scholarly journals Monitoring of Urban Growth with Improved Model Accuracy by Statistical Methods

2019 ◽  
Vol 11 (20) ◽  
pp. 5579 ◽  
Author(s):  
Ayazli
Keyword(s):  
Urban Growth ◽  
Growth Models ◽  
Simulation Models ◽  
Growth Simulation ◽  
Urban Growth Model ◽  
Growth Properties ◽  
Force Calibration ◽  
Improved Model ◽  
Best Fit

While the rural population is decreasing day by day, the urban population is increasing rapidly. Urban growth, which occurs as a result of this increase, is sprawling toward natural and environmental areas in urban fringes, and constitutes the main source of many environmental, physical, social, and economic problems. In order to overcome these problems, the direction and rate of urban growth should be determined with simulation models. In this context, many urban growth models have been developed since the 1990s; the SLEUTH urban growth model is one of the most popular among them and has been used in many projects around the world. The brute force calibration process in which the best fit values of growth coefficients are determined is the most important stage of simulation models. The coefficient ranges are initially defined as being between 0 and 100 and are then narrowed in this step according to 13 separate regression scores, which are used to specify the characterization of urban growth. Consensus has not yet been reached as to which metrics should be used for calculating the best fit values, but the Lee–Sallee and Optimum SLEUTH Metric (OSM) methods have been mostly used in past studies. However, in rapidly growing study areas, these methods cannot truly explain urban growth properties. The main purpose of this paper is to precisely calibrate urban growth simulation models. Therefore, Exploratory Factor Analysis (EFA) was used to calculate the growth coefficients, as a new statistical approach for calibration, in this study. The district of Sancaktepe, Istanbul, which experienced population growth of 80% between 2008 and 2018, was selected as the study area in order to test the achievement of the EFA method, and two urban growth simulation models were generated for the years 2030 and 2050. According to the results, despite the fact that there is little effect of urban growth in the short term, more than 70% of forests and agricultural lands are at risk of urbanization by 2050.

Using spatial heterogeneity to strengthen the neighbourhood effects of urban growth simulation models

2021 ◽  
pp. 1-19
Author(s):  
Shuting Zhai ◽  
Yongjiu Feng ◽  
Xinlei Yan ◽  
Yongliang Wei ◽  
Rong Wang ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Urban Growth ◽  
Simulation Models ◽  
Neighbourhood Effects ◽  
Growth Simulation

Location Choice and Urban Growth in a Rent-Seeking Society

1988 ◽  
Vol 16 (2) ◽  
pp. 158-177 ◽  
Author(s):  
Bruce L. Benson ◽  
M. D. Faminow
Keyword(s):  
Urban Growth ◽  
Specific Growth ◽  
Growth Models ◽  
Empirical Test ◽  
Location Theory ◽  
Rent Seeking ◽  
Growth Patterns ◽  
Growth Theory ◽  
Complete Understanding ◽  
Urban Growth Model

Gordon Tullock suggested that as rent-seeking becomes increasingly important, location choices and urban growth patterns will be affected. Resources should be diverted to cities where government units are most able to grant rents. The implications of this argument are expanded upon using principles of location theory and location-specific growth theory. An empirical test of an urban growth model provides support for Tullock's contentions. By considering rent seeking in the context of location and urban growth models, the implications of the rent-seeking paradigm are extended. Simultaneously, a more complete understanding of relative urban growth rates is gained.

scholarly journals A Review of Simulation Urban Growth Model

2018 ◽  
Vol 7 (4.11) ◽  
pp. 17 ◽  
Author(s):  
Feri Nugroho ◽  
Omar Ismael Al-Sanjary ◽  
. .
Keyword(s):  
Growth Model ◽  
Urban Growth ◽  
Growth Models ◽  
Model Identification ◽  
Urban Modeling ◽  
Rapid Population Growth ◽  
Urban Growth Model ◽  
Urban Growth Modeling ◽  
Growth Planning ◽  
The City

Urban development has become a problem in many cities, especially in developing countries. The availability of areas for development is needed to deal with rapid population growth and urbanization. The purpose of this study was to identify urban growth models. Due to urban growth planning, the city will be more manageable and organized. From the conclusions of urban modeling identification can provide an idea of what model is appropriate for use in urban growth studies. The results of this urban growth model identification could be a reference in urban growth modeling in better urban planning.  

scholarly journals Comparative Evaluation of Open Source Urban Simulation Models Applied to Colombo City and Environs in Sri Lanka

2021 ◽  
pp. 49-60
Author(s):  
P. Jayasinghe ◽  
L.N. Kantakumar ◽  
V. Raghavan ◽  
G. Yonezawa
Keyword(s):  
Open Source ◽  
Urban Growth ◽  
Urban Areas ◽  
Comparative Evaluation ◽  
Growth Models ◽  
Simulation Models ◽  
Good Choice ◽  
Surface Generation ◽  
Suitable Model ◽  
Urban Simulation

Availability of a variety of urban growth models make model selection to be an important factor in urban simulation studies. In this regard, a comparative evaluation of available urban growth models helps to choose a suitable model for the study area. Thus, we selected three open-source simulation models namely FUTURES, SLEUTH and MOLUSCE to compare in their simplest state to provide a guidance for selection of an urban growth model for Colombo. The urban extent maps of 1997, 2005, 2008, 2014 and 2019 derived from Landsat imageries were used in calibration and validation of models. Models were implemented with the minimum required data with default settings. The simulation results indicate that the estimated quantity of urban growth (148.91 km2) during 2008-2019 by FUTURES model is matching closely with observed urban growth (127.37 km2) during 2008-2019. On the other hand, the SLEUTH model showed an overestimation (250.56 km2) and MOLUSCE showed an underestimation (77.11 km2). Further, the spatial accuracy of urban growth simulation of SLEUTH (Figure of Merit = 0.26) is relatively better in comparison to FUTURES (0.20) and MOLUSCE (0.20). Considering the tradeoff between computational overheads and obtained results, FUTURES could be a good choice over SLEUTH and MOLUSCE, when these models implemented in their simplest form with minimum required datasets. As a future work, we propose the incorporation of exclusion factor for potential surface generation to mitigate the overestimation of urban areas in SLUETH.

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

2020 ◽  
Vol 12 (17) ◽  
pp. 6801
Author(s):  
Alvin Christopher G. Varquez ◽  
Sifan Dong ◽  
Shinya Hanaoka ◽  
Manabu Kanda
Keyword(s):  
Urban Growth ◽  
Urban Areas ◽  
Historical Data ◽  
Growth Models ◽  
Urban Expansion ◽  
Grid Spacing ◽  
Urban Growth Model ◽  
Sleuth Model ◽  
The Right ◽  
Better Than

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.

scholarly journals INVESTIGATION OF URBAN GROWTH EFFECTS ON FOREST AREA BY CREATING DIFFERENT SIMULATION SCENARIOS

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 87-92
Author(s):  
I. E. Ayazli ◽  
S. Baslik ◽  
A. E. Yakup ◽  
F. Kilic Gul ◽  
D. Kotay ◽  
...  
Keyword(s):  
Land Cover ◽  
Urban Growth ◽  
Simulation Models ◽  
Urban Life ◽  
Background Information ◽  
Urban Management ◽  
Growth Simulation ◽  
Cadastral Maps ◽  
Land Cover Data

<p><strong>Abstract.</strong> It is necessary to keep urban growth under control according to the understanding of sustainable urban management in rapidly growing cities. In other words, sustainable urban management requires estimating how the land cover will change and in which direction the urbanization will be in the coming years, as well as knowing the current structures of the cities. Therefore, simulation models are frequently used for monitoring urban growth. The results of simulation models help to obtain background information that will be the basis for the formation of a “sustainable urban life” by allowing the determination of the natural areas that can face the threat of urbanization. The cadastral structure is one of the basic variables affecting the growth of a city. Therefore, the purpose of the study is to investigate urban growth by producing cadastral parcel-based simulation models. The land cover data required to create a simulation model were generated from cadastral maps and land registry data, in four different time periods. Within the scope of the study, cellular automata-based urban growth simulation models for the years 2030, 2050 and 2070 were produced, and the land cover changes that occurred in Sancaktepe were investigated.</p>

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