Land use scenarios and projections simulation using an integrated GIS cellular automata algorithms

Abstract

Over the years, urban growth models have proven to be effective in describing and estimating urban development and have consequently proven to be valuable for informed urban planning decision. Therefore, this paper investigates the implementation of an urban growth Cellular automata (CA) model using a GIS platform as a support tool for city planners, economists, urban ecologists and resource managers to help them establish decision making strategies and planning towards urban sustainable development. The area used as a test case is the River Shannon Basin in Ireland. This paper investigates the spatio-temporally varying effects of urbanization using a combined method of CA and GIS rasterization. The results generated from Cellular automata model indicated that the historical urban growth patterns in the River Shannon Basin area, in considerable part, be affected by distance to district centres, distance to roads, slope, neighbourhood effect, population density, and environmental factors with relatively high levels of explanation of the spatial variability. The optimal factors and the relative importance of the driving factors varied over time, thus, providing a valuable insight into the urban growth process. The developed model for Shannon catchment has been calibrated, validated, and used for predicting the future land use scenarios for the future time intervals 2020, 2050 and 2080. By involving natural and socioeconomic variables, the developed Cellular automata (CA) model had proved to be able to reproduce the historical urban growth process and assess the consequence of future urban growth. This paper presented as a novel application to the integrated CA-GIS model using a complicated land use dynamic system for Shannon catchment. The major conclusion from this paper was that land use simulation and projection without GIS rasterization formats cannot perform a multi-class, multi factors analysis which makes high accuracy simulation is impossible.

Modeling the Impact of Urbanization on Land-Use Change in Bahir Dar City, Ethiopia: An Integrated Cellular Automata–Markov Chain Approach

“…CA is a simulation technique for modeling the dynamics of various complex phenomena using discrete models; it uses a countable outcome of a specific feature at a particular time [43,45,46]. This is achieved by representing spatial complexities in a grid/lattice of cells that have a specific land use [15,47]. To this end, CA models have four components: the cell, the state, the transition rule, and the neighborhoods [41].…”

“…This is, in turn, often considered to be adversely affecting the ecosystem and posing major socio-economic challenges for a broad range of actors [1,8,9]. In particular, in many African cities, the absence and/or the implementation problem of spatial planning policies [10,11] worsen the uncontrolled land-use changes, governance complexities, and widening socio-economic gaps across different scales [12][13][14].Unsurprisingly, then, these LUCs have become a major point of attention in scientific research [15,16]. One salient area of research has been modeling the nature and the geographies of the massive conversion of "other" land-use types into built-up areas because of rapid urban expansion [17,18].…”

“…Unsurprisingly, then, these LUCs have become a major point of attention in scientific research [15,16]. One salient area of research has been modeling the nature and the geographies of the massive conversion of "other" land-use types into built-up areas because of rapid urban expansion [17,18].…”

Past, Present and Future: Combining habitat suitability and future landcover simulation for long-term conservation management of Indian rhino

“…As MOLUSCE only work with raster data, all vector data sets were converted into raster, resampled at 30 × 30 m cell and were projected at WGS_1984_UTM_ZONE_ (45 N). For projecting the simulated results, the cellular-automata simulation was used, based on the Monte Carlo algorithm 69,[71][72][73][74] . The simulated map for the year 2028 was based on classified images of 2008 and 2018.…”

Proyeksi Spasial Under Bounded City Di Kota Jambi Berbasis Smce Ca (Spatial Multy Criteria Evaluation Cellular Automata)

“…Laju perluasan fisik kota yang terus meningkat dapat mengancam keberlanjutan kegiatan pertanian akibat adanya konversi penggunaan lahan. Perubahan kondisi daerah akan terus berlanjut dan yang terpenting adalah keberlanjutan perubahan tersebut harus diperhitungkan sejauh mungkin (Ward, Murray, & Phinn, 2003) karena pertumbuhan yang tidak direncanakan merupakan salah satu faktor yang bertanggungjawab akan timbulnya berbagai permasalahan yang mengarah pada pembangunan yang tidak berkelanjutan (Jat, Choudhary, & Saxena, 2017).Model ekspansi fisik kota yang dinamis mampu menggambarkan proses urbanisasi, memproyeksikan dinamika spasio temporalnya, dan memberikan informasi yang berguna mengenai implikasi urbanisasi yang akan terjadi (Wu, Liu, Wang, & Wang, 2010), serta terbukti bermanfaat untuk pengambilan keputusan perencanaan kota (Fuglsang, Münier, & Hansen, 2013;Gharbia et. al., 2016); Leao et.…”