Modeling Determinants of Urban Growth in Conakry, Guinea: A Spatial Logistic Approach

Arafan Traore,Teiji Watanabe

doi:10.3390/urbansci1020012

Abstract

The main objective of the present study was to integrate a logistic regression model (LRM), a geographic information system (GIS) and remote sensing (RS) techniques to analyze and quantify urban growth patterns and investigate the relationship between urban growth and various driving forces. Landsat images from 1986, 2000, and 2016 derived from the TM, ETM+, and OLI sensors respectively were used to simulate an urban growth probability map for Conakry. To better explain the effects of the drivers on the urban growth processes in the study area, variables for two groups of drivers were considered: socioeconomic proximity and physical topography. The results of the LRM using IDRISI Selva indicated that the variables elevation (β7 = 1.76) and distance to major roads (β4 = 0.67) resulted in models with the best fit and the highest regression coefficients. These results indicate a high probability of urban growth in areas with high elevation and near major roads. The validation of the model was conducted using the relative operating characteristic (ROC) method; which result exhibited high accuracy of 0.89 between the simulated urban growth probability map and the actual one. A land use/land cover (LULC) change analysis showed that the urban area had undergone continuous growth over the study period resulting in an extent of 143.5 km2 for the urban area class in 2016.

Highlights

The process of urbanization has been traditionally associated with other important economic and social transformations, resulting in greater geography mobility, lower fertility, longer life expectancy and population ageing [1]
The study area consists of Conakry city, which is located in the maritime region of the Republic of Guinea (Figure 1)
The logistic regression model (LRM) results (Table 8) indicated that the socioeconomic proximity variables and the topography variables were positive and were significantly correlated with the urban growth processes in the study area

Summary

Introduction

The process of urbanization has been traditionally associated with other important economic and social transformations, resulting in greater geography mobility, lower fertility, longer life expectancy and population ageing [1]. City growth and changes in land-use patterns have various important social and environmental impacts, including the loss of natural spaces, increased vehicular congestion, urban heat island effects, landscape fragmentation and homogenization, the loss of highly productive agricultural lands, alterations in natural drainage systems, and reduced water quality [3]. To understand the spatial and temporal dynamics of these processes, the factors that drive urban growth must be identified and examined, especially those factors that can be used to predict future changes and their potential environmental effects [4]

Objectives

Methods

Results

Discussion

Conclusion