Progressive merging of blocks taking into account functional semantics and grid features

References

• Ai, T., & Cheng, J. (2005). Key issues of multi-scale representation of spatial data. Geomatics and Information Science of Wuhan University, 30(5), 377–382. https://doi.org/10.3321/j.issn:1671-8860.2005.05.001
   Google Scholar
• Ai, T., & Liu, Y. (2002). Aggregation and amalgamation in land-use data generalization. Geomatics and Information Science of Wuhan University, 27(5), 486–492. https://doi.org/10.1080/12265080208422884
   Google Scholar
• Atallah, M. J. (1983). A linear time algorithm for the Hausdorff distance between convex polygons. Information Processing Letters, 17(4), 207–209. https://doi.org/10.1016/0020-0190(83)90042-X
   Web of Science ®Google Scholar
• Bai, Y. B., Yong, J. H., Liu, C. Y., Liu, X. M., & Meng, Y. (2011). Polyline approach for approximating Hausdorff distance between planar free-form curves. Computer-Aided Design, 43(6), 687–698. https://doi.org/10.1016/j.cad.2011.02.008
   Web of Science ®Google Scholar
• Bijun, L. (2011). Extracting hierarchical landmarks from urban POI data. Journal of Remote Sensing, 15(5), 973–988. https://doi.org/10.11834/jrs.20110173
   Google Scholar
• Chen, J., Hu, Y., Li, Z., Zhao, R., & 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. https://doi.org/10.1080/13658810802070730
   Web of Science ®Google Scholar
• Dijkstra, E. W. (1959). A note on two problems in connection with graphs. Numerische Mathematics, 1(1), 269–271. https://doi.org/10.1007/bf01386390
   Google Scholar
• Gedicke, S., Oehrlein, J., & Haunert, J.-H. (2021). Aggregating land-use polygons considering line features as separating map elements. Cartography and Geographic Information Science, 48(2), 124–139. https://doi.org/10.1080/15230406.2020.1851613
   Web of Science ®Google Scholar
• Goodchild, M. F. (2007). Citizens as sensors: The world of volunteered geography. Geo Journal, 69(4), 211–221. https://doi.org/10.1007/s10708-007-9111-y
   Google Scholar
• Grubbs, F. E. (1948). On estimating precision of measuring instruments and product variability. Journal of the American Statistical Association, 43(242), 243–264. https://doi.org/10.1080/01621459.1948.10483261
   Web of Science ®Google Scholar
• Guo, Q. (1998). Study on progressive approach to graphic generalization of linear feature. Journal of Wuhan Technical University of Surveying & Mapping, 23(1), 52–56.
   Google Scholar
• Guo, Q., Wang, X., & Liu, J. (2012). Progressive combination of polygon groups. Geomatics and Information Science of Wuhan University, 37(2), 220–223. https://doi.org/10.13203/j.whugis2012.02.020
   Google Scholar
• Hart, P. E., Nilsson, N. J., & Raphael, B. (1972). A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, 4(2), 28–29. https://doi.org/10.1109/TSSC.1968.300136
   Google Scholar
• Haunert, J.-H., Dilo, A., & Oosterom, P. V. (2009). Constrained set-up of the tGAP structure for progressive vector data transfer. Computers & Geosciences, 35(11), 2191–2203. https://doi.org/10.1016/j.cageo.2008.11.002
   Web of Science ®Google Scholar
• Haunert, J.-H., & Wolff, A. (2010). Area aggregation in map generalisation by mixed-integer programming. International Journal of Geographical Information Science, 24(12), 1871–1897. https://doi.org/10.1080/13658810903401008
   Web of Science ®Google Scholar
• Huang, L., Meijers, M., Suba, R., & Oosterom, P. V. (2016). Engineering web maps with gradual content zoom based on streaming vector data. Isprs Journal of Photogrammetry and Remote Sensing, 114, 274–293. https://doi.org/10.1016/j.isprsjprs.2015.11.011
   Web of Science ®Google Scholar
• Huimin, L., Wenke, H., Jianbo, T., Yan, S., & Min, D. (2020). A method for recognizing building clusters by considering functional features of buildings. Acta Geodaetica et Cartographica Sinica, 49(5), 622. https://doi.org/10.11947/j.AGCS.2020.20190222
   Google Scholar
• Hu, T., Yang, J., Li, X., & Gong, P. (2016). Mapping urban land use by using Landsat images and open social data. Remote. Sensing, 8(2), 151. https://doi.org/10.3390/rs8020151
   Web of Science ®Google Scholar
• Jie, W., Qingsheng, G., Xiaoyan, W., & Peng, L. (2012). An improved algorithm for combination of land-use data. Geomatics and Information Science of Wuhan University, 37(9), 1116–1119. http://ch.whu.edu.cn/en/article/id/329
   Google Scholar
• Liu, X., & Long, Y. (2016). Automated identification and characterization of parcels with OpenStreetMap and points of interest. Environment and Planning B: Planning and Design, 43(2), 341–360. https://doi.org/10.1177/0265813515604767
   Google Scholar
• Li, C., Yin, Y., Liu, X.-L., & Wu, P. (2018). An automated processing method for agglomeration areas. ISPRS International Journal of Geo-Information, 7(6), 204. https://doi.org/10.3390/ijgi7060204
   Web of Science ®Google Scholar
• Li, C., Yin, Y., Wu, P., & Wu, W. (2021). An area merging method in map generalization considering typical characteristics of structured geographic objects. Cartography and Geographic Information Science, 48(3), 210–224. https://doi.org/10.1080/15230406.2020.1863862
   Web of Science ®Google Scholar
• Luan, X., Yang, B., & Li, Q. (2014). A mixed integer programming model of block aggregation for grid pattern maintenance in urban network. Acta Geodaetica et Cartographica Sinica, 43(4), 426–434. https://doi.org/10.13485/j.cnki.11-2089.2014.0063
   Google Scholar
• Moore, E. F. (1959). The shortest path through a maze. Proc. of the International Symposium on the Theory of Switching, Part II, Harvard University Press, 285–292.
   Google Scholar
• Oosterom, P. V. (2005). Variable-scale topological data structures suitable for progressive data transfer: The GAP- face tree and GAP-edge forest. Cartography and Geographic Information Science, 32(4), 331–345. https://doi.org/10.1559/152304005775194782
   Google Scholar
• Peng, D. (2019). An optimization-based approach for continuous map generalization. Julius-Maximilians-Universität Würzburg. https://doi.org/10.25972/WUP-978-3-95826-105-1
   Google Scholar
• Pohl, I. (1973). The avoidance of (relative) catastrophe, heuristic competence, genuine dynamic weighting and computational issues in heuristic problem solving. Proceedings of the 3rd international joint conference on Artificial intelligence, Stanford, USA.
   Google Scholar
• Rock, I., & Palmer, S. (1990). The legacy of Gestalt psychology. Scientific American, 263(6), 84–91. https://doi.org/10.1038/SCIENTIFICAMERICAN1290-84
   PubMed Web of Science ®Google Scholar
• Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65. https://doi.org/10.1016/0377-0427(87)90125-7
   Web of Science ®Google Scholar
• Schwering, A. (2008). Approaches to semantic similarity measurement for geo‐spatial data: A survey. Transactions in Gis, 12(1), 5–29. https://doi.org/10.1111/j.1467-9671.2008.01084.x
   Google Scholar
• Sester, M., & Brenner, C. (2005). Continuous generalization for visualization on small mobile devices. International Symposium on Spatial Data Handing. https://doi.org/10.1007/3-540-26772-7_27
   Google Scholar
• Shen, Y., Ai, T., Li, W., Yang, M., & Feng, Y. (2019). A polygon aggregation method with global feature preservation using superpixel segmentation. Computers, Environment and Urban Systems, 75, 117–131. https://doi.org/10.1016/j.compenvurbsys.2019.01.009
   Web of Science ®Google Scholar
• Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46(sup1), 234–240. https://doi.org/10.2307/143141
   Web of Science ®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
• Van Kreveld, M. (2001). Smooth generalization for continuous zooming. Proc. 20th Intl. Geographic Conference Beijing (China), 2180–2185.
   Google Scholar
• Wang, Z., Ma, D., Sun, D., Zhang, J., & Yang, J. (2021). Identification and analysis of urban functional area in Hangzhou based on OSM and POI data. PLOS ONE, 16(5), e0251988. https://doi.org/10.1371/journal.pone.0251988
   PubMed Web of Science ®Google Scholar
• Wang, D., Qian, H., & Zhao, Y. (2022). Review and prospect: Management, multi-scale transformation and representation of geospatial data. Geo-Information Science, 24(12), 2265–2281. https://doi.org/10.12082/dqxxkx.2022.220163
   Google Scholar
• Wu, P., Zhang, S., Li, H., Dale, P., Ding, X., & Lu, Y. (2019). Urban parcel grouping method based on urban form and functional connectivity characterisation. ISPRS International Journal of Geo-Information, 8(6), 282. https://doi.org/10.3390/ijgi8060282
   Web of Science ®Google Scholar
• Xue, B., Li, J., Xiao, X., Xie, X., Lu, C., Ren, W., & Jiang, L. (2019). Overview of man-land relationship research based on POI data: Theory, method and application. Geogr Geo-Inf Sci, 35(6), 51–60. https://doi.org/10.3969/j.issn.1672-0504.2019.06.009
   Google Scholar
• Yang, B., Luan, X., & Li, Q. (2010). An adaptive method for identifying the spatial patterns in road networks. Computers Environment & Urban Systems, 34(1), 40–48. https://doi.org/10.1016/j.compenvurbsys.2009.10.002
   Web of Science ®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
• Yoshida, H., & Omae, M. (2005). An approach for analysis of urban morphology: Methods to derive morphological properties of city blocks by using an urban landscape model and their interpretations. Computers Environment & Urban Systems, 29(2), 223–247. https://doi.org/10.1016/j.compenvurbsys.2004.05.008
   Google Scholar