Spatial data analysis and geoinformation extraction

Advanced Geoinformation Science

Volumn , Issue , 2010, Pages 145-203

  Croitoru, Arie ^a   Guo, Diansheng ^b   Wang, Fahui ^c   Wong, David ^d   Agouris, Peggy ^d   Stefanidis, Anthony ^d  

^a UNIVERSITY OF ALBERTA   (Canada)

^b UNIVERSITY OF SOUTH CAROLINA   (United States)

^c LOUISIANA STATE UNIVERSITY   (United States)

^d GEORGE MASON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DISTRIBUTED COMPUTER SYSTEMS;

COMPUTERIZED SYSTEMS; DISTRIBUTED COMPUTING ENVIRONMENT; EXTRACTING INFORMATION; GEO-INFORMATION; NETWORKED COMPUTING; SPATIAL DATA; SPATIAL DATA ANALYSIS; STANDALONE SYSTEMS;

GEOGRAPHIC INFORMATION SYSTEMS;

EID: 84887545163     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1201/b10280     Document Type: Chapter

References (154)

1. Agouris, P., A.F. Schenk, and A. Stefanidis. 1989. Zero-crossings for edge detection. Proceedings of the 1989 ASPRS-ACSM Fall Convention, pp. 91-99. Baltimore, MD.
2. Agouris, P., A. Stefanidis, and S. Gyftakis. 2001. Differential snakes for change detection in road segments. Photogrammetric Engineering & Remote Sensing, 67(12):1391-1399.
3. Agouris, P. and A. Stefanidis. 2003. Efficient summarization of spatiotemporal events. Communications of the ACM, 46(1):65-66.
4. Agrawal, R., T. Imielinski, and A. Swami. 1993. Mining association rules between sets of items in large databases. In ACM SIGMOD International Conference on Management of Data, pp. 207-216.
5. Andrews, D.F. 1972. Plots of high-dimensional data. Biometrics 29:125-136.
6. Andrienko, G. and N. Andrienko. 1999. Data mining with C4.5 and interactive cartographic visualization. In User Interfaces to Data Intensive Systems, eds. N.W. Paton and T. Griffiths, pp. 162-165. IEEE Computer Society, Los Alamitos, CA.
7. Anselin, L. 1995. Local indicators of spatial association-LISA. Geographical Analysis, 27:93-115.
8. Anselin, L. 1999. Interactive techniques and exploratory spatial data analysis. In Geographical Information Systems-Principles and Technical Issues, eds. P.A. Longley, M.F. Goodchild, D.J. Maguire, and D.W. Rhind, pp. 253-266. John Wiley & Sons, Inc., New York, NY.
9. Anselin, L. and D.A. Griffith. 1988. Do spatial effects really matter in regression? Papers in Regional Science 65(1):11-34.
10. Appice, A., M. Ceci, A. Lanza, F.A. Lisi, and D. Malerba. 2003. Discovery of spatial association rules in geo-referenced census data: A relational mining approach. Intelligent Data Analysis, 7:541-566.
11. Augustin, N.H., M.A. Mugglestone, and S.T. Buckland. 1996. An autologistic model for the spatial distribution of wildlife. Journal of Applied Ecology, 33:339-347.
12. Bailey, T.C. and A.C. Gatrell. 1995. Interactive Spatial Data Analysis. John Wiley & Sons, Inc., New York, NY.
13. Baller, R.D., L. Anselin, S.F. Messner, G. Deane, and D.F. Hawkins. 2001. Structural covariates of U.S. county homicide rates: Incorporating spatial effects. Criminology, 39:561-590.
14. Band, L.E. 1986. Topographic partition of watersheds with digital elevation models. Water Resource Research, 22:15-24.
15. Benenson, I. and P. Torrens. 2004. Geosimulation:Automata-Based Modeling of Urban Phenomena. John Wiley & Sons, West Sussex, England.
16. Bernardin, K. and R. Stiefelhagen. 2007. Audio-visual multiperson tracking and identification for smart environments. Proceedingsof the 15th International Conference on Multimedia, pp. 661-670, Augsburg, Germany.
17. Besag, J. 1974. Spatial interaction and the statistical analysis of lattice systems (with Discussion). Journal of the Royal Statistical Society, Series B, 36:192-236.
18. Besag, J. 1975. Statistical analysis of non-lattice data. The Statistician, 24:179-195.
19. Besag J., J. York, and A. Mollie. 1991. Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics, 1:1-59.
20. Black, W.R. 2003. Transportation: A Geographical Analysis. The Guilford Press, New York.
21. Blake, A. and M. Isard. 1998. Active Contours. London, Springer-Verlag.
22. Bouchara, F. and S. Ramdani. 2007. Statistical behavior of edge detectors. Signal, Image and Video Processing, 1(3):273-285.
23. Canny, J., 1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6):679-698.
24. Carr, D.B., D. White, and A.M. MacEachren. 2005. Conditioned choropleth maps and hypothesis generation. Annals of the Association of American Geographers, 95:32-53.
25. Chawla, S., S. Shekhar, W. Wu, and U. Ozesmi. 2000. Extending data mining for spatial applications: A case study in predicting nest locations. In ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD 2000). Dallas, TX.
26. Chernoff, H. and M.H. Rizvi. 1975. Effect on classification error of random permutations of features in representing multivariate data by faces. Journal of American Statistical Association, 70:548-554.
27. Clarke, K.C., S. Hoppen, and L.J. Gaydos. 1997. A self-modifying cellular automation model of historical urbanization in the San Francisco Bay area. Environment and Planning B, 24:247-261.
28. Cliff, A.D. and J.K. Ord. 1973. Spatial Autocorrelation. Pion, London.
29. Cohen, L.D. 1991. On active contour models and balloons. Computer Vision, Graphics, and Image Processing. Image Understanding, 53(2):211-218.
30. Cohn, A., D. Magee, A. Galata, D. Hogg, and S. Hazarika, 2003. Towards an architecture for cognitive vision using qualitative spatiotemporal representations and abduction. Spatial Cognition III, LNCS, 2685:1034-1045.
31. Colasanti, R.L. and J.P. Grime. 1993. Resource dynamics and vegetation processes: A deterministic model using two-dimensional cellular automata. Functional Ecology, 7:169-176.
32. Comaniciu, D., V. Ramesh, and P. Meer. 2000. Real-time tracking of non-rigid objects using mean shift. Proceedings IEEE CVPR’00, pp. 142-149.
33. Cook, D., A. Buja, J. Cabrera, and C. Hurley. 1995. Grand tour and projection pursuit. Journal of Computational and Graphical Statistics, 4:155-172.
34. Croitoru, A. and Y. Doytsher. 2004. Right-angle rooftop polygon extraction in regularized urban areas: Cutting the corners. The Photogrammetric Record, 19(108): 311-341.
35. DiBiase, D., C. Reeves, J. Krygier, A.M. MacEachren, M.V. Weiss, J. Sloan, and M. Detweiller. 1994. Multivariate display of geographic data: Applications in earth system science. In Visualization in Modern Cartography, eds. A.M. MacEachren and D.R.F. Taylor, pp. 287-312. Pergamon, Oxford, UK.
36. Dobrokhodov, V., I. Kaminer, K. Jones, and R. Ghabcheloo. 2006. Vision-based tracking and motion estimation for moving targets using small UAVs. Proceedings of the IEEE American Control Conference, Minneapolis, MN, pp. 1428-1433.
37. Durrani, T.S. and D. Bisset. 1984. The radon transform and its properties. Geophysics, 49(8):1180-1187.
38. Dykes, J. 1998. Cartographic visualization: Exploratory spatial data analysis with local indicators of spatial association using Tcl/Tk and cdv’. The Statistician, 47:485-497.
39. Dykes, J., A.M. MacEachren, and M.-J. Kraak. 2005. Exploring Geovisualization. Elsevier, Amsterdam.
40. Ester, M., H.P. Kriegel, and J. Sander. 1997. Spatial data mining: A database approach. In Advances in Spatial Databases, Vol. 33, pp. 47-66. Springer-Verlag, Berlin.
41. Fayyad, U., G. Piatetsky-Shapiro, and P. Smyth. 1996. From data mining to knowledge discovery-an review. In Advances in Knowledge Discovery, eds. U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusay, pp. 1-33. AAAI Press/The MIT Press, Cambridge, MA.
42. Fischer, M.M. 1998. Computational neural networks: A new paradigm for spatial analysis. Environment and Planning A, 30:1873-1891.
43. Fischer, M.M., M. Reismann, and K. Hlavackova-Schindler. 2003. Neural network modeling of constrained spatial interaction flows: Design, estimation, and performance issues. Journal of Regional Science, 43:35-61.
44. Fotheringham, A.S., C. Brunsdon, and M. Charlton. 2000. Quantitative Geography: Perspectives on Spatial Data Analysis. Sage, London.
45. Fotheringham, A.S. and M.E. O’Kelly. 1989. Spatial Interaction Models: Formulations and Applications. Kluwer Academic Publishers, Dordrecht.
46. Fovell, R.G. and M.-Y.C. Fovell. 1993. Climate zones of the conterminous United States defined using cluster analysis. Journal of Climate, 6:2103-2135.
47. Gabelaia, D., R. Kontchakov, A. Kurucz, F. Wolter, and M. Zakharyaschev, 2005. Combining spatial and temporal logics: Expressiveness vs. complexity. Journal of Artificial Intelligence Research, 23:167-243.
48. Gahegan, M. 1998. Scatterplots and scenes: Visualization techniques for exploratory spatial analysis. Computers, Environment and Urban Systems, 22:43-56.
49. Gahegan, M. 2003. Is inductive machine learning just another wild goose (or might it lay the golden egg)? International Journal of Geographical Information Science, 17:69-92.
50. Geary, R. 1954. The contiguity ratio and statistical mapping. The Incorporated Statistician, 5:115-145.
51. Getis, A. and D.A. Griffith. 2002. Comparative spatial filtering in regression analysis. Geographical Analysis, 34:130-140.
52. Getis, A. and J.K. Ord. 1992. The analysis of spatial association by use of distance statistics. Geographical Analysis, 24:189-206.
53. Gopal, S., W. Liu, and C. Woodcock. 2001. Visualization based on the fuzzy ARTMAP neural network for mining remotely sensed data. In Geographic Data Mining and Knowledge Discovery, eds. H.J. Miller and J. Han, pp. 315-336. Taylor & Francis, London and New York.
54. Gordon, A.D. 1996. Hierarchical classification. In Clustering and Classification, eds. P. Arabie, L. J. Hubert, and G. D. Soete, pp. 65-122. World Scientific Publisher, River Edge, NJ.
55. Gottfried, B., H. Guesgen, and S. Huebner, 2006. Spatiotemporal reasoning for Smart Homes. Designing Smart Homes, LNCS, 4008:16-34.
56. Gould, P.R. 1970. Is Statistic Inference the geographical name for a wild goose? Economic Geography, 46:439-448.
57. Griffith. D.A. 2003. Spatial Autocorrelation and Spatial Filtering. Springer, Berlin.
58. Griffith, D.A. and C.G. Amrhein. 1997. Multivariate Statistical Analysis for Geographers. Prentice-Hall, Upper Saddle River, NJ.
59. Griffith D.A. and R. Haining. 2005. Beyond mule kicks: The Poisson distribution in geographical analysis. Geographical Analysis, 38:123-139.
60. Grinstein, G., J.C.J. Sieg, S. Smith, and M.G. Williams. 1992. Visualization for knowledge discovery. International Journal of Intelligent Systems, 7:637-648.
61. Guo, D. 2008. Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP). International Journal of Geographical Information Science, 22:801-823.
62. Guo, D. 2009. Multivariate spatial clustering and geovisualization. In Geographic Data Mining and Knowledge Discovery, eds. H.J. Miller and J. Han, pp. 325-345. Taylor & Francis, London and New York.
63. Guo, D., J. Chen, A.M. MacEachren, and K. Liao. 2006. A visualization system for space-time and multivariate patterns (VIS-STAMP). IEEE Transactions on Visualization and Computer Graphics, 12:1461-1474.
64. Guo, D., M. Gahegan, A.M. MacEachren, and B. Zhou. 2005. Multivariate analysis and geovisualization with an integrated geographic knowledge discovery approach. Cartography and Geographic Information Science, 32:113-132.
65. Guo, D., D. Peuquet, and M. Gahegan. 2003. ICEAGE: Interactive clustering and exploration of large and high-dimensional geodata. GeoInformatica, 7:229-253.
66. Gurnell, A.M. and D.R. Montgomery. (eds.) 2000. Hydrological Applications of GIS. John Wiley & Sons, West Sussex, England.
67. Gyftakis, S., P. Agouris, and A. Stefanidis, 2005. Image-based change detection of aerial objects using differential snakes. Proceedings of ACM-GIS`05, pp. 135-142. Bremen, Germany.
68. Haggett, P., A.D. Cliff, and A. Frey. 1977. Locational Methods. John Wiley & Sons, New York.
69. Haining, R.P., S.M. Wise, and M. Blake. 1994. Constructing regions for small area analysis: Material deprivation and colorectal cancer. Journal of Public Health Medicine, 16:429-438.
70. Hamilton, L.C. 1992. Regression with Graphics. Duxbury, Belmont, CA.
71. Han, J. and M. Kamber. 2001. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco, CA.
72. Han, J., K. Koperski, and N. Stefanovic. 1997. GeoMiner: A system prototype for spatial data mining. In ACM SIGMOD International Conference on Management of Data, pp. 553-556. Tucson, AZ.
73. Hanna, S.R., M.J. Brown, F. Camelli, S.T. Chan, W.J. Coirier, O.R. Hansen, A.H. Huber, S. Kim, and R.M. Reynolds. 2006. Detailed simulations of atmospheric flow and dispersion in downtown Manhattan: An Application of five computational fluid dynamics models. Bulletin of the American Meteorological Society, 87:1713-1726.
74. Harris, C. and M. Stephens, 1988. A combined corner and edge detector. Proceedings of The Fourth Alvey Vision Conference, pp 147-151. Manchester.
75. Harris, R.L. 1999. Information Graphics: A Comprehensive Illustrated Reference. Oxford Press, Oxford, UK.
76. Huang, Y., J. Pei, and H. Xiong. 2006. Mining co-location patterns with rare events from spatial data sets. Geoinformatica 10:239-260.
77. Inselberg, A. 1985. The plane with parallel coordinates. The Visual Computer 1:69-97.
78. Isaaks, E.H. and R.M. Srivastava. 1990. An Introduction to Applied Geostatistics. Oxford University Press, New York.
79. Jain, A.K. and R.C. Dubes. 1988. Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs, NJ.
80. Jain, A.K., M.N. Murty, and P.J. Flynn. 1999. Data clustering: A review. ACM Computing Surveys (CSUR), 31:264-323.
81. Jakkula, V. and D. Cook, 2007. Using temporal relations in smart environment data for activity prediction, Proceedings of the 24th International Conference on Machine Learning, Corvallis, OR.
82. Jin, H. and D. Guo. 2009. Understanding climate change patterns with multivariate geovisualization. In Proceedings-IEEE International Conference on Data Mining Workshops, pp. 217-222. IEEE Press, Miami, FL.
83. Kasahara, A. and W.M. Washington. 1967. NCAR global general circulation model of the atmosphere. Monthly Weather Review, 95(7):389-402.
84. Kaski, S., J. Kangas, and T. Kohonen. 1998. Bibliography of self-organizing map (SOM) papers: 1981-1997. Neural Computing Surveys, 1:102-350.
85. Kass, M., A. Witkin, and D. Terzopoulos, 1988. Snakes: Active contour models. International Journal of Computer Vision, 1(4):321-331.
86. Kastrinaki, V., M. Zervakis, and K. Kalaitzakis, 2003. A survey of video processing techniques for traffic applications. Image and Vision Computing, 21:359-381.
87. Keim, D.A., C. Panse, M. Sips, and S.C. North. 2004. Visual data mining in large geospatial point sets. IEEE Computer Graphics and Applications, 24:36-44.
88. Kirtman, B.P. and J. Shukla. 2002. Interactive coupled ensemble: A new coupling strategy for CGCMs. Geophysical Research Letters, 29(10):5.1-5.4
89. Knorr, E.M. and R.T. Ng. 1996. Finding aggregate proximity relationships and commonalities in spatial data mining. IEEE Transactions Knowledge and Data Engineering, 8:884-897.
90. Kohonen, T. 2001. Self-Organizing Maps. Springer, Berlin, New York.
91. Koperski, K. and J. Han. 1995. Discovery of spatial association rules in geographic information databases. In The 4th International Symposium on Large Spatial Databases (SSD95), pp. 47-66. Maine, USA.
92. Koperski, K., J. Han, and N. Stefanovic. 1998. An efficient two-step method for classification of spatial data. In 1998 International Symposium on Spatial Data Handling SDH’98, pp. 45-54. Canada, Vancouver, BC.
93. Kulldorff, M. 1997. A spatial scan statistic. Communications in Statistics-Theory and Methods, 26:1481-1496.
94. Kulldorff, M., R. Heffernan, J. Hartman, R.M. Assunção, and F. Mostashari. 2005. A space-time permutation scan statistic for the early detection of disease outbreaks. PLoS Medicine, 2:216-224.
95. Lillesand, T., R. Kiefer, and J. Chipman. 2007. Remote Sensing and Image Interpretation. Wiley, New York.
96. Lim, S.N., L. Davis, and A. Elgammal, 2003. A scalable image-based multi-camera visual surveillance system. IEEE Conference on Advanced Video & Signal Based Surveillance (AVSS), pp. 205-212. Miami.
97. Liu, Y. 2008. Modelling Urban Development with Geographical Information Systems and Cellular Automata. CRC Press, Boca Raton, FL.
98. Lu, G.Y. and D.W. Wong. 2008. An adaptive inverse-distance weighting spatial interpolation technique. Computers and Geosciences, 34(9):1044-1055.
99. Luthon, F., M. Lievin, and F. Faux, 2004. On the use of entropy power for threshold selection, Signal Processing, 84(10):1789-1804.
100. MacEachren, A. 1994. Visualization in modern cartography: Setting the agenda. In Visualization in Modern Cartography, eds. A.M. MacEachren and D.R.F. Taylor, pp. 1-12. Pergamon, Oxford, UK.
101. MacEachren, A.M. and M.J. Kraak. 1997. Exploratory cartographic visualization: Advancing the agenda. Computers and Geosciences, 23:335-343.
102. MacEachren, A. and M.J. Kraak. 2001. Research challenges in geovisualization. Cartography and Geographic Information Science, 28:3-12.
103. MacEachren, A.M., M. Wachowicz, R. Edsall, D. Haug, and R. Masters. 1999. Constructing knowledge from multivariate spatiotemporal data: Integrating geographical visualization with knowledge discovery in database methods. International Journal of Geographical Information Science, 13:311-334.
104. Malladi, R., J.A. Sethian, and B.C. Vemuri, 1995. Shape modeling with front propagation: A level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 17(2):158-175.
105. Mark, D.M. 1983. Automatic detection of drainage networks from digital elevation models. Cartographica, 21:168-178.
106. Meegama, R.G.N. and J.C. Rajapakse, 2003. NURBS Snakes. Image and Vision Computing, 21(2003):551-562.
107. Mehrotra, R., S. Nichani, and N. Ranganathan, 1990. Corner detection. Pattern Recognition, 23(11):1223-1233.
108. Mennis, J. and J.W. Liu. 2005. Mining association rules in spatio-temporal data: An analysis of urban socioeconomic and land cover change. Transactions in GIS, 9:5-17.
109. Menon, V., B. Jayaraman, and V. Govindaraju, 2008. Integrating recognition and reasoning in smart environments. IET International Conference on Intelligent Environments, pp. 1-8. Seattle, WA.
110. Miller, H. and J. Han. 2009. Geographic data mining and knowledge discovery: An overview. In Geographic Data Mining and Knowledge Discovery, eds. H. Miller and J. Han, pp. 1-26. CRC Press, Taylor & Francis Group, Boca Raton, FL.
111. Miller, H.J. and J. Han. 2001. Geographic Data Mining and Knowledge Discovery: An overview. In Geographic Data Mining and Knowledge Discovery, eds. H.J. Miller and J. Han, pp. 3-32. Taylor & Francis, London and New York.
112. Miller, H.J. and S.L. Shaw. 2001. Geographic Information Systems for Transportation: Principles and Applications. Oxford University Press, New York.
113. Mitchell, T.M. 1997. Machine Learning. McGraw-Hill, New York.
114. Mollie A. 1996. Bayesian mapping of disease. In Markov Chain Monte Carlo in Practice, eds. W.R. Gilks, S. Richardson, and D.J. Spiegelhalter, pp. 359-379. Chapman & Hall, New York.
115. Monmonier, M. 1989. Geographic brushing: Enhancing exploratory analysis of the scatterplot matrix. Geographical Analysis, 21:81-84.
116. Mu, L. and F. Wang. 2008. A scale-space clustering method: Mitigating the effect of scale in the analysis of zone-based data. Annals of the Association of American Geographers, 98:85-101.
117. Nittel, S., A. Labrinidis, and A. Stefanidis, 2008. Advances in GeoSensor Networks, Lecture Notes in Computer Science, Vol. 4540, Springer, Berlin.
118. Openshaw, S., M. Charlton, C. Wymer, and A. Craft. 1987. A mark 1 geographical analysis machine for the automated analysis of point data sets. International Journal of Geographical Information Science, 1:335-358.
119. Openshaw, S., A. Cross, and M. Charlton. 1990. Building a prototype geographical correlates exploration machine. International Journal of Geographical Information Science, 4:297-311.
120. Osnes, K. 1999. Iterative random aggregation of small units using regional measures of spatial autocorrelation for cluster localization. Statistics in Medicine, 18:707-725.
121. Parker, D.C., S.M. Mason, M.A. Janssen, M.J. Hoffmann, and P. Deadman. 2003. Multiagent systems for the simulation of land-use and land-cover change: A review. Annals of the Association of American Geographers, 93:314-337.
122. Pickett, R.M., G. Grinstein, H. Levkowitz and S. Smith. 1995. Harnessing preattentive perceptual processes in visualization. In Perceptual Issues in Visualization, eds. G. Grinstein and H. Levkowitz, pp. 33-45. Springer, New York.
123. Quinlan, J.R. 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA.
124. Rittscher, J., J. Kato, S. Joga, and A. Blake, 2000. A probabilistic background model for tracking, European Conference on Computer Vision (ECCV’00), Lecture Notes in Computer Science, Vol. 1843, pp. 336-350, Springer-Verlag, Berlin.
125. Robinson, G.M. 1998. Methods and Techniques in Human Geography. John Wiley and Sons, West Sussex, UK.
126. Rogerson, P. and I. Yamada. 2009. Statistical Detection and Surveillance of Geographic Clusters. Taylor & Francis Group, London and New York.
127. Rosin, P., 2002. Thresholding for change detection, Computer Vision and Image Understanding, 86(2):79-95.
128. Schmid, C., R. Mohr, and C. Buckhage, 2000. Evaluation of interest point detectors. International Journal of Computer Vision, 37(2):151-172.
129. Skamarock, W.C., J.B. Klemp, and J. Dudhia, 2001. Prototypes for the WRF (Weather Research and Forecasting) model. Preprints, Ninth Conference on Mesoscale Processes, pp. J11-J15. Ft. American Meteorological Society, Ft. Lauderdale, FL.
130. Shekhar, S. and Y. Huang. 2001. Discovering spatial co-location patterns: A summary of results. In Advances In Spatial And Temporal Databases, Proceedings, Lecture Notes in Computer Science, eds. C. Jensen, M. Schneider, B. Seeger, and V. Tsotras, pp. 236-256. Springer-Verlag, Berlin.
131. Shekhar, S., P. Zhang, Y. Huang, and R. Vatsavai. 2004. Trends in spatial data mining. In Data Mining: Next Generation Challenges and Future Directions, eds. H. Kargupta, A. Joshi, K. Sivakumar, and Y. Yesha, pp. 357-381. AAAI/MIT Press, Cambridge, MA.
132. Shen, Q. 1994. An application of GIS to the measurement of spatial autocorrelation. Computer, Environment and Urban Systems, 18:167-191.
133. Skupin, A. and R. Hagelman. 2003. Attribute space visualization of demographic change. In Proceedings of the Eleventh ACM International Symposium on Advances in Geographic Information Systems, pp. 56-62. ACM Press, New Orleans, LA.
134. Smith, S.M. and J. Brady, 1995. SUSAN-A new approach to low level image processing. International Journal of Computer Vision, 23(1):45-78.
135. Taylor, D.R.F., 2005. Cybercartography: Theory and Practice. Elsevier Science, Amsterdam.
136. Thomas, J.J. and K.A. Cook. 2005. Illuminating the Path: The Research and Development Agenda for Visual Analytics. IEEE Computer Society, Los Alametos, CA.
137. Tobler, W.R. 1970. A computer movie simulating urban growth in the Detroit region. Economic Geography, 46:234-240.
138. Toft, P., 1996. The radon transform-Theory and implementation. PhD thesis, Department of Mathematical Modeling, Section for Digital Signal Processing, University of Denmark.
139. Tomlin, D. 1990. Geographic Information Systems and Cartographic Modeling. Prentice-Hall, Englewood Cliffs, NJ.
140. Tukey, J. 1977. Exploratory Data Analysis. Addison-Wesley, Reading, MA.
141. Valera, M. and S. Velastin, 2005. Intelligent distributed surveillance systems: A review. IEEE Vision, Image and Signal Processing, 152(2):192-204.
142. Wang, F. 2006. Quantitative Methods and Applications in GIS. CRC Press/Taylor & Francis, Boca Raton, FL.
143. Wang, F., L. Luo, and S. McLafferty. 2010. Healthcare access, socioeconomic factors and late-stage cancer diagnosis: An exploratory spatial analysis and public policy implication. International Journal of Public Policy, 5:237-258.
144. Ward, J.H. 1963. Hierarchical grouping to optimise an objective function. Journal of the American Statistic Association, 58:236-244.
145. Ward, M.O. 2004. Finding needles in large-scale multivariate data haystacks. Computer Graphics and Applications, 24:16-19.
146. Wise, S.M., R.P. Haining, and J. Ma. 1997. Regionalization tools for the exploratory spatial analysis of health data. In Recent Developments in Spatial Analysis: Spatial Statistics, Behavioural Modelling and Neuro-Computing, eds. M. Fischer and A. Getis. Springer-Verlag, Berlin.
147. Wittenbrink, C.M., E. Saxon, J.J. Furman, A. Pang, and S. Lodha. 1995. Glyphs for visualizing uncertainty in environmental vector fields. IEEE Transactions on Visualization and Computer Graphics, 2:266-279.
148. Wong, D. 2009. The Modifiable Areal Unit Problem (MAUP). In The SAGE Handbook of Spatial Analysis, eds. A.S. Fotheringham and P.A. Rogerson, pp. 95-112. Sage, London.
149. Wong, D.W.S. and J. Lee. 2005. Statistical Analysis and Modeling of Geographic Information. John Wiley & Sons, Hoboken, NJ.
150. Wu, H. and F.W. Huffer. 1997. Modelling the distribution of plant species using the autologistic regression model. Environmental and Ecological Statistics, 4:49-64.
151. Xu, C. and J.L. Prince, 1997. Gradient Vector flow: A new external force for snakes. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 66-71. San Juan, Puerto Rico.
152. Yilmaz, A. and M. Shah, 2004. Contour-based object tracking with occlusion handling in video acquired using mobile cameras, IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(11):1531-1536.
153. Zhang, X. and M. Pazner. 2004. The icon image map technique for multivariate geospatial data visualization: Approach and software system. Cartography and Geographic Information Science, 31:29-41.
154. Ziou, D. and S. Tabbone, 1998. Edge detection techniques-An overview. International Journal of Pattern Recognition and Image Analysis, 1998(8):537-559.