An Approach to Measuring the Spatial Information Content of an Area Feature

  • School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

Received date: 2012-11-01

  Revised date: 2012-12-01

  Online published: 2012-12-25


Map is a visualization representation of geospatial entities and their distribution. Users often can obtain large amount of information through reading a map. The measurement of map information content is one of the most important basic research issues in the theory of map information transmission. It has been preliminarily applied to map generalization and many other aspects of map applications. Spatial information of a map contains that of features and their distributions. Existing methods of measuring spatial information content only consider the information content of spatial distribution among the features. In other words, the information content of spatial features is not involved. Therefore, the results of the information content obtained by existing methods are inaccurate. For this purpose, in this paper we focused on the development of a methodology for the information content measurement of individual spatial features, where area features are chosen as an example. As a matter of fact, it has been extensively accepted that geometric shape is deemed to be the carrier of geospatial information content of an area feature. As a result, the convex hull is firstly used for shape decomposition of individual area features and a hierarchical structure called convex hull tree is proposed to represent an area feature from the view of spatial cognition. Secondly, geometric shape of area features is analyzed according to the nodes of convex hull tree at three levels, namely, node level, neighborhood level and global level. Moreover, quantitative indicators at each level are defined and utilized for the description of geometric shape, including edge number as the indicator of shape complexity, and convexity as that of shape pattern at node level, out-degree at neighborhood level and layer at global level as indicators of geometry distributions. Sequentially, the corresponding computational models are respectively developed based on geometry characteristics at three levels, which are further used to measure spatial information content of individual area features. At last, an example is provided to illustrate the rationality and the accuracy of the proposed methods.

Cite this article

LIU Hui-Min, DENG Min, HE Tie-Jun, XU Shen . An Approach to Measuring the Spatial Information Content of an Area Feature[J]. Journal of Geo-information Science, 2012 , 14(6) : 744 -750,774 . DOI: 10.3724/SP.J.1047.2012.00744


[1] Kolácny A. Cartographic information: A fundamental concept and term in modern cartography [J]. The Cartographic Journal, 1969, 6(1): 47-49.

[2] Stigmar H. Amount of information in mobile maps: A study of user preference [J]. Mapping and Image Science, 2006, 21(4): 68-74.

[3] 王家耀. 我国地图制图学与地理信息工程学科发展研究[J].测绘通报,2007(5):1-6.

[4] Sukhov V I. Information capacity of a map entropy [J]. Geodesy and Aerophotography, 1967, X: 212-215.

[5] 祝国瑞,王建华.现代地图分析有关问题的探讨[J].测绘学报,1995,24(1):77-79.

[6] 田晶,艾廷华.街道渐进性选取的信息传输模型[J].武汉大学学报(信息科学版),2010,35(4):415-418.

[7] 邓敏,徐震,赵彬彬,等. 地图综合中空间目标空间信息传递模型研究[J].地球信息科学学报, 2010, 12(5): 655-661.

[8] 刘慧敏, 樊子德, 邓敏,等. 地图上等高线信息度量的层次方法研究[J].测绘学报, 2012, 41(5): 777-783.

[9] Stoter J, Burghardt D, Duchéne C, et al. Methodology for evaluating automated map generalization in commercial software[J]. Computers, Environment and Urban Systems, 2009(33): 311-324.

[10] Harrie L, Weibel R. Modeling the overall process of generalization [C].//Ruas A, Mackaness W, Sarjakoski T (Eds.). Generalization of Geographic Information: Cartographic Modeling and Applications. Elsevier, 2007, 67-88.

[11] Sukhov V I. Application of information theory in generalization of map contents [J]. International Yearbook of Cartography, 1970, X: 41-47.

[12] Neumann J. The topological information content of a map: An attempt at a rehabilitation of information theory in cartography [J]. Cartographical, 1994, 31: 26-34.

[13] Bjorke J T. Framework for entropy-based map evaluation [J]. Cartography and Geographical Information Systems, 1996, 23(2): 78-95.

[14] Wang S Y, Du Q Y, Wang Z. A quantitative measurement approach for metric information of maps based on spatial cognition [C]. Fourth International Conference on Natural Computation, IEEE Computer Society, 2008, 235-239. doi: 10.1109/ICNC.2008.379

[15] Li Z L, Huang P Z. Quantitative measures for spatial information of maps [J]. International Journal of Geographical Information Science, 2002, 16(7): 699-709.

[16] Harrie L, Stijmar H. An evaluation of measures for quantifying map information[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2010, 65(3): 266-274.

[17] 陈杰,邓敏,徐枫,等. 面状地图空间信息度量方法研究[J].测绘科学, 2010, 35(1): 74-76.

[18] Batchelor B. Hierarchical shape description based upon convex hulls of concavities [J]. Journal of Cybernetics, 1980(10): 205-210.

[19] Xu J. Hierarchical representation of 2-D shapes using convex polygons: A morphological approach [J]. Pattern Recognition Letters, 1997, 18: 1009-1017.

[20] Whelan B M, McBratney A B. Prediction uncertainty and implications for digital map resolution [C].//Robert P C, Rust R H, Larson W E (Eds.). Proceedings of the Fourth International Conference on Precision Agriculture, Madison, WI, USA, 1998, 4: 1185-1196.

[21] Palmer S. Hierarchical structure in perceptual representation [J]. Cognitive Psychology, 1977, 9: 441-474.

[22] Siddiqi K, Kimia B B. Parts of visual form: computational aspects [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(3): 239-251.

[23] 艾廷华,李志林,刘耀林,等.面向流媒体传输的空间数据变化累积模型[J].测绘学报, 2009, 38(6): 514-519.

[24] 丁险峰,吴洪,张宏江,等. 形状匹配综述[J].自动化学报, 2001, 27(5): 678-693.

[25] 刘颖. 空间图形的表达、识别与综合.郑州:中国人民解放军信息工程大学, 2005.

[26] Liu H R, Liu W Y, Latecki L J. Convex shape decomposition [J]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, 97-104.

[27] Latecki L, Lakamper R. Convexity rule for shape decomposition based on discrete contour evolution [J]. Computer Vision and Image Understanding, 1999, 73(3): 441-454.

[28] 鲁学军. 空间认知模式研究[J].地理信息世界, 2004(6):9-13.