
Published 2023-11-18
Keywords
- Micro-mobility,
- visualization,
- cartography,
- transport geography
How to Cite
Copyright (c) 2023 Nikola Koktavá, Jiří Horák

This work is licensed under a Creative Commons Attribution 4.0 International License.
Accepted 2023-11-18
Published 2023-11-18
Abstract
The growth in technology has led to the enhancement of open data sources and the development of user-friendly open-source visualization and analysis tools. The evolution of these tools has resulted in the expansion of various analytical and visualization techniques. This research concentrates on the visualization methods used in micro-mobility studies. It briefly defines micro-mobility, including the key factors that influence it. The motivation for writing this paper was to identify visualization methods that are suitable for representing a variety of micro-mobility data types. The aim of this paper is to briefly review a number of visualization methods that are widely used in micro-mobility research.
Downloads
References
- Abduljabbar, R. L., Sohani Liyanage, and Hussein D. (2021). ‘The Role of Micro-Mobility in Shaping Sustainable Cities: A Systematic Literature Review’. Transportation Research Part D: Transport and Environment 92:102734. https://10.1016/j.trd.2021.102734
- Agriesti, S. A. M., Soe, R.-M., & Saif, M. A. (2022). Framework for connecting the mobility challenges in low density areas to smart mobility solu-tions: The case study of Estonian municipalities. European Transport Research Review, 14(1), 32. https://doi.org/10.1186/s12544-022-00557-y
- Ayfantopoulou, G., Salanova Grau, J.M., Maleas, Z. & Siomos, A. (2022). ‘Micro-Mobility User Pattern Analysis and Station Location in Thessaloni-ki’. Sustainability 14(11):6715. https://10.3390/su14116715
- Bartzokas-Tsiompras, A., Bakogiannis, E., & Nikitas, A. (2023). Global microscale walkability ratings and rankings: A novel composite indicator for 59 European city centres. Journal of Transport Geography, 111, 103645. https://doi.org/10.1016/j.jtrangeo.2023.103645
- Bartzokas-Tsioumpras, A. (2022). Utilizing OpenStreetMap data to measure and compare pedestrian street lengths in 992 cities around the world. European Journal of Geography, 13(2), 127-141. https://doi.org/10.48088/ejg.a.bar.13.2.127.138
- Bartzokas-Tsiompras, A., Photis, Y. N., Tsagkis, P., & Panagiotopoulos, G. (2021). Microscale walkability indicators for fifty-nine European central urban areas: An open-access tabular dataset and a geo-spatial web-based platform. Data in Brief, 37, 107048. https://doi.org/10.1016/j.dib.2021.107048
- Bartzokas-Tsiompras, A., Photis, Y. N. (2019). Measuring rapid transit accessibility and equity in migrant communities across 17 European cities. International Journal of Transport Development and Integration, 3(3), 245–258. https://doi.org/10.2495/TDI-V3-N3-245-258
- Fortini, P.M., & Davis, C.A. (2018). ‘Analysis, Integration and Visualization of Urban Data From Multiple Heterogeneous Sources’. Pp. 17–26 in Proceedings of the 1st ACM SIGSPATIAL Workshop on Advances on Resilient and Intelligent Cities. Seattle WA USA: ACM.
- Gehrke, S. R., Sadeghinasr, B., Wang, Q. & Reardon, T.G. (2021). ‘Patterns and Predictors of Dockless Bikeshare Trip Generation and Duration in Boston’s Suburbs’. Case Studies on Transport Policy 9(2):756–66. https://doi.org/10.1016/j.cstp.2021.03.012
- Heumann, M., Kraschewski, T., Brauner, T. Tilch, L. & Breitner, M.H. (2021). ‘A Spatiotemporal Study and Location-Specific Trip Pattern Categori-zation of Shared E-Scooter Usage’. Sustainability 13(22):12527. https://doi.org/10.3390/su132212527
- Jinghai, H., Yang, H., Li, C., Zheng, R., Yang, L. and Wen, Y. (2021). ‘Influence of the Built Environment on E-Scooter Sharing Ridership: A Tale of Five Cities’. Journal of Transport Geography 93:103084. https://doi.org/10.1016/j.jtrangeo.2021.103084
- Jiao, J., Degen, N. & Azimian, A. (2022). ‘Understanding the Relationships Among E-Scooter Ridership, Transit Desert Index, and Health-Related Factors’. Transportation Research Record: Journal of the Transportation Research Board 2676(12):728–39. https://doi.org/10.1177/03611981221097094
- KAM Brno (2023). KAM Brno. Průzkum maloobchodu. http://webmaps.kambrno.cz/maloobchod/
- Kimpton, A., Pojani, D., Conno, R., Ouyang, L., Sipe, N. & Corcoran, J. (2021). ‘Contemporary Parking Policy, Practice, and Outcomes in Three Large Australian Cities’. Progress in Planning 153:100506. https://doi.org/10.1016/j.progress.2020.100506
- Li, A., Zhao, P., Liu, X., Mansourian, A., Axhausen, K.W., & Qu, X. (2022). ‘Comprehensive Comparison of E-Scooter Sharing Mobility: Evidence from 30 European Cities’. Transportation Research Part D: Transport and Environment 105:103229. https://doi.org/10.1016/j.trd.2022.103229
- Li, H., Yuan, Z., Novack, T., Huang, W., & Zipf, A. (2022). ‘Understanding Spatiotemporal Trip Purposes of Urban Micro-Mobility from the Lens of Dockless e-Scooter Sharing’. Computers, Environment and Urban Systems 96:101848. https://doi.org/10.1016/j.compenvurbsys.2022.101848
- Liu, L., Miller, H.J. (2022). ‘Measuring the Impacts of Dockless Micro-Mobility Services on Public Transit Accessibility’. Computers, Environment and Urban Systems 98:101885. https://doi.org/10.1016/j.compenvurbsys.2022.101885
- Lovelace, R. (2021). Open source tools for geographic analysis in transport planning. Journal of Geographical Systems. https://doi.org/10.1007/s10109-020-00342-2
- Lovelace, R., Tennekes, M., & Carlino, D. (2022). ClockBoard: A zoning system for urban analysis. Journal of Spatial Information Science, 24, Article 24. https://doi.org/10.5311/JOSIS.2022.24.172
- MacEachren, A. M., & Ganter., J.H. (1990). ‘A pattern identification approach to cartographic visualization’. Cartographica: The International Journal for Geographic Information and Geovisualization 27(2):64–81. https://doi.org/10.3138/M226-1337-2387-3007
- Manetos, P., Bartzokas-Tsiompras, A., & Koutsopoulos, K. (2022). Editorial: A new EJG section entitled “Geographic Insights in Brief.” European Journal of Geography, 13(3), 42–43. https://doi.org/10.48088/ejg.p.man.13.2.editoriral
- McKenzie, G. (2019). ‘Spatiotemporal Comparative Analysis of Scooter-Share and Bike-Share Usage Patterns in Washington, D.C.’ Journal of Transport Geography 78:19–28. https://doi.org/10.1016/j.jtrangeo.2019.05.007
- McKenzie, G. (2020). ‘Urban Mobility in the Sharing Economy: A Spatiotemporal Comparison of Shared Mobility Services’. Computers, Environ-ment and Urban Systems 79:101418. https://doi.org/10.1016/j.compenvurbsys.2019.101418
- Milias, V., Psyllidis, A. (2023). Measuring spatial age segregation through the lens of co-accessibility to urban activities. Computers, Environment and Urban Systems, 91(101829). https://doi.org/10.1016/j.compenvurbsys.2022.101829
- O’Hern, S., & Estgfaeller, N. (2020). ‘A Scientometric Review of Powered Micromobility’. Sustainability 12(22):9505. https://doi.org/10.3390/su12229505
- Pérez-Fernández, O., & García-Palomares, J.C. (2021). ‘Parking Places to Moped-Style Scooter Sharing Services Using GIS Location-Allocation Models and GPS Data’. ISPRS International Journal of Geo-Information 10(4):230. https://doi.org/10.3390/ijgi10040230
- Peters, L., & MacKenzie, D. (2019). ‘The Death and Rebirth of Bikesharing in Seattle: Implications for Policy and System Design’. Transportation Research Part A: Policy and Practice 130:208–26. https://doi.org/10.1016/j.tra.2019.09.012
- Qian, X., Jaller, M. & Niemeier, D. (2020). ‘Enhancing Equitable Service Level: Which Can Address Better, Dockless or Dock-Based Bikeshare Sys-tems?’ Journal of Transport Geography 86:102784. https://doi.org/10.1016/j.jtrangeo.2020.102784
- Rhoads, D., Rames, C., Solé-Ribalta, A., González, M. C., Szell, M., & Borge-Holthoefer, J. (2023). Sidewalk networks: Review and outlook. Com-puters, Environment and Urban Systems, 106, 102031. https://doi.org/10.1016/j.compenvurbsys.2023.102031
- Reimann, R., Benedikt G., & Schmitt, P. (2017). ‘All Roads to Rome: Visualizing Mobility at Scale’. Pp. 1–4 in 2017 IEEE VIS Arts Program (VISAP). Phoenix, AZ: IEEE.
- Slocum, T. A., & McMaster, R.B. (2022). Thematic Cartography and Geovisualization. 4e ed. London: Routledge.
- Štraub, D, & Gajda, A. (2020). ‘E-Scooter Sharing Schemes Operational Zones in Poland: Dataset on Voivodeship Capital Cities’. Data in Brief 33:106560. https://doi.org/10.1016/j.dib.2020.106560
- Thévenin, T., & Vuidel, G. (2021). Exploring daily mobility in space and time: The geographer project. Journal of Transport Geography, 93(103082). https://doi.org/10.1016/j.jtrangeo.2021.103082
- Yang, H., Ma, Q., Wang, Z., Cai, Q., Xie, K., & Yang, D. (2020). ‘Safety of Micro-Mobility: Analysis of E-Scooter Crashes by Mining News Reports’. Accident Analysis & Prevention 143:105608. https://doi.org/10.1016/j.aap.2020.105608
- Zaragozí, B., Trilles, S., & Gutiérrez, A. (2021). ‘Passive Mobile Data for Studying Seasonal Tourism Mobilities: An Application in a Mediterranean Coastal Destination’. ISPRS International Journal of Geo-Information 10(2):98. https://doi.org/10.3390/ijgi10020098
- Zhao, P., Haitao, H., Li, A., & Mansourian, A. (2021). ‘Impact of Data Processing on Deriving Micro-Mobility Patterns from Vehicle Availability Data’. Transportation Research Part D: Transport and Environment 97:102913. https://doi.org/10.1016/j.trd.2021.102913