Urban mobility patterns reveal important socio-spatial phenomena, yet visualizing these patterns from massive datasets presents significant challenges. We introduce MobilityHexVis, an interactive visualization platform for analyzing urban mobility flows from mobile phone data classified by socioeconomic status. Our approach employs small multiples visualization with hexagonal glyphs, enabling simultaneous comparison of mobility patterns across different socioeconomic groups and distance ranges. Each hexagon displays origin-destination proportions using a bi-color split representation, while a Gini index quantifies social mixture in urban areas. Through a case study in Santiago, Chile, we demonstrate how the platform reveals employment centers and socioeconomic differences in mobility patterns. A pilot study confirms the platform’s effectiveness while identifying areas for improvement. MobilityHexVis contributes to urban mobility understanding and provides a tool to support evidence-based planning toward more inclusive cities.
BertinJ (1983) Semiology of Graphics. University of Wisconsin Press.
2.
BillgerMThuvanderLWästbergBS (2017) In search of visualization challenges: the development and implementation of visualization tools for supporting dialogue in urban planning processes. Environment and Planning B: Urban Analytics and City Science44(6): 1012–1035. SAGE. https://doi.org/10.1177/0265813516657341
3.
BirchCPOomSPBeechamJA (2007) Rectangular and hexagonal grids used for observation, experiment and simulation in ecology. Ecological Modelling206(3-4): 347–359. Elsevier. https://doi.org/10.1016/j.ecolmodel.2007.03.041
BlumenstockJCadamuroGOnR (2015) Predicting poverty and wealth from mobile phone metadata. Science350(6264): 1073–1076. American Association for the Advancement of Science. https://doi.org/10.1126/science.aac4420
6.
BostockMOgievetskyVHeerJ (2011) D data-driven documents. IEEE Transactions on Visualization and Computer Graphics17(12): 2301–2309. IEEE. https://doi.org/10.1109/TVCG.2011.185
7.
Brůhová FoltýnováHVejchodskáERybováK, et al. (2020) Sustainable urban mobility: one definition, different stakeholders’ opinions. Transportation Research Part D: Transport and Environment87: 102465. Elsevier. https://doi.org/10.1016/j.trd.2020.102465
8.
CrespoRHernandezI (2020) On the spatially explicit gini coefficient: the case study of Chile—A high-income developing country. Letters in Spatial and Resource Sciences13(1): 37–47. https://doi.org/10.1007/s12076-020-00243-4
9.
De SabbataSBennettKGardnerZ (2024) Towards a study of everyday geographic information: bringing the everyday into view. Environment and Planning B: Urban Analytics and City Science51(6): 1354–1368. SAGE. https://doi.org/10.1177/23998083231217606
10.
DemissieMGAlmeida CorreiaGHDEBentoC (2013) Exploring cellular network handover information for urban mobility analysis. Journal of Transport Geography31: 164–170. https://doi.org/10.1016/j.jtrangeo.2013.06.016
11.
Di LorenzoGSbodioMCalabreseF, et al. (2016) AllAboard: visual exploration of cellphone mobility data to optimize public transport. IEEE Transactions on Visualization and Computer Graphics22(2): 1036–1050. https://doi.org/10.1109/TVCG.2015.2440259
12.
Dios OrtúzarJDEWillumsenLG (2021) Modelling Transport. John Wiley & Sons.
13.
DongLDuarteFDurantonG, et al. (2024) Defining a city — delineating urban areas using cell-phone data. Nature Cities1(2): 117–125. https://doi.org/10.1038/s44284-023-00019-z
14.
DuGDongLDuarteF, et al. (2021) Wanderlust: 3D impressionism in human journeys In: 2021 IEEE VIS Arts Program (VISAP), p. 2021.
15.
DuncanODDuncanB (1955) A methodological analysis of segregation indexes. American Sociological Review20(2): 210–217. https://doi.org/10.2307/2088328
16.
Escolano UtrillaSOrtiz VélizJMoreno MoraR (2015) La estructura de los núcleos de empleo de la Región Metropolitana de Santiago (Chile): Centralización funcional y espacial. Boletín de la Asociación de Geógrafos Españoles2015: 263–290. Available at: https://doi.org/10.21138/bage.1897
17.
FarberSNeutensTMillerHJ, et al. (2013) The social interaction potential of metropolitan regions: a time-geographic measurement approach using joint accessibility. Annals of the Association of American Geographers103(May 2011): 483–504. https://doi.org/10.1080/00045608.2012.689238
18.
García-AlbertosPCantú RosOHerranzR, et al. (2017) Understanding door-to-door travel times from opportunistically collected mobile phone records. SESAR Innovation Days 20177: 1–8.
19.
GarretonMBasauriAValenzuelaL (2020) Exploring the correlation between city size and residential segregation: comparing Chilean cities with spatially unbiased indexes. Environment and Urbanization32(2): 569–588. SAGE. https://doi.org/10.1177/0956247820918983
20.
GleicherMAlbersDWalkerR, et al. (2011) Visual comparison for information visualization. Information Visualization10(4): 289–309. SAGE. https://doi.org/10.1177/1473871611416549
21.
Graells-GarridoEOpitzDRoweF, et al. (2023a) A data fusion approach with mobile phone data for updating travel survey-based mode split estimates. Transportation Research Part C: Emerging Technologies155: 104285. Elsevier. https://doi.org/10.1016/j.trc.2023.104285
22.
Graells-GarridoESchifanellaROpitzD, et al. (2023b) Measuring the local complementarity of population, amenities and digital activities to identify and understand urban areas of interest. Environment and Planning B: Urban Analytics and City Science50(4): 942–957. SAGE. https://doi.org/10.1177/23998083221117830
23.
GraserASchmidtJRothF, et al. (2019) Untangling origin-destination flows in geographic information systems. Information Visualization18(1): 153–172. SAGE. https://doi.org/10.1177/1473871617738122
24.
HägerstrandT (1970) What about people in regional science?Transport Sociology: Social Aspects of Transport Planning143–158.
25.
JamesDRTaeuberKE (1985) Measures of segregation. Sociological Methodology15: 1–32. JSTOR. https://doi.org/10.2307/270845
26.
LenormandMSamaniegoH (2023) Uncovering the socioeconomic structure of spatial and social interactions in cities. Urban Science7(1): 15. https://doi.org/10.3390/urbansci7010015
27.
MoroECalacciDDongX, et al. (2021) Mobility patterns are associated with experienced income segregation in large US cities. Nature Communications12(1): 4633. Nature Publishing Group. https://doi.org/10.1038/s41467-021-24899-8
28.
MunznerT (2009) A nested model for visualization design and validation. IEEE Transactions on Visualization and Computer Graphics15(6): 921–928. IEEE. https://doi.org/10.1109/TVCG.2009.111
29.
MüüriseppKJärvOTammaruT, et al. (2022) Activity spaces and big data sources in segregation research: a methodological review. Frontiers in Sustainable Cities4: 861640. https://doi.org/10.3389/frsc.2022.861640
OdellHNavarro-LópezEMPintoN, et al. (2022) Detecting shifts in metropolitan structure: a spatial network perspective. Environment and Planning B: Urban Analytics and City Science49(7): 1912–1928. SAGE. https://doi.org/10.1177/23998083211065767
32.
OpenshawS (1984) The modifiable areal unit problem. In: Concepts and Techniques in Modern Geography. GeoBooks.
33.
Osorio-ArjonaJGarcı́a-PalomaresJC (2019) Big Data and universidades: análisis de movilidad de los estudiantes universitarios a partir de datos de Twitter. GeoFocus: Revista Internacional de Ciencia and Tecnología de la Información Geográfica24(24): 37–57. https://doi.org/10.21138/gf.648
34.
ÖsthJShuttleworthINiedomyslT (2018) Spatial and temporal patterns of economic segregation in Sweden’s metropolitan areas: a mobility approach. Environment and Planning A: Economy and Space50(4): 809–825. SAGE. https://doi.org/10.1177/0308518x18763167
35.
Peña-ArayaVBezerianosAPietrigaE (2020) A comparison of geographical propagation visualizations. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems,Honolulu, HI, USA, 2020: 1–14.
36.
Pérez-MessinaIGraells-GarridoE (2019) Visualizing transportation flows with mode split using glyphs. In: JohanssonJSadloFMaraiGE (eds) Eurovis 2019 - Short Papers. The Eurographics Association.
37.
Pérez-MessinaIGraells-GarridoELoboM-J, et al. (2020) Modalflow: cross-Origin flow data visualization for urban mobility. Algorithms13(11): 298. https://doi.org/10.3390/a13110298
Ruiz-TagleJRomanoS (2019) Mezcla social e integración urbana: aproximaciones teóricas and discusión del caso chileno. Revista INVI34(95): 45–69. https://doi.org/10.4067/s0718-83582019000100045
40.
SahrKWhiteDKimerlingAJ (2003) Geodesic discrete global grid systems. Cartography and Geographic Information Science30(2): 121–134. Taylor & Francis. https://doi.org/10.1559/152304003100011090
41.
ShareckMKestensYFrohlichKL (2014) Moving beyond the residential neighborhood to explore social inequalities in exposure to area-level disadvantage: results from the interdisciplinary study on inequalities in smoking. Social Science & Medicine108: 106–114. Elsevier. https://doi.org/10.1016/j.socscimed.2014.02.044
42.
SlingsbyALoonEVAN (2016) Exploratory visual analysis for animal movement ecology. Computer Graphics Forum35(3): 471–480. https://doi.org/10.1111/cgf.12923
43.
SlingsbyAKellyMDykesJ (2014) OD maps for showing changes in Irish female migration between 1851 and 1911. Environment and Planning A: Economy and Space46(12): 2795–2797. https://doi.org/10.1068/a140112g
44.
SotoVFrias-MartinezVVirsedaJ, et al. (2011) Prediction of socioeconomic levels using cell phone records. Springer, 377–388.
45.
Sotomayor-GómezBSamaniegoH (2020) City limits in the age of smartphones and urban scaling. Computers, Environment and Urban Systems79: 101423. Elsevier. https://doi.org/10.1016/j.compenvurbsys.2019.101423
ToroFOrozcoH (2018) Concentración y homogeneidad socioeconómica: Representación de la segregación urbana en seis ciudades intermedias de Chile. Revista de Urbanismo38: 1–21. https://doi.org/10.5354/0717-5051.2018.48834
48.
TorresJOlivosCPicornellM, et al. (2021) Estudio de la movilidad interprovincial en España mediante la fusión de datos de telefonía móvil con otras fuentes de datos. 2021.
VergaraL (2021) Mixtura social y sociabilidad: ¿inducen los proyectos de integración social a vínculos socioeconómicamente diversos?Eure47: 5–25. https://doi.org/10.7764/eure.47.142.01
51.
WangFChenWZhaoY, et al. (2017) Adaptively exploring population mobility patterns in flow visualization. IEEE Transactions on Intelligent Transportation Systems18: 2250–2259. https://doi.org/10.1109/tits.2017.2711644
52.
WickhamHHofmannHWickhamC, et al. (2012) Glyph-maps for visually exploring temporal patterns in climate data and models. Environmetrics23(5): 382–393. Wiley Online Library. https://doi.org/10.1002/env.2152
53.
WongDW (2016) From aspatial to spatial, from global to local and individual: are we on the right track to spatialize segregation measures? In: HowellFMPorterJRMatthewsSA (eds) Recapturing Space: New Middle-Range Theory in Spatial Demography. Springer, pp. 77–98.
XuFWangQMoroE, et al. (2025) Using human mobility data to quantify experienced urban inequalities. Nature Human Behaviour. Springer Nature9: 654–664. https://doi.org/10.1038/s41562-024-02079-0
56.
YaoJWongDWSBaileyN, et al. (2019) Spatial segregation measures: a methodological review. Tijdschrift voor Economische en Sociale Geografie110(3): 235–250. https://doi.org/10.1111/tesg.12305
57.
YuYWangYZhangY, et al. (2025) InclusiViz: visual analytics of human mobility data for understanding and mitigating urban segregation. IEEE Transactions on Visualization and Computer Graphics31(6): 3836–3849. https://doi.org/10.1109/TVCG.2025.3567117
58.
ZhouZYuJGuoZ, et al. (2018) Visual exploration of urban functions via spatio-temporal taxi OD data. Journal of Visual Languages & Computing48: 169–177. https://doi.org/10.1016/j.jvlc.2018.08.009
