GIS Cellular Automata Using Artificial Neural Network for Land Use Change Simulation of Lagos, Nigeria

Tremendous land use change has occurred in Lagos in recent times. Modelling urban systems now extends beyond the use of geographic information systems models. This research therefore presents a loose coupling of geographic information systems and artificial neural network for simulating land use change in Lagos. The experiment is based on three land use epochs of Lagos: 1963-1978, 1978-1984, and 1984-2000. Twelve salient land use explanatory variables (distance to water, distance to residential structures, distance to industrial and commercial centres, distance to major roads, distance to railway, distance to Lagos Island, distance to international airport, distance to international seaport, distance to University of Lagos, distance to Lagos State University, income potential, and population potential) are used for the simulation. Using the Kappa statistic, the result of the simulation in terms of the order of best-fit of the reference data is: 1978-1984, 1984-2000, and 1963-1978. An evaluation of the simulation using the receiver operating characteristics corroborates the Kappa estimates. A non black-box experiment using a one-neuron neural network to assess the performance of the spatial independent variables used for the simulation indicates that for all three epochs distance to residential structures has the highest impact in the simulation while population potential has the lowest impact.

[1]  M. Gandy.  Planning, Anti-planning and the Infrastructure Crisis Facing Metropolitan Lagos , 2006 .

[2]  Emiliano Ferreira Castejon,et al.  Using neural networks and cellular automata for modelling intra‐urban land‐use dynamics , 2008, Int. J. Geogr. Inf. Sci..

[3]  Anthony Gar-On Yeh,et al.  Neural-network-based cellular automata for simulating multiple land use changes using GIS , 2002, Int. J. Geogr. Inf. Sci..

[4]  Fulong Wu,et al.  Calibration of stochastic cellular automata: the application to rural-urban land conversions , 2002, Int. J. Geogr. Inf. Sci..

[5]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[6]  Keith C. Clarke,et al.  Loose-Coupling a Cellular Automaton Model and GIS: Long-Term Urban Growth Prediction for San Francisco and Washington/Baltimore , 1998, Int. J. Geogr. Inf. Sci..

[7]  José I. Barredo,et al.  Modelling Future Urban Scenarios in Developing Countries: An Application Case Study in Lagos, Nigeria , 2004 .

[8]  Yves Chauvin,et al.  Backpropagation: theory, architectures, and applications , 1995 .

[9]  J. O. Abiodun.  Urban Growth and Problems in Metropolitan Lagos , 1974 .

[10]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[11]  P. Torrens,et al.  Cellular Automata and Urban Simulation: Where Do We Go from Here? , 2001 .

[12]  Fangju Wang,et al.  The Use of Artificial Neural Networks in a Geographical Information System for Agricultural Land-Suitability Assessment , 1994 .

[13]  Tom Fawcett,et al.  ROC Graphs: Notes and Practical Considerations for Data Mining Researchers , 2003 .

[14]  Patrick van der Smagt,et al.  Introduction to neural networks , 1995, The Lancet.

[15]  O. Okwuashi.  The Application of Geographic Information Systems Cellular Automata Based Models to Land Use Change Modelling of Lagos, Nigeria , 2011 .

[16]  Soohong Park,et al.  Incorporating Cellular Automata simulators as analytical engines in GIS , 1997, Trans. GIS.