]

Job van Eldijk, Jorge Gil, and Lars Marcus

doi:10.1016/j.tbs.2023.100708. Job van Eldijk, Jorge Gil, and Lars Marcus. Disentangling barrier effects of transport infrastructure: synthesising research for the practice of impact assessment.European transport research review, 14(1):1,

work page doi:10.1016/j.tbs.2023.100708 2023

[8] [8]

Populations légales des régions en 2020 – recensement de la population

INSEE. Populations légales des régions en 2020 – recensement de la population. https://www.insee.fr/fr/ statistiques/6683011?sommaire=6683037,

work page arXiv 2020

[9] [9]

La Grande Conversation

Accessed: 2025-08-19. La Grande Conversation. Fewer parisians but more greater parisians: Den- sity in the Île-de-france. https://www.lagrandeconversation.com/en/society/ fewer-parisians-but-more-greater-parisians-density-in-the-ile-de-france ,

work page 2025

[10] [10]

Île-de-France Mobilités

Accessed: 2025-08-18. Île-de-France Mobilités. Reference Guide for Mobility-as-a-Service (MaaS). PDF document on the PRIM platform, February

work page 2025

[11] [11]

Biao Yin and Fabien Leurent

Online at: https://prim.iledefrance-mobilites.fr/content/files/2023/02/IDFM_ Reference-guide-for-Mobility-as-a-Service_english.pdf, accessed 2025-08-19. Biao Yin and Fabien Leurent. What are the multimodal patterns of individual mobility at the day level in the paris region? a two-stage data-driven approach based on the 2018 household travel survey.Transp...

work page 2023

[12] [12]

Uber Technologies, Inc

URL https://arxiv.org/ abs/2506.05903. Uber Technologies, Inc. H3: A hexagonal hierarchical spatial index. https://h3geo.org/,

work page arXiv

[13] [13]

Institut national de la statistique et des études économiques (INSEE)

Accessed: 2025-08-18. Institut national de la statistique et des études économiques (INSEE). Revenus, pauvreté et niveau de vie en 2021 (iris). Web page,

work page 2025

[14] [14]

Overture Maps Foundation

URLhttps://www.insee.fr/fr/statistiques/8229323. Overture Maps Foundation. Overture maps api.https://overturemaps.org/,

work page arXiv

[15] [15]

Mei-Po Kwan

Accessed: 2025-08-18. Mei-Po Kwan. Gender and individual access to urban opportunities: a study using space–time measures.The Professional Geographer, 51(2):210–227,

work page 2025

[16] [16]

URLhttps://doi.org/10.32866/001c.21262

doi:10.32866/001c.21262. URLhttps://doi.org/10.32866/001c.21262. Nick Huntington-Klein.The effect: An introduction to research design and causality. Chapman and Hall/CRC,

work page doi:10.32866/001c.21262

[17] [17]

Author contributions Y .L

Acknowledgements This research is funded by the Swedish Research Council (Project Number 2022-06215). Author contributions Y .L. conceptualized the study. All authors designed the methods. Y .L. processed the data and the model. All authors wrote the manuscript. Competing interests The authors declare that there are no conflicts of interest. Additional in...

work page 2022