References
- Andradóttir, S., 2006. An overview of simulation optimization via random search. Handbooks in Operations Research and Management Science, 13, 617–631.
- Anselin, L., 1995. Local Indicators of Spatial Association—LISA. Geographical Analysis, 27 (2), 93–115. doi:10.1111/j.1538-4632.1995.tb00338.x
- Anselin, L., et al., 1996. Simple diagnostic tests for spatial dependence. Regional Science and Urban Economics, 26 (1), 77–104. doi:10.1016/0166-0462(95)02111-6
- Arribas, I., et al., 2016. Mass appraisal of residential real estate using multilevel modelling. International Journal of Strategic Property Management, 20 (1), 77–87. doi:10.3846/1648715X.2015.1134702
- Atack, J. and Margo, R.A., 1998. “Location, location, location!” The price gradient for vacant urban land: New York, 1835 to 1900. The Journal of Real Estate Finance and Economics, 16 (2), 151–172. doi:10.1023/A:1007703701062
- Atkins, D., 2003. Revolutionizing science and engineering through cyberinfrastructure: report of the National Science Foundation Blue-Ribbon Advisory Panel on cyberinfrastructure.
- Attoh-Okine, N.O., 1999. Analysis of learning rate and momentum term in backpropagation neural network algorithm trained to predict pavement performance. Advances in Engineering Software, 30 (4), 291–302. doi:10.1016/S0965-9978(98)00071-4
- Batista, G.E.A.P.A. and Monard, M.C., 2003. An analysis of four missing data treatment methods for supervised learning. Applied Artificial Intelligence, 17 (5–6), 519–533. doi:10.1080/713827181
- Bergstra, J., et al., 2015. Hyperopt: a python library for model selection and hyperparameter optimization. Computational Science & Discovery, 8 (1), 014008. doi:10.1088/1749-4699/8/1/014008
- Bergstra, J. and Bengio, Y., 2012. Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13 (Feb), 281–305.
- Bergstra, J., Yamins, D., and Cox, D.D. 2013a. “Hyperopt: a python library for optimizing the hyperparameters of machine learning algorithms.” Proceedings of the 12th Python in Science Conference, Austin, TX.
- Bergstra, J., Yamins, D., and Cox, D.D., 2013b. Making a science of model search: hyperparameter optimization in hundreds of dimensions for vision architectures. Icml (1), 28, 115–123.
- Bergstra, J.S., et al., 2011. Algorithms for hyper-parameter optimization. In: J. Shawe-Taylor et al., eds. Advances in neural information processing systems. Granada, Spain: Neural Information Processing Systems Conference 2011, 2546–2554.
- Bishop, C.M., 2006. Pattern recognition and machine learning. New York, NY: springer.
- Brigham, E.F., 1965. The determinants of residential land values. Land Economics, 41 (4), 325–334. doi:10.2307/3144665
- Brown, C.E., 1998. Coefficient of variation. In: Applied multivariate statistics in geohydrology and related sciences. Berlin: Springer, 155–157.
- Chen, S., Billings, S.A., and Grant, P.M., 1990. Non-linear system identification using neural networks. International Journal of Control, 51 (6), 1191–1214. doi:10.1080/00207179008934126
- Claesen, M. and Bart, D.M. 2015. “Hyperparameter search in machine learning.” arXiv preprint arXiv:1502.02127.
- Creutin, J.D. and Obled, C., 1982. Objective analyses and mapping techniques for rainfall fields: an objective comparison. Water Resources Research, 18 (2), 413–431. doi:10.1029/WR018i002p00413
- Cunningham, C.R., 2006. House price uncertainty, timing of development, and vacant land prices: evidence for real options in Seattle. Journal of Urban Economics, 59 (1), 1–31. doi:10.1016/j.jue.2005.08.003
- Dai, E., et al., 2005. Modeling change-pattern-value dynamics on land use: an integrated GIS and artificial neural networks approach. Environmental Management, 36 (4), 576–591. doi:10.1007/s00267-004-0165-z
- Delmelle, E.M., 2014. Spatial sampling. In: M.M. Fischer. and P. Nijkamp, eds. Handbook of regional science. Berlin: Springer, 1385–1399.
- Delmelle, E.M. and Goovaerts, P., 2009. Second-phase sampling designs for non-stationary spatial variables. Geoderma, 153 (1–2), 205–216. doi:10.1016/j.geoderma.2009.08.007
- Demuth, H.B., et al., 2014. Neural network design. Stillwater, OK: Martin T. Hagan; Boulder, CO: Howard B. Demuth.
- Dreiseitl, S. and Ohno-Machado, L., 2002. Logistic regression and artificial neural network classification models: a methodology review. Journal of Biomedical Informatics, 35 (5), 352–359.
- Fawcett, T., 2006. An introduction to ROC analysis. Pattern Recognition Letters, 27 (8), 861–874. doi:10.1016/j.patrec.2005.10.010
- Flatman, G.T. and Yfantis, A.A., 1984. Geostatistical strategy for soil sampling: the survey and the census. Environmental Monitoring and Assessment, 4 (4), 335–349. doi:10.1007/BF00394172
- Fujita, M., et al., 1999. The spatial economy: cities, regions and international trade. Vol. 213. Hoboken, NJ: Wiley Online Library.
- Girouard, N. and Blöndal, S., 2001. House prices and economic activity. OECD Economics Department Working Papers. Paris: OECD.
- Godden, B., 2004. Sample size formulas. Retrieved on December, 3, 2013.
- Goethals, P.L.M., et al., 2007. Applications of artificial neural networks predicting macroinvertebrates in freshwaters. Aquatic Ecology, 41 (3), 491–508. doi:10.1007/s10452-007-9093-3
- Gopal, S., 2017. Artificial neural networks in geospatial analysis. In: D. Richardson, ed. The international encyclopedia of geography. Hoboken, NJ: Wiley-Blackwell,1–7
- Govindaraju, R.S. and Rao, A.R., 2013. Artificial neural networks in hydrology. Vol. 36. Berlin: Springer Science & Business Media.
- Grekousis, G., Manetos, P., and Photis, Y.N., 2013. Modeling urban evolution using neural networks, fuzzy logic and GIS: the case of the Athens metropolitan area. Cities, 30, 193–203. doi:10.1016/j.cities.2012.03.006
- Grekousis, G. and Photis, Y.N., 2014. Analyzing high-risk emergency areas with GIS and neural networks: the case of Athens, Greece. The Professional Geographer, 66 (1), 124–137. doi:10.1080/00330124.2013.765300
- Griffith, D.A., 2005. Effective geographic sample size in the presence of spatial autocorrelation. Annals of the Association of American Geographers, 95 (4), 740–760. doi:10.1111/j.1467-8306.2005.00484.x
- Hahnloser, R.H.R., et al., 2000. Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature, 405 (6789), 947–951. doi:10.1038/35016072
- Handcock, M.S. and Wallis, J.R., 1994. An approach to statistical spatial-temporal modeling of meteorological fields. Journal of the American Statistical Association, 89 (426), 368–378. doi:10.1080/01621459.1994.10476754
- Heermann, P.D. and Khazenie, N., 1992. Classification of multispectral remote sensing data using a back-propagation neural network. IEEE Transactions on Geoscience and Remote Sensing, 30 (1), 81–88. doi:10.1109/36.124218
- Heikkila, E., et al., 1989. What happened to the CBD-distance gradient?: land values in a policentric city. Environment and Planning A, 21 (2), 221–232. doi:10.1068/a210221
- Hornik, K., Stinchcombe, M., and White, H., 1989. Multilayer feedforward networks are universal approximators. Neural Networks, 2 (5), 359–366. doi:10.1016/0893-6080(89)90020-8
- Hu, S., et al., 2016. Spatially non-stationary relationships between urban residential land price and impact factors in Wuhan city, China. Applied Geography, 68, 48–56. doi:10.1016/j.apgeog.2016.01.006
- Jeffrey, S.J., et al., 2001. Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environmental Modelling & Software, 16 (4), 309–330. doi:10.1016/S1364-8152(01)00008-1
- Kalos, M.H. and Whitlock, P.A., 2008. Monte carlo methods. Hoboken, NJ: John Wiley & Sons.
- Karsoliya, S., 2012. Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture. International Journal of Engineering Trends and Technology, 3 (6), 714–717.
- Kavzoglu, T. and Mather, P.M., 2003. The use of backpropagating artificial neural networks in land cover classification. International Journal of Remote Sensing, 24 (23), 4907–4938. doi:10.1080/0143116031000114851
- Kotsiantis, S.B., Zaharakis, I., and Pintelas, P., 2007. Supervised machine learning: a review of classification techniques. Emerging Artificial Intelligence Applications in Computer Engineering, 160, 3–24.
- Kotthoff, L., et al., 2016. Auto-WEKA 2.0: automatic model selection and hyperparameter optimization in WEKA. Journal of Machine Learning Research, 17, 1–5.
- Krejcie, R.V. and Morgan, D.W., 1970. Determining sample size for research activities. Educational and Psychological Measurement, 30 (3), 607–610. doi:10.1177/001316447003000308
- Krige, D.G., 1978. Lognormal-de Wijsian geostatistics for ore evaluation. Johannesburg, South African: South African Institute of mining and metallurgy Johannesburg.
- Lam, N.S.-N., 1983. Spatial interpolation methods: a review. The American Cartographer, 10 (2), 129–150. doi:10.1559/152304083783914958
- Lark, R.M., 2002. Optimized spatial sampling of soil for estimation of the variogram by maximum likelihood. Geoderma, 105 (1–2), 49–80. doi:10.1016/S0016-7061(01)00092-1
- LaValle, S.M., Branicky, M.S., and Lindemann, S.R., 2004. On the relationship between classical grid search and probabilistic roadmaps. The International Journal of Robotics Research, 23 (7–8), 673–692. doi:10.1177/0278364904045481
- Legendre, P. and Fortin, M.J., 1989. Spatial pattern and ecological analysis. Vegetatio, 80 (2), 107–138. doi:10.1007/BF00048036
- Lerman, P.M., 1980. Fitting segmented regression models by grid search. Applied Statistics, 77–84. doi:10.2307/2346413
- Li, X., et al., 2014. A spatial–temporal Hopfield neural network approach for super-resolution land cover mapping with multi-temporal different resolution remotely sensed images. ISPRS Journal of Photogrammetry and Remote Sensing, 93, 76–87. doi:10.1016/j.isprsjprs.2014.03.013
- Li, X. and Yeh, A.G.-O., 2002. Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Science, 16 (4), 323–343. doi:10.1080/13658810210137004
- Maa, C.Y. and Schanblatt, M.A., 1992. A two-phase optimization neural network. IEEE Transactions on Neural Networks, 3 (6), 1003–1009. doi:10.1109/72.165602
- Mas, J.F. and Flores, J.J., 2008. The application of artificial neural networks to the analysis of remotely sensed data. International Journal of Remote Sensing, 29 (3), 617–663. doi:10.1080/01431160701352154
- Matheron, G., 1963. Principles of geostatistics. Economic Geology, 58 (8), 1246–1266.
- McBratney, A.B. and Webster, R., 1983. How many observations are needed for regional estimation of soil properties? Soil Science, 135 (3), 177–183. doi:10.1097/00010694-198303000-00007
- McDonald, J.H., 2009. Handbook of biological statistics. Vol. 2, Baltimore, MD: Sparky House Publishing.
- McKay, M.D., Beckman, R.J., and Conover, W.J., 1979. Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics : a Journal of Statistics for the Physical, Chemical, and Engineering Sciences, 21 (2), 239–245.
- Mera, K. and Renaud, B., 2016. Asia’s financial crisis and the role of real estate. Abingdon: Routledge.
- Mitas, L. and Mitasova, H., 1999. Spatial interpolation. Geographical Information Systems: Principles, Techniques, Management and Applications, 1, 481–492.
- Moran, P.A.P., 1950. Notes on continuous stochastic phenomena. Biometrika, 37 (1/2), 17–23.
- Nevtipilova, V., et al., 2014. Testing artificial neural network (ANN) for spatial interpolation. International Journal of Geology and Geosciences (JGG), ISSN, 2329 (6755), 01–09.
- Nourani, V., et al., 2013. Using self-organizing maps and wavelet transforms for space–time pre-processing of satellite precipitation and runoff data in neural network based rainfall–runoff modeling. Journal of Hydrology, 476, 228–243. doi:10.1016/j.jhydrol.2012.10.054
- Openshaw, S. and Openshaw, C., 1997. Artificial intelligence in geography. Chichester: John Wiley & Sons, Inc.
- Paola, J.D. and Schowengerdt, R.A., 1995. A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery. International Journal of Remote Sensing, 16 (16), 3033–3058. doi:10.1080/01431169508954607
- Pebesma, E.J., 2004. Multivariable geostatistics in S: the gstat package. Computers & Geosciences, 30 (7), 683–691. doi:10.1016/j.cageo.2004.03.012
- Pijanowski, B.C., et al., 2002. Using neural networks and GIS to forecast land use changes: a land transformation model. Computers, Environment and Urban Systems, 26 (6), 553–575. doi:10.1016/S0198-9715(01)00015-1
- Pijanowski, B.C., et al., 2005. Calibrating a neural network‐based urban change model for two metropolitan areas of the Upper Midwest of the United States. International Journal of Geographical Information Science, 19 (2), 197–215. doi:10.1080/13658810410001713416
- Pijanowski, B.C., et al., 2014. 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 (HPC) environment. Environmental Modelling & Software, 51, 250–268. doi:10.1016/j.envsoft.2013.09.015
- Quan, D.C. and Titman, S., 1999. Do real estate prices and stock prices move together? An international analysis. Real Estate Economics, 27 (2), 183–207. doi:10.1111/1540-6229.00771
- Quigley, J.M., 2002. Real estate prices and economic cycles. In: C.-O. Chang et al., eds. Berkeley program on housing and urban policy. Rockville, MD: International Real Estate Review, 1–20.
- Robinson, T.P. and Metternicht, G., 2006. Testing the performance of spatial interpolation techniques for mapping soil properties. Computers and Electronics in Agriculture, 50 (2), 97–108. doi:10.1016/j.compag.2005.07.003
- Rumelhart, D.E. and James, L.M., PDP Research Group, 1988. Parallel distributed processing. Vol. 1. Piscataway, NJ: IEEE.
- Specht, D.F., 1990. Probabilistic neural networks. Neural Networks, 3 (1), 109–118. doi:10.1016/0893-6080(90)90049-Q
- Stathakis, D., 2009. How many hidden layers and nodes? International Journal of Remote Sensing, 30 (8), 2133–2147. doi:10.1080/01431160802549278
- Tabios, G.Q. and Salas, J.D., 1985. A comparative analysis of techniques for spatial interpolation of precipitation. JAWRA Journal of the American Water Resources Association, 21 (3), 365–380. doi:10.1111/j.1752-1688.1985.tb00147.x
- Tang, W. and Jia, M., 2014. Global sensitivity analysis of a large agent-based model of spatial opinion exchange: a heterogeneous multi-GPU acceleration approach. Annals of the Association of American Geographers, 104 (3), 485–509. doi:10.1080/00045608.2014.892342
- Thornton, C., et al. 2013. “Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms.” Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, Chicago, IL.
- Tilman, D. and Kareiva, P.M., 1997. Spatial ecology: the role of space in population dynamics and interspecific interactions. Vol. 30. Princeton, NJ: Princeton University Press.
- Tobler, W.R., 1970. A computer movie simulating urban growth in the Detroit region. Economic Geography, 46 (sup1), 234–240. doi:10.2307/143141
- Wackernagel, H., 2013. Multivariate geostatistics: an introduction with applications. Berlin: Springer Science & Business Media.
- Wilkinson, B. and Allen, M., 1999. Parallel programming. Vol. 999, Upper Saddle River, NJ: Prentice hall.
- Willmott, C.J., 1982. Some comments on the evaluation of model performance. Bulletin of the American Meteorological Society, 63 (11), 1309–1313. doi:10.1175/1520-0477(1982)063<1309:SCOTEO>2.0.CO;2
- Yamazaki, R., 2001. Empirical testing of real option pricing models using land price index in Japan. Journal of Property Investment & Finance, 19 (1), 53–72. doi:10.1108/14635780110365361
- Yu, X.-H. and Chen, G.-A., 1997. Efficient backpropagation learning using optimal learning rate and momentum. Neural Networks, 10 (3), 517–527. doi:10.1016/S0893-6080(96)00102-5
- Zimmerman, D., et al., 1999. An experimental comparison of ordinary and universal kriging and inverse distance weighting. Mathematical Geology, 31 (4), 375–390. doi:10.1023/A:1007586507433