Modeling multiple land use changes using ANN, CART and MARS: Comparing tradeoffs in goodness of fit and explanatory power of data mining tools

International Journal of Applied Earth Observation and Geoinformation

Volumn 28, Issue 1, 2014, Pages 102-116

  Tayyebi, Amin ^a,b   Pijanowski, Bryan C ^b  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

^b PURDUE UNIVERSITY   (United States)

Author keywords

Classification and Regression Trees (CART); Classifications (LTM MC); Land Transformation Model Multiple; Multiple Classifications (MC); Multivariate Adaptive Regression Splines (MARS)

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; DATA MINING; ELEVATION; LAND USE CHANGE; MULTIVARIATE ANALYSIS; NUMERICAL MODEL; REGRESSION ANALYSIS;

MICHIGAN; MUSKEGON RIVER; UNITED STATES; WISCONSIN;

EID: 84897442036     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2013.11.008     Document Type: Article

References (58)

1. Alexandridis, K., Pijanowski, B.C., Lei, Z., 2007. Assessing multi-agent parcelization performance in the MABEL simulation model using Monte Carlo replication experiments. Environment and Planning B 34, 223-244.
2. Alexandridis, K., Pijanowski, B.C., 2013. Spatially-explicit Bayesian information entropy metrics for calibrating landscape transformation models. Entropy 15 (7), 2480-2509.
3. Allwein, E., Shapire, R., Singer, Y., 2000. Reducing multiclass to binary: a unifying approach for margin classifiers. Journal of Machine Learning Research, 113-141.
4. Anderson, J.R., Hardy, E.E., Roach, J.T., Witmer, R.E., 1976. A Land Use and Land Cover Classification System for Use with Remote Sensor Data. US Geological Survey, Professional Paper 964: 28, Reston, VA.
5. Ayoubloo, M.K., Azamathulla, H.M., Jabbari, E., Zanganeh, M., 2011. Predictive model-based for the critical submergence of horizontal intakes in open channel flows with different clearance bottoms using CART ANN and linear regression approaches. Expert Systems with Applications 38, 10114-10123.
6. Ballestores Jr., F., Qiu, Z., Nedorezova, B.N., Nedorezov, L.V., Ferrarini, A., Ramathilaga, A., Ackah, M., 2012. An integrated parcel-based land use change model using cellular automata and decision tree. Proceedings of the International Academy of Ecology and Environmental Sciences 2 (2), 53-69.
7. Blockeel, H., Raedt, L.D., Jacobs, N., Demoen, B., 1999. Scaling up inductive logic programming by learning from interpretations. Data Mining and Knowledge Discovery 3 (1), 59-93.
8. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J., 1984. Classification and Regression Trees. Wadsworth, Belmont, CA.
9. Caruana, R., 1997. Multi-task learning. Machine Learning 28 (1), 41-75.
10. Chen, M.Y., 2011. Predicting corporate financial distress based on integration of decision tree classification and logistic regression. Expert Systems with Applications 38 (2011), 11261-11272.
11. Clarke, K.C., Hoppen, S., Gaydos, L., 1997. A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B: Planning and Design 24, 247-261.
12. Dietterich, T.G., Bakiri, G., 1995. Solving multiclass learning problems via error correcting output codes. Journal of Artificial Intelligence Research 39, 1-38.
13. Dubchak, I., Muchnik, I., Mayor, C., Dralyuk, I., Kim, S.H., 1999. Recognition of a protein fold in the context of the Structural Classification of Proteins (SCOP) classification. Proteins 35, 401-407.
14. Friedman, J.H., 1991. Multivariate Adaptive Regression Splines (with discussion). Annals of Statistics 19, 1.
15. Friedman, J.H., 1996. Another approach to polychotomous classification. Technical report. Stanford University.
16. Gooijer, D.J., Ray, G.B.K., 2003. Modeling vector nonlinear time series using POLYMARS. Computational Statistics & Data Analysis 42, 73-90.
17. Grzesiak, W., Zaborski, D., Sablik, P., Zukiewicz, A., Dybus, A., Szatkowsk, I., 2011. Detection of cows with insemination problems using selected classification models. Computers and Electronics in Agriculture 74 (2010), 265-273.
18. Hastie, T., Tibshirani, R., 1998. Classification by pairwise coupling. In: Michael, I., Jordan, Michael, J., Kearns, Sara, A., Solla (Eds.), Advances in Neural Information Processing Systems, vol. 10. The MIT Press.
19. Hand, D.J., Till, R.J., 2001. A simple generalization of the area under the ROC curve for multiple class classification problems. Machine Learning 45, 171-186.
20. Ho, T.K., 2000. Complexity of classification problems and comparative advantages of combined classifiers. Lecture Notes in Computer Science 1857, 97-106.
21. Irwin, E., Geoghegan, J., 2001. Theory, data, methods: developing spatially-explicit economic models of land use change. Agriculture, Ecosystems & Environment 85, 7-24.
22. Jenerette, G.D., Wu, J., 2001. Analysis and simulation of land-use change in the central Arizona-Phoenix region, USA. Landscape Ecology 16, 611-626.
23. Lambin, E.F., Geist, H.J. (Eds.)), 2006. Land-use and Land-Cover Change: Local Processes and Global Impacts. Springer, Berlin.
24. Li, X., Yeh, A.G.O., 2002a. Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Science 16 (4), 323-343.
25. Li, X., Yeh, A.G.O., 2002b. Urban simulation using principal components analysis and cellular automata for land use planning. Photogrammetric Engineering and Remote Sensing 68 (4), 341-351.
26. Li, Y.F., Ng, S.H., Xie, M., Goh, T.N., 2011. A systematic comparison of metamodeling techniques for simulation optimization in Decision Support Systems. Applied Soft Computing 10 (2010), 1257-1273.
27. Loh, W.-Y., 2010. Tree-structured classifiers. WIREs Computational Statistics 2, 364-369.
28. Menéndeza, L.A., de Cos Juez, F.J., Lasheras, F.S., Riesgo, J.A.A., 2011. Artificial neural networks applied to cancer detection in a breast screening programme. Mathematical and Computer Modelling 52 (2010), 983-991.
29. Parker, D.C., Manson, S.M., Janssen, M.A., Hoffmann, M.J., Deadman, P., 2003. Multiagent systems for the simulation of land-use and land-cover change: a review. Annals of the Association of American Geographers 93 (2), 314-337.
30. Paulsen, P., Smulders, F.J.M., Tichy, A., Aydin, A., Höck, C., 2011. Application of Classification and Regression Tree (CART) analysis on the microflora of minced meat for classification according to Reg. (EC) 2073/2005. Meat Science 88 (2011), 531-534.
31. Pijanowski, B.C., Brown, D.G., Shellito, B.A., Manik, G.A., 2002. Using neural networks and GIS to forecast land use changes: a Land Transformation Model. Computers Environment and Urban Systems 26, 553-575.
32. Pijanowski, B.C., Pithadia, S., Shellito, B.A., Alexandridis, K., 2005. Calibrating a neural network based urban change model for two metropolitan areas of Upper Midwest of the United States. International Journal of Geographical Information Science 19, 197-215.
33. Pijanowski, B.C., Alexandridis, K., Mueller, D., 2006. Modeling urbanization patterns in two diverse regions of the world. Journal of Land Use Science 1, 83-108.
34. Pijanowski, B.C., Tayyebi, A., Delavar, M.R., Yazdanpanah, M.J., 2009. Urban expansion simulation using geographic information systems and artificial neural networks. International Journal of Environmental Research 3 (4), 493-502.
35. Pijanowski, B.C., Tayyebi, A., Doucette, J., Pekin, B.K., Braun, D., Plourde, J., 2013. A big data urban growth simulation at a national scale: configuring the GIS and neural network based Land Transformation Model to run in a high performance computing environment. Environmental Modeling & Software 51, 250-268.
36. Pontius Jr., R.G., Cornell, J., Hall, C., 2001. Modeling the spatial pattern of land-use change with GEOMOD2: application and validation for Costa Rica. Agriculture, Ecosystems & Environment 85 (1-3), 191-203.
37. Pontius Jr., R.G., Batchu, K., 2003. Using the relative operating characteristic to quantify certainty in prediction of location of land cover change in India. Transactions in GIS 7 (4), 467-484.
38. Pontius Jr., R.G., Connors, J., 2006. Expanding the conceptual, mathematical, and practical methods for map comparison. In: Conference Proceedings of the Meeting of Spatial Accuracy, Lisbon, Portugal, 16 pp.
39. Pontius Jr., R.G., Boersma, W., Castella, J.C., Clarke, K., de Nijs, T., Dietzel, C., Duan, Z., Fotsing, E., Goldstein, N., Kok, K., Koomen, E., Lippitt, C.D., McConnell, W., Mohd Sood, A., Pijanowski, B., Pithadia, S., Sweeney, S., Trung, T.N., Veldkamp, A.T., Verburg, P.H., 2008. Comparing input, output, and validation maps for several models of land change. Annals of Regional Science 42, 11-47.
40. Refaeilzadeh, P., Tang, L., Liu, H., 2008. Cross-validation. http://www.public.asu.edu/~ltang9/papers/ency-cross-validation.pdf
41. Ralha, C.G., Abreu, C.G., Coelho, C.G., Zaghetto, A., Macchiavello, B., Machado, R.B., 2013. A multi-agent model system for land-use change simulation. Environmental Modeling & Software 42, 30-46.
42. Ray, D.K., Pijanowski, B.C., Kendall, A.D., Hyndman, D.W., 2012. Coupling land use and groundwater models to map land use legacies: assessment of model uncertainties relevant to land use planning. Applied Geography 34 (2012), 356-370.
43. Rifkin, R., Klautau, A., 2004. Parallel networks that learn to pronounce English text. Journal of Machine Learning Research, 101-141.
44. Steinberg, D., Colla, P., 1997. CART-Classification And Regression Trees: A Supplementary Manual for Windows. Salford Systems Inc., San Diego.
45. Steinberg, D., Golovnya, M., 2006. CART 6. 0 User's Manual. Salford Systems, San Diego, CA.
46. Tayyebi, A., Delavar, M.R., Yazdanpanah, M.J., Pijanowski, B.C., Saeedi, S., Tayyebi, A.H., 2010. A Spatial Logistic Regression Model for Simulating Land Use Patterns: A Case Study of the Shiraz Metropolitan Area of Iran Advances in Earth Observation of Global Change. Springer, Netherlands, pp. 27-42.
47. Tayyebi, A., Pijanowski, B.C., Tayyebi, A.H., 2011a. An urban growth boundary model using neural networks, GIS and radial parameterization: an application to Tehran, Iran. Landscape and Urban Planning 100, 35-44.
48. Tayyebi, A., Pijanowski, B.C., Pekin, B., 2011b. Two rule-based urban growth boundary models applied to the Tehran metropolitan area, Iran. Applied Geography 31, 908-918.
49. Tayyebi, A., Pekin, B.K., Pijanowski, B.C., Plourde, J.D., Doucette, J., Braun, D., 2012. Hierarchical modeling of urban growth across the conterminous USA: developing meso-scale quantity drivers for the Land Transformation Model. Journal of Land Use Science, 1-12.
50. Tayyebi, A., Perry, P.C., Tayyebi, A.H., 2013a. Predicting the expansion of an urban boundary using spatial logistic regression and hybrid raster-vector routines with remote sensing and GIS. International Journal of Geographical Information Science, 1-21 (in press).
51. Tayyebi, A.H., Tayyebi, A., Khanna, N., 2013b. Assessing uncertainty dimensions in land use change models using swap and multiplicative error models by injecting attribute and positional errors in spatial data. International Journal of Remote Sensing 35 (1), 149-170.
52. Timofeev, R., 2004. Classification and regression trees (CART) theory and applications. Master's thesis. Humboldt University Berlin.
53. Tsoumakas, G., Katakis, I., Vlahavas, I., 2010. Mining multi-label data. In: Maimon, O., Rokach, L. (Eds.), Data Mining and Knowledge Discovery Handbook., 2nd ed. Springer, pp. 667-685 (Chapter 34).
54. Veldkamp, A., Lambin, E.F., 2001. Editorial: predicting land-use change. Agriculture, Ecosystems & Environment 85, 1-6.
55. Verburg, P.H., Soepbaoer, W., Veldkamp, A., Limpiada, R., Espaldon, V., Mastura, S., 2002. Modeling the spatial dynamics of regional land use: the CLUE S model. Environmental Management 30 (2), 391-405.
56. Wang, K., Zhou, S., Liew, S., 1999. Building hierarchical classifiers using class proximities. In: Proceedings of the International Conference on Very Large Data Bases (VLDB).
57. Washington, C., Pijanowski, B., Campbell, D., Olson, J., Kinyamario, J., Irandu, E., Nganga, J., Gicheru, P., 2010. Using a role-playing game to inform the development of land-use models for the study of a complex socio-ecological system. Agricultural Systems, http://dx.doi.org/10.1016/j.agsy.2009.10.002.
58. Yap, B.W., Ong, S.H., Husain, N.H.M., 2011. Using data mining to improve assessment of credit worthiness via credit scoring models. Expert Systems with Applications 38, 13274-13283.