Determining the capacity infill development in growing metropolitans: A case study of Urmia city

Abedini, Asghar; Khalili, Amin

doi:10.1016/j.jum.2019.04.001

Cited by 22 publications

(9 citation statements)

References 9 publications

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“…The climate of this region is cold and semiarid, with an average of 360 mm annual precipitation and 11 • C annual temperature [81], making it one of the coldest cities in Iran [82]. Urmia is one of Iran's most important historical and growing metropolises and has grown considerably in recent years [83]. According to the 2016 census, it had a population of about 750,000 [84,85], which according to the 2018 census, has increased to 800,000 [86].…”

Section: Study Areamentioning

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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Section: Study Areamentioning

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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show abstract

“…The climate of this region is cold, semi-arid with an average of 360 mm annual pre-cipitation and 11 ° C annual temperature (Chitsazan et al, 2019), making it one of the coldest cities in Iran (Abedini et al, 2020). Urmia is one of the most important historical and growing metropolises of Iran that has grown a lot in recent years (Abedini & Khalili, 2019). According to the 2016 census, it had about 750,000 population (Gharakhanlou & Hooshangi, 2020), which according to the 2018 census has increased to 800,000 (Mo-hammadi et al, 2020).…”

Section: Study Areamentioning

confidence: 99%

Simulation of Urban Sprawl by Comparison Cellular Automata-Markov and ANN

Asadi¹,

Oshnooei-Nooshabadi²,

Saleh³

et al. 2022

Preprint

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A correctly obtained Land-use/land-cover (LULC) prediction map is essential to under-standing and assessing future patterns. In the study, the LULC map of Urmia/Iran in 2030 was produced using two different prediction methods CA-Markov and Artificial Neural Network (ANN). In general, the study followed a methodology consisting of three steps. In the first steps, Landsat satellite images acquired in 2000, 2010 and 2020 were classified with maximum likelihood algorithm and LULC maps were prepared for each year. In the second stage, to investigate the LULC prediction methods' validation (CA-Markov and ANN) the LULC prediction map of 2020 was produced using the LULC map of 2000 and 2010; In this step, the predicted LULC map of 2020 and the actual LULC map of 2020 were evaluated by correctness, completeness and quality indexes. Finally, The LULC map for 2030 was prepared using all two algorithms and the change map was extracted. The results show that the area of soil and vegetation decreased, and built-up regions increased during the research period. The methods validation results show that the two algorithms are much closer to each other. Nevertheless, in general, ANN has the highest completeness (96.21%) and quality (93.8%) and CA-Markov the most correctness (96.47). This study shows that the CA-Markov algorithm is most successful in predicting the future that had larger areas and a higher percentage in the region (urban and vegetation cover) and the ANN algorithm in predicting phenomena that had smaller levels with fewer percentages (soil and rock).

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“…Infill development in many metropolises has a significant share in the areas where urbanisation is rapidly taking place [29]. Following this, due to the land scarcity in Kuala Lumpur, flexible land use activity and optimisation of infill development have been given attention in the KLSP2040 (KLSP2040, chapter [8][9][10][11][12].…”

Section: Adopting Infill Developmentmentioning

confidence: 99%

Retrofitting Makerspace: Unifying Outdoor-Indoor activities for Civic Engagement at Sentul Park, Kuala Lumpur

Ahmad

Rahman

Anuar

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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A contemporary approach of makerspace that combines art, technology, learning and collaboration has been widely integrated into the 21st-century civic infrastructures. However, it has resembled chiefly the maker movement of indoor spaces compared to outdoor or hybrid indoor-outdoor spaces. Hence, this paper intends to explore the potential of retrofit makerspaces that merges the usage of the outdoors-indoor spaces for civic engagement. The objectives of this paper are threefold: (1) To examine a learning-design partnership in hybrid spaces through community’s engagement; (2) To explore a wide range of creative civic spaces that would foster business start-ups, job opportunities, products, services, and income generation; and (3) To develop a working, learning, and recreational space design concept that is more tactile and outdoor environment-oriented. The primary data was gathered through site observation and participation in the workshops and events at Sentul Park. Limited access to the perimeter of the private areas of Sentul Park became a setback in this study. Thus, the data collected for the inaccessible areas was derived mainly from reports published by the local authority, the site’s developer, and previously published researchers. The three types of retrofitted makerspaces created for education, community, and workplace, which unify the outdoor and indoor spaces, could become a trendsetter and potential platform for future civic engagement in Malaysia. More significant efforts and attention are needed to understand a community’s needs and preferences in the city’s ever-evolving physical and cultural aspect to make makerspaces a new form of civic infrastructure.

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Determining the capacity infill development in growing metropolitans: A case study of Urmia city

Cited by 22 publications

References 9 publications

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

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

Simulation of Urban Sprawl by Comparison Cellular Automata-Markov and ANN

Retrofitting Makerspace: Unifying Outdoor-Indoor activities for Civic Engagement at Sentul Park, Kuala Lumpur

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