Design and development of a system for vario-scale maps
DOI:
https://doi.org/10.7480/abe.2017.18.1877Keywords:
geo-information, vario-scale, maps, tu delft, bouwkundeAbstract
Nowadays, there are many geo-information data sources available such as maps on the Internet, in-car navigation devices and mobile apps. All datasets used in these applications are the same in principle, and face the same issues, namely:
- Maps of different scales are stored separately. With many separate fixed levels, a lot of information is the same, but still needs to be included, which leads to duplication.
- With many redundant data throughout the scales, features are represented again and again, which may lead to inconsistency.
- Currently available maps contain significantly more levels of detail (twenty map scales on average) than in the past. These levels must be created, but the optimal strategy to do so is not known.
- For every user’s data request, a significant part of the data remains the same, but still needs to be included. This leads to more data transfer, and slower response.
- The interactive Internet environment is not used to its full potential for user navigation. It is common to observe lagging, popping features or flickering of a newly retrieved map scale feature while using the map.
This research develops principles of variable scale (vario-scale) maps to address these issues. The vario-scale approach is an alternative for obtaining and maintaining geographical data sets at different map scales. It is based on the specific topological structure called tGAP (topological Generalized Area Partitioning) which addresses the main open issues of current solutions for managing spatial data sets of different scales such as: redundancy data, inconsistency of map scales and dynamic transfer. The objective of this thesis is to design, to develop and to extend the variable-scale data structures and it is expressed as the following research question:
How to design and develop a system for vario-scale maps?
To address the above research question, this research has been conducted using the following outline:
To address the above research question, this research has been conducted using the following outline: 1) Investigate state-of-the-art in map generalization. 2) Study development of vario-scale structure done so far. 3) Propose techniques for generating better vario-scale map content. 4) Implement strategies to process really massive datasets. 5) Research smooth representation of map features and their impact on user interaction. Results of our research led to new functionality, were addressed in prototype developments and were tested against real world data sets. Throughout this research we have made following main contributions to the design and development of a system of vario-scale maps. We have:
- studied vario-scale development in the past and we have identified the most urgent needs of the research.
- designed the concept of granularity and presented our strategy where changes in map content should be as small and as gradual as possible (e. g. use groups, maintain road network, support line feature representation).
- introduced line features in the solution and presented a fully-automated generalization process that preserves a road network features throughout all scales.
- proposed an approach to create a vario-scale data structure of massive datasets.
- demonstrated a method to generate an explicit 3D representation from the structure which can provide smoother user experience.
- developed a software prototype where a 3D vario-scale dataset can be used to its full potential.
- conducted initial usability test.
All aspects together with already developed functionality provide a more complex and more unified solution for vario-scale mapping. Based on our research, design and development of a system for vario-scale maps should be clearer now. In addition, it is easier to identified necessary steps which need to be taken towards an optimal solution. Our recommendations for future work are:
- One of the contributions has been an integration of the road features in the structure and their automated generalization throughout the process. Integrating more map features besides roads deserve attention.
- We have investigated how to deal with massive datasets which do not fit in the main memory of the computer. Our experiences consisted of dataset of one province or state with records in order of millions. To verify our findings, it will be interesting to process even bigger dataset with records in order of billions (a whole continent).
- We have introduced representation where map content changes as gradually as possible. It is based on process where: 1) explicit 3D geometry from the structure is generated. 2) A slice of the geometry is calculated. 3) Final maps based on the slice is constructed. Investigation of how to integrate this in a server-client pipeline on the Internet is another point of further research.
- Our research focus has been mainly on one specific aspect of the concept at a time. Now all aspects may be brought together where integration, tuning and orchestration play an important role is another interesting research that desire attention.
- Carry out more user testing including; 1) maps of sufficient cartographic quality, 2) a large testing region, and 3) the finest version of visualization prototype.
References
Ai, T. and van Oosterom, P. (2002). Gap-tree extensions based on skeletons. In Ad- vances in Spatial Data Handling, 10th International Symposium on Spatial Data Han- dling, Ottawa, Canada. (Cited on pages 5, 21, and 22).
Armstrong, M. P. and Densham, P. J. (1992). Domain decomposition for parallel pro- cessing of spatial problems. Computers, Environment and Urban Systems, 16(6):497
– 513. (Cited on page 73).
Ballard, D. H. (1981). Strip trees: A hierarchical representation for curves. Commun. ACM, 24(5):310–321. (Cited on page 20).
Bereuter, P., Weibel, R., and Burghardt, D. (2012). Content zooming and exploration for mobile maps. In Gensel, J., Josselin, D., and Vandenbroucke, D., editors, Multidis- ciplinary Research on Geographical Information in Europe and Beyond Proceedings of the AGILE’2012 International Conference on Geographic Information Science, Avi- gnon, April, 24-27, 2012, pages 74–80. International Conference on Geographic Information Science, Avignon. (Cited on page 18).
Bertolotto, M. and Egenhofer, M. J. (2001). Progressive transmission of vector map data over the world wide web. GeoInformatica, 5(4):345–373. (Cited on page 20).
Bregt, A. and Bulens, J. (1996). Application-oriented generalization of area object. In Molenaar, M., editor, Methods for The Generalization of Geo-databases, number 43, pages 57–64. Netherlands Geodetic Commission, Delft, The Netherlands. (Cited on page 22).
Brewer, C. A. and Buttenfield, B. P. (2007). Framing guidelines for multi-scale map design using databases at multiple resolutions. Cartography and Geographic Infor- mation Science, 34(1):3–15. (Cited on page 15).
Buttenfield, B. P. (2002). Transmitting Vector Geospatial Data across the Internet, pages 51–64. Springer Berlin Heidelberg, Berlin, Heidelberg. (Cited on page 20).
Buttenfield, B. P. and McMaster, R. B. (1991). Map Generalization : Making Rules for Knowledge Representation. Longman Scientific & Technical, Harlow, UK. (Cited on page 14).
Cecconi, A. (2003). Integration of cartographic generalization and multi-scale databases for enhanced web mapping. PhD thesis, University of Zurich. (Cited on page 15).
Cecconi, A., Weibel, R., and Barrault, M. (2002). Improving automated generalisation for on-demand web mapping by multiscale databases. In Advances in Spatial Data Handling, pages 515–531. Springer Berlin Heidelberg. (Cited on page 14).
Chen, J., Hu, Y., Li, Z., Zhao, R., and Meng, L. (2009). Selective omission of road features based on mesh density for automatic map generalization. International Journal of Geographical Information Science, 23(8):1013–1032. (Cited on page 42).
Chimani, M., van Dijk, T. C., and Haunert, J.-H. (2014). How to eat a graph: Computing selection sequences for the continuous generalization of road networks. In Pro- ceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geo- graphic Information Systems, SIGSPATIAL ’14, pages 243–252, New York, NY, USA. ACM. (Cited on pages 15, 37, 40, and 88).
Danciger, J., Devadoss, S. L., Mugno, J., Sheehy, D., and Ward, R. (2009). Shape defor- mation in continuous map generalization. GeoInformatica, 13(2):203–221. (Cited on pages 15, 16, and 88).
Dean, J. and Ghemawat, S. (2004). MapReduce: Simplified Data Processing on Large Clusters. In Proceedings of the 6th Conference on Symposium on Opearting Systems Design & Implementation - Volume 6, OSDI’04, pages 10–10, Berkeley, CA, USA. USENIX Association. (Cited on page 73).
Dilo, A., van Oosterom, P., and Hofman, A. (2009). Constrained tgap for generalisation between scales: the case of dutch topographic data. Computers, Environment and Urban Systems, 33(5):388–402. (Cited on pages 5, 26, 28, and 63).
Ding, Y. and Densham, P. J. (1996). Spatial strategies for parallel spatial modelling. International journal of geographical information systems, 10(6):669–698. (Cited on page 73).
DMA USDMA (1986). Defense Mapping Agency Product Specifications for Digital Fea- ture Analysis Data (DFAD) Level 1-C and Level 3-C. (DFAD): Level 1 and level 2. DMA Aerospace Center, St. Louis, Mo. (Cited on page 21).
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. Cartographica: The International Journal for Geographic Information and Geovisualization, 10(2):112–
(Cited on page 20).
Driel, M., Šuba, R., Meijers, M., van Oosterom, P., and Eisemann, E. (2016). Real-time space-scale cube slicing for interactive geovisualization. to be submitted in Comput- ers & Graphics. (Cited on page 105).
Duchêne, C., Baella, B., Brewer, C. A., Burghardt, D., Buttenfield, B. P., Gaffuri, J., Käuferle, D., Lecordix, F., Maugeais, E., Nijhuis, R., Pla, M., Post, M., Regnauld, N., Stanislawski, L. V., Stoter, J., Tóth, K., Urbanke, S., van Altena, V., and Wiedemann, A. (2014). Abstracting Geographic Information in a Data Rich World: Method- ologies and Applications of Map Generalisation, volume 2014 of Lecture Notes in Geoinformation and Cartography, chapter Generalization in Practice Within National Mapping Agencies, pages 329–391. Springer International Publishing. (Cited on pages 12, 13, and 14).
Dumont, M., Touya, G., and Duchêne, C. (2015). Automated generalisation of interme- diate levels in a multi-scale pyramid. In Proceedings of 18th ICA Workshop on Gener- alisation and Multiple Representation, Rio de Janeiro, Brazil. (Cited on page 18).
Dumont, M., Touya, G., and Duchêne, C. (2016). Assessing the variation of visual com- plexity in multi-scale maps with clutter measures. In Proceedings of 19th ICA Work- shop on Generalisation and Multiple Representation, Helsinki, Finland. (Cited on page 18).
Eastman, C. M. and Weiler, K. J. (1979). Geometric Modeling Using the Euler Operators. DRC. Institute of Physical Planning, Carnegie-Mellon University. (Cited on page 15).
Edwardes, A. and Mackaness, W. (2000). Intelligent road network simplification in urban areas. GIS Research UK 2000 Conference (GISRUK 2000), University of York. (Cited on page 42).
Eldawy, A., Li, Y., Mokbel, M. F., and Janardan, R. (2013). Cg_hadoop: Computational geometry in mapreduce. In Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL’13, pages 294–303, New York, NY, USA. ACM. (Cited on page 73).
Foerster, T., Stoter, J., and Kraak, M.-J. (2010). Challenges for automated generali- sation at european mapping agencies: a qualitative and quantitative analysis. The Cartographic Journal, 47(1):41–54. (Cited on pages 12 and 13).
Frank, A. U. and Barrera, R. (1990). The Fieldtree: A data structure for Geographic Information Systems. In Proceedings of the First Symposium on Design and Imple- mentation of Large Spatial Databases, SSD ’90, pages 29–44, New York, NY, USA. Springer-Verlag New York, Inc. (Cited on pages 72 and 74).
Fuchs, H., Goldfeather, J., Hultquist, J. P., Spach, S., Austin, J. D., Brooks, Jr., F. P., Eyles,
J. G., and Poulton, J. (1985). Fast spheres, shadows, textures, transparencies, and image enhancements in pixel-planes. SIGGRAPH Comput. Graph., 19(3):111–120. (Cited on page 108).
Guan, X., Wu, H., and Li, L. (2012). A Parallel Framework for Processing Massive Spatial Data with a Split-and-Merge Paradigm. Transactions in GIS, 16(6):829–843. (Cited on page 73).
Guha, S., Krishnan, S., Munagala, K., and Venkatasubramanian, S. (2003). Application of the two-sided depth test to CSG rendering. In Proceedings of the 2003 symposium on Interactive 3D graphics, pages 177–180. ACM. (Cited on page 105).
Günther, O. (1988). Efficient Structures for Geometric Data Management. Springer- Verlag New York, Inc., New York, NY, USA. (Cited on page 20).
Guttman, A. (1984). R-trees: A dynamic index structure for spatial searching. SIGMOD Rec., 14(2):47–57. (Cited on page 21).
Hable, J. and Rossignac, J. (2005). Blister: GPU-based rendering of boolean combi- nations of free-form triangulated shapes. In ACM Transactions on Graphics (TOG), volume 24, pages 1024–1031. ACM. (Cited on page 105).
Hägerstrand, T. (1970). What about people in regional science? Papers of the Regional Science Association, 24(1):6–21. (Cited on page 30).
Hampe, M., Sester, M., and Harrie, L. (2004). Multiple representation databases to support visualization on mobile devices. In Proceedings of the XXth ISPRS congress, July 12-23, 2004, Istanbul, Turkey, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXV(B4:IV), pages 135–140. (Cited on page 32).
Harrie, L., Sarjakoski, L. T., and Lehto, L. (2002). A variable-scale map for small-display cartography. In Joint International Symposium on GeoSpatial Theory, Processing and Applications (ISPRS/Commission IV, SDH2002), Ottawa, Canada. (Cited on page 32).
Harrie, L., Stigmar, H., and Djordjevic, M. (2015). Analytical estimation of map read- ability. ISPRS International Journal of Geo-Information, 4(2):418. (Cited on page 18).
Haunert, J.-H., Dilo, A., and van Oosterom, P. (2009). Constrained set-up of the tgap structure for progressive vector data transfer. Computers & Geosciences, 35(11):2191–2203. (Cited on pages 26, 28, and 63).
Haunert, J.-H. and Wolff, A. (2006). Generalization of land cover maps by mixed integer programming. In Proceedings of the 14th Annual ACM International Symposium on Advances in Geographic Information Systems, GIS ’06, pages 75–82, New York, NY, USA. ACM. (Cited on page 63).
Hawick, K. A., Coddington, P. D., and James, H. A. (2003). Distributed frameworks and parallel algorithms for processing large-scale geographic data. Parallel Computing, 29(10):1297–1333. (Cited on page 73).
Hildebrandt, J., Owen, M., and Hollamby, R. (2010). Cluster raptor: Dynamic geospatial imagery visualisation using backend repositories. In Proceedings of the 5th Interna- tional Command and Control Research and Technology Symposium (ICCRTS). (Cited on page 20).
Huang, L., Meijers, M., Šuba, R., and van Oosterom, P. (2016). Engineering web maps with gradual content zoom based on streaming vector data. ISPRS Journal of Pho- togrammetry and Remote Sensing, 114:274 – 293. (Cited on pages 5, 8, 19, 34, 35,
, 40, 87, and 131).
ICA and EuroSDR (2013). ICA/EuroSDR NMA Symposium 2013. Accessed 17 June 2013. (Cited on pages 12 and 13).
ICA and EuroSDR (2015). 2nd ICA/EuroSDR NMA Symposium. Accessed 29 June 2016. (Cited on pages 12 and 13).
Jones, C. B., Abdelmoty, A. I., Lonergan, M. E., van der Poorten, P., and Zhou, S. (2000). Multi-scale spatial database design for online generalisation. In 9th International Symposium on Spatial Data Handling, Beijing, International Geographical Union, volume 7b, pages 34–44. (Cited on page 20).
Jones, C. B. and Abraham, I. M. (1987). Line generalisation in a global cartographic database. Cartographica: The International Journal for Geographic Information and Geovisualization, 24(3):32–45. (Cited on page 20).
Käuferle, D. (2015). New national maps for switzerland. Presented at 2nd ICA/Eu- roSDR NMA Symposium 2015, Amsterdam, The Netherlands. Accessed 10 August 2016. (Cited on page 12).
Lagrange, J.-P., Weibel, R., and Muller, J.-C. (1995). GIS and Generalisation: Methodol- ogy and Practice. Number 1. CRC Press. (Cited on page 14).
Lazaridis, I. and Mehrotra, S. (2001). Progressive approximate aggregate queries with a multi-resolution tree structure. In Proceedings of the 2001 ACM SIGMOD Interna- tional Conference on Management of Data, SIGMOD ’01, pages 401–412, New York, NY, USA. ACM. (Cited on page 20).
Li, Z. and Zhou, Q. (2012). Integration of linear and areal hierarchies for continuous multi-scale representation of road networks. International Journal of Geographical Information Science, 26(5):855–880. (Cited on page 42).
MacEachren, A. M. and Kraak, M.-J. (1997). Exploratory cartographic visualization: ad- vancing the agenda. Computers & Geosciences, 23(4):335–343. (Cited on page 14).
Mackaness, W., Burghardt, D., and Duchêne, C. (2014). Abstracting Geographic In- formation in a Data Rich World: Methodologies and Applications of Map Generali- sation, volume 2014 of Lecture Notes in Geoinformation and Cartography, chapter Map generalisation: Fundamental to the Modelling and Understanding of Geo- graphic Space, pages 1–14. Springer International Publishing. (Cited on pages 9, 10, and 11).
Mackaness, W. A., Ruas, A., and Sarjakoski, L. T. (2007). Generalisation of Geographic Information: Cartographic Modelling and Applications. Elsevier, Oxford, UK. (Cited on page 14).
Meijers, M. (2011). Variable-scale Geo-information. PhD thesis, Delft University of Technology. (Cited on pages 3, 5, 26, 27, 29, 30, 31, 32, 41, and 46).
Meijers, M. and Ledoux, H. (2013). EdgeCrack: a parallel Divide-and-Conquer algo- rithm for building a topological data structure. In Ellul, C., Zlatanova, S., Rumor, M., and Laurini, R., editors, Urban and Regional Data Management: Annual 2013, pages 107–116. CRC Press. (Cited on page 73).
Meijers, M., Savino, S., and van Oosterom, P. (2016). SPLITAREA : an algorithm for weighted splitting of faces in the context of a planar partition. International Journal of Geographical Information Science, 30:1522–1551. (Cited on page 53).
Meijers, M. and van Oosterom, P. (2011). The space-scale cube: An integrated model for 2d polygonal areas and scale. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(4):95–102. (Cited on pages 3 and 29).
Meijers, M., van Oosterom, P., and Quak, W. (2009). A storage and transfer efficient data structure for variable scale vector data. In Sester, M., Bernard, L., and Paelke, V., editors, Advances in GIScience, Lecture Notes in Geoinformation and Cartography, pages 345–367. Springer Berlin Heidelberg. (Cited on pages 23 and 26).
Meijers, M., Šuba, R., and van Oosterom, P. (2015). Parallel creation of vario-scale data structures for large datasets. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-4/W7:1–9. (Cited on pages 8 and 71).
Meng, L., Huang, C., Zhao, C., and Lin, Z. (2007). An improved Hilbert curve for parallel spatial data partitioning. Geo-spatial Information Science, 10(4):282–286. (Cited on page 73).
Midtbø, T. and Nordvik, T. (2007). Effects of animations in zooming and panning operations on web maps: A web-based experiment. The Cartographic Journal, 44(4):292–303. (Cited on pages 40 and 88).
Nöllenburg, M., Merrick, D., Wolff, A., and Benkert, M. (2008). Morphing polylines: A step towards continuous generalization. Computers, Environment and Urban Sys- tems, 32(4):248–260. (Cited on pages 15 and 88).
Penner, R. (2002). Robert Penner’s Programming Macromedia Flash MX, chapter 7: Motion, Tweening, and Easing, pages 191–240. McGraw-Hill, Inc., New York, NY, USA, 1 edition. (Cited on pages 119 and 120).
Read, P. and Meyer, M.-P. (2000). Restoration of Motion Picture Film (Butterworth- Heinemann Series in Conservation and Museology). Butterworth-Heinemann. (Cited on page 114).
Reilly, D. F. and Inkpen, K. M. (2004). Map morphing: Making sense of incongruent maps. In Proceedings of Graphics Interface 2004, GI ’04, pages 231–238, School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada. Canadian Human-Computer Communications Society. (Cited on page 88).
Reilly, D. F. and Inkpen, K. M. (2007). White rooms and morphing don’t mix: setting and the evaluation of visualization techniques. In Proceedings of the SIGCHI Confer- ence on Human Factors in Computing Systems, CHI ’07, pages 111–120, New York, NY, USA. ACM. (Cited on page 88).
Robinson, A. H., Morrison, J. L., Muehrcke, P. C., Kimerling, A. J., and Guptill, S. C. (1995). Elements of cartography, chapter 29. Dynamic/interactive mapping. Wi- ley, Wiley, New York, 6th edition. (Cited on page 14).
Rosenbaum, R. and Schumann, H. (2004). Remote raster image browsing based on fast content reduction for mobile environments. In Proceedings of the Seventh Euro- graphics Conference on Multimedia, EGMM’04, pages 13–19, Aire-la-Ville, Switzer- land, Switzerland. Eurographics Association. (Cited on page 20).
Samet, H. (1984). The quadtree and related hierarchical data structures. ACM Comput. Surv., 16(2):187–260. (Cited on page 20).
Sester, M. and Brenner, C. (2005). Continuous generalization for visualization on small mobile devices. In Developments in Spatial Data Handling, pages 355–368. Springer Berlin Heidelberg. (Cited on pages 15, 40, and 88).
Sharker, M. H. and Karimi, H. A. (2014). Big Data, chapter Distributed and Parallel Computing, pages 1–30. CRC Press. (Cited on page 72).
Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. In Proceedings of the 1968 23rd ACM National Conference, ACM ’68, pages 517–524, New York, NY, USA. ACM. (Cited on page 110).
Shewchuk, J. R. (1996). Triangle: Engineering a 2D Quality Mesh Generator and De- launay Triangulator. In Lin, M. C. and Manocha, D., editors, Applied Computational Geometry: Towards Geometric Engineering, volume 1148 of Lecture Notes in Com- puter Science, pages 203–222. Springer-Verlag. From the First ACM Workshop on Applied Computational Geometry. (Cited on page 97).
Stoter, J. (2005). Generalisation within NMA’s in the 21st century. In Proceedings of the International Cartographic Conference. (Cited on page 12).
Stoter, J., Meijers, M., van Oosterom, P., Grunreich, D., and Kraak, M.-J. (2010). Apply- ing dlm and dcm concepts in a multi-scale data environment. In GDI 2010, a Sym- posium on Generalization and Data Integration, Boulder, page 7. (Cited on page 4).
Stoter, J., Post, M., van Altena, V., Nijhuis, R., and Bruns, B. (2014). Fully automated generalization of a 1:50k map from 1:10k data. Cartography and Geographic Infor- mation Science, 41(1):1–13. (Cited on pages 17 and 73).
Stoter, J., van Altena, V., Post, M., Burghardt, D., and Duchêne, C. (2016). Automated generalisation within NMAs in 2016. In Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B4, 647-652, 2016, Prague. (Cited on pages 12, 13, and 14).
Thiemann, F., Warneke, H., Sester, M., and Lipeck, U. (2011). A Scalable Approach for Generalization of Land Cover Data. In Geertman, S., Reinhardt, W., and Toppen, F., editors, Advancing Geoinformation Science for a Changing World, Lecture Notes in Geoinformation and Cartography, pages 399–420. Springer Berlin Heidelberg. (Cited on page 73).
Thomson, R. C. and Richardson, D. E. (1999). The ‘good continuation’ principle of per- ceptual organization applied to the generalization of road networks. In Proceedings of the 19th International Cartographic Conference, pages 1215–1225. Ottwawa. (Cited on page 42).
Touya, G. and Girres, J.-F. (2013). Scalemaster 2.0: a scalemaster extension to monitor automatic multi-scales generalizations. Cartography and Geographic Information Science, 40(3):192–200. (Cited on page 15).
Turner, A. (2007). From axial to road-centre lines: a new representation for space syntax and a new model of route choice for transport network analysis. Environment and Planning B: Planning and Design, 34(3):539–555. (Cited on pages 42 and 47).
Tutorials Point (2015). Software development life cycle. In Software Engineering Tuto- rial. Accessed 8 August 2016. (Cited on page 6).
Uitermark, H., Vogels, A., and van Oosterom, P. (1999). Semantic and geometric as- pects of integrating road networks. In 2nd International conference on Interoper- ating Geographic Information Systems, 10-12 March’99, Zurich, LNCS 1580, pages 177–188. Springer-Verlag. (Cited on page 47).
van Kreveld, M. (2001). Smooth generalization for continuous zooming. In Proceedings 20th International Cartographic Conference (ICC’01), pages 2180–2185, Beijing, China. (Cited on page 14).
van Oosterom, P. (1989). Reactive data structures for geographic information systems. In AutoCarto 9, Baltimore, Maryland, 1986, pages 665–674. (Cited on page 20).
van Oosterom, P. (1990). Reactive Data Structures for Geographic Information Sys- tems. PhD thesis, Department of Computer Science, Leiden University, The Nether- lands. (Cited on pages 20 and 95).
van Oosterom, P. (1992). A storage structure for a multi-scale database: The reactive- tree. 16(3):239–247. (Cited on page 20).
van Oosterom, P. (1994). Reactive Data Structures for Geographic Information Sys- tems. Oxford University Press, Inc., New York, NY, USA. (Cited on page 21).
van Oosterom, P. (1995). GIS and Generalization, Methodology and Practice, chapter The GAP-tree, an approach to “On-the-Fly” Map Generalization of an Area Partition- ing., pages 120–132. (Cited on pages 21 and 22).
van Oosterom, P. (2005). Variable-scale Topological Data Structures Suitable for Pro- gressive Data Transfer: The GAP-face Tree and GAP-edge Forest. Cartography and Geographic Information Science, 32(11):331–346. (Cited on pages 2, 5, 23, 24, 25, 28, 33, and 41).
van Oosterom, P. and Meijers, M. (2011a). Method and system for generating maps in an n-dimensional space. Dutch patent application 2006630, WO 2012/144983, filed April 19, 2011, published October 26, 2012. (Cited on page 5).
van Oosterom, P. and Meijers, M. (2011b). Towards a true vario-scale structure sup- porting smooth-zoom. In Proceedings of 14th ICA/ISPRS Workshop on Generalisa- tion and Multiple Representation, pages 1–19, Paris. (Cited on pages 2 and 90).
van Oosterom, P. and Meijers, M. (2012). Vario-scale data structures supporting smooth zoom and progressive transfer of 2d and 3d data. In Jaarverslag 2011, pages 21–42. Nederlandse Commissie voor Geodesie. (Cited on page 33).
van Oosterom, P., Meijers, M., Stoter, J., and Šuba, R. (2014). Abstracting Geographic Information in a Data Rich World: Methodologies and Applications of Map General- isation, volume 2014 of Lecture Notes in Geoinformation and Cartography, chap- ter Data Structures for Continuous Generalisation: tGAP and SSC, pages 83–118. Springer International Publishing. (Cited on pages 2, 8, and 19).
van Oosterom, P. and Schenkelaars, V. (1995). The development of an interac- tive multi-scale GIS. International Journal of Geographical Information Systems, 9(5):489–507. (Cited on page 21).
van Oosterom, P. and Vijlbrief, T. (1996). The Spatial Location Code. In Proceedings of the 7th International Symposium on Spatial Data Handling, SDH’96. (Cited on page 74).
van Putten, J. and van Oosterom, P. (1998). New results with Generalized Area Parti- tionings. In Proceedings of the International Symposium on Spatial Data Handling, SDH’98, pages 485–495. (Cited on pages 21, 22, 63, and 72).
van Smaalen, J. W. N. (2003). Automated aggregation of geographic objects. A new approach to the conceptual generalisation of geographic databases. PhD thesis, Wageningen University, The Netherlands. (Cited on page 22).
Vermeij, M., van Oosterom, P., Quak, W., and Tijssen, T. (2003). Storing and using scale-less topological data efficiently in a client-server dbms environment. 7th Inter- national conference on GeoComputation. (Cited on pages 21, 23, and 29).
Visvalingam, M. and Whyatt, J. D. (1993). Line generalisation by repeated elimination of points. The Cartographic Journal, 30(1):46–51. (Cited on page 91).
Šuba, R. (2012). Content of variable-scale maps. Number 64, pages 1–36. ISBN: 978-90-77029-38-1 ISSN: 1569-0245. (Cited on page 33).
Šuba, R., Driel, M., Meijers, M., van Oosterom, P., and Eisemann, E. (2016a). Usability test plan for truly vario-scale maps. In Proceedings of the 19th ICA Workshop on Generalisation and Multiple Representation, Helsinki, Finland. (Cited on pages 8 and 87).
Šuba, R., Meijers, M., Huang, L., and van Oosterom, P. (2014a). An area merge opera- tion for smooth zooming. In Huerta, J., Schade, S., and Granell, C., editors, Connect- ing a Digital Europe Through Location and Place, Springer Lecture Notes in Geoinfor- mation and Cartography, pages 275–293. Springer International Publishing. ISBN: 978-3-319-03611-3. (Cited on pages 8, 19, and 87).
Šuba, R., Meijers, M., Huang, L., and van Oosterom, P. (2014b). Continuous Road Net- work Generalisation. In Proceedings of the 17th ICA Workshop on Generalisation and Multiple Representation, Vienna, Austria, September 23, 2014, pages 1–12. (Cited on pages 8, 39, 52, and 78).
Šuba, R., Meijers, M., and Oosterom, P. v. (2016b). Continuous road network gen- eralization throughout all scales. ISPRS International Journal of Geo-Information, 5(8):145. (Cited on pages 8 and 39).
Šuba, R., Meijers, M., and van Oosterom, P. (2013). 2D vario-scale representations based on real 3D structure. In Proceedings of the 16th ICA Generalization Workshop, pages 1–11. The paper for ICA worshop 2013/Dresden. (Cited on pages 8 and 102).
Šuba, R., Meijers, M., and van Oosterom, P. (2015). Large scale road network gener- alization for vario-scale map. In Proceedings of the 18th ICA Workshop on General- isation and Multiple Representation, Rio de Janeiro, Brazil, 21 August, 2015, pages 1–10. (Cited on pages 8, 39, and 43).
Wang, H., Fu, X., Wang, G., Li, T., and Gao, J. (2011). A common parallel computing framework for modeling hydrological processes of river basins. Parallel Computing, 37(6–7):302 – 315. (Cited on page 73).
Weibel, R. (1997). Generalization of spatial data: Principles and selected algorithms, volume 1340 of Algorithmic Foundations of Geographic Information Systems.
Springer, Berlin, Germany, Berlin, Heidelberg. (Cited on page 14).
Weiss, R. and Weibel, R. (2014). Road network selection for small-scale maps using an improved centrality-based algorithm. Journal of Spatial Information Science, 2014(9):71–99. (Cited on page 41).
Zhang, X. (2012). Automated evaluation of generalized topographic maps. PhD thesis, University of Twente. (Cited on pages 63 and 66).
Zhou, Q. and Li, Z. (2012). A comparative study of various strategies to concatenate road segments into strokes for map generalization. International Journal of Geo- graphical Information Science, 26(4):691–715. (Cited on pages 42 and 48).
Zhou, X., Abel, D., and Truffet, D. (1998). Data Partitioning for Parallel Spatial Join Processing. GeoInformatica, 2(2):175–204. (Cited on page 73).
Zhou, X., Prasher, S., Sun, S., and Xu, K. (2004). Multiresolution spatial databases: Making web-based spatial applications faster. In Advanced Web Technologies and Applications: 6th Asia-Pacific Web Conference, APWeb 2004, Hangzhou, China, April 14-17, 2004. Proceedings, pages 36–47, Berlin, Heidelberg. Springer Berlin Heidel- berg. (Cited on page 20).
