Modeling Urban Growth and Land Use Transition Using SLEUTH Model: The Case of Dilla City, Ethiopia

Urban growth / urban sprawl are the extension of a residential region into the surrounding area. The negative face of urban development is urban sprawl, criticizing the cause of environmental deterioration, growing inequality and diminishing the viability of aesthetic and urban areas. An effective and efficient planning of urban development and changes in land use and its effects on the environment needs, among other important details, details on development trends and patterns. Over the years, several models of urban growth have been developed and used to predict trends of growth. SLEUTH models are used to simulate and predict urban growth and land use transition for 2020-2050 in the City of Dilla (Ethiopia) in the analysis of Geographic Information System (GIS). The word SLEUTH was derived from the model's input image specifications: slope, land cover, exclusion, urban, transport, and Hill shade. Input data preparation used a cumulative time series dataset of 30 years, i.e. 1989, 1999, 2009 and 2019, such as historical topographical maps and satellite imagery. The SLEUTH model uses the parameters of the best fit growth rule by narrowing coefficients in the calibration mode and passing them down to forecast potential urban growth trends, creating different probability maps and LULC maps. The models generated future urban growth pattern predicted in the 31 years' from 2019, there will be nearly 41.14% urban rise in 2020, 52.95% in 2030, 59.91% in 2040 and 64.30% in 2050. In general, the extension of the urban growth trend introduces new spreading centers that are indicative of urban growth.

USING THE TOTAL EXPLORATORY FACTOR ANALYSIS (T-EFA) AS A CALIBRATION TECHNIQUE FOR SLEUTH MODEL

Developments in information technologies (IT) allow to modelling dynamic and complex form of cities and several studies have been implemented since 1990s. The cellular automata based urban growth simulation model, SLEUTH is the most well-known one among the simulation models. Calibration is the most important stage of the model created in three stages such as test, calibration, prediction. The more precise the calibration is completed, the more accurate the model generates. Several methods have been developed for the calibration step in which growth coefficients values are calculated by metrics. The study aims to investigate success of the Total Exploratory Factor Analysis (T-EFA) technique, which provides using the 13 metrics all together, in rapid grown settlement areas using high resolution data. In this context, the Sancaktepe district of Istanbul was selected as the study area and a simulation model was generated for the year 2050. The obtained results are promising to apply the T-EFA method in different studies.

Improvement of an Urban Growth Model for Railway-Induced Urban Expansion

Increasing population in urban areas drives urban cover expansion and spatial growth. Developing urban growth models enables better understanding and planning of sustainable urban areas. The SLEUTH model is an urban growth simulation model which uses the concept of cellular automata to predict land cover change using six spatial inputs of historical data (slope, land use, exclusion, urban, transportation, and hill-shade). This study investigates the potential of SLEUTH to capture railway-induced urban growth by testing methods that can consider railways as input to the model, namely (1) combining the exclusion layer with a station map; (2) creating a new input layer representing stations in addition to the default six inputs. Districts in Tsukuba, Japan and Gurugram, India which historically showed evidence of urban growth by railway construction are investigated. Results reveal that both proposed methods can capture railway impact on urban growth, while the former algorithm under the right settings may perform better than the latter at finer resolutions. Coarser resolution representation (300-m grid-spacing) eventually reduces the differences in accuracy among the default SLEUTH model and the proposed algorithms.

A BOTTOM-UP QUANTITATIVE MECHANISM FOR UPDATING THE SELF-MODIFICATION PARAMETERS IN THE SLEUTH MODEL

Generally, the default Self-Modification Parameters (SMPs) values, rather than the proper SMPs parameters group, has been applied in the SLEUTH model. However, various pre-setting of SMPs will simulate different morphology and structure of urban sprawl. The study is intended to propose a practical tool for the quantification response to model input variables on modelling complex urban systems. In this research, the parameter weight sorting job has been carried out to provide an adjusting priority experience. Besides, the model output imagery indices were used to describe the morphology, distribution of the urban prediction sprawl and the correlationship with urban road network effectively. Finally, the adjusting factors have been calculated with AHP method. The work helps geographers to determine how to make further use of the inner forward transmission mechanism on the SLEUTH model for further improving it in performance.