Abstract
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.
Highlights
Urbanization, which is oftentimes quantified by a population shift from rural to urban areas, is an unavoidable global phenomenon
We mainly focused on the further improvement of SLEUTH to consider the effect of railway stations on urban growth by evaluating the two following methods: (1) Exploring suitable pixel values influencing the probability of urban growth (i.e., Extended SLEUTH); (2) Adding a new input layer to represent stations with expected urban growth (i.e., SLEUTsH)
The main objective of this study is to evaluate the methods of introducing railway-induced urban growth, it is informative to discuss the urban cover projections for both the target areas, Tsukuba City and Gurugram City
Summary
Urbanization, which is oftentimes quantified by a population shift from rural to urban areas, is an unavoidable global phenomenon. Drastic urbanization driven by rapid economic growth in developing countries has posed huge challenges. Countries in which the pace of urbanization is considered to be rapid will face many land-use difficulties to meet the needs of their growing urban populations. Urban planners and policymakers who are concerned about the long-term condition of the society need to spatially predict land-use changes under various policy scenarios. Land-use information is a vital input for various numerical models (e.g., climate and hydrologic models, socio-economic models, food-supply chain). This means that land use is directly or indirectly linked with other crucial factors affecting society such as weather, food supply, and economy. Other studies [5,6] suggest strong links between urban landscapes and lifestyle of citizens; health and well-being
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