Simulating Urban Growth Using Cellular Automata Approach (SLEUTH)-A Case Study of Praia City, Cabo Verde

Tania Mendes Goncalves,Xie Zhong,Brefo Yaw Dwamena,Yao Yevenyo Ziggah

doi:10.1109/access.2019.2949689

Abstract

In recent years, urban growth has become the center of attention and preoccupation for many researchers especially those concerned with urban planning. Some scholars only take into consideration the social dynamism and demographic data of an area of interest while analyzing the urban growth. However, those approaches fail to consider other physical conditions such as the urban extent and exclusion zones (e.g. rivers and forest reserves). This makes it difficult to correctly model the urban expansion as well as simulate for future forecast, which is vital for policymakers and city planners when making informed decisions. This study, therefore, used the Geographic Information System (GIS) to compile the data obtained from a time series of satellite images between 1984 and 2018 for the urban growth model calibration. A modified cellular automata methodology called SLEUTH was then applied to supervise and forecast the urban sprawl in Praia city, the capital of Cabo Verde from the year 1984 to 2050. The forecasted result showed that breed (100) and road (62) coefficients would influence the urban growth in Praia city. This means that there is a likelihood of newly generated detached settlements and many new buildings along the roadside.

Highlights

Urbanization is a complex phenomenon, which can be assessed and monitored through modeling and simulation processes [1], [2], [3]
Praia city? b) Using the Simulating Urban Growth Using Cellular Automata Approach (SLEUTH) model techniques, what may be the future urban growth in Praia city? To this end, the goal of this study is to model urban growth in Praia city using the power of integrated remote sensing data, Geographic Information Systems (GIS) tool and SLEUTH model to monitor and forecast the urban growth
Our study illustrated that the metrics with high summary correlation were ‘‘population’’ and urbanization (% urban), both having a value of 0.99, which is similar to results from Rafiee et al [61], Feng et al [62], Akin et al [63] and Sandamali et al [64]. This means that the SLEUTH model predictions based on the initial seed year of the present urban pattern using those refined values are very similar to what happened in reality

Summary

Introduction

Urbanization is a complex phenomenon, which can be assessed and monitored through modeling and simulation processes [1], [2], [3]. Sector-based economic methods, spatially disaggregate economic methods, agent-based methods and hybrid methods [3], [5] have been developed in recent years. These new developments have accelerated a more sophisticated approach to simulating the future processes of the Earth’s surface. This is because monitoring and modeling of urban growth and land transitions have become a trend that satisfies the need for policymakers and planners to achieve precise and accurate LULC information [5]

Objectives

Methods

Results

Conclusion