Quantitative expressions of spatial similarity between road networks in multiscale map spaces

References

• Ai, T., Zhang, X., Zhou, Q., & Yang, M. (2015). A vector field model to handle the displacement of multiple conflicts in building generalization. International Journal Geographic Information Science, 29(8), 1310–1331. https://doi.org/10.1080/13658816.2015.1019886
   Web of Science ®Google Scholar
• Bader, M., & Weibel, R. (1997). Detecting and resolving size and proximity conflicts in the generalization of polygonal maps. Proceedings of the 18th International Cartographic Conference, Stockholm, Sweden, 23, 1–27.
   Google Scholar
• Bard, S. (2004). Quality assessment of cartographic generalization. Transactions in GIS, 8(1), 63–81. https://doi.org/10.1111/j.1467-9671.2004.00168.x
   Google Scholar
• Bard, S., & Ruas, A. (2004). Why and how evaluating generalised data? In Proceedings of the 11th International Symposium on Progress in Spatial Data Handling (pp. 327–342). Springer. https://doi.org/10.1007/3-540-26772-7_25
   Google Scholar
• Barrault, M., Regnauld, N., Duchêne, C., Haire, K., Baeijs, C., Demazeau, Y., Hardy, P., Mackaness, W. A., Ruas, A., & Weibel R. (2001). Integrating multi-agent, object-oriented and algorithmic techniques for improved automated map generalization. In Proceedings of the 20th International Cartographic Conference (Vol. 3, pp. 2110–2116). Beijing, China.
   Google Scholar
• Beard, M. K. (1991). Constraints on rule formation. Map generalization (pp. 121–135). Longman.
   Google Scholar
• Brassel, K. E., & Weibel, R. (1988). A review and framework of automated map generalization. International Journal of Geographical Information Systems, 2(3), 229–244. https://doi.org/10.1080/02693798808927898
   Google Scholar
• Brazile, F. L. (2000). Semantic infrastructure and methods to support quality evaluation in cartographic generalization [Doctorate thesis]. University of Zurich.
   Google Scholar
• Buttenfield, B. P., & McMaster, R. B. (1991). Map generalization: Making rules for knowledge representation. Longman.
   Google Scholar
• Chaudhry, O., & Mackaness, W. A. (2008). Automatic identification of urban settlement boundaries for multiple representation databases. Computers, Environment and Urban Systems, 32(2), 95–109. https://doi.org/10.1016/j.compenvurbsys.2007.09.001
   Web of Science ®Google Scholar
• Cheng, T., & Li, Z. (2006). Toward quantitative measures for the semantic quality of polygon generalization. Cartographica: The International Journal for Geographic Information and Geovisualization, 41(2), 135–148. https://doi.org/10.3138/0172-6733-227U-8155
   Google Scholar
• Courtial, A., Touya, G., & Zhang, X. (2022). Constraint-based evaluation of map images generalized by deep learning. Journal of Geovisualization and Spaital Analysis, 6, 13. https://doi.org/10.1007/s41651-022-00104-2
   Google Scholar
• Daniel, C. B., Mathew, S., & Subbarayan, S. (2022). GIS-based study on the association between road centrality and socio-demographic parameters: A case study. Journal of Geovisualization and Spatial Analysis, 6(1), 1. https://doi.org/10.1007/s41651-021-00095-6
   Google Scholar
• Duchêne, C., Ruas, A., & Cambie, C. (2012). The CartACom model: Transforming cartographic features into communicating agents for cartographic generalisation. International Journal of Geographical Information Science, 26(9), 1533–1562. https://doi.org/10.1080/13658816.2011.639302
   Web of Science ®Google Scholar
• Duchêne, C., Touya, G., Taillandier, P., et al. (2018). Multi-agents systems for cartographic generalization: feedback from past and on-going research (Research Report). IGN (Institut National de l’Information Géographique et Forestière); LaSTIG, équipe COGIT.
   Google Scholar
• Dumont, M., Touya, G., & Duche^ne, C. (2020). Designing multi-scale maps: Lessons learned from existing practices. International Journal of Cartography, 6(1), 121–151. https://doi.org/10.1080/23729333.2020.1717832
   Google Scholar
• Edwardes, A., & Mackaness, W. A. (2000). Modelling knowledge for automated generalisation of categorical maps-a constraint-based approach. In P. Atkinson, & D. Martin (Eds.), Innovations in GIS 7. GIS and geocomputation (pp. 161–174). Taylor & Francis.
   Google Scholar
• Filippovska, Y., Walter, V., & Fritsch, D. (2008). Quality evaluation of generalization algorithms. The international archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XXXVII. Part B2. Beijing, China.
   Google Scholar
• Forghani, A., Kazemi, S., & Bruce, D. (2021). A machine-learning approach to generalisation of GIS data. International Journal of Geoinformatics, 17(2), 41–59. https://doi.org/10.52939/ijg.v17i2.1757
   Google Scholar
• Frank, R., & Ester, M. (2006). A quantitative similarity measure for maps. In Proceedings of 12th International Symposium. Progress in Spatial Data Handling (pp. 435–450). Springer. https://doi.org/10.1007/3-540-35589-8_28
   Google Scholar
• Harrie, L. (2001). An optimization approach to cartographic generalization [Doctorate thesis]. Lund University.
   Google Scholar
• Harrie, L., & Weibel, R. (2007). Modelling the overall process of generalisation. In W. A. Mackaness, A. Ruas, & L.T. Sarjakoski (Eds.), Generalisation of geographic information: Cartographic modelling and applications (pp. 67–87). Elsevier.
   Google Scholar
• ICA. (1973). Multilingual dictionary of technical terms in cartography. Franz Steiner Verlag GMBH.
   Google Scholar
• Joao, E. (1998). Causes and consequences of map generalization. CRC Press.
   Google Scholar
• Jones, C. B., & Ware, J. M. (2005). Map generalization in the web age. International Journal of Geographical Information Science, 19(8–9), 859–870. https://doi.org/10.1080/13658810500161104
   Web of Science ®Google Scholar
• Kazemi, S., & Forghani, A. (2016). Knowledge-based generalisation of road networks. International Journal of Geoinformatics, 12(1), 1–13.
   Google Scholar
• Kolb, W. M. (1984). Curve fitting for programmable calculators. Syntec Inc.
   Google Scholar
• Kronenfeld, B. J., Stanislawski, L. V., Buttenfield, B. P., & Brockmeyer, T. (2020). Simplification of polylines by segment collapse: Minimizing areal displacement while preserving area. International Journal of Cartography, 6(11), 22–46. https://doi.org/10.1080/23729333.2019.1631535
   Google Scholar
• Li, B., & Fonseca, F. (2006). TDD: A comprehensive model for qualitative spatial similarity assessment. Spatial Cognition & Computation: An Interdisciplinary Journal, 6(1), 31–62. https://doi.org/10.1207/s15427633scc0601_2
   Google Scholar
• Liu, H., Zhang, L., Long, Y., & Zheng, Y. (2019). Real-time displacement of point symbols based on spatial distribution characteristics. ISPRS International Journal of Geo-Information, 8(10), 426. https://doi.org/10.3390/ijgi8100426
   Web of Science ®Google Scholar
• Lu, X., Yan, H., Li, W., Li, X., & Wu, F. (2019). An algorithm based on the weighted Voronoi diagram for point cluster simplification. ISPRS International Journal of Geo-Information, 8(3), 105. https://doi.org/10.3390/ijgi8030105
   Web of Science ®Google Scholar
• Ma, D., Zhao, Z., Zheng, Y., Guo, R., & Zhu, W. (2020). Polysimp: A tool for polygon simplification based on the underlying scaling hierarchy. ISPRS International Journal of Geo-Information, 9(10), 594. https://doi.org/10.3390/ijgi9100594
   Web of Science ®Google Scholar
• Mackaness, W. A., & Beard, M. K. (1993). Use of graph theory to support map generalization. Cartography and Geographic Information Systems, 20(4), 210–221. https://doi.org/10.1559/152304093782637479
   Google Scholar
• Mackaness, W. A., Burghardt, D., & Duchêne, C. (2017). Map generalization. John Wiley & Sons, Ltd.
   Google Scholar
• Mackaness, W. A., & Mackechnie, G. A. (1999). Automating the detection and simplification of junctions in road networks. GeoInformatica, 3(2), 185–200. https://doi.org/10.1023/A:1009807927991
   Google Scholar
• Mackaness, W. A., & Ruas, A. (2007). Chapter 5-evaluation in the map generalisation process. In W. A. Mackaness, A. Ruas, & L. T. Sarjakoski (Eds.), Generalisation of geographic information. International cartographic association (pp. 89–111). Elsevier Science B.V.
   Google Scholar
• Mandelbrot, B. (1967). How long is the coast of Britain? Statistical self-similarity and fractional dimension. Science, 156(3775), 636–638. https://doi.org/10.1126/science.156.3775.636
   PubMed Web of Science ®Google Scholar
• Maudet, A., Touya, G., Duchêne, C., et al. (2017). Multi-agent multi-level cartographic generalisation in CartAGen. International conference on practical applications of agents & multi-agent systems. Springer International Publishing.
   Google Scholar
• Nedas, K. A., & Egenhofer, M. J. (2008). Spatial-scene similarity queries. Transactions in GIS, 12(6), 661–681. https://doi.org/10.1111/j.1467-9671.2008.01127.x
   Google Scholar
• Peter, B. (2001). Measures for the generalization of polygonal maps with categorical data. In Fourth ACI Workshop on Progress in Automated Map generalization, Beijing, China.
   Google Scholar
• Rodriguez, M. A., & Egenhofer, M. J. (2004). Comparing geospatial entity classes: An asymmetric and context-dependent similarity measure. International Journal of Geographic Information Science, 18(3), 229–256. https://doi.org/10.1080/13658810310001629592
   Web of Science ®Google Scholar
• Ruas, A., & Duchêne, C. (2007). A prototype generalisation system based on the multi-agent system paradigm. In W. A. Mackaness, A. Ruas, & L.T. Sarjakoski (Eds.), Generalisation of geographic information: Cartographic modelling and applications (pp. 269–283). Elsevier.
   Google Scholar
• Ruas, A., & Plazanet, C. (1996). Strategies for automated generalization. In Proceedings of the 7th International Symposium on Spatial Data Handling (pp. 6.1–6.18). Delft, The Netherlands.
   Google Scholar
• Sayidov, A., Aliakbarian, M., & Weibel, R. (2020). Geological map generalization driven by size constraints. ISPRS International Journal of Geo-Information, 9(4), 284. https://doi.org/10.3390/ijgi9040284
   Web of Science ®Google Scholar
• Sester, M. (2005). Optimization approaches for generalization and data abstraction. International Journal of Geographical Information Science, 19(8–9), 871–897. https://doi.org/10.1080/13658810500161179
   Web of Science ®Google Scholar
• Skopeliti, A., & Tsoulos, L. (2001). Methodology of the assessment of generalization accuracy. In Fourth ACI Workshop on Progress in Automated Map Generalization, Beijing, China.
   Google Scholar
• Steiniger, S., & Weibel, R. (2005, July 11–16). A conceptual framework for automated generalization and its application to geologic and soil maps. In Proceedings of the 22nd International Cartographic Conference, La coruña, Spain.
   Google Scholar
• Stoter, J., Burghardt, D., Duchêne, C., Baella, B., Bakker, N., Blok, C., Pla, M., Regnauld, N., Touya, G., & Schmid, S. (2009). Methodology for evaluating automated map generalization in commercial software. Computers, Environment and Urban Systems, 33(5), 311–324. https://doi.org/10.1016/j.compenvurbsys.2009.06.002
   Web of Science ®Google Scholar
• Stoter, J., Zhang, X., Stigmar, H., & Harrie, L. (2014). Evaluation in generalisation. In D. Burghardt, C. Duchêne, & W. Mackaness (Eds.), Abstracting geographic information in a data rich world. Lecture notes in geoinformation and cartography (pp. 259–297). Springer.
   Google Scholar
• Tomko, M., Winter, S., & Claramunt, C. (2008). Experiential hierarchies of streets. Computers Environment & Urban Systems, 32(1), 41–52. https://doi.org/10.1016/j.compenvurbsys.2007.03.003
   Web of Science ®Google Scholar
• Töpfer, F., & Pillewizer, W. (1966). The principles of selection. The Cartographic Journal, 3(1), 10–16. https://doi.org/10.1179/caj.1966.3.1.10
   Google Scholar
• Touya, G. (2010). A road network selection process based on data enrichment and structure detection. Transactions in GIS, 14(5), 595–614. https://doi.org/10.1111/j.1467-9671.2010.01215.x
   Web of Science ®Google Scholar
• Weibel, R. (1997). Generalization of spatial data: Principles and selected algorithms. In M. van Kreveld, J. Nievergelt, T. Roos, & P. Widmayer (Eds.), Algorithmic foundations of geographic information systems. CISM school 1996. Lecture notes in computer science (Vol. 1340, pp. 99–152). Springer. https://doi.org/10.1007/3-540-63818-0_5
   Google Scholar
• Xu, Y., Xie, Z., Chen, Z., & Wu, L. (2017). Shape similarity measurement model for holed polygons based on position graphs and Fourier descriptors. International Journal of Geographical Information Science, 31(2), 253–279. https://doi.org/10.1080/13658816.2016.1192637
   Web of Science ®Google Scholar
• Yan, H. (2015). Quantitative relations of spatial similarity degree and map scale change of individual linear objects in multi-scale map spaces. Geocarto International, 30(4), 472–482. https://doi.org/10.1080/10106049.2014.902115
   Web of Science ®Google Scholar
• Yan, H. (2019). Description approaches and automated generalization algorithms for groups of map objects. Springer Nature Singapore President Ltd.
   Google Scholar
• Yan, H., & Li, J. (2014). Spatial similarity relations in multi-scale map spaces. Springer International Publishing Switzerland.
   Google Scholar
• Yan, H., Zhang, X., Lu, X., et al. (2022). Approach to automating the DP algorithm—taking river simplification as an example. Geomatics and Information Science of Wuhan University.
   Google Scholar
• Yang, B., Luan, X., & Li, Q. (2011). Generating hierarchical strokes from urban street networks based on spatial pattern recognition. International Journal of Geographical Information Science, 25(12), 2025–2050. https://doi.org/10.1080/13658816.2011.570270
   Web of Science ®Google Scholar
• Yu, H., Ai, T., Yang, M., Huang, W., & Harrie, L. (2023). A graph autoencoder network to measure the geometric similarity of drainage networks in scaling transformation. International Journal of Digital Earth, 16(1), 1828–1852. https://doi.org/10.1080/17538947.2023.2212920
   Web of Science ®Google Scholar
• Yu, W., Zhang, Y., Ai, T., Guan, Q., Chen, Z., & Li, H. (2020). Road network generalization considering traffic flow patterns. International Journal of Geographical Information Science, 34(1), 119–149. https://doi.org/10.1080/13658816.2019.1650936
   Web of Science ®Google Scholar
• Zhang, X., & Yan, H. (2022). Relationship between scale change and median hausdorff distance of river elements. Acta Geodaetica et Cartographica Sinica.
   Google Scholar