A Review of Simulation Urban Growth Model

Nugroho, Feri; Al-Sanjary, Omar Ismael; ., .

doi:10.14419/ijet.v7i4.11.20681

Cited by 14 publications

(5 citation statements)

References 41 publications

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“…Population growth is increasing every year, thus encouraging an increase in land demand (Nugroho & Al-sanjary, 2018). With the increase in land demand, the rate of land change is higher.…”

Section: Figure 1 Bekasi Regencymentioning

confidence: 99%

Monitoring the Impact of Land Cover Change on Urban Heat Island with Remote Sensing & GIS

Nugroho,

Sugara,

Priana

et al. 2023

Geos. Ind.

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The increasing need for land has resulted in a higher rate of land conversion and urbanization, leading to a rise in urban density and the occurrence of an Urban Heat Island (UHI) effect. The application of remote sensing and GIS can serve as a substitute for data collection in monitoring the UHI phenomena. This work utilizes Landsat 8 OLI satellite image data, namely band 10, to analyze Land Surface Temperature (LST). Bands 5 and 4 are employed to assess the distribution of Normalized Difference Vegetation Index (NDVI) in Bekasi Regency during the years 2014 and 2020. The relationship between NDVI and LST is highly correlated as they can effectively forecast the influence of areas with sparse vegetation on temperature. The guided classification approach, employing the maximum likelihood algorithm and kappa validation, is utilized to evaluate alterations in land use. The kappa accuracy test yielded a score of 0.90% for 2014 and 0.99% for 2020. The research conducted between 2014 and 2020 revealed changes in land distribution. Specifically, the built-up land area increased by 99.92 Km2, empty land expanded by 280.82 Km2, bodies of water covered an additional 46.13 Km2, and vegetation expanded by 293.91 Km^2. According to the UHI research, it is evident that there has been a rise in surface temperature in Bekasi Regency from 2014 to 2020. In 2014, the minimum temperature reached 30 °C, and the maximum temperature reached 51 °C. In 2020, the minimum temperature was recorded at 34 °C, while the maximum temperature reached 52 °C.

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“…Population growth is increasing every year, thus encouraging an increase in land demand (Nugroho & Al-sanjary, 2018). With the increase in land demand, the rate of land change is higher.…”

Section: Figure 1 Bekasi Regencymentioning

confidence: 99%

Monitoring the Impact of Land Cover Change on Urban Heat Island with Remote Sensing & GIS

Nugroho,

Sugara,

Priana

et al. 2023

Geos. Ind.

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“…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] also suggest strong links between urban landscapes and lifestyle of citizens; specifically, health and well-being (e.g., incidents of obesity [5], healthy environment for the Sustainability 2020, 12 elderly [6]) are largely affected by urban cover changes. Thus, predicting future urban growth patterns is a necessary step for sustainable land-use management.…”

Section: Introductionmentioning

confidence: 98%

“…The SLEUTH model has the advantage of simplicity and ease of data preparation as inputs are geospatial images. With this feature, SLEUTH has the potential for use in global scale urban growth studies [11][12][13]. Meanwhile, the model is coded in the C language and the protocol code could be downloaded for free from the website, which enables users to extend the model flexibly by adding or rewriting code for achieving specific prediction [8,14].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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“…Urban growth is a problem in developing countries where every city has its form and complexity [39] and has the most damaging effects on changing landscapes [40]. Understanding the complex nature of urban dynamics, especially in cities in developing countries, is very important from the point of view of smart city projects because developing a prosperous smart city relies on proper planning and analysis of urban growth [41].…”

Section: Introductionmentioning

confidence: 99%

Urban Sprawl Simulation Mapping of Urmia (Iran) by Comparison of Cellular Automata–Markov Chain and Artificial Neural Network (ANN) Modeling Approach

Asadi¹,

Oshnooei-Nooshabadi

Saleh

et al. 2022

Sustainability

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Considering urbanization can lead to irreversible land transformations, it is crucial to provide city managers, environmental resources managers, and even people with accurate predicting land use/land cover (LULC) to accomplish sustainable development goals. Although many methods have been used to predict land use/land cover (LULC), few studies have compared them. Therefore, by analyzing the results of various prediction models and, consequently, recognizing the most accurate and reliable ones, we can assist city managers, environmental resources managers, and researchers.. In this regard, this research compares Cellular Automata–Markov Chain and Artificial Neural Network (ANN) as frequently used models to overcome this gap and help those concerned about sustainable development to predict urban sprawl with the most reliable accuracy. In the first step, Landsat satellite images acquired in 2000, 2010, and 2020 were classified with Maximum Likelihood Classification (MLC), and LULC maps were prepared for each year. In the second step, to investigate the LULC prediction, validation of the CA–Markov and ANN methods was performed. In this way, the LULC simulation map of 2020 was prepared based on the LULC map of 2000 and 2010; next, the predicted LULC map of 2020 and the actual LULC map for 2020 were compared using correctness, completeness, and quality indices. Finally, the LULC map for 2030 was generated using both algorithms, and the corresponding change map was extracted, showing a reduction in soil and vegetation areas (respectively, 39% and 12%) and an expansion (58%) in built-up regions. Moreover, the validation test of the methods showed that the two algorithms were closer to each other; however, ANN had the highest completeness (96.21%) and quality (93.8%), while CA–Markov had the most correctness (96.47%). This study showed that the CA–Markov algorithm is more accurate in predicting the future of larger areas with higher allocations (urban and vegetation cover) while the ANN algorithm is more accurate in predicting the future of small areas with fewer allocations (soil and rock).

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A Review of Simulation Urban Growth Model

Cited by 14 publications

References 41 publications

Monitoring the Impact of Land Cover Change on Urban Heat Island with Remote Sensing & GIS

Monitoring the Impact of Land Cover Change on Urban Heat Island with Remote Sensing & GIS

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

Urban Sprawl Simulation Mapping of Urmia (Iran) by Comparison of Cellular Automata–Markov Chain and Artificial Neural Network (ANN) Modeling Approach

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