Focusing on Greater Kumasi Metropolitan Area (GKMA), the fastest growing metropolitan area in Ghana, this study aims to understand the spatio-temporal dynamics of GKMA’s growth, the factors driving this process and to quantify their relative contributions. First, growth patterns during the periods 1986-2001 and 2001-2014 in were analysed. Spatial metrics were used to deepen the understanding of the patterns of growth. This revealed that of the three growth types identified in both periods, edge-expansion was predominant in both cases.
Analyses of the driving forces of GKMA’s growth was done using Spatial Logistic Regression modelling approach. A review of literature coupled with consultations with experts during fieldwork assisted in identifying locally relevant driving forces of the area’s growth. Two models were constructed on basis of the identified drivers for the periods 1986-2001 and 2001-2014. The performance of both models was evaluated and validated to identify the one that best simulates GKMA’s growth. The estimated coefficients and associated odds ratio of the models were used in assessing the individual contributions of the driving forces. The results from the analysis showed that distance to urban cluster, distance to CBD, distance to major roads and the proportion of urban cells in 7x7 neighbourhood which are common to both time periods were among the top four drivers of urban growth in both periods though with varying levels of influence. Population density was identified as the most important driver of growth during 2001-2014.
Finally, predictions of future growth were made based on the 2001-2014 model. The results of the model’s prediction mimic past trends of the area’s growth. This is because predicted growth is shown to mimic the layout of major roads as observed in reality. The study also simulated future growth based on proposed public investment on new roads so as to understand how this will influence future growth. The results from the predicted scenario showed new growth occurring which were however not associated with the updated factor. This study attributed the decline in influence of the updated factor to correlation among the variables.
Overall the results of the study shows that the integration of remote sensing, GIS, spatial metrics and logistic regression provide a powerful collection of tools for understanding the spatio-temporal dynamics of urban growth.
Table of Contents
1. BACKGROUND OF STUDY
1.1. Introduction
1.2. Justification
1.3. Study area
1.4. Urbanisation in Greater Kumasi
1.5. Research Problem
1.6. Objectives and Research questions
1.7. Conceptual Framework
1.8. Thesis Structure
2. LITERATURE REVIEW
2.1. Introduction
2.2. Urban development policies/plans
2.3. Remote Sensing and GIS in urban growth studies
2.3.1. Urban land cover extraction using image classification techniques.
2.4. Spatial metrics in urban growth pattern analysis
2.4.1. Urban growth types
2.4.2. Spatial metrics
2.5. Urban growth modelling
2.5.1. Driving Forces of urban growth
3. METHODOLOGY
3.1. Introduction
3.2. Research Methodology
3.3. Data Source and type
3.4. Identifying and Analysing urban growth typologies
3.4.1. Image Classification
3.4.2. Classification Accuracy Assessment
3.4.3. Post-classification Change detection
3.4.4. Distinguishing and analysing growth typologies
3.4.5. Analysis of growth types using spatial metrics
3.5. Logistic Regression modelling and driving factors of urban growth
3.5.1. Driving factors of urban growth
3.5.2. Logic underlying the selection of driving factors for modelling
3.5.3. Preparation of input data for logistic regression modelling
3.5.4. Multicollinearity diagnostics
3.5.5. Sampling scheme
3.5.6. Logistic regression model
3.5.7. Model Evaluation and Validation
3.5.8. Simulating future urban growth
3.6. Possibilty of errors
3.7. Tools used
4. RESULTS AND DISCUSSIONS
4.1. Results of image classification
4.2. Trend in land cover change between 1986 and 2014
4.3. Urban growth typologies
4.4. Spatial metrics
4.5. Results of logistic regression models
4.6. Multicollinearity check
4.7. Model results
4.7.1. Model interpretation and discussions
4.8. Model evaluation and validation
4.8.1. Probable areas of future urban growth
4.8.2. Influence of public investment on urban growth
4.9. Comparing LR results with other studies
5. CONCLUSIONS
5.1. Introduction
5.2. Sub-objective specific conclusion
5.3. Future research avenues
6. APPENDICES
Research Objectives and Themes
The primary aim of this research is to identify and analyze the key drivers of urban growth in the Greater Kumasi Metropolitan Area, Ghana. By employing a spatial-statistical approach, the study seeks to quantify the influence of various factors on urban expansion and project future growth patterns to support more informed planning decisions.
- Spatio-temporal analysis of urban growth patterns (1986–2014)
- Modelling the drivers of urban growth using logistic regression
- Evaluation of the influence of public investment on infrastructure and expansion
- Simulation and prediction of future urban growth for 2023 and 2033
- Integration of remote sensing, GIS, and spatial metrics in urban studies
Excerpt from the Book
3.4.1. Image Classification
One of the objectives of this study is to analyse and understand the urban growth typologies over the chosen time steps. The first step towards this objective was to classify the satellite imagery so as to generate maps of the land cover classes of interest in this case urban, non-urban, and water bodies. Detailed description of these classes is given below (Table 4);
Both supervised and unsupervised classification were done to generate the above land cover classes. Two of the images, namely 2001 and 2014 were classified using supervised classification because these two times have reference data. Training samples, collected with the help of Land use maps of 2000 and 2012 and very high resolution images from Google Earth are used for 2001 and 2014 Landsat images while the land cover classes for 1986 were derived using visual image interpretation techniques of the false colour composite of bands 4, 3 and 2 of the Landsat TM image coupled with expert knowledge of the study landscape during that time. Some of the experts namely Mr. Joseph Edusei, Professor Romanus Dinye and the Director of Town Planning, Mr. Emmanuel Christian Coffie provided valuable information on the land cover types existing during that time. The image was printed in colour on an A 3 sheet and this was presented to the experts to indicate by sketching sample polygons on each identified land cover. The knowledge obtained from this was used in generating training sites in ERDAS IMAGINE to supervise the classification of the image.
Summary of Chapters
1. BACKGROUND OF STUDY: Provides global urbanisation trends and introduces the research problem and objectives regarding Greater Kumasi's expansion.
2. LITERATURE REVIEW: Reviews urban development policies, applications of remote sensing/GIS, and various modelling approaches for urban growth.
3. METHODOLOGY: Details the data collection, classification techniques, spatial metrics, and the logistic regression modelling framework used for analysis.
4. RESULTS AND DISCUSSIONS: Presents the analysis of land cover change, growth typologies, and the performance and implications of the developed logistic regression models.
5. CONCLUSIONS: Synthesizes the research findings regarding spatio-temporal dynamics and driving forces of urban growth, offering suggestions for future research.
6. APPENDICES: Contains supporting information, including expert interview details, correlation matrices, and buffer zone level spatial metrics.
Keywords
Urbanisation, Greater Kumasi, Spatial-Statistical Modelling, Logistic Regression, Remote Sensing, GIS, Spatial Metrics, Land Cover Change, Urban Sprawl, Infilling, Edge-Expansion, Urban Growth Drivers, Population Density, Urban Planning, Infrastructure Development
Frequently Asked Questions
What is the core focus of this research?
The research focuses on understanding the spatio-temporal dynamics of urban growth in the Greater Kumasi Metropolitan Area, Ghana, specifically identifying the driving forces behind this rapid urban expansion.
What are the primary themes addressed in the work?
Key themes include the quantification of urban growth patterns, the application of spatial metrics to analyze land-use change, the construction of logistic regression models to identify urban drivers, and the simulation of future urban development scenarios.
What is the primary objective of this study?
The main objective is to identify and analyze the key drivers of urban growth in Kumasi through a spatial-statistical modelling approach.
Which scientific methodologies are applied?
The study utilizes remote sensing for land cover extraction, GIS-based spatial metric analysis, and binary logistic regression modelling to quantify the influence of various socio-economic and biophysical factors on urban growth.
What topics are discussed in the main chapters?
The chapters cover the background and justification of the study, a review of relevant literature, detailed methodological steps for data processing, the presentation of results including classification and regression outputs, and final conclusions.
Which keywords characterize this research?
Important keywords include Urbanisation, Spatial-Statistical Modelling, Greater Kumasi, Logistic Regression, GIS, Remote Sensing, Urban Sprawl, and Land Cover Change.
How does this study handle the lack of historical data for 1986?
For the 1986 imagery, which lacked reference data, the author employed visual image interpretation techniques using false-color composites, supplemented by expert knowledge from local urban planners.
What role does public investment play in the predicted future growth?
The research incorporates planned road infrastructure into a specific model (Model C) to evaluate how proposed investments shift future urban growth patterns compared to the status quo.
How does the author evaluate the accuracy of the growth models?
Model performance is validated using a combination of the Percentage of Correct Predictions (PCP) and the Kappa statistic, ensuring the models are representative of observed land-use changes.
Why are spatial metrics utilized in this research?
Spatial metrics are used to quantitatively analyze the structure and patterns of urban development, helping to distinguish between infilling, edge-expansion, and outlying growth types.
- Quote paper
- Abdul-Fatawu Mohammed (Author), 2015, Spatial-statistical Modelling of Urban Growth In GKMA, Munich, GRIN Verlag, https://www.grin.com/document/1036800
