Flowmapper.org: a web-based framework for designing origin–destination flow maps

References

• Andrienko, G., Andrienko, N., Fuchs, G., & Wood, J. (2017). Revealing patterns and trends of mass mobility through spatial and temporal abstraction of origin-destination movement data. IEEE Transactions on Visualization and Computer Graphics, 23(9), 2120–2136. https://doi.org/10.1109/TVCG.2016.2616404
   PubMed Web of Science ®Google Scholar
• Aoghs.org Editors. (2020). Oklahoma oil history. https://aoghs.org/oil-almanac/oklahoma-oil-history/
   Google Scholar
• Bostock, M., Ogievetsky, V., & Heer, J. (2011). D³ data-driven documents. IEEE Transactions on Visualization and Computer Graphics, 17(12), 2301–2309. https://doi.org/10.1109/TVCG.2011.185
   PubMed Web of Science ®Google Scholar
• Boyandin, I. (2021). flowmap.blue. https://flowmap.blue
   Google Scholar
• Boyandin, I., Bertini, E., Bak, P., & Lalanne, D. (2011). Flowstrates: An approach for visual exploration of temporal origin-destination data. Paper presented at the Computer Graphics Forum.
   Google Scholar
• Dent, B., Torguson, J., & Hodler, T. (2008). Thematic map design. McGraw-Hill.
   Google Scholar
• Divvy. (2021). Historical bike trip data. https://www.divvybikes.com/system-data
   Google Scholar
• Dong, W., Wang, S., Chen, Y., & Meng, L. (2018). Using eye tracking to evaluate the usability of flow maps. ISPRS International Journal of Geo-Information, 7(7), Article 281. https://doi.org/10.3390/ijgi7070281
   PubMed Web of Science ®Google Scholar
• FAO. (2021). Food and Agriculture Organization of United Nations (FAO). http://www.fao.org/faostat/en/#data/TM
   Google Scholar
• Ghoniem, M., Fekete, J.-D., & Castagliola, P. (2005). On the readability of graphs using node-link and matrix-based representations: A controlled experiment and statistical analysis. Information Visualization, 4(2), 114–135. https://doi.org/10.1057/palgrave.ivs.9500092
   Google Scholar
• Graser, A., Schmidt, J., Roth, F., & Brändle, N. (2019). Untangling origin-destination flows in geographic information systems. Information Visualization, 18(1), 153–172. https://doi.org/10.1177/1473871617738122
   Web of Science ®Google Scholar
• Guo, D. (2008). Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP). International Journal of Geographical Information Science, 22(7), 801–823. https://doi.org/10.1080/13658810701674970
   Web of Science ®Google Scholar
• Guo, D. (2009). Flow mapping and multivariate visualization of large spatial interaction data. IEEE Transactions on Visualization and Computer Graphics, 15(6), 1041–1048. https://doi.org/10.1109/TVCG.2009.143
   PubMed Web of Science ®Google Scholar
• Guo, D., & Zhu, X. (2014). Origin-destination flow data smoothing and mapping. IEEE Transactions on Visualization and Computer Graphics, 20(12), 2043–2052. https://doi.org/10.1109/TVCG.2014.2346271
   PubMed Web of Science ®Google Scholar
• He, S. (2018). From beautiful maps to actionable insights: Introducing Kepler.gl, Uber's open source geospatial toolbox. Uber Engineering, 29.
   Google Scholar
• Holten, D., Isenberg, P., Van Wijk, J. J., & Fekete, J. D. (2011, March 1–4). An extended evaluation of the readability of tapered, animated, and textured directed-edge representations in node-link graphs. IEEE Pacific Visualization Symposium, Hong Kong, China (pp. 195–202). IEEE. https://doi.org/10.1109/PACIFICVIS.2011.5742390
   Google Scholar
• Holten, D., & van Wijk, J. J. (2009). Force-directed edge bundling for graph visualization. Computer Graphics Forum, 28(3), 983–990. https://doi.org/10.1111/j.1467-8659.2009.01450.x
   Web of Science ®Google Scholar
• Jenny, B., Stephen, D. M., Muehlenhaus, I., Marston, B. E., Sharma, R., Zhang, E., & Jenny, H. (2017). Force-directed layout of origin-destination flow maps. International Journal of Geographical Information Science, 31(8), 1521–1540. https://doi.org/10.1080/13658816.2017.1307378
   Web of Science ®Google Scholar
• Jenny, B., Stephen, D. M., Muehlenhaus, I., Marston, B. E., Sharma, R., Zhang, E., & Jenny, H. (2018). Design principles for origin-destination flow maps. Cartography and Geographic Information Science, 45(1), 62–75. https://doi.org/10.1080/15230406.2016.1262280
   Web of Science ®Google Scholar
• Koylu, C. (2014). Understanding geo-social network patterns: Computation, visualization, and usability [Ph.D.]. University of South Carolina. ProQuest Dissertations Publishing.
   Google Scholar
• Koylu, C., & Guo, D. (2017). Design and evaluation of line symbolizations for origin–destination flow maps. Information Visualization, 16(4), 309–331. https://doi.org/10.1177/1473871616681375
   Web of Science ®Google Scholar
• Koylu, C., Guo, D., Huang, Y., Kasakoff, A., & Grieve, J. (2021). Connecting family trees to construct a population-scale and longitudinal geo-social network for the U.S. International Journal of Geographical Information Science, 35(12), 2380–2423. https://doi.org/10.1080/13658816.2020.1821885
   Web of Science ®Google Scholar
• Natural Earth. (2021). Country shapefiles. https://www.naturalearthdata.com
   Google Scholar
• Nost, E., Rosenfeld, H., Vincent, K., Moore, S. A., & Roth, R. E. (2017). Hazmatmapper: An online and interactive geographic visualization tool for exploring transnational flows of hazardous waste and environmental justice. Journal of Maps, 13(1), 14–23. https://doi.org/10.1080/17445647.2017.1282384
   Web of Science ®Google Scholar
• Parisi, T. (2012). WebGL: Up and running. O'Reilly Media, Inc.
   Google Scholar
• Phan, D., Xiao, L., Yeh, R., & Hanrahan, P. (2005). Flow map layout. Paper presented at the IEEE Symposium on Information Visualization.
   Google Scholar
• Slocum, T. A., McMaster, R. B., Kessler, F. C., & Howard, H. H. (2009). Thematic cartography and geovisualization. Pearson Prentice Hall.
   Google Scholar
• Stephen, D. M., & Jenny, B. (2017). Automated layout of origin–destination flow maps: US county-to-county migration 2009–2013. Journal of Maps, 13(1), 46–55. https://doi.org/10.1080/17445647.2017.1313788
   Web of Science ®Google Scholar
• Tao, R., & Thill, J. C. (2016). Spatial cluster detection in spatial flow data. Geographical Analysis, 48(4), 355–372. https://doi.org/10.1111/gean.12100
   Web of Science ®Google Scholar
• Tobler, W. R. (1987). Experiments in migration mapping by computer. American Cartographer, 14(2), 155–163. https://doi.org/10.1559/152304087783875273
   Web of Science ®Google Scholar
• Vincent, K., Roth, R. E., Moore, S. A., Huang, Q., Lally, N., Sack, C. M., Nost, E., & Rosenfeld, H. (2019). Improving spatial decision making using interactive maps: An empirical study on interface complexity and decision complexity in the North American hazardous waste trade. Environment and Planning B: Urban Analytics and City Science, 46(9), 1706–1723. https://doi.org/10.1177/2399808318764122
   Google Scholar
• von Landesberger, T., Brodkorb, F., Roskosch, P., Andrienko, N., Andrienko, G., & Kerren, A. (2016). Mobilitygraphs: Visual analysis of mass mobility dynamics via spatio-temporal graphs and clustering. IEEE Transactions on Visualization and Computer Graphics, 22(1), 11–20. https://doi.org/10.1109/TVCG.2015.2468111
   PubMed Web of Science ®Google Scholar
• Ware, C., Kelley, J. G., & Pilar, D. (2014). Improving the display of wind patterns and ocean currents. Bulletin of the American Meteorological Society, 95(10), 1573–1581. https://doi.org/10.1175/BAMS-D-13-00135.1
   Web of Science ®Google Scholar
• Wood, J., Dykes, J., & Slingsby, A. (2010). Visualisation of origins, destinations and flows with OD maps. Cartographic Journal, 47(2), 117–129. https://doi.org/10.1179/000870410(12658023467367
   Web of Science ®Google Scholar
• Xu, K., Rooney, C., Passmore, P., Ham, D.-H., & Nguyen, P. H. (2012). A user study on curved edges in graph visualization. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2449–2456. https://doi.org/10.1109/TVCG.2012.189
   PubMed Web of Science ®Google Scholar
• Yang, Y., Dwyer, T., Goodwin, S., & Marriott, K. (2017). Many-to-many geographically-embedded flow visualisation: An evaluation. Ieee Transactions on Visualization and Computer Graphics, 23(1), 411–420. https://doi.org/10.1109/TVCG.2016.2598885
   PubMed Web of Science ®Google Scholar
• Zhu, X., Guo, D., Koylu, C., & Chen, C. (2019). Density-based multi-scale flow mapping and generalization. Computers, Environment and Urban Systems, 77, Article 101359. https://doi.org/10.1016/j.compenvurbsys.2019.101359
   Web of Science ®Google Scholar