MODELLING URBAN GROWTH DYNAMICS OF UYO METROPOLIS USING LINEAR AND NON LINEAR MODELSBYEYOH, ANIEKAN EFFIONGPG/Ph.D/13/65673DEPARTMENT OF GEOINFORMATICS & SURVEYINGFACULTY OF ENVIRONMENTAL STUDIESUNVERSITY OF NIGERIAENUGU CAMPUSSUPERVISOR:PROF. F. I. OKEKEDOCTORAL RESEARCH PROPOSAL

STATEMENT OF PROBLEM Urban growth in southern cities in Nigeria has put profound pressure on housing, infrastructure and the environment and is generally viewed by most Nigerians as an intractable problem (Braimoh et al. 2007). The social and environmental repercussion of loosely planned urban cities could be catastrophic especially in the present situation in Uyo that has constantly experienced remarkable urban expansion in recent time. Understanding urban growth and it's dynamics is critical to city planners and resource managers in this rapidly changing city. Currently, technological methods such as Geographic Information Systems (GIS), Remote sensing, along with analytical models among other requirements for sustainable physical planning, are not presently utilised by urban planners in Uyo and other cities in Nigeria. Oduwaye (2009) argued that sustainable physical planning will remain a mirage in cities in Nigeria unless contemporary e-planning techniques are adopted. Uyo is one of the foremost cities currently experiencing unprecedented urban sprawl. with modelling and simulation, it will be possible to reduce urban growth uncertainties and thereby increase the understanding of urban growth dynamics. Taking urban planning process as an example, planning is a future-oriented activity, strongly conditioned by the past and present. Urban planners need to enhance their analytical problem solving and decision making capabilities by having an understanding of the urban growth drivers and its influence over the past and a possible effect in future sprawls. With the help of analytical models, this scenario building can be facilitated, thus providing an important aid to future directed decision- making. In this way, urban planning can be more scientific, thus reducing the subjectivity brought by decision makers. It is on this background that this research work is undertaken to support Uyos sustainable development and assist city planners in having a better understanding of past and future urban growth patterns and the underlying factors influencing the spatial and temporal processes of urban growth dynamics.

Research Aim and ObjectivesThe specific aim of this research is to: Model and assess urban growth dynamics of Uyo metropolis using linear and non linear model so as to reveal the underlying factors influencing the spatial and temporal processes of past urban growth thereby aiding the forecast of future growth patterns.The above stated aim will be accomplished with the following specific objectives:Spatial-temporal assessment of urban growth dynamics of Uyo metropolis from 1978-2013 (Four Epoch).Extraction/Generation of independent spatial and weighted variables (causal factors driving urban growth) from remote sensing data.Modelling and validation; using linear model(Geographically Weighted Regression) and non-linear model (Artificial Neural Networks).Assessment of the level of significance/contribution of each driving factor influencing Urban growth in the study area.Juxtapose performance of the linear model(GWR) and non linear model( ANN)Predicting future urban growth in 2020 and 2030 using validated calibrations.

RESEARCH QUESTIONS

SPECIFIC OBJECTIVESRESEARCH QUESTIONS1.Spatial-temporal assessment of urban growth dynamics of Uyo metropolis from 1978-2013(four epoch).ii. What are the spatial extent of urban growth in each epoch?i. what are the patterns of the urban growths? Is it ;Infill? or Urban Extension/Expansion? or Urban sprawl?2. Extraction/Generation of independent spatial and weighted variables (causal factors driving urban growth) from remote sensing data.i. What are the causal factors driving urban growth in the study area? ii. Can this drivers be extracted/generated from remote sensing data?3. Modelling and validation of the models (GWR and ANN).i. Can Geographically Weighted Regression (GWR) model be implemented to simulate and predict urban growth with satisfactory accuracy?iii. Can Artificial Neural Network (ANN) be implemented to simulate and predict urban growth with satisfactory accuracy?

RESEARCH QUESTIONS CONTINUES

SPECIFIC OBJECTIVESRESEARCH QUESTIONS4. Assessment of the significance/contribution of each variables(factor driving Urban growth) used in the models.i. Are all the drivers of urban growth identified in the study area significant?ii. What are the level of significance/contribution of each driver for each epoch under consideration?5. Juxtapose performance of the linear model(GWR) and the non linear model (ANN)ii. Does the Linear model (GWR ) yield better results and accuracies than the non linear model (ANN)?iii. Which of the model is the most reliable and why?6. Predicting future urban growth in 2020 and 2030 using validated calibrationsCan future urban growth in 2020 and 2030 be predicted using the validated calibrations from linear model and non linear model?

RESEARCH SIGNIFICANCE There has been a concerted effort in using GIS and remote sensing to provide information on existing land use and land cover changes, and it has been increasingly used to characterize urban areas and to show urban changes in Nigerian cities. But most time the spatio-temporal pattern of the urban growth are not explicitly expounded and analytical models are not used to simulate and build scenario that can aid the understanding of complex urban growth dynamics, thus limiting the usefulness/applicability of such studies. This research will attempt to breach this gap by using various analytical robust models(linear and non linear) like Geographically Weighted Regression (GWR), and Artificial Neural Network (ANN) to implement the modelling of urban growth dynamics of Uyo metropolis. Metropolitan planners have always sought tools to enhance their analytical, problem-solving and decision-making capabilities and this research is set out to meet this need. Consequently this research should be able to : Assist municipal planners and policy makers in having a better understanding of past and future urban growth patterns from historical remote sensing data. Reveal the underlying factors influencing the spatial and temporal processes of urban growth dynamics. Provide an important aid to future-directed decision-making on urban planning. Help in generating scientific plans which will foster sustainable development in Uyo metropolis, Nigeria.

STUDY AREALocation and Extent;- Uyo metropolis is the area chosen for this study. This city serves as the capital of Akwa Ibom State. It is currently the number one oil producing state in Nigeria. It lies between Longitude 07o 54 and 07 o 58 East of the Greenwich Meridian and between Latitude 04o 58 and 05o 08 North of the Equator. Uyo metropolis is situated at the north central part of the state and occupies a model position with road links to all local government areas in the state. The city can be accessed by road via various Highways such as Calabar-Itu Highway, Abak Road, Oron-Nwaniba Road,, and Aka Road. Nearby airport is the Akwa Ibom International Airport. The total area the study will cover will be approximately 160km2 covering greater part of Uyo Local Government Area, part of Itu Local Government Area, Ibesikpo/Asutan Local Government Area, Ibiono Ibom Local Government Area and Nsit Ibom Local Government Area. Topography:- Uyo metropolis lies within the lowland coastal plain of Nigeria with alternating denudation and aggradation activities. The terrain is fairly flat except for some part which has dots of ravine resulting from geomorphologic processes of the past. Climate:-Uyo metropolis lies within the equatorial rain forest belt. It is in a tropical climatic region that has long rainy season occurring between the month of March and October, with a short dry season between November and February.

LITERATURE REVIEW

S/NOAUTHOR/YEARTOPICSUMMARYRELATIONSHIPMY REMARKS/CONTRIBUTION1Mohammady et al. (2013)Urban growth modelling with Artificial Neural Network and Logistic Regression. Case study: Sanandaj city, Iran.It employed two models; Artificial Neural Network and Logistic Regression, in modelling dynamic urban growth in Sanandaj city, Iran from a single epoch of Remote sensing image (2000-2006). The dataset used includes distance to main roads, distance to the residence region, elevation, slope, and distance to green space. Percent Area Match (PAM) obtained from modelling of these changes with ANN was 90.47% and the accuracy achieved for urban growth modelling with Logistic Regression (LR) was 88.91%. Percent Correct Match (PCM) and Figure of Merit for ANN method were 91.33% and 59.07% and then for LR were 90.84% and 57.07%, respectively. The coefficients obtained from LR method showed that distance to main road had the biggest impact on urban growth in this area and, on the other hand, elevation had the minimum impact. It is related. These researchers used both linear and non-linear models. Artificial Neural Network is also the non-linear model chosen for this study.The work demonstrated that linear and non-linear models can be used to model complex urban systems with satisfactory accuracy . However , the used of a single epoch of a low resolution image over a very short period (6years) will definitely not reveal conclusively the significance of the driving factors of urban growth. Hence, I propose to use 4 epochs (35years span), Aerial photo and high resolution images to enhance performance and reliability of my research. Also, these researchers used mostly proximity variables and did not consider other important causal factors of urban growth like; Demographic(Population size and Population density), Economic( cost of Land; Business potential and rent), Social (Human attitudes and values; Affluence), Neighbourhood (Availability of useable sites and Agglomeration of developed areas ), constraints (Water body and Environment sensitive area), and rule making(Zoning laws) were not considered. I hope to incorporate these variables in my research. Furthermore, these researchers only simulated urban growth but did not predict future growth. I intend to predict future urban growth based on my validated model calibrations in two perspective; using linear model and non-linear model.2Moghadam et al. (2013)Spatiotemporal urbanization processes in the megacity of Mumbai, India: A Markov chains-cellular automata urban growth model.It used Remote sensing data collected between 1973 and 2010 with an integrated Markov Chainse Cellular Automata (MCeCA) urban growth model to predict the citys expansion for the years 2020 and 2030. The variables used included; distance from roads, distance from water bodies, distances from built up areas and slope. It analysis demonstrated that the integration of GIS, remote sensing, and urban modelling offers an enhanced understanding of the futures and trends that megacities will face. It also provides important information for strategies directed at fostering sustainable development.It is related. These researchers predicted future urban growth in 2020 and 2030 using a different model from what I intend to used.Markov chain analysis uses a transition matrix to describe the change of land use but cannot reveal the causal factors and their significance. But the linear model (GWR ) and non-linear model (ANN ) proposed for this research can simulate and as well reveal the level of significance/contribution of each urban growth drivers. In addition to physical and proximity variables the researchers only used, other variables stated in my remarks in literature review-1 will be introduced by me.3Abebe (2013)Quantifying Urban growth pattern in developing countries using Remote sensing and Spatial Matrics: A case study of Kampala Uganda.In this study, the spatial temporal patterns and process of urban growth of kampala and its drivers were investigated from 1989 to 2010 using satellite remote sensing images, spatial matrics and Logistic Regression modelling. Four different land cover maps derived from Landsat TM images of 1989, 1995, 2003 and 2010 were used to evaluate a set of nine selected spatial matrices to reveal patterns and dynamics of urban growth in the study area. The result proved that quantifying urban growth patterns and development process of the past trends can help better understanding of built up areas and guide sustainable development planning of future urban growth. It is related. These researchers used Logistic Regression; which is one of my linear models.The researcher did a very notable work. He considered the urban growth patterns (Infill, expansion/extension and sprawl) which is a very important aspect of urban studies that has not been taken into consideration by researchers. His work is really an inspiration. But in addition, I intend to use both linear and non-linear model to carry out my own research to give room for comparative analysis.

LITERATURE REVIEW CONTINUES

S/NOAUTHOR/YEARTOPICSUMMARYRELATIONSHIPMY REMARKS/CONTRIBUTION4Soltani, & Karimzadeh (2013)The spatio-temporal modelling of urban growth case study: Mahabad, IranThis study model and simulated the complex patterns of land use change by utilizing remote sensing data (1989, 2000, and 2005) and artificial intelligence techniques in the city of Mahabad, north-west of Iran. Cellular Automata (CA) was used as the principal motor of the model and then ANN applied to find suitable scale of parameters and relations between potential factors affecting urban growth. The final step of modelling was prediction of urban growth for 2025 based on the parameters and results of model validation. The driving factors considered were four groups; physical attribute, accessibility and neighbourhood, zoning and land use data for the period of 1989 to 2005. The general accuracy of the model obtained from Kappa Coefficient confirms that ANN model can be used in simulating nonlinear urban evolution process.It is related. These researchers used Artificial Neural Network; which is one of my non-linear model and they also predicted future urban growth which I also anticipate to do.They work showed the effectiveness of Artificial Neural Network in simulation and prediction of urban growth. But the level of significance of the drivers of urban growth were not highlighted and evaluated by these researchers. I hope to evaluate the level of significance of the of causal factors of urban growth in my own research . Also the used of linear model will make my research more analytical since GWR model have tremendous explanatory properties, such that the significant contribution of the independent variables(urban growth drivers) in the models can be explored, as well as tests for detecting multicollinearity, normality, spatial autocorrelation, and determining R2 .5Eyoh (2012)Modelling and predicting future urban expansion of Lagos, Nigeria from remote sensing data using logistic regression and GISThis research explores the implementation of a loosely coupled logistic regression model and geographic information systems in modelling and predicting future urban expansion of Lagos from historical remote sensing data (Landsat TM images of Lagos acquired on 1984, 2000 and 2005). The ten land use explanatory variables that were used for the modelling were all significant at 95% CL which implied that all the explanatory variables contributed to the urban expansion of Lagos. Distance to urban cells (Residential and commercial/industrial) had the highest impact in the model indicating that urban growth tends to occur close to the nearest urban area. The spatial growth of Lagos in 2030 was believed to be a direct consequence of urban sprawl as urban areas tend to expand close to the nearest urban cluster.It is related. The researcher used Logistic Regression; which is a linear model.Due to the complication of urban systems, a linear model alone cannot be solely relied upon in the modelling of such a complex system. Also the researcher used only proximity variables and did not consider other important causal factors of urban growth like; Demographic(Population size and Population density), Economic( cost of Land; Business potential and rent), Social (Human attitudes and values; Affluence), Neighbourhood (Availability of useable sites and Agglomeration of developed areas ), constraints (Water body and Environment sensitive area), and rule making(Zoning laws). Also the use of linear model (Geographic Weighted Regression) and a robust non-linear models (ANN) will give room for juxtapose performance of the models.6Shariff et al. (2010)Modelling Urban Land Use Change Using GeographicallyWeighted Regression and the Implications for SustainableEnvironmental PlanningAn ordinary least squares regression (OLS) and Geographically Weighted Regression (GWR) was used to model urban land use changes in Penang Island from 1990 to 2005. Spatial variables describing environment, physical and socioeconomic factors (proximity only) which are assumed to influence the change in the land use in the study area were extracted and used for the modelling. An ordinary least squares regression (OLS) model was first applied to the variables followed by a GWR model. The results were compared. The results show that the GWR outputs explained considerably more variance in the relationship of the explanatory factors compared to conventional OLS models and provided significantly better results. In addition, GWR also provided important insights on location where changes happen.It is related. The researcher used Geographically weighted Regression; which is my linear model.The researchers demonstrated that GWR can be effectively implemented for modelling urban growth but they only used proximity variables and did not consider other important causal factors of urban growth. I vividly believe that the additional use of robust non-linear models (ANN) will present a more comprehensive studies needed to understand long term spatio-temporal dynamics inherent in urban systems. Furthermore, I intend to predict future urban growth based on my validated model calibrations in two perspective; linear model and non-linear model.

LITERATURE REVIEW CONTINUES

S/NOAUTHOR/YEARTOPICSUMMARYRELATIONSHIPMY REMARKS/CONTRIBUTION7Huang et al. (2009)Spatio-temporal Analysis of Rural-urban Land ConversionGIS coupled with a logistic regression model and exponential smoothing techniques was used for exploring the effects of various factors on land use change(1984-2002) in New Castle County, Delaware USA. These factors include population density, slope, proximity to roads, and surrounding land use. The modelling results reveal that Logistics Regression model can achieved a reasonable goodness-of-fit of the actual land use development thus assisting in the forecast of the sites prone to urbanization.It is related. Logistic Regression was used; which is a linear model.Rural-urban Land Conversion is stochastic in behaviour and might not be normally distributed, thus the use of linear model alone cannot be solely relied upon in the modelling growth dynamics in a metropolis like Uyo. Thus employing robust non linear predictive tools that can realistically model urban complexity, dynamism, and future growth efficiently is pertinent.9Alkheder (2008)Urban growth simulation using remote sensing imagery and neural networksThis work applies neural network (NN) algorithms in simulating urban growth of Indianapolis city using historical satellite images of Indianapolis city over a study period of three decades (1973-2003). Their focus was directed to the residential and commercial classes and their growth. Two NN algorithms were used to simulate the urban growth: Simple Adaptive Linear Neural Network (SALNN) and Back Propagation Neural Network Algorithm (BPNN). Results showed that both algorithms after increasing the volume of the dataset succeeded in simulating the growth trends with better results achieved using the Simple Adaptive Linear Neural Network (SALNN).It is related. These researchers used Artificial Neural Network; which is my chosen non-linear model.The researcher used only proximity variables and did not consider other important fundamental factors of urban growth which are very pertinent in order to efficiently and effectively reveal the main drivers of Urban growth dynamics. I believe that the use of linear model (GWR ) will enable juxtapose performance of Urban growth models and the forecast of future urban growth by both linear and non linear model will enhance vivid conclusions on urban growth dynamics. 9Xie (2006)Support Vector Machines for Land Use ChangeModellingThis research implemented a Support Vector Machines (SVMs) model for land use change modelling of Calgary city,Canada from 1985 to 2001(1985, 1990, 1992, 1999, 2000, and2001 Landsat Images). He used three categories of causal factors: (1) site specific characteristics, (2) proximity, and (3) neighbourhood characteristics. He also implemented a Spatial logistic regression so as to compare the performance of Logistic Regression with Support Vector Machines. The modelling result demonstrated that SVMs can achieve high and reliable performances than Logistic RegressionIt is related. It use Support Vector Machines (SVMs) a non-linear model and Logistic Regression a non linear model.Support Vector Machines (SVMs) is a very robust non linear model like ANN model. It has the capability to effectively address urban growth dynamics modelling. Despite this, there is still a need to use linear model. This researcher still used linear model to enable juxtapose performance and this corroborate my stand on using both linear and non linear model. He also did a remarkable work on the choice of his drivers. His work is really an inspiration. But the non linear model (SVMs) he used did not reveal the significance of the causal factors of urban growth in his study area.10Cheng (2003)Modelling Spatial & Temporal Urban GrowthThis research was centred on monitoring and evaluating Urban Growth in Wuhan city, china from 1955-2000(1955, 1965, 1986, 1993 and 2000) based on aerial photographs, SPOT images and other data sources. The main quantitative analysis he carried out was; morphology analysis, spatial pattern analysis and land use structure change. Fractal analysis, regression analysis and landscape metrics were used as analytical methods for the evaluation of urban growth. His result reveals temporal variations in the spatial urban growth process of the Wuhan city.It is related. Logistic Regression is a non linear model.The researcher did a very good work in his quantitative analysis but linear model alone cannot be solely relied upon in the modelling complex urban system like Uyo. Also the researcher used only proximity variables and did not consider other important categorical drivers. Am using a different linear model (Geographically Weighted Regression) and two formidable non-linear models (ANN )

Reasons for choosing Linear model(GWR) and non-linear model(ANN) and Overview of the two models Linear model (GWR)Geographically Weighted Regression (GWR) is a local form of spatial analysis model adapted from Ordinary Least Square Regression. It was introduced in 1996 by Stewart Fotheringham .Stewart Fotheringham is a Professor and Director of the National Centre for Geocomputation (NCG) at the National University of Ireland, Maynooth.The reasons for choosing Geographically Weighted Regression (GWR) model as the linear model for the modelling of urban growth dynamics of Uyo Metropolis are;Geographically Weighted Regression (GWR) is a powerful tool for exploring spatial heterogeneity. Spatial heterogeneity exists when the structure of the process being modelled varies across the study area.(Fotheringham et al., 2002).GWR models have tremendous explanatory properties, such that the significant contribution of the independent variables in the models can be explored (Fotheringham et al., 2002).Also, test detecting multicollinearity, normality, spatial autocorrelation, and determining R2 are carried out to ascertain its results(Fotheringham et al., 1997).GWR has the potential to investigate non-stationary relations in regression analysis (Fotheringham et al., 2002).Several researchers corroborate that its yields high accuracy when used for modelling land use change (Noresah & Ruslan, 2009; Thapa & Murayama, 2009 and Fotheringham et al., 2002).

Mathematical Expression of Geographically Weighted Regression Model

Non-linear model (ANN)

Metropolis are complex urban system that are likely to be stochastic in behaviour. Triantakonstantis (2012) opined that the spatial complexity inherent in an urban environment reflects the impact of numerous physical and socioeconomic factors and as a result heterogeneous patterns appear across location and scale thus making urban development a dynamic and non-linear process.Consequently, linear models cannot be exclusively relied upon for modelling its growth dynamics. Hence a robust predictive tools that can realistically model their complexity, dynamism, and growth is required. Thus the choice of ANN as non-linear model.

Introduction to ANNAn Artificial Neural Network (ANN) is an information processing model that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this model is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. This is true of ANNs as well.

Why the Choice of Artificial Neural Networks(ANN) as the Non-Linear model?Artificial Neural networks, has remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an "expert" in the category of information it has been given to analyse. This expert can then be used to provide projections given new situations of interest and answer "what if" questions. Other benefits include: Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience. Self-Organisation: An ANN can create its own organisation or representation of the information it receives during learning time. Li and Yeh (2002) identify a series of ANN advantages:(a) The structure of algorithms enables neural networks to be robust and noise resistant regardless of poor data.(b) They can solve highly nonlinear problems in complex systems. (c) The method is rather simple because no exact questions or expressions are required.(d) The best level of performance can be obtained.(e) There are no restrictions about using nonnumeric data. (f) They adapt to nonnormal frequency distribution.(g) Mixtures of measurement types can be used.(h) They can use many variables, some of which may be redundant.

Explanation of Artificial Neural Network Model

Explanation of Artificial Neural Network Model Continues

Causal Factors(Drivers) of Urban Growth to be Considered for the ResearchTypically, urban growth are influenced by a few recurrent factors that cannot be overlooked. Demographic factors (population size, and population density) are widely treated as major causal factors of land use change (Verburg et al.,2001). It is obvious that a city will grow if its population increases. Consequently, new residential areas will emerge in close proximity to transportation facilities (roads, railways and bus lines) and commercial centres also develop concurrently. In the meantime, industrial buildings develop in the vicinity of those previously existing. On the whole, urban expansion will transform vacant or low rent areas into built-up land. Additionally, the agglomeration of developed areas and the availability of exploitable sites will significantly influence land use change patterns. Hence the following urban growth drivers will be considered in my research.

CONCLUSIONI strongly believe that this research will be exploratory and its results will aid urban planners and policy makers to effectively and efficiently understand urban growth processes and its driving forces, make more precise projections of future urban growth, thus helping them generate plans which will foster sustainable development.

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