Spatial-statistical Modelling of Urban Growth In GKMA

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

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.