SPAWNN: A Toolkit for SPatial Analysis With Self-Organizing Neural Networks

Transactions in GIS

Volumn 20, Issue 5, 2016, Pages 755-774

  Hagenauer, Julian ^a   Helbich, Marco ^b  

^a LEIBNIZ INSTITUTE OF ECOLOGICAL URBAN AND REGIONAL DEVELOPMENT   (Germany)

^b UTRECHT UNIVERSITY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

NEURAL NETWORKS; OPEN SOURCE SOFTWARE; OPEN SYSTEMS; SPATIAL VARIABLES MEASUREMENT;

CUTTING EDGES; EXPLORATIVE DATA ANALYSIS; FREE AND OPEN SOURCE SOFTWARES; NETWORK CONTEXTS; SELF-ORGANISING; SELF-ORGANISING NEURAL NETWORKS; SPATIAL ANALYSIS; SPATIAL CONTEXT MODELING; SPATIAL DATA; SPATIAL DEPENDENCE;

CLUSTERING ALGORITHMS;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; SOCIOECONOMIC CONDITIONS; SPATIAL ANALYSIS; SPATIAL DATA;

PENNSYLVANIA; PHILADELPHIA; UNITED STATES;

EID: 84957570422     PISSN: 13611682     EISSN: 14679671     Source Type: Journal    
DOI: 10.1111/tgis.12180     Document Type: Article

References (76)

1. Adams C 1993 Philadelphia: Neighborhoods, Division, and Conflict in a Postindustrial City. Philadelphia, PA, Temple University Press
2. Andrienko G, Andrienko N, Bremm S, Schreck T, van Landesberger T, Bak P, and Keim D 2010 Space-in-time and time-in-space self-organizing maps for exploring spatiotemporal patterns. Computer Graphics Forum 29: 913–22
3. Anselin L 1995 Local indicators of spatial association: LISA. Geographical Analysis 27: 93–115
4. Anselin L, Syabri I, and Kho Y 2006 GeoDa: An introduction to spatial data analysis. Geographical Analysis 38: 5–22
5. Assuncao R M, Neves M C, Camara G, and da Costa Freitas C 2006 Efficient regionalization techniques for socio-economic geographical units using minimum spanning trees. International Journal of Geographical Information Science 20: 797–811
6. Augustijn E-W and Zurita-Milla R 2013 Self-organizing maps as an approach to exploring spatio-temporal diffusion patterns. International Journal of Health Geographics 12(1): 60
7. Bacao F, Lobo V, and Painho M 2005 The self-organizing map, the Geo-SOM, and relevant variants for geosciences. Computational Geosciences 31: 155–63
8. Barreto-Sanz M A and Perez-Uribe A 2007 Improving the correlation hunting in a large quantity of SOM component planes. In de Sa J M, Alexandre L, Duch W, and Mandic D (eds) Artificial Neural Networks: ICANN 2007. Berlin, Springer Lecture Notes in Computer Science Vol. 4669: 379–88
9. Bavaud F 1998 Models for spatial weights: A systematic look. Geographical Analysis 30: 153–71
10. Becker S 1991 Unsupervised learning procedures for neural networks. International Journal of Neural Systems 2: 17–33
11. Blondel V D, Guillaume J-L, Lambiotte R, and Lefebvre E 2008 Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 10: P10008
12. Brandes U, Eiglsperger M, Herman I, Himsolt M, and Marshall M S 2002 GraphML progress report: Structural layer proposal. In Mutzel P, Jünger M, and Leipert S (eds) Graph Drawing (GD 2001). Berlin, Springer Lecture Notes in Computer Science Vol. 2265: 501–12
13. Carpenter G A and Grossberg S 1991 Pattern Recognition by Self-organizing Neural Networks. Cambridge, MA, MIT Press
14. Chon T-S 2011 Self-organizing maps applied to ecological sciences. Ecological Informatics 6: 50–61
15. Cottrell M, Hammer B, Hasenfus A, and Villmann T 2006 Batch and median neural gas. Neural Networks 19: 762–71
16. Estevez P A and Figueroa C J 2006 Online data visualization using the neural gas network. Neural Networks 19: 923–34
17. Fayyad U, Piatetsky-Shapiro G, and Smyth P 1996 From data mining to knowledge discovery: An overview. In Fayyad U M, Piatetsky-Shapiro G, Smyth P, and Uthurusamy R (eds) Advances in Knowledge Discovery and Data Mining. Cambridge, MA, MIT Press: 1–34
18. Fischer M M 1998 Computational neural networks: A new paradigm for spatial analysis. Environment and Planning A 30: 1873–91
19. Flexer A 2001 On the use of self-organizing maps for clustering and visualization. Intelligent Data Analysis 5: 373–84
20. Fruchterman T M and Reingold E M 1991 Graph drawing by force-directed placement. Software: Practice and Experience 21: 1129–64
21. Getis, A 2009 Spatial weights matrices. Geographical Analysis 41(4): 404–10
22. Girvan M and Newman M E 2002 Community structure in social and biological networks. Proceedings of the National Academy of Sciences, USA 99: 7821–26
23. Goodchild, M. F 1986 Spatial Autocorrelation. Norwich, UK, Geo Books.
24. Grubesic T H, Wei R, and Murray A T 2014 Spatial clustering overview and comparison: Accuracy, sensitivity, and computational expense. Annals of the Association of American Geographers 104: 1134–56
25. Guo D 2008 Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP). International Journal of Geographical Information Science 22: 801–23
26. Guo D, Chen J, MacEachren A M, and Liao K 2006 A visualization system for space-time and multivariate patterns (VIS-STAMP). IEEE Transactions on Visualization and Computer Graphics 12: 1461–74
27. Guo D, Gahegan M, MacEachren A M, and Zhou B 2005 Multivariate analysis and geovisualization with an integrated geographic knowledge discovery approach. Cartography and Geographic Information Science 32: 113–32
28. Hagenauer J 2014 Clustering contextual neural gas: A new approach for spatial planning and analysis tasks. In Helbich M, Jokar Arsanjani J, and Leitner M (eds) Computational Approaches for Urban Environments. Berlin, Springer: 77–94
29. Hagenauer J 2015 Weighted merge context for clustering and quantizing spatial data with self-organizing neural networks. Journal of Geographical Systems 17: in press
30. Hagenauer J and Helbich M 2013 Contextual neural gas for spatial clustering and analysis. International Journal of Geographical Information Science 27: 251–66
31. Hagenauer J, Helbich M, and Leitner M 2011 Visualization of crime trajectories with self-organizing maps: A case study on evaluating the impact of hurricanes on spatio-temporal crime hotspots. In Proceedings of the Twenty-fifth International Cartographic Conference, Paris, France
32. Han J, Fu Y, Chiang W W J, Gong W, Koperski K, Li D, Lu Y, Rajan A, Xia N S B, and Zaiane O 1996 DBMiner: A system for mining knowledge in large relational databases. In Proceedings of the International Conference on Data Mining and Knowledge Discovery, Portland, Oregon: 250–55
33. Han J, Koperski K, and Stefanovic N 1997 GeoMiner: A system prototype for spatial data mining. ACM SIGMOD Record 26: 553–56
34. Harrower M and Brewer C A 2003 Colorbrewer.org: An online tool for selecting colour schemes for maps. Cartographic Journal 40: 27–37
35. Helbich M, Brunauer W, Hagenauer J, and Leitner M 2013a Data-driven regionalization of housing markets. Annals of the Association of American Geographers 103: 871–89
36. Helbich M, Hagenauer J, Leitner M, and Edwards R 2013b Exploration of unstructured narrative crime reports: An unsupervised neural network and point pattern analysis approach. Cartography and Geographic Information Science 40: 326–36
37. Henriques R, Bacao F, and Lobo V 2012 Exploratory geospatial data analysis using the GeoSOM suite. Computers, Environment and Urban Systems 36: 218–32
38. Jain A K 2010 Data clustering: 50 years beyond K-means. Pattern Recognition Letters 31: 651–66
39. Kalteh A M, Hjorth P, and Berndtsson R 2008 Review of the self-organizing map (SOM) approach in water resources: Analysis, modelling and application. Environmental Modelling and Software 23: 835–45
40. Kamada T and Kawai S 1989 An algorithm for drawing general undirected graphs. Information Processing Letters 31: 7–15
41. Kangas J 1992 Temporal knowledge in locations of activations in a self-organizing map. In Aleksander I and Taylor J (eds) Artificial Neural Networks, 2. Amsterdam, Netherlands, North-Holland: 117–20
42. Kaski S and Kohonen T 1998 Bibliography of self-organizing map (SOM) papers: 1981–1997. Neural Computing Surveys 1: 102–350
43. Keim D A 2002 Information visualization and visual data mining. IEEE Transactions on Visualization and Computer Graphics 8: 1–8
44. Kohonen T 1982 Self-organized formation of topologically correct feature maps. Biological Cybernetics 43: 59–69
45. Kohonen T 2001 Self-Organizing Maps. New York, Springer
46. Korting T S, Garcia Fonseca L M, and Camara G 2013 GeoDMA–geographic data mining analyst. Computers and Geosciences 57: 133–45
47. Lacayo-Emery M A 2011 SOM Analyst. WWW document, https://github.com/mlacayoemery/somanalyst
48. Liu Y and Weisberg R H 2011 A Review of Self-organizing Map Applications in Meteorology and Oceanography. Rijeka, Croatia, InTech
49. Logan J R 2011 Separate and unequal: The neighborhood gap for Blacks, Hispanics and Asians in Metropolitan America. WWW document, http://www.s4.brown.edu/us2010/Data/Report/report0727.pdf
50. MacEachren A M, Wachowicz M, Edsall R, Haug D, and Masters R 1999 Constructing knowledge from multivariate spatiotemporal data: integrating geographical visualization with knowledge discovery in database methods. International Journal of Geographical Information Science 13: 311–34
51. Martinetz T 1993 Competitive hebbian learning rule forms perfectly topology preserving maps. In Gielen S and Kappen B (eds) ICANN ’93. London, Springer: 427–34
52. Martinetz T, Berkovich S, and Schulten K 1993 “Neural-gas” network for vector quantization and its application to time-series prediction. IEEE Transactions on Neural Networks 4: 558–69
53. Martinetz T and Schulten K 1991 A “neural-gas” network learns topologies. Artificial Neural Networks 1: 397–402
54. Mayer R, Dittenbach M, Frank J, Neumayer R, and Lidy T 2011 Data mining with the Java SOMToolbox. WWW document, http://www.ifs.tuwien.ac.at/dm/somtoolbox/
55. Mennis J and Guo D 2009 Spatial data mining and geographic knowledge discovery: An introduction. Computers, Environment and Urban Systems 33: 403–08
56. Miller H J and Goodchild M F 2014 Data-driven geography. GeoJournal 80: 449–61
57. Miller H J and Han J 2009 Geographic Data Mining and Knowledge Discovery. Boca Raton, FL, CRC Press
58. Munoz A and Muruzabal J 1998 Self-organizing maps for outlier detection. Neurocomputing 18: 33–60
59. Murray A T and Shyy T-K 2000 Integrating attribute and space characteristics in choropleth display and spatial data mining. International Journal of Geographical Information Science 14: 649–67
60. Murtagh F 1995 Interpreting the Kohonen self-organizing feature map using contiguity-constrained clustering. Pattern Recognition Letters 16: 399–408
61. Oja M, Kaski S, and Kohonen T 2003 Bibliography of self-organizing map (SOM) papers: 1998-2001 addendum. Neural Computing Surveys 3: 1–156
62. Openshaw S 1999 Geographical data mining: Key design issues. In Proceedings of the Fourth International Conference on Geocomputation, Fredericksburg, Virginia (CD-ROM)
63. Parimala M, Lopez D, and Senthilkumar N 2011 A survey on density based clustering algorithms for mining large spatial databases. International Journal of Advanced Science and Technology 31: 59–66
64. Rey S J 2009 Show me the code: Spatial analysis and open source. Journal of Geographical Systems 11: 191–207
65. Skupin A and Agarwal P 2008 Introduction: What is a self-organizing map? In Agarwal P and Skupin A (eds) Self-organising Maps: Applications in Geographic Information Science. New York, John Wiley and Sons: 1–20
66. Stojkovic M, Simic V, Milosevic D, Mancev D, and Penczak T 2013 Visualization of fish community distribution patterns using the self-organizing map: A case study of the Great Morava River system (Serbia). Ecological Modelling 248: 20–29
67. Strickert M and Hammer B 2003 Neural gas for sequences. In Proceedings of the Workshop on Self-Organizing Networks, Fukuoka, Japan: 53–57
68. Strickert M and Hammer B 2005 Merge SOM for temporal data. Neurocomputing 64: 39–71
69. Sui D Z 2004 Tobler's first law of geography: A big idea for a small world? Annals of the Association of American Geographers 94: 269–77
70. Takatsuka M and Gahegan M 2002 GeoVISTA studio: A codeless visual programming environment for geoscientific data analysis and visualization. Computers and Geosciences 28: 1131–44
71. Tasdemir K and Merenyi E 2009 Exploiting data topology in visualization and clustering of self-organizing maps. IEEE Transactions on Neural Networks 20: 549–62
72. Vesanto J 1999 SOM-based data visualization methods. Intelligent Data Analysis 3: 111–26
73. Vesanto J and Ahola J 1999 Hunting for correlations in data using the self-organizing map. In Proceedings of the International ICSC Congress on Computational Intelligence Methods and Applications, Rochester, New York: 279–85
74. Vincent L and Soille P 1991 Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence 13: 583–98
75. Ware C 2012 Information Visualization: Perception for Design. Oxford, UK, Elsevier
76. Yuan M, Buttenfield B, Gahegan M, and Miller H 2004 Geospatial data mining and knowledge discovery. In McMaster R B and Usery E L (eds) A Research Agenda for Geographic Information Science. Boca Raton, FL, CRC Press: 365–88