The division of internal structures and external space of geographical entities is foundation of spatial analysis, query and reasoning. Most current division methods are crisp, and inconsistent with human cognitive habits. Usually existing geographic information systems(GISs) analyze spatial data directly by certain spatial analysis methods and then use natural language or words to explain analysis results, so the encoding process is absence but it is necessary in intelligent GIS (IGIS). Fuzzy geographical entities and phenomena occur throughout the real world, and these semantics of words related spatial locations and relations usually involve uncertainties. First, the geographical perceptual computing (GPC) model based on CWW is proposed and it includes four modules: input, geo-encoder, geographical CWW engine and geo-decoder. Then, the trapezoidal fuzzy set is adopted to represent spatial words. A fine fuzzy spatial partitioning model of line objects based on CWW is proposed. The interior of a line object is divided into several parts according to fuzzy logic and human cognitive habits. The exterior of a line entity is then divided into several parts by combining direction relation and distance relation models with fuzzy logic methods. This model provides a full natural language description of the interior and exterior of crisp or fuzzy line entities and consistent with human cognitive habits. An application case of this model is provided in the last and proves the superiority of this model.
Aja-FernándezS., De Luis-GarcíaR., Martín-FernándezM.A. and Alberola-LópezC., A computational TW3 classifier for skeletal maturity assessment. A Computing with Words approach, Journal of Biomedical Informatics37 (2004), 99–107.
2.
ClementiniE., CohnA.G., RCC*-9 and CBM*, In: DuckhamM., PebesmaE., StewartK., FrankA.U.. (Eds.), Geographic Information Science 8th Int Conf, GIScience 2014, Geographic Information, Science Publishing House, Vienna, 2014, pp. 349–365.
3.
DengM., XuR. and LiZ., et al., A spatial-query-driven transformation between metric spatial relations and natural- language spatial relations: Taking regions as example, Acta Geodaetica et Cartographica Sinica38 (2009), 527–531.
4.
DengM., HuangX., LiuH. and LiG., An approach for spatial query based on natural-language spatial relations, Geomatics and Information Science of Wuhan University36 (2011), 1089–1093.
5.
EgenhoferM.J. and ShariffA.R.B.M., Metric details for natural-language spatial relations, ACM Transactions on Information Systems16 (1998), 295–321.
6.
GoyalA.R., Similarity Assessment for Cardinal Directions between Extended Spatial Objects, University of Maine, Orono, ME, 2000.
7.
GuoJ.F. and CuiW.H., 8-directions fuzzy asymmetric model and analysis of its uncertainty conducted by reference point’s location error, Journal of Remote Sensing14 (2010), 879–892.
8.
GuoJ.F., CuiW.H., LiW.X. and PengG., A direction Relation description model based on IT2 FLS, Geomatics and Information Science of Wuhan University35 (2010), 1365–1368.
9.
JifaG., JianM., TiejunC. and ChongweiL., A Multi-scale fuzzy spatial analysis framework for large data based on IT2 FS, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems23 (2015), 73–104.
10.
GuoJ. and CuiT., Geometric properties of interval Type-II fuzzy regions, Journal of Intelligent and Fuzzy Systems26 (2014), 563–575.
11.
GuoJ., CuiW., LiuZ. and PengG., Investigation of uncertainty integrative description on fuzzy geographical object, Geomatics and Information Science of Wuhan University35 (2010), 46–50.
12.
GuoJ.F., Multi-Scale Fuzzy Geographical Spatial Analysis-Based on Interval Type-II Fuzzy Set, Survering and Mapping Press, 2014.
13.
KerreE.E., A walk through fuzzy relations and their applications to information retrieval, medical diagnosis and expert systems, analysis and management of uncertainty: Theory and applications, In: AyyubB.M., GutaM.M. and KanalL.N. (Eds.), First Int Sym, Institute of Electrical and Electronics Engineers, College Park, MD, 1990, pp. 84–89.
14.
LiuK. and ShiW., Quantitative fuzzy topological relations of spatial objects by induced fuzzy topology, International Journal of Applied Earth Observation and Geoinformation11 (2009), 38–45.
15.
LiuL., spatial frames of reference and topological descriptions in Chinese discourse, Journal of Beijing International Studies University10 (2014), 24–32.
16.
MendelJ.M. and WuD., Perceptual Computing: Aiding People in Making Subjective Judgments, Books in the IEEE Press Series on Computational Intelligence, Wiley-IEEE Press, 2010.
17.
MendelJ.M., Fuzzy sets for words: A new beginning, The 12th IEEE International Conference on Fuzzy systems, In: FUZZ’03, Institute of Electrical and Electronics Engineers, St. Louis, 2003, pp. 37–42.
18.
BohnemeyerJ., DonelsonK.T., MooreR.E., BenedictoE., EgglestonA., O’MearaC.K., BáezG.P., GarzaA.C., GreenN.H. and GómezM.D.J.S.H., et al., The contact diffusion of linguistic practices, Language Dynamics and Change5 (2015), 169–201.
19.
SchneiderM., ChenT., ViswanathanG. and YuanW., Cardinal directions between complex regions, ACM Transactions on Database Systems37 (2012), 1–40.
20.
SchneiderM., Spatial Plateau algebra for implementing fuzzy spatial objects indatabases and GIS: Spatial plateau data types and operations, Applied Soft Computing16 (2014), 148–170.
21.
SchockaertS., Reasoning about fuzzy temporal and spatial information from the Web, Doctoral Dissertation, Ghent University, Ghent, Belgium, 2008.
22.
SharifA.R., EgenhoferM.J. and MarkD., Natural-language spatial relations between linear and areal objects: The topology and metric of English-language terms, International Journal of Geographical Information Science12 (1998), 215–246.
23.
XuJ., Natural Language representation and query of liner geographic objects in GIS, Doctoral Dissertation, State University of at Buffalo, Buffalo, New York, 2005.
24.
XuJ., Natural language understanding of spatial relations between linear geographic objects, Geo-Information, Science10 (2008), 363–369.
25.
YaoX. and ThillJ., How Far is too Far? - A statistical approach to context-contingent proximity modeling, Transactions in GIS9 (2005), 157–178.
26.
ZadehL.A., Studies in fuzziness and Soft computing, Computing with words: Principal concepts and ideas, Springer-Verlag, Berlin Heidelberg, 2012.
27.
ZadehL.A., From computing with numbers to computing with words - from manipulation of measurements to manipulation of perceptions, IEEE Transactions on Circuit and Systems—I, Fundamental Theory and Application45 (1999), 105–109.
28.
ZadehL.A., Fuzzy logic=computing with words, IEEE Transactions on Fuzzy Systems4 (1996), 103–111.
29.
ZhangX. and LvG., Natural language spatial relations and their applications in GIS, Geo-Information Science9 (2007), 77–81.
30.
EgenhoferM.J. and FranzosaR.D., Point-set topological spatial relations, International Journal of Geographical Information Systems5 (1991), 161–174.
31.
ClementiniE. and Di FeliceP. and van OosteromP., A Small Set of Formal Topological Relationships Suitable for End-User Interaction, in Advances in Spatial Databases - Third International Symposium, SSD ’93, vol. 692, AbelD. and OoiB.C., Eds. Berlin: Springer-Verlag, 1993, pp. 277–295.
32.
ClementiniE. and DiP., Felice, A comparison of methods for representing topological relationships, Information Sciences3 (1995), 149–178.
33.
TangX.M., Spatial Object Modeling in Fuzzy Topological Spaces with Applications to Land Cover Change, Doctoral Dissertation. University of Twente, Enschede, Netherlands, 2004.
34.
LiuK. and ShiW., Computing the fuzzy topological relations of spatial objects based on induced fuzzy topology, International Journal of Geographical Information Science20(8) (2006), 857–883.
35.
BjørkeJ.T., Topological relations between fuzzy regions: Derivation of verbal terms, Fuzzy Sets and Systems141 (2004), 449–467.
36.
GuoJ.F. and CuiW.H., 8-directions fuzzy asymmetric model and analysis of its uncertainty conduced by reference point’s location error, Journal of Remote Sensing14(5) (2010), 879–892.
37.
FrankU., Qualitative spatial reasoning about cardinal directions[C]. In: MarkD. and WhiteD., eds. Proceedings of Austrian Conference on Artificial Intelligence, 1991, pp. 157–167.
38.
GoyalR., Similarity Assessment for Cardinal Directions between Extended Spatial Objects, Doctoral Dissertation, University of Maine, Maine, 2000.
39.
ChangK.T., Introduction to Geographic Information Systems (3e). The McGraw-Hill Companies, Inc. 2009.
40.
PopovichV., AviloffC., ErmolaevV., et al., Sound propagation modelling on intelligent GIS base.In: PapadakisJ.S. and BjørnøL., (eds) Proceedings of UAM 2009, 2009, Nafplion.
41.
PopovichV., et al., (eds.), Information Fusion and Geographic Information Systems (IF&GIS 2013), Lecture Notes in Geoinformation and Cartography, Springer-Verlag, Berlin, Heidelberg, 2014.
42.
DubeM.P., Algebraic Refinements of Direction Relations through Topological Augmentation, Doctoral Dissertation, Spatial Information Science and Engineering, The University of Maine, 2016.
43.
WangJ.H. and HaoJ., A new version of 2-tuple fuzzy linguistic representation model for computing with words, IEEE Transaction on Fuzzy Systems14(3) (2006), 435–445.
44.
WuD., A reconstruction decoder for computing with words[J], Information Sciences255 (2014), 1–15.
45.
RodríguezR.M., LabellaÁ. and MartínezL., An overview on fuzzy modelling of complex linguistic preferences in decision making[J], International Journal of Computational Intelligence Systems9(Supp 1) (2016), 81–94.
46.
LiC.C., DongY.C. and HerreraF., et al., Personalized individual semantics in computing with words for supporting linguistic group decision making. An application on consensus reaching, Information Fusion33 (2017), 29–40.
