Pith. sign in

REVIEW 4 major objections 5 minor 1 cited by

Unraveling the hidden organisation of urban systems and their mobility flows

T0 review · 4 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper claims that the functional organization of cities—how people's flows connect urban areas—follows size-driven regularities and that the transport layer is what holds a city together, with removing it able to cut integration by…

desk verdict A useful empirical mapping of urban integration and segregation from Foursquare check-ins, but the unvalidated trip proxy and small sample mean the transport-layer headline should be read as suggestive, not conclusive. read the letter →

arxiv 1908.02538 v2 pith:HFL3RI6Z submitted 2019-08-07 physics.soc-ph cond-mat.dis-nn

Riccardo Gallotti , Giulia Bertagnolli , Manlio De Domenico This is my paper · ORCID
classification physics.soc-phcond-mat.dis-nn
keywords urbansystemshumanmobilityintegrationsegregationmultilayernetworkssmall-worldactivity-awarecityscaling
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper asks how the physical street layout of a city and the way people actually move through it jointly shape two measurable properties: integration, or how efficiently flows circulate between areas, and segregation, or how strongly the city splits into weakly connected clusters. Using two years of anonymized location check-ins and street data for ten megacities, it builds activity-aware networks and finds that larger cities tend to be more segregated and less integrated, with a functional organization close to small-world networks. The load-bearing result is that the transport layer is the main integrator: removing transport flows increases segregation in every city and cuts integration by up to half in several, while closing other activity layers can leave integration roughly unchanged. If correct, this gives quantitative grounds for deciding which activities can be restricted during an emergency without fragmenting the city.

What carries the argument

The argument runs on two normalized measures applied to paired structural and functional networks. Integration is measured by the global communication efficiency GCE, a weighted generalization of average inverse shortest-path distance, normalized by a physically grounded ideal network so weighted and unweighted networks can be compared; segregation is the maximal modularity Q* found by the Louvain algorithm. The functional network is a multilayer network whose nodes are 500m by 500m cells and whose weighted edges are consecutive check-ins between venues, stratified into fifteen activity layers with intra- and inter-layer flows. Null models—Watts-Strogatz small-world networks and random geometric networks with rewiring—are used to show that edge density and spatial scale reproduce the integration-segregation trade-off, and layer-removal attacks identify which activity types carry the integrating role.

What would settle it

Take one of the ten cities, rebuild the functional network from an independent mobility source such as mobile-phone location data over the same area, and compare the integration and segregation values with and without the transport layer; if the integration drop after removing transport disappears or reverses, the central claim fails for that city.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that a city's functional organization—how people's flows connect its areas—obeys regular, partly size-driven patterns that differ from its street geometry. For ten world megacities, larger cities tend to be more segregated and less integrated: functional segregation grows with city size and structural integration falls with it, while functional integration is not explained by size alone. The flow weights matter: deviations of functional from structural integration align with the city's flow hierarchy, so cities whose flows connect central and marginal areas more directly are more integrated than their size predicts. Finally, targeted removal of activity layers shows that the transport layer is the backbone: its removal raises segregation in all ten cities and reduces integration by up to about half in some, whereas removing short-range layers such as restaurants or leisure can slightly improve integration.

Load-bearing premise

The entire analysis assumes that consecutive anonymous check-ins by the same user are a faithful proxy for actual trips between city areas, so any bias in who checks in or which stops are recorded would change the measured integration and segregation.

Editorial extensions

If this is right

  • Across the ten cities, functional segregation and structural integration scale with city size, so size alone explains most of their variance ($R^2=0.67$ and $R^2=0.71$), whereas functional integration does not ($R^2=0.05$).
  • A city with low flow hierarchy—more direct connections between hubs and marginal areas—is more integrated than its size predicts, so flow distribution, not just geometry, shapes integration.
  • Closing transport-related flows increases segregation in all ten cities and can halve integration; closing short-range layers such as restaurants or leisure can slightly increase integration.
  • The multilayer, activity-aware view shows that the city's functional organization differs by activity type and time of day, so the same urban area contains several distinct 'cities within the city'.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the transport-layer result generalizes, emergency mobility restrictions should be evaluated not only by trip counts but also by the resulting change in integration and segregation; the paper's layer-removal protocol offers a ready-made stress test for such policies.
  • The flow-hierarchy finding suggests a testable design rule for urban planning: adding direct links between peripheral and central areas should raise functional integration even without changing city size or street density.
  • Because the integration measure is scale-independent, the same comparative approach could be applied to non-urban systems such as multimodal regional transport or online activity flows to see whether the small-world and transport-backbone patterns are universal.
  • A direct validation would compare the check-in-derived flows against independent mobility data for the same cities; if the proxy misses intermediate stops or underrepresents certain populations, the transport layer's integrating role could be overestimated.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper studies the structural and functional organization of ten megacities by combining OpenStreetMap street networks with Foursquare check-in flows. Functional networks are built from coarse-grained 500m-by-500m cells, with edge weights derived from consecutive anonymized check-ins; structural networks are topological undirected street networks on the same grid. Integration is measured by a normalized global communication efficiency (GCE) and segregation by modularity Q*. The authors report that functional segregation and structural integration scale with city size L, that functional integration is not size-determined but instead correlates with flow hierarchy from an independent dataset, that topological versions of the functional networks lie on the Watts-Strogatz small-world curve, and that targeted removal of the transport layer increases segregation in all cities and strongly reduces integration in most, with outliers such as Singapore, Jakarta, and Istanbul. The paper presents these results as evidence for cross-city regularities in urban functional organization and as quantitative support for restriction-policy decisions during emergencies.

Significance. If the empirical claims hold, the paper would provide measurable, cross-city regularities connecting urban size, structural topology, and functional mobility, and would offer a quantitative framework for evaluating activity-layer closures. Strengths include the use of a purpose-built normalized integration measure from the authors' companion work, comparisons against two spatial null models, explicit significance checks for modularity against configuration-model ensembles, and the use of an independent dataset for flow hierarchy in Fig. 4(d). The paper is also commendably candid about several limitations, including the narrow size range for scaling exponents, the need for better segregation normalization, and undersampling issues in monthly topological networks. However, the central empirical claims rest on a mobility-flow proxy that is not validated and on a small number of cities without uncertainty quantification, which substantially tempers the strength of the conclusions as currently supported.

major comments (4)
  1. [Methods: Geographic coarse-graining] The definition of flows from 'subsequent anonymized check-ins into Foursquare venues' is load-bearing for every result in the paper, yet the manuscript provides no time-window or trip-segmentation rule. Because the data are pre-aggregated by venue pair, month, and hour-of-day, two check-ins by the same user separated by days are counted as a single mobility flow. This can systematically over-represent long, unlikely transitions and distort edge weights, shortest-path efficiency, modularity, and the topological network used for the small-world comparison. I therefore request an explicit validation of the consecutive-check-in proxy against independent origin-destination data (e.g., smart-card, GPS, or mobile-phone data) for at least a subset of cities, or, failing that, a carefully argued sensitivity analysis using temporal strata or trip-length filtering that shows the main results are robust.
  2. [Fig. 5 and Fig. 6] The headline transport-layer finding is vulnerable to a check-in-behavior confound. Whether removal of the transport layer strongly drops integration (Tokyo, Seoul) or leaves it essentially unchanged (Singapore, Jakarta, Istanbul) may reflect how often Foursquare users check in at transit stations in those cities rather than the true role of transport in mobility. Since transport venues are precisely the places where station-based check-ins are common, the pattern in Fig. 6(b) could be an artifact of venue-check-in propensity. The paper needs a control for this, for example by excluding transport check-ins whose origin or destination is a transit station and recomputing the layer-attack analysis, or by validating the transport-layer result against an independent mobility dataset.
  3. [Fig. 4, Supplementary Fig. 2, Supplementary Table I] The central scaling and cross-city correlations are based on only ten cities, with no error bars, bootstrapping, or confidence intervals reported for the Pearson correlations, the power-law fits, or the R² values. With n=10, a single city such as Los Angeles can drive the apparent deviation in Fig. 4(d), and the reported exponents in Fig. 4(a,b) and Supplementary Fig. 3 are accompanied by the authors' own caveat that the size range is not diverse enough for meaningful exponent estimation. In addition, Supplementary Fig. 2 explicitly states that 'an improved and correctly normalized definition of segregation is still needed' because the segregation values across the three network types are not consistent. Since segregation is one of the two principal measures, this admission directly weakens the comparisons in Fig. 2 and the scaling result in Fig. 4(a) and should be addressed, for instance by reporting a normalized modularity or by restricting segregation claims to comparisons within, not across, network types.
  4. [Fig. 3] The claim that city functional networks resemble small-world networks is supported only by the visual proximity of the topological functional-network points to the Watts-Strogatz regression line in the integration-segregation plane. The paper does not report clustering coefficients, characteristic path lengths, or small-world coefficients, nor does it quantify the fit between the empirical points and the WS curve relative to the random-geometric-network curve. Given that the small-world claim appears in the abstract and is presented as a central organizational finding, a quantitative comparison is needed, for example a goodness-of-fit or model-selection statistic on the (integration, segregation) pairs against the two generative models.
minor comments (5)
  1. [Methods] The model name 'Watts-Strograts' should be 'Watts-Strogatz'.
  2. [Fig. 5 caption] The phrase 'which strongly influence a the urban functional connectivity' contains a typo; it should read 'which strongly influence the urban functional connectivity.'
  3. [Fig. 6, results text] In the sentence 'for others (notably Singapore, Jakarta and Istanbul) integration is unchained, or even slightly increased', the word 'unchained' is likely intended to be 'unchanged'.
  4. [Data availability] The data availability statement only offers aggregated data 'upon request' and no code is provided; for a network-science paper whose claims rely on specific data-processing choices, releasing the reconstruction code and a de-identified aggregate network would materially improve reproducibility.
  5. [Fig. 4(d)] The use of an independent flow-hierarchy dataset is a strength, but the text should state more explicitly that this comparison is at the city level and therefore has n=10; a scatter plot with city labels and a correlation coefficient with confidence interval would help assess the robustness of the reported relation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's central claims are empirical measurements from external flow and street data, with self-citations limited to methodological tools and independent supporting data.

full rationale

The paper's derivation chain is self-contained and does not reduce any claimed result to its own inputs. The core integration measure (normalized GCE) is cited to the authors' own prior work [71], but that prior work supplies a parameter-free normalization method whose stated construction (artificial shortcut links carrying the total flow of each weighted shortest path) does not assume the urban integration/segregation patterns reported here. It is a methodological self-citation, not a circular reduction: the empirical anti-correlation between GCE and modularity, the deviation of Los Angeles, and the scaling with city size are all measured outcomes rather than properties enforced by the definitions. The flow hierarchy values used to explain deviations from structural integration are taken from a different dataset in [74], with numerical values available externally, so this is independent support rather than a self-citation loop. The power-law fits in Fig. 4 are explicitly described as rough indications ('the sizes of the cities considered are not diverse enough for initiating a meaningful discussion based on the value of the exponents observed'), and are not used as predictions. The layer-removal transport analysis is a direct perturbation of the measured flow network, not a fitted parameter renamed as a prediction. The only serious weakness is the validity of consecutive Foursquare check-ins as a proxy for real trips, but that is a data-assumption/correctness concern, not a circularity concern: no equation or citation in the paper makes a conclusion equivalent to an input by construction.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

No new physical entities are introduced. The Gideal artificial flow construction in the normalization method comes from ref [71], not this paper. The free parameters are the fitted power-law exponents, which the authors themselves describe as rough. The main assumptions are the representativeness of the data and the validity of the chosen network measures.

free parameters (3)
  • Power-law exponent for functional segregation vs L = 0.31 ± 0.08
    Fitted in Fig. 4a to 10 cities; the paper says the size range is too narrow for a meaningful exponent.
  • Power-law exponent for structural integration vs L = 1.7 ± 0.4
    Fitted in Fig. 4b; same caveat. These exponents are used only to support the qualitative claim that size matters.
  • Power-law exponents for edge density, average weight, hotspot fraction = 2.2 ± 0.4, 1.1 ± 0.3, -1.3 ± 0.5
    Reported in Supplementary Fig. 3 as rough indications; not central to the main claims.
assumptions (4)
  • standard math Efficiency of communication between two areas is inversely proportional to topological distance.
    Stated in Methods as a reasonable assumption for defining integration.
  • domain assumption Normalized global communication efficiency from Bertagnolli et al. [71] correctly measures integration and allows comparison across weighted and unweighted networks.
    The paper relies entirely on this measure, which is defined in a companion paper by the same authors.
  • domain assumption Foursquare consecutive check-ins are a valid proxy for human mobility between city areas.
    All functional networks are built from this assumption; it is load-bearing and unvalidated against census or other mobility data.
  • domain assumption OpenStreetMap street networks completely and correctly represent the structural road network in the selected areas.
    The structural network is extracted with OSMnx; incomplete OSM coverage in some cities would bias the structural measures.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Unraveling the hidden organisation of urban systems and their mobility flows." pith.science (2026). https://pith.science/paper/HFL3RI6Z

@misc{pith2026190802538,
  author       = {Pith},
  title        = {Pith review of: Unraveling the hidden organisation of urban systems and their mobility flows},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HFL3RI6Z}},
  note         = {Machine review of arXiv:1908.02538}
}
read the original abstract

Increasing evidence suggests that cities are complex systems, with structural and dynamical features responsible for a broad spectrum of emerging phenomena. Here we use a unique data set of human flows and couple it with information on the underlying street network to study, simultaneously, the structural and functional organisation of 10 world megacities. We quantify the efficiency of flow exchange between areas of a city in terms of integration and segregation using well defined measures. Results reveal unexpected complex patterns that shed new light on urban organisation. Large cities tend to be more segregated and less integrated, while their overall topological organisation resembles that of small world networks. At the same time, the heterogeneity of flows distribution might act as a catalyst for further integrating a city. Our analysis unravels how human behaviour influences, and is influenced by, the urban environment, suggesting quantitative indicators to control integration and segregation of human flows that can be used, among others, for restriction policies to adopt during emergencies and, as an interesting byproduct, allows us to characterise functional (dis)similarities of different metropolitan areas, countries, and cultures.

Figures

Figures reproduced from arXiv: 1908.02538 by the authors.

Figure 3
Figure 3. This because the urban functional networks are [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 1
Figure 1. FIG. 1 [PITH_FULL_IMAGE:figures/full_fig_p011_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2 [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗
Figures from the paper (4 more)
Figure 3
Figure 3. Figure 3: FIG. 3 [PITH_FULL_IMAGE:figures/full_fig_p013_3.png]
Figure 4
Figure 4. Figure 4: FIG. 4 [PITH_FULL_IMAGE:figures/full_fig_p014_4.png]
Figure 5
Figure 5. Figure 5: FIG. 5 [PITH_FULL_IMAGE:figures/full_fig_p015_5.png]
Figure 6
Figure 6. Figure 6: FIG. 6 [PITH_FULL_IMAGE:figures/full_fig_p016_6.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Role Detection in Bicycle-Sharing Networks Using Multilayer Stochastic Block Models

    cs.SI 2019-08 conditional novelty 6.0 of 10

    A time-dependent stochastic block model with mixed or discrete membership classifies bicycle-sharing stations into home and work roles in Los Angeles, San Francisco, and a Manhattan subnetwork of New York City.

Reference graph

Works this paper leans on

79 extracted references · 74 canonical work pages · cited by 1 Pith paper

  1. [1]

    The statistical physics of cities

    Marc Barthelemy. The statistical physics of cities. Nature Reviews Physics, page 1, 2019

    work page 2019

  2. [2]

    Community structure in time-dependent, multiscale, and multiplex networks

    Peter J Mucha, Thomas Richardson, Kevin Macon, Ma- son A Porter, and Jukka-Pekka Onnela. Community structure in time-dependent, multiscale, and multiplex networks. science, 328(5980):876–878, 2010

    work page 2010

  3. [3]

    Multirelational organization of large-scale social net- works in an online world

    Michael Szell, Renaud Lambiotte, and Stefan Thurner. Multirelational organization of large-scale social net- works in an online world. Proceedings of the National Academy of Sciences, 107(31):13636–13641, 2010

    work page 2010

  4. [4]

    Mathematical formu- lation of multilayer networks

    Manlio De Domenico, Albert Sol´ e-Ribalta, Emanuele Cozzo, Mikko Kivel¨ a, Yamir Moreno, Mason A Porter, Sergio G´ omez, and Alex Arenas. Mathematical formu- lation of multilayer networks. Physical Review X , 3(4): 041022, 2013

    work page 2013

  5. [5]

    Multilayer network modeling of inte- grated biological systems: Comment on” network science of biological systems at different scales: A review” by gosak et al

    M De Domenico. Multilayer network modeling of inte- grated biological systems: Comment on” network science of biological systems at different scales: A review” by gosak et al. Physics of Life Reviews , 24:149, 2018

    work page 2018

  6. [6]

    Multilayer networks

    Mikko Kivel¨ a, Alex Arenas, Marc Barthelemy, James P Gleeson, Yamir Moreno, and Mason A Porter. Multilayer networks. Journal of complex networks , 2(3):203–271, 2014

    work page 2014

  7. [7]

    The physics of spreading processes in multilayer networks

    Manlio De Domenico, Clara Granell, Mason A Porter, and Alex Arenas. The physics of spreading processes in multilayer networks. Nature Physics, 12(10):901, 2016

    work page 2016

  8. [8]

    Measuring information transfer

    Thomas Schreiber. Measuring information transfer. Physical review letters, 85(2):461, 2000

    work page 2000

Show all 79 references
  1. [9]

    Granger causality and transfer entropy are equivalent for gaussian variables

    Lionel Barnett, Adam B Barrett, and Anil K Seth. Granger causality and transfer entropy are equivalent for gaussian variables. Physical review letters , 103(23): 238701, 2009

    2009

  2. [10]

    Escaping the curse of dimensionality in estimating multivariate transfer entropy

    Jakob Runge, Jobst Heitzig, Vladimir Petoukhov, and J¨ urgen Kurths. Escaping the curse of dimensionality in estimating multivariate transfer entropy. Physical review letters, 108(25):258701, 2012

    2012

  3. [11]

    De- tecting causality in complex ecosystems

    George Sugihara, Robert May, Hao Ye, Chih-hao Hsieh, Ethan Deyle, Michael Fogarty, and Stephan Munch. De- tecting causality in complex ecosystems. science, 338 (6106):496–500, 2012

    2012

  4. [12]

    Synergy and redundancy in the granger causal analysis of dynamical networks

    Sebastiano Stramaglia, Jesus M Cortes, and Daniele Marinazzo. Synergy and redundancy in the granger causal analysis of dynamical networks. New Journal of Physics, 16(10):105003, 2014

    2014

  5. [13]

    Causal feedbacks in climate change

    Egbert H Van Nes, Marten Scheffer, Victor Brovkin, Timothy M Lenton, Hao Ye, Ethan Deyle, and George Sugihara. Causal feedbacks in climate change. Nature Climate Change, 5(5):445, 2015

    2015

  6. [14]

    Information flow between resting-state networks

    Ibai Diez, Asier Erramuzpe, Inaki Escudero, Beat- riz Mateos, Alberto Cabrera, Daniele Marinazzo, Ernesto J Sanz-Arigita, Sebastiano Stramaglia, Jesus M Cortes Diaz, and Alzheimer’s Disease Neuroimaging Ini- tiative. Information flow between resting-state networks. Brain connec...

    2015

  7. [15]

    Integrated information theory: from con- sciousness to its physical substrate

    Giulio Tononi, Melanie Boly, Marcello Massimini, and Christof Koch. Integrated information theory: from con- sciousness to its physical substrate. Nature Reviews Neu- roscience, 17(7):450, 2016

    2016

  8. [16]

    Information flows? a critique of transfer entropies

    Ryan G James, Nix Barnett, and James P Crutchfield. Information flows? a critique of transfer entropies. Phys- ical review letters, 116(23):238701, 2016

    2016

  9. [17]

    Information leverage in interconnected ecosystems: Overcoming the curse of di- mensionality

    Hao Ye and George Sugihara. Information leverage in interconnected ecosystems: Overcoming the curse of di- mensionality. Science, 353(6302):922–925, 2016

    2016

  10. [18]

    Efficient behavior of small-world networks

    Vito Latora and Massimo Marchiori. Efficient behavior of small-world networks. Physical Review Letters, 87(19): 198701, 2001

    2001

  11. [19]

    Analysis of weighted networks

    Mark EJ Newman. Analysis of weighted networks. Phys- ical review E, 70(5):056131, 2004

    2004

  12. [20]

    Functional cartography of complex metabolic networks

    Roger Guimera and Luis A Nunes Amaral. Functional cartography of complex metabolic networks. nature, 433 (7028):895, 2005

    2005

  13. [21]

    Detecting rich-club or- dering in complex networks

    Vittoria Colizza, Alessandro Flammini, M Angeles Ser- rano, and Alessandro Vespignani. Detecting rich-club or- dering in complex networks. Nature physics , 2(2):110, 2006

    2006

  14. [22]

    Human brain networks in health and disease

    Danielle S Bassett and Edward T Bullmore. Human brain networks in health and disease. Current opinion in neu- rology, 22(4):340, 2009

    2009

  15. [23]

    Complex network mea- sures of brain connectivity: uses and interpretations

    Mikail Rubinov and Olaf Sporns. Complex network mea- sures of brain connectivity: uses and interpretations. Neuroimage, 52(3):1059–1069, 2010

    2010

  16. [24]

    Rich-club organization of the human connectome

    Martijn P Van Den Heuvel and Olaf Sporns. Rich-club organization of the human connectome. Journal of Neu- roscience, 31(44):15775–15786, 2011

    2011

  17. [25]

    Network attributes for segregation and in- tegration in the human brain

    Olaf Sporns. Network attributes for segregation and in- tegration in the human brain. Current opinion in neuro- biology, 23(2):162–171, 2013

    2013

  18. [26]

    The social origins of networks and diffu- sion

    Damon Centola. The social origins of networks and diffu- sion. American Journal of Sociology , 120(5):1295–1338, 2015

    2015

  19. [27]

    Rethinking segregation and inte- gration: contributions of whole-brain modelling

    Gustavo Deco, Giulio Tononi, Melanie Boly, and Morten L Kringelbach. Rethinking segregation and inte- gration: contributions of whole-brain modelling. Nature Reviews Neuroscience, 16(7):430, 2015

    2015

  20. [28]

    The segregation and integration of distinct brain networks and their re- lationship to cognition

    Jessica R Cohen and Mark D’Esposito. The segregation and integration of distinct brain networks and their re- lationship to cognition. Journal of Neuroscience, 36(48): 12083–12094, 2016

    2016

  21. [29]

    Brain networks under attack: ro- bustness properties and the impact of lesions

    Hannelore Aerts, Wim Fias, Karen Caeyenberghs, and Daniele Marinazzo. Brain networks under attack: ro- bustness properties and the impact of lesions. Brain, 139 (12):3063–3083, 2016

    2016

  22. [30]

    The diverse club

    MA Bertolero, BTT Yeo, and M D’esposito. The diverse club. Nature communications, 8(1):1277, 2017

    2017

  23. [31]

    A mechanistic model of con- nector hubs, modularity and cognition

    Maxwell A Bertolero, BT Thomas Yeo, Danielle S Bas- sett, and Mark D’Esposito. A mechanistic model of con- nector hubs, modularity and cognition. Nature human behaviour, 2(10):765, 2018

    2018

  24. [32]

    Impact of modular organization on dynamical richness in cortical networks

    Hideaki Yamamoto, Satoshi Moriya, Katsuya Ide, Takeshi Hayakawa, Hisanao Akima, Shigeo Sato, Shigeru Kubota, Takashi Tanii, Michio Niwano, Sara Teller, et al. Impact of modular organization on dynamical richness in cortical networks. Science advances, 4(11):eaau4914, 2018

    2018

  25. [33]

    Influence of augmented humans in on- line interactions during voting events

    Massimo Stella, Marco Cristoforetti, and Manlio De Domenico. Influence of augmented humans in on- line interactions during voting events. PloS one , 14(5): e0214210, 2019

    2019

  26. [34]

    Big data, smart cities and city planning

    Michael Batty. Big data, smart cities and city planning. Dialogues in Human Geography, 3(3):274–279, 2013

    2013

  27. [35]

    Quantifying urban form: compactness ver- sus’ sprawl’

    Yu-Hsin Tsai. Quantifying urban form: compactness ver- sus’ sprawl’. Urban studies, 42(1):141–161, 2005. 8

    2005

  28. [36]

    Built-up en- croachment and the urban field: A comparison of forty european cities

    Marianne Guerois and Denise Pumain. Built-up en- croachment and the urban field: A comparison of forty european cities. Environment and Planning A: Economy and Space, 40(9):2186–2203, 2008. doi: 10.1068/a39382

    2008 doi

  29. [37]

    Urban form revisited?selecting indicators for characterising european cities

    Nina Schwarz. Urban form revisited?selecting indicators for characterising european cities. Landscape and urban planning, 96(1):29–47, 2010

    2010

  30. [38]

    From mobile phone data to the spatial structure of cities

    Thomas Louail, Maxime Lenormand, Oliva G Cantu Ros, Miguel Picornell, Ricardo Herranz, Enrique Frias- Martinez, Jos´ e J Ramasco, and Marc Barthelemy. From mobile phone data to the spatial structure of cities. Sci- entific reports, 4:5276, 2014

    2014

  31. [39]

    Gately, Lucy R

    Conor K. Gately, Lucy R. Hutyra, and Ian Sue Wing. Cities, traffic, and CO2: A multidecadal assessment of trends, drivers, and scaling relationships. Proceed- ings of the National Academy of Sciences , 112(16):4999– 5004, April 2015. doi: 10.1073/pnas.1421723112. URL https://doi...

    2015 doi

  32. [40]

    Compactness versus sprawl: A review of recent evidence from the united states

    Reid Ewing and Shima Hamidi. Compactness versus sprawl: A review of recent evidence from the united states. Journal of Planning Literature , 30(4):413–432, 2015

    2015

  33. [41]

    Modelling the scaling properties of human mo- bility

    Chaoming Song, Tal Koren, Pu Wang, and Albert-L´ aszl´ o Barab´ asi. Modelling the scaling properties of human mo- bility. Nature Physics, 6(10):818–823, September 2010. doi: 10.1038/nphys1760. URL https://doi.org/10. 1038/nphys1760

    2010 doi

  34. [42]

    Uncov- ering the spatial structure of mobility networks

    Thomas Louail, Maxime Lenormand, Miguel Picornell, Oliva Garc´ ıa Cant´ u, Ricardo Herranz, Enrique Frias- Martinez, Jos´ e J Ramasco, and Marc Barthelemy. Uncov- ering the spatial structure of mobility networks. Nature communications, 6:6007, 2015

    2015

  35. [43]

    A stochastic model of ran- domly accelerated walkers for human mobility

    Riccardo Gallotti, Armando Bazzani, Sandro Rambaldi, and Marc Barthelemy. A stochastic model of ran- domly accelerated walkers for human mobility. Na- ture Communications , 7(1), August 2016. doi: 10. 1038/ncomms12600. URL https://doi.org/10.1038/ ncomms12600

    2016

  36. [44]

    Human mobility: Models and appli- cations

    Hugo Barbosa, Marc Barthelemy, Gourab Ghoshal, Charlotte R James, Maxime Lenormand, Thomas Louail, Ronaldo Menezes, Jos´ e J Ramasco, Filippo Simini, and Marcello Tomasini. Human mobility: Models and appli- cations. Physics Reports, 734:1–74, 2018

    2018

  37. [45]

    Traffic and related self-driven many- particle systems

    Dirk Helbing. Traffic and related self-driven many- particle systems. Reviews of modern physics, 73(4):1067, 2001

    2001

  38. [46]

    Percolation transition in dynamical traffic network with evolving critical bottlenecks

    Daqing Li, Bowen Fu, Yunpeng Wang, Guangquan Lu, Yehiel Berezin, H Eugene Stanley, and Shlomo Havlin. Percolation transition in dynamical traffic network with evolving critical bottlenecks. Proceedings of the National Academy of Sciences, 112(3):669–672, 2015

    2015

  39. [47]

    Gonz´ alez

    Serdar C ¸ olak, Antonio Lima, and Marta C. Gonz´ alez. Understanding congested travel in urban areas. Na- ture Communications , 7(1), March 2016. doi: 10. 1038/ncomms10793. URL https://doi.org/10.1038/ ncomms10793

    2016

  40. [48]

    De- congestion of urban areas with hotspot pricing

    Albert Sol´ e-Ribalta, Sergio G´ omez, and Alex Arenas. De- congestion of urban areas with hotspot pricing. Networks and Spatial Economics, 18(1):33–50, 2018

    2018

  41. [49]

    From global scaling to the dynamics of individual cities

    Jules Depersin and Marc Barthelemy. From global scaling to the dynamics of individual cities. Proceedings of the National Academy of Sciences, 115(10):2317–2322, 2018

    2018

  42. [50]

    Urban spatial structure, daily mo- bility and energy consumption: a study of 34 european cities

    Florent Le N´ echet. Urban spatial structure, daily mo- bility and energy consumption: a study of 34 european cities. Cybergeo: European Journal of Geography, 2012

    2012

  43. [51]

    Urban sprawl and air quality in large US cities

    Brian Stone. Urban sprawl and air quality in large US cities. Journal of Environmental Management , 86(4): 688–698, March 2008. doi: 10.1016/j.jenvman.2006.12

    2008 doi

  44. [52]

    URL https://doi.org/10.1016/j.jenvman.2006. 12.034

    2006 doi

  45. [53]

    Transport impacts on atmosphere and climate: Land transport

    Elmar Uherek, Tomas Halenka, Jens Borken-Kleefeld, Yves Balkanski, Terje Berntsen, Carlos Borrego, Michael Gauss, Peter Hoor, Katarzyna Juda-Rezler, and Jos Lelieveld. Transport impacts on atmosphere and climate: Land transport. Atmospheric Environment, 44(37):4772– 4816, Dece...

    2010 doi

  46. [54]

    An idealized study of city structure, urban climate, energy consumption, and air quality

    Alberto Martilli. An idealized study of city structure, urban climate, energy consumption, and air quality. Ur- ban Climate, 10:430–446, December 2014. doi: 10.1016/ j.uclim.2014.03.003. URL https://doi.org/10.1016/j. uclim.2014.03.003

    2014 doi

  47. [55]

    Reid Ewing, Gail Meakins, Shima Hamidi, and Arthur C. Nelson. Relationship between urban sprawl and physi- cal activity, obesity, and morbidity – update and refine- ment. Health & Place , 26:118–126, March 2014. doi: 10.1016/j.healthplace.2013.12.008. URL https://doi. org/10.10...

    2014 doi

  48. [56]

    Newby, Pier M

    David E. Newby, Pier M. Mannucci, Grethe S. Tell, Andrea A. Baccarelli, Robert D. Brook, Ken Donald- son, Francesco Forastiere, Massimo Franchini, Oscar H. Franco, Ian Graham, Gerard Hoek, Barbara Hoffmann, Marc F. Hoylaerts, Nino K¨ unzli, Nicholas Mills, Juha Pekkanen, Annett...

    2014 doi

  49. [57]

    Rice, Petter L

    Mary B. Rice, Petter L. Ljungman, Elissa H. Wilker, Kirsten S. Dorans, Diane R. Gold, Joel Schwartz, Pet- ros Koutrakis, George R. Washko, George T. O’Connor, and Murray A. Mittleman. Long-term exposure to traffic emissions and fine particulate matter and lung function decline in...

    2015 doi

  50. [58]

    Dorans, Elissa H

    Wenyuan Li, Kirsten S. Dorans, Elissa H. Wilker, Mary B. Rice, Michelle T. Long, Joel Schwartz, Brent A. Coull, Petros Koutrakis, Diane R. Gold, Caroline S. Fox, and Murray A. Mittleman. Residential proximity to major roadways, fine particulate matter, and hep- atic steatosis. ...

    2017 doi

  51. [59]

    Nicholl, J

    J. Nicholl, J. West, S. Goodacre, and J. Turner. The re- lationship between distance to hospital and patient mor- tality in emergencies: an observational study. Emer- gency Medicine Journal, 24(9):665–668, September 2007. doi: 10.1136/emj.2007.047654. URL https://doi.org/ 10.1...

    2007 arXiv

  52. [60]

    Growth, innovation, scaling, and the pace of life in cities

    Lu´ ıs MA Bettencourt, Jos´ e Lobo, Dirk Helbing, Chris- tian K¨ uhnert, and Geoffrey B West. Growth, innovation, scaling, and the pace of life in cities. Proceedings of the national academy of sciences , 104(17):7301–7306, 2007

    2007

  53. [61]

    The origins of scaling in cities

    Lu´ ıs MA Bettencourt. The origins of scaling in cities. 9 Science, 340(6139):1438–1441, 2013

    2013

  54. [62]

    The spatial organization of cities: Delib- erate outcome or unforeseen consequence? UC Berkeley IURD Working Paper Series , 2004

    Alain Bertaud. The spatial organization of cities: Delib- erate outcome or unforeseen consequence? UC Berkeley IURD Working Paper Series , 2004

    2004

  55. [63]

    The spatial organi- zation of the population density in cities

    Valerio Volpati and Marc Barthelemy. The spatial organi- zation of the population density in cities. arXiv preprint arXiv:1804.00855, 2018

    2018 arXiv

  56. [64]

    Modeling the polycen- tric transition of cities

    R´ emi Louf and Marc Barthelemy. Modeling the polycen- tric transition of cities. Physical review letters , 111(19): 198702, 2013

    2013

  57. [65]

    How congestion shapes cities: from mobility patterns to scaling

    R´ emi Louf and Marc Barthelemy. How congestion shapes cities: from mobility patterns to scaling. Scientific Re- ports, 4(1), July 2014. doi: 10.1038/srep05561. URL https://doi.org/10.1038/srep05561

    2014 doi

  58. [66]

    Activity-aware map: Identifying human daily activity pattern using mobile phone data

    Santi Phithakkitnukoon, Teerayut Horanont, Giusy Di Lorenzo, Ryosuke Shibasaki, and Carlo Ratti. Activity-aware map: Identifying human daily activity pattern using mobile phone data. In International Work- shop on Human Behavior Understanding , pages 14–25. Springer, 2010

    2010

  59. [67]

    Exploiting foursquare and cellular data to infer user activity in urban environments

    Anastasios Noulas, Cecilia Mascolo, and Enrique Frias- Martinez. Exploiting foursquare and cellular data to infer user activity in urban environments. In 2013 IEEE 14th International Conference on Mobile Data Management . IEEE, June 2013. doi: 10.1109/mdm.2013.27. URL https://...

    2013 doi

  60. [68]

    The economy of brain network organization

    Ed Bullmore and Olaf Sporns. The economy of brain network organization. Nature Reviews Neuroscience, 13 (5):336, 2012

    2012

  61. [69]

    Osmnx: New methods for acquiring, con- structing, analyzing, and visualizing complex street net- works

    Geoff Boeing. Osmnx: New methods for acquiring, con- structing, analyzing, and visualizing complex street net- works. Computers, Environment and Urban Systems , 65: 126–139, 2017

    2017

  62. [70]

    https://www

    Future cities challenge. https://www. futurecitieschallenge.com. Accessed: 2019-08-05

    2019

  63. [71]

    Patterns of residential segregation

    R´ emi Louf and Marc Barthelemy. Patterns of residential segregation. PloS one, 11(6):e0157476, 2016

    2016

  64. [72]

    Quantifying efficient information exchange in real network flows

    Giulia Bertagnolli, Riccardo Gallotti, and Manlio De Domenico. Quantifying efficient information exchange in real network flows. arXiv preprint arXiv:2003.11374 , 2020

    2003 arXiv

  65. [73]

    Fast algorithm for detecting com- munity structure in networks

    Mark EJ Newman. Fast algorithm for detecting com- munity structure in networks. Physical review E , 69(6): 066133, 2004

    2004

  66. [74]

    Anatomy and efficiency of urban multimodal mobility

    Riccardo Gallotti and Marc Barthelemy. Anatomy and efficiency of urban multimodal mobility. Scientific re- ports, 4(1):1–9, 2014

    2014

  67. [75]

    Hierarchical organization of urban mobility and its connection with city livability

    Aleix Bassolas, Hugo Barbosa-Filho, Brian Dickinson, Xerxes Dotiwalla, Paul Eastham, Riccardo Gallotti, Gourab Ghoshal, Bryant Gipson, Surendra A Hazarie, Henry Kautz, et al. Hierarchical organization of urban mobility and its connection with city livability. Nature communicat...

    2019

  68. [76]

    Structural and func- tional brain networks: from connections to cognition

    Hae-Jeong Park and Karl Friston. Structural and func- tional brain networks: from connections to cognition. Science, 342(6158):1238411, 2013

    2013

  69. [77]

    https: //developer.foursquare.com/docs/api/venues/ categories

    Foursquare developers venue categories. https: //developer.foursquare.com/docs/api/venues/ categories. Accessed: 2019-08-02

    2019

  70. [78]

    Economic small- world behavior in weighted networks

    Vito Latora and Massimo Marchiori. Economic small- world behavior in weighted networks. The European Physical Journal B-Condensed Matter and Complex Sys- tems, 32(2):249–263, 2003

    2003

  71. [79]

    Small Worlds

    Vincent D Blondel, Jean-Loup Guillaume, Renaud Lam- biotte, and Etienne Lefebvre. Fast unfolding of commu- nities in large networks. Journal of statistical mechanics: theory and experiment, 2008(10):P10008, 2008. 10 11 FIG. 1: Modeling Structure and Function of Urban Systems. ...

    2008

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.