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GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY

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Spatial variation of feature density in multiscale topographic data

https://doi.org/10.24057/2071-9388-2022-127

Abstract

Digital topographic maps are created in a series of scales from large to small, and the underlying spatial data is commonly organized as a multiscale database consisting of several levels of detail (LoDs). Spatial density of features (or spatial objects) in such database varies both between LoDs (coarser levels are less densely populated with features) and within each LoD (feature density changes over the area). While the former type of density variation is caused by generalization, the latter one is mainly conditioned by geographic location and its properties, such as landscape complexity or fraction of urban areas. Since topographic database LoDs are derived using different data sources and generalization techniques, there is a need for a method that can help with automated evaluation of resulting feature density in terms of its appropriateness for the specified location and level of detail. This paper provides such method by uncovering dependencies between the location properties and the density of spatial data in multiscale topographic database. Changes in feature density are modeled as a function of spatial (landscape complexity and terrain ruggedness) and non-spatial (land cover types ratio) measures estimated via independent data sources. Resulting model predicts how much higher or lower is the expected spatial density of features over the area in comparison to the average density for the LoD. This information can be used further to assess the fitness of the data to the desired level of detail of the topographic map.

About the Authors

T. E. Samsonov
Lomonosov Moscow State University
Russian Federation

 Faculty of Geography

Leninskiye Gory 1, 119234, Moscow



O. P. Yakimova
Demidov Yaroslavl State University
Russian Federation

Faculty of Mathematics

Souyznaya str. 144, 150008, Yaroslavl



D. A. Potemkin
Demidov Yaroslavl State University
Russian Federation

Faculty of Mathematics

Souyznaya str. 144, 150008, Yaroslavl



O. A. Guseva
Demidov Yaroslavl State University
Russian Federation

Faculty of Biology and Ecology



References

1. Biljecki F., Ledoux H., and Stoter J. (2016). An Improved LOD Specification for 3d Building Models. Computers, Environment and Urban Systems, 59, 25–37, DOI: 10.1016/j.compenvurbsys.2016.04.005.

2. Buchhorn M., Lesiv M., Tsendbazar N.-E., Herold M., Bertels L., and Smets B. (2020). Copernicus Global Land Cover Layers Collection 2. Remote Sensing, 12 (6), 1044, DOI: 10.3390/rs12061044.

3. Buchin K., Meulemans W., Van Renssen A., and Speckmann B. (2016). Area-Preserving Simplification and Schematization of Polygonal Subdivisions. ACM Transactions on Spatial Algorithms and Systems, 2(1), 1-36, DOI: 10.1145/2818373.

4. Burrough P.A. (1981). Fractal Dimensions of Landscapes and Other Environmental Data. Nature, 294 (5838), 240-42, DOI: 10.1038/294240a0.

5. Cheng X., Liu Z., and Zhang Q. (2021). MSLF: Multi-Scale Legibility Function to Estimate the Legible Scale of Individual Line Features. Cartography and Geographic Information Science, 48(2), 151-68, DOI: 10.1080/15230406.2020.1857307.

6. Cheng X., Wu H., Ai T., and Yang M. (2017). Detail Resolution: A New Model to Describe Level of Detail Information of Vector Line Data. In

7. Advances in Geographic Information Science, edited by Chenghu Zhou, Fenzhen Su, Francis Harvey, and Jun Xu, Singapore: Springer Singapore, 167-77, DOI: 10.1007/978-981-10-4424-3_12.

8. De Floriani L., Marzano M., and Puppo E. (1996). Multiresolution Models for Topographic Surface Description. The Visual Computer, 12 (7), 317-45, DOI: 10.1007/s003710050068.

9. Douglas D.H. and Peucker T.K. (1973). Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or Its Caricature. The Canadian Cartographer, 10(2), 112-22, DOI: 10.3138/FM57-6770-U75U-7727.

10. Haunert J.-H. and Wolff A. (2010). Area Aggregation in Map Generalisation by Mixed-Integer Programming. International Journal of Geographical Information Science, 24(12), 1871-97, DOI: 10.1080/13658810903401008.

11. Hesselbarth M. H. K., Sciaini M., With K.A., Wiegand K, and Nowosad J. (2019). Landscapemetrics : An Open-Source R Tool to Calculate Landscape Metrics. Ecography, 42 (10), 1648-57, DOI: 10.1111/ecog.04617.

12. Imhof E. (1982). Cartographic Relief Presentation, Berlin: Walter der Gruyter, 416.

13. Jones C.B. and Abraham I.M. (1986). Design Considerations for a Scale-Independent Database. In Proceedings of Second International Symposium on Spatial Data Handling, Seattle, 384-98.

14. Kilpeläinen T. (2000). Maintenance of Multiple Representation Databases for Topographic Data. The Cartographic Journal, 37(2), 101-7, DOI: 10.1179/0008704.37.2.p101.

15. Kolbe T.H., Gröger G., and Plümer L. (2005). CityGML: Interoperable Access to 3d City Models. In: Geo-Information for Disaster Management, edited by Peter van Oosterom, Siyka Zlatanova, and Elfriede M. Fendel, Berlin, Heidelberg: Springer Berlin Heidelberg, 883-99, DOI: 10.1007/3-540-27468-5_63.

16. Lemmens M. (2011). Quality of Geo-Information. In: Geo-Information, Dordrecht: Springer Netherlands, 211-27, DOI: 10.1007/978-94-007-1667-4_11.

17. Li Z. and Openshaw S. (1992). Algorithms for Automated Line Generalization Based on a Natural Principle of Objective Generalization. International Journal of Geographical Information Systems, 6(5), 373-89, DOI: 10.1080/02693799208901921.

18. Lin P., Pan M., Wood E. F., Yamazaki D., and Allen G. H. (2021). A New Vector-Based Global River Network Dataset Accounting for Variable Drainage Density. Scientific Data, 8(1), 28, DOI: 10.1038/s41597-021-00819-9.

19. Meijer J.R., Huijbregts M.A.J., Schotten K.C.G.J., and Schipper A.M. (2018). Global Patterns of Current and Future Road Infrastructure. Environmental Research Letters, 13(6), 064006, DOI: 10.1088/1748-9326/aabd42.

20. Meng L. and Forberg A. (2007). 3D Building Generalisation. In: Generalisation of Geographic Information: Cartographic Modelling and Applications, Elsevier, 211-31, DOI: 10.1016/B978-008045374-3/50013-2.

21. Military Topographic Service. (1978). Guide to cartographic and cartographic works. Part 1. Drafting and preparation for publication of 1:25 000, 1:50 000, and 1:100 000 scale topographic maps [in Russian]. Moscow: Editorial; Publishing Department of Military Topographic Service.

22. Military Topographic Service. (1980). Guide to cartographic and cartographic works. Part 2. Drafting and preparation for publication of 1:200 000 and 1:500 000 topographic maps [in Russian]. Moscow: Editorial; Publishing Department of Military Topographic Service.

23. Military Topographic Service. (1985). Guide to cartographic and cartographic works. Part 3. Drafting and preparation for publication of 1:1 000 000 scale topographic maps [in Russian]. Moscow: Editorial; Publishing Department of Military Topographic Service.

24. Nowosad J. and Stepinski T. F. (2019). Information Theory as a Consistent Framework for Quantification and Classification of Landscape Patterns. Landscape Ecology, 34(9), 2091-101, DOI: 10.1007/s10980-019-00830-x.

25. Phillips J. (1999) Earth surface systems : complexity, order and scale – Malden, Mass: Blackwell Publishers.

26. Riitters K.H., O’Neill R.V., Wickham J.D., and Jones K.B. (1996). A Note on Contagion Indices for Landscape Analysis. Landscape Ecology, 11(4), 197-202, DOI: 10.1007/BF02071810.

27. Riley S.J., De Gloria S.D., and Elliot R. (1999). A Terrain Ruggedness That Quantifies Topographic Heterogeneity. Intermountain Journal of Science, 5 (1-4), 23-27.

28. Ruas A., and Bianchin A. (2002). Echelle Et Niveau de Detail. In: Generalisation et Representation Miltiple, edited by Anne Ruas, Paris: Hermes Lavoisier, 25-44.

29. Russian Federal State Statistics Service. (2022). Database of Municipal Indicators. Available at: https://www.gks.ru/free_doc/new_site/bd_munst/munst.htm. [Accessed 10 June 2022].

30. Samsonov T. (2022). Granularity of Digital Elevation Model and Optimal Level of Detail in Small-Scale Cartographic Relief Presentation. Remote Sensing, 14(5), 1270, DOI: 10.3390/rs14051270.

31. Samsonov T. and Yakimova O. (2020). Regression Modeling of Reduction in Spatial Accuracy and Detail for Multiple Geometric Line Simplification Procedures. International Journal of Cartography, 6(1), 47-70, DOI: 10.1080/23729333.2019.1615745.

32. Töpfer F. and Pillewizer W. (1966). The Principles of Selection. The Cartographic Journal, 3(1), 10-16, DOI: 10.1179/caj.1966.3.1.10.

33. Touya G. and Brando-Escobar C. (2013). Detecting Level-of-Detail Inconsistencies in Volunteered Geographic Information Data Sets. Cartographica: The International Journal for Geographic Information and Geovisualization, 48(2), 134-43, DOI: 10.3138/carto.48.2.1836.

34. Touya G., Duchêne C., and Ruas A.. (2010). Collaborative Generalisation: Formalisation of Generalisation Knowledge to Orchestrate Different Cartographic Generalisation Processes. In: Theories and Methods of Spatio-Temporal Reasoning in Geographic Space, Berlin, Heidelberg: Springer Berlin Heidelberg, 264-78, DOI: 10.1007/978-3-642-15300-6_19.

35. Visvalingam M. and Whyatt J.D. (1993). Line Generalisation by Repeated Elimination of Points. The Cartographic Journal, 30(1), 46-51, DOI: 10.1179/caj.1993.30.1.46.

36. Yakimova O., Samsonov T., Potemkin D., Usmanova E. (2021). QGIS processing tool for spatial data detail assessment. InterCarto. InterGIS, 27(2), 260-279, DOI: 10.35595/2414-9179-2021-2-27-268-279.


Review

For citations:


Samsonov T.E., Yakimova O.P., Potemkin D.A., Guseva O.A. Spatial variation of feature density in multiscale topographic data. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY. 2023;16(1):86-102. https://doi.org/10.24057/2071-9388-2022-127

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ISSN 2071-9388 (Print)
ISSN 2542-1565 (Online)