Spatial-statistical Modelling of Urban Growth In GKMA

Table of Contents

• 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
• 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
• 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
• 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

Objectives and Key Themes

This study aims to understand the spatio-temporal dynamics of Greater Kumasi Metropolitan Area's (GKMA) growth, the driving forces behind this growth, and to quantify their relative influences. It focuses on analyzing GKMA's growth patterns, modeling urban growth based on key driving forces, and predicting future spatial growth patterns.

• Spatio-temporal patterns of urban growth in GKMA.
• Identification and quantification of key driving forces behind GKMA's urban growth.
• Development and validation of a logistic regression model to predict future urban growth.
• Analysis of urban growth typologies (infilling, edge-expansion, outlying growth).
• Assessment of the impact of public investment on future urban growth patterns.

Chapter Summaries

BACKGROUND OF STUDY: This introductory chapter establishes the context of the research by discussing global urbanization trends, focusing on the unique challenges of rapid urbanization in developing countries, particularly in sub-Saharan Africa. It highlights the rapid and uncontrolled growth of Greater Kumasi Metropolitan Area (GKMA) in Ghana, emphasizing the need for understanding the driving forces behind this expansion to implement effective urban management strategies. The chapter introduces the research problem, objectives, and questions, along with a conceptual framework that outlines the methodology.

LITERATURE REVIEW: This chapter reviews existing literature on urban development policies in GKMA, highlighting the historical context and challenges in planning and implementation. It explores the application of remote sensing and GIS in urban growth studies, detailing methods for urban land cover extraction and the use of spatial metrics in analyzing urban growth patterns. The chapter also delves into various urban growth modelling approaches, focusing on logistic regression as the chosen method for this study, and discusses the different driving forces identified in previous research.

METHODOLOGY: This chapter details the research methodology, data collection methods, and analytical techniques employed in the study. It describes the collection of both primary (expert interviews and ground truthing) and secondary data (remote sensing imagery, GIS data, demographic data). The chapter explains the image classification process, accuracy assessment, post-classification change detection, and the use of spatial metrics to analyze urban growth typologies. The application of logistic regression modeling for identifying and quantifying the driving forces of urban growth, as well as the model evaluation and validation methods, are thoroughly described.

RESULTS AND DISCUSSIONS: This chapter presents and analyzes the results of the study. It begins with the results of image classification and accuracy assessment, followed by an analysis of land cover change trends between 1986 and 2014. The chapter then presents a detailed analysis of urban growth typologies, using spatial metrics to quantify the spatial patterns of growth. The results of the logistic regression models are discussed extensively, interpreting the model parameters and assessing the relative contributions of the different driving forces. The chapter concludes with a discussion of model evaluation and validation, and a comparison with findings from other related studies.

Keywords

Urban growth, Greater Kumasi, Ghana, spatial-statistical modelling, logistic regression, remote sensing, GIS, spatial metrics, land cover change, urbanization dynamics, driving forces, model prediction, urban planning.

Frequently Asked Questions: A Comprehensive Language Preview

What is the main topic of this research?

This research focuses on understanding the spatio-temporal dynamics of Greater Kumasi Metropolitan Area's (GKMA) urban growth, identifying the driving forces behind this growth, and quantifying their relative influences. It involves analyzing growth patterns, modeling urban growth using key driving forces, and predicting future spatial growth patterns.

What are the key objectives of this study?

The study aims to achieve the following objectives: analyze spatio-temporal patterns of urban growth in GKMA; identify and quantify key driving forces behind GKMA's urban growth; develop and validate a logistic regression model to predict future urban growth; analyze urban growth typologies (infilling, edge-expansion, outlying growth); and assess the impact of public investment on future urban growth patterns.

What methodology is used in this research?

The research employs a mixed-methods approach. It uses remote sensing and GIS techniques for analyzing land cover change and urban growth patterns. Spatial metrics are utilized to quantify these patterns. A logistic regression model is developed to identify and quantify the driving forces of urban growth. The model is then validated and used to simulate future urban growth. Primary data (expert interviews, ground truthing) and secondary data (remote sensing imagery, GIS data, demographic data) are collected and analyzed.

What data sources are used?

The study utilizes both primary and secondary data sources. Primary data includes expert interviews and ground truthing. Secondary data includes remote sensing imagery (covering multiple time periods), GIS data, and demographic data.

What are the key stages of the research process?

The research process includes: Background of Study (establishing context and research problem); Literature Review (examining relevant research); Methodology (detailing data collection and analysis techniques); and Results and Discussions (presenting and interpreting the findings).

What are the key findings of the research?

The research presents results from image classification, analysis of land cover change trends, identification of urban growth typologies using spatial metrics, and results from the logistic regression model. The model's performance is evaluated, and future urban growth is simulated. The findings are compared with results from other studies.

What are the key driving forces of urban growth identified in this study?

The study identifies and quantifies various driving forces behind GKMA's urban growth through the logistic regression model. Specific factors are analyzed and their relative contributions to urban expansion are assessed. Public investment's influence on future growth is also investigated.

What type of spatial modeling is employed?

The study utilizes logistic regression modeling, a spatial-statistical technique, to predict future urban growth based on identified driving forces. The model's accuracy is evaluated and validated.

How is the accuracy of the model assessed?

The study employs appropriate model evaluation and validation techniques to assess the accuracy and reliability of the logistic regression model used to predict future urban growth. Specific metrics and methods are used for this purpose.

What are the implications of this research?

This research provides valuable insights into the dynamics of urban growth in GKMA, contributing to a better understanding of urban expansion in developing countries. The findings can inform urban planning strategies and policies for more sustainable and effective urban management.

What are the keywords associated with this research?

Urban growth, Greater Kumasi, Ghana, spatial-statistical modelling, logistic regression, remote sensing, GIS, spatial metrics, land cover change, urbanization dynamics, driving forces, model prediction, urban planning.