Intelligent initial map scale generation based on rough-set rules

Abstract

A proper initial map scale can help improve map legibility. However, the existing initial scale designs for electronic maps cannot make active adjustments according to the differences in the surrounding geographic information distributions, during map panning or navigation. This causes many redundant zooming operations, which reduce the reading efficiency. To solve this problem, we propose a method based on the rough set, which chooses an initial map scale according to the spatial distribution of the road network. First, the spatial distribution of the road network is evaluated using the neighborhood relation model, with Delaunay triangulations. Next, the data of the road network’s spatial distributions and the corresponding map scale data from user operations are collected at different locations. Then, the relationship rules are extracted based on rough set. Finally, an intelligent initial map scale service is developed according to the rules, and its feasibility and effectiveness are tested using an experimental system. The test results show that the intelligent initial map method can adjust the map scale adaptively and dynamically according to distribution of the road network. Consequently, the map legibility is improved significantly because of the reduction in the number of zooming operations.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

References

  1. Ai TH, Liang R (2007) Variable-scale visualization in navigation electronic map. Geomatics Inf Sci Wuhan Univ 32:127–130

    Google Scholar 

  2. Ai TH, He YK, Du X (2007) Information entropy change in GIS data scale transformation. Geog Geo-Inf Sci 32(2):7–11. https://doi.org/10.3969/j.issn:1672-0504.2015.02.002

    Article  Google Scholar 

  3. Ai T, Zhang X, Zhou Q, Yang M (2015) A vector field model to handle the displacement of multiple conflicts in building generalization. Int J Geogr Inf Sci 29(8):1310–1331. https://doi.org/10.1080/13658816.2015.1019886

    Article  Google Scholar 

  4. Ai TH, Ke S, Yang M, Li JZ (2016) Envelope generation and simplification of polylines using Delaunay triangulation. Int J Geogr Inf Sci 31(2):297–319. https://doi.org/10.1080/13658816.2016.1197399

    Article  Google Scholar 

  5. Bertone A, Burghardt D (2017) A survey on visual analytics for the spatio-temporal exploration of microblogging content. J Geovis Spat Anal 1(1–2):2. https://doi.org/10.1007/s41651-017-0002-6

    Article  Google Scholar 

  6. Chalmers D, Sloman M, Dulay N (2001) Map adaptation for users of mobile systems. Proc10th Int Conf on World Wide Web (WWW10) Hong Kong, ACM, pp 735–744

  7. Chen J, Yan CD, Zhao RL, Zhao XS (2009) Voronoi neighbor-based self-adaptive clipping model for mobile maps. Acta Geodaetica et Cartographica Sinica 38:152–156. https://doi.org/10.3321/j.issn:1001-1595.2009.02.010

    Article  Google Scholar 

  8. Deng HY, Wu F, Zhai RJ, Wang HL (2007) A DFQR tree for quality control of cartographical generalization. Acta Geodaetica Et Cartographica Sinica 36(2):237–243. https://doi.org/10.3321/j.issn:1001-1595.2007.02.021

    Article  Google Scholar 

  9. Du XC, Guo QS (2004) Spatial neighborhood relation reasoning based on Delaunay triangulation. Sci Surveying Mapp 29:65–67. https://doi.org/10.3771/j.issn.1009-2307.2004.06.015

    Article  Google Scholar 

  10. Guo W (2013) The method of variable-scale mobile map. Master Thesis, Zhengzhou University, Zhengzhou, China

  11. Harrie L, Sarjakoski LT, Lehto L (2002) A mapping function for variable-scale maps in small-display cartography. J Geospatial Eng 4:111–123

    Google Scholar 

  12. Heitzler M, Lam JC, Hackl J, Adey BT (2017) GPU-accelerated rendering methods to visually analyze large-scale disaster simulation data. J Geovis Spat Anal 1(1–2):1–20. https://doi.org/10.1007/s41651-017-0004-4

    Article  Google Scholar 

  13. Jiang N, Cao YN, Sun QH, Zhang H, Gu YH (2014) Research and application of two-peak changing law of basic electronic map load. Acta Geodaetica et Cartographica Sinica 43:306–313. https://doi.org/10.13485/j.cnki.11-2089.2014.0044

    Article  Google Scholar 

  14. Kratz S, Brodien I, Rohs M (2010) Semi-automatic zooming for mobile map navigation. Proc 12th Int Conf Human-Computer Interaction with Mobile Devices and Services, pp 63–72

  15. Li Q (2009) Variable-scale representation of road networks on small mobile devices. Comput Geosci 35:2185–2190. https://doi.org/10.1016/j.cageo.2008.12.009

    Article  Google Scholar 

  16. Li JT, Yang DG, Zhang T, Lian XM (2014) Fuzzy homogenization of road density in vehicle navigation map. J Tsinghua Univ(Sci & Technol) 54(11):1434–1439. https://doi.org/10.16511/j.cnki.qhdxxb.2014.11.012

    Article  Google Scholar 

  17. Liang JY, Li DY (2005) The uncertainty and knowledge acquisition in information system. Science Press, Beijing

    Google Scholar 

  18. Liang HM, Zhao J (2001a) Application of geographic information system on spatial distribution characteristics of settlement. J Northwest University (Nat Sci) 37:76–80. https://doi.org/10.16783/j.cnki.nwnuz.2001.02.019

    Article  Google Scholar 

  19. Liang HM, Zhao J (2001b) Study on the spatial distribution characteristics of settlement in Loess Plateau by GIS. Hum Geogr 16:81–83. https://doi.org/10.3969/j.issn.1003-2398.2001.06.021

    Article  Google Scholar 

  20. Lingas A (1986) The greedy and Delaunay triangulations are not bad in the average case and minimum weight triangulation of multi-connected polygons in NP-complete. Inf Process Lett 22:25–31

    Article  Google Scholar 

  21. Meng L (2005) Egocentric design of map-based mobile services. Cartogr J 42:5–13. https://doi.org/10.1179/000870405X57275

    Article  Google Scholar 

  22. Nivala A, Sarjakoski LT, Jakobsson A, Kaasinen E (2003) Usability evaluation of topographic maps in mobile devices. Proc 21st Int Cartographic Conf:1903–1913

  23. Partala T, Luimula M, Saukko O (2006) Automatic rotation and zooming in mobile roadmaps. Proc 8th Conf Human-Computer Interaction with Mobile Devices and Services (HCI’06), pp 255–258

  24. Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11(5):341–356

    Article  Google Scholar 

  25. Peng YP, Liu WX (2002) Study on Delaunay triangulation and Voronoi diagram application in GIS. Eng Surv Mapp 11:39–41

    Google Scholar 

  26. Qiu SS, Quan GR, Kong LH (2000) A rule learning algorithm on continuous attributes space. J Harbin Inst Tech 32:42–47. https://doi.org/10.3321/j.issn:0367-6234.2000.03.011

    Article  Google Scholar 

  27. Reichenbacher T (2004) Mobile cartography–adaptive visualization of geographic information on mobile device. Doctoral dissertation, Technical University Munich

  28. Shi JF (2010) On the relationship between user speed and mobile map scale. Comput Knowl Technol 06:6250–6251. https://doi.org/10.3969/j.issn.1009-3044.2010.22.051

    Article  Google Scholar 

  29. Stoter J, Burghardt D, Cécile D, Baella B, Bakker N, Blok C et al (2009) Methodology for evaluating automated map generalization in commercial software computers. Environ Urban Syst 33(5):311–324. https://doi.org/10.1016/j.compenvurbsys.2009.06.002

    Article  Google Scholar 

  30. Töpfer F, Pillewizer W (1966) The principles of selection: a means of cartographic generalization. Cartogr J 3(1):10-16.

  31. Tsai VJD (1993) Delaunay triangulations in TIN creation: an overview and a linear-time algorithm. Int J GIS 7:501–524

    Google Scholar 

  32. Wan G, Gao J, Liu YZ (2008) Research on cognitive map formation based on reading experiments. J Rem Sens 12:339–344

    Google Scholar 

  33. Wang GY (2001) Rough sets and knowledge acquisition. Xi’an Jiaotong University Press, Xi’an, pp 102–111

    Google Scholar 

  34. Wang Y, Ai TH (2018) Graphic simplification of complex road network intersections based on spatial relationship. J Geom 43(2):97–100. https://doi.org/10.14188/j.2095-6045.2017261

    Article  Google Scholar 

  35. Wang P, Liu LN, Pan CH (2001) Delaunay triangulation fast creation and topology automatic form in GIS. J Geom 26:24–27. https://doi.org/10.14188/j.2095-6045.2001.04.007

    Article  Google Scholar 

  36. Yan CD, Zhao RL, Chen J (2006) Adaptive model of mobile map. Geog Geo-Inf Sci 22(2):42–45

    Google Scholar 

  37. Yan QW, Bian ZF, Wang Z (2009) A spatial analysis on patterns of settlements distribution in Xuzhou. Sci Surveying Mapp 34:160–163

    Google Scholar 

  38. Yan CD, Zhao YK, Guo W (2013) The measurement and application of adjacent area based on Delaunay triangulation. Geog Geo-Inf Sci 2:125–126

    Google Scholar 

  39. Zhang HT (2005) Research on spatial information mobile service model, algorithm and transmission technology. Doctoral dissertation, Chinese PLA Information Engineering University, Zhengzhou

  40. Zhang C, Yang BG (1984) Quantitative Geography Foundation. Higher Education Press, Beijing

    Google Scholar 

  41. Zhang ZJ, Li L, Jiang WL (2008) Research on dynamic eagle-eye technique in the multi-level display of electronic map. J Geom 33(3):36–37. https://doi.org/10.14188/j.2095-6045.2008.03.019

    Article  Google Scholar 

  42. Zhong YX (1994) A metrical research on map legibility. J Wuhan Tech Univ Surv Mapp 19:346–351. https://doi.org/10.13203/j.whugis1994.04.012

    Article  Google Scholar 

Download references

Funding

This work is supported by the National Natural Science Foundation of China (No. 41671455, No. 40971238) and the key research projects of the Henan Provincial Education Department (15A420007, 16A420005).

Author information

Affiliations

Authors

Contributions

CY, LY, and GG conceived the idea presented in this paper. CY and LY designed the experiments and performed the modeling. QZ developed the experimental system. LY and XL performed the tests. CY and LY wrote the paper.

Corresponding author

Correspondence to Chaode Yan.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yan, C., Yang, L., Gartner, G. et al. Intelligent initial map scale generation based on rough-set rules. Arab J Geosci 12, 109 (2019). https://doi.org/10.1007/s12517-019-4265-8

Download citation

Keywords

  • Map
  • Initial scale
  • Rough set
  • Spatial distribution
  • Road network