Application of Geographic Information Systems to Assessing the Dynamics of Slum and Land Use Changes in Urban Core of Akure, Nigeria

Thesis Overview

<p> </p><div><b>1.0 Introduction</b> </div><div><b>1.1 Background study</b></div><div><br></div><div>The decay in the built environment in many developing countries, Nigeria inclusive, is widespread. The factors responsible can be traced to rapid urbanization, rural-urban migration, steady economic downturn, decay of urban infrastructure, poor quality of original construction, lack of integrated planning, negligent urban housekeeping, inadequate methods of preservation of historic value, disaster and war (Omole, 2000; World Bank, 2005; Omole &amp; Owoeye, 2006; Ahiamba, Dimuna &amp; Okogun, 2008). This decay manifests in different forms, including squalor, squalid, blight, slum etc. The urban core of Akure exhibits such deplorable conditions where substandard houses are prevalent in unkempt environment (Owoeye, 2006). Among factors that contribute to the continued formation and expansion of slums, include rapid rural-to-urban migration, policy failure, increasing urban poverty and inequality, population growth and globalization. While more people are migrating from rural areas to towns and cities, urban areas are not expanding enough, inadequate affordable houses, and municipalities are not able to provide enough accommodation. Other factors are failed government policies, corruption, inappropriate regulation, dysfunctional land markets, and unresponsive financial systems to provide low-income people with essential public infrastructure and services (UN habitat, 2003).</div><div> <div>Kengne (2000) argued that there is a close correlation between the informal economy and informal settlement. Another important factor that helps to explain the proliferation of slums is the rigidity of urban planning regulations associated with other factors such as poor governance, corruption, and nepotism, which all lead to a severe shortage of land and urban housing, squatting, and infringements of building regulations (Fekade, 2000). A slum according to UN-HABITAT (2007) is an area that combines, to various extents, the following characteristics: inadequate access to safe water, inadequate access to sanitation and other infrastructure, poor structural quality of housing, overcrowding and insecure residential status. These characteristics are being proposed because they are largely quantifiable and can be used to measure progress toward the Millennium Development Goal to significantly improve the lives of at least 100 million slum dwellers by 2020 (UN-HABITAT, 2007).</div><div><br></div><div>Slums manifest in different ways and vary from country to country. Two major ones have been identified. </div><div>These are slums of hope or progressing settlements and slums of despair or declining neighborhoods. The first is made of ‘old’ city centre slums and ‘new’ slum estates whilst the latter is made of squatter settlements and semi-legal sub-divisions (UN- Habitat, 2003). These two major ones are sub divided into four categories of slums. These are inner city slums; slum estates, squatter settlements and illegal sub-divisions which differ in terms of their formation, condition and extent of deprivation.</div><div>Recently, Abebe (2011) described informal settlements into three phases namely infancy, consolidation and saturation based on the availability of open space in the neighbourhood. According to Abebe (2011), infancy is the starting stage at which 50 percent of the settlement area would be built-up; consolidation stage refers to booming stage at which up to 80 percent of the land would be used for housing construction; and saturation stage is the stage whereby further construction is mainly continued through vertical densification. Urban land uses and their areal distributions are fundamental data required for a wide range of studies in the physical and social sciences, as well as by municipalities for land planning purposes (Stefanov, 2001). To this end, geographic information systems (GIS) and remote sensing data and techniques provide efficient methods for analysis of land use issues and tools for land use planning and modelling. Understanding the driving forces of land use development in the past, managing the current situation with modern GIS tools, and modelling the future will help to develop plans for multiple uses of natural resources and nature conservation. Fortifying data from GIS techniques and applications with the regular field survey/research approaches can lead to greater accuracy and efficiency in solving myriads of social and environmental concerns. These include delineating land use changes, slum development, identifying causative factor for slum development, observing the trend of land</div><div>use changes (to forecast future trends) and also in identifying solutions to the attendant problems of slum and urban decay generally.</div><div>Remote Sensing and GIS has been applied severally for change detection of informal settlements and exploiting spatial patterns (Hurskainen &amp; Pellikka, 2004; Stasolla &amp; Gamba, 2007; Abbott, 2003; Sartori, Nembrini &amp; Stauffer, 2002). The Object-based Image Classification (OBIA) approach has been employed for detecting and mapping slum settlements through the integration of semantic information (Benz et al., 2004; Hofmann, 2001; Nobrega, Quintanilha &amp; Ohara, 2006).</div><div>In many countries, local authorities have limited understanding of the slum location, extent and their dynamics. Given the expected increase in the number of slum dwellers, there is also a growing need for efficient methods to effectively identify and monitor slums and informal developments (Sliuzas, Mboup &amp; de Sherbinin., 2008). Reliable spatial information about informal settlements is vital for any action of improvement of the living conditions (Hofrnann et al., 2008). Over the years, several approaches have been used to detect slums. These include the participatory approach as used by Karanja (2010), the livelihood approach, and the census data approach as used by Weeks, Hill, Stow, Getis and Fugate (2007). These current practices in spatial analysis related to slums are based on simple aggregations of slum household data according to Enumeration Areas (EAs) in which the households reside. Any EA in which more than 50% of the population is deprived in terms of one of the four operational slum indicators of the UN Habitat is considered a slum (Sliuzas et al., 2008). This approach of spatially defining slums has been adopted out of pragmatic considerations largely relating to available data. This often results in several problems since variables or characteristics specific to the settlement level such as condition of the roads, drainage, air pollution, and location are not considered (Sliuzas et al., 2008). Traditional methods like statistical, regional analyses and fieldwork are limited to capture the urban process (Niebergall, Leow &amp; Mauser, 2007).</div> <br></div> <br><p></p>