Literature DB >> 26183153

Urban change analysis and future growth of Istanbul.

Anıl Akın1, Filiz Sunar2, Süha Berberoğlu3.   

Abstract

This study is aimed at analyzing urban change within Istanbul and assessing the city's future growth potential using appropriate approach modeling for the year 2040. Urban growth is a major driving force of land-use change, and spatial and temporal components of urbanization can be identified through accurate spatial modeling. In this context, widely used urban modeling approaches, such as the Markov chain and logistic regression based on cellular automata (CA), were used to simulate urban growth within Istanbul. The distance from each pixel to the urban and road classes, elevation, and slope, together with municipality and land use maps (as an excluded layer), were identified as factors. Calibration data were obtained from remotely sensed data recorded in 1972, 1986, and 2013. Validation was performed by overlaying the simulated and actual 2013 urban maps, and a kappa index of agreement was derived. The results indicate that urban expansion will influence mainly forest areas during the time period of 2013-2040. The urban expansion was predicted as 429 and 327 km(2) with the Markov chain and logistic regression models, respectively.

Keywords:  Istanbul; Logistic regression; Markov chain; Urban growth

Mesh:

Year:  2015        PMID: 26183153     DOI: 10.1007/s10661-015-4721-1

Source DB:  PubMed          Journal:  Environ Monit Assess        ISSN: 0167-6369            Impact factor:   2.513


  3 in total

1.  Modeling the spatial dynamics of regional land use: the CLUE-S model.

Authors:  Peter H Verburg; Welmoed Soepboer; A Veldkamp; Ramil Limpiada; Victoria Espaldon; Sharifah S A Mastura
Journal:  Environ Manage       Date:  2002-09       Impact factor: 3.266

2.  Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore.

Authors:  K C Clarke; L J Gaydos
Journal:  Int J Geogr Inf Sci       Date:  1998 Oct-Nov       Impact factor: 4.186

Review 3.  Measuring the accuracy of diagnostic systems.

Authors:  J A Swets
Journal:  Science       Date:  1988-06-03       Impact factor: 47.728

  3 in total
  4 in total

1.  Scenario prediction of emerging coastal city using CA modeling under different environmental conditions: a case study of Lingang New City, China.

Authors:  Yongjiu Feng; Yan Liu
Journal:  Environ Monit Assess       Date:  2016-08-31       Impact factor: 2.513

2.  Monitoring and assessment of urban growth patterns using spatio-temporal built-up area analysis.

Authors:  Maher Milad Aburas; Yuek Ming Ho; Mohammad Firuz Ramli; Zulfa Hanan Ash'aari
Journal:  Environ Monit Assess       Date:  2018-02-20       Impact factor: 2.513

3.  A Review of Driving Factors, Scenarios, and Topics in Urban Land Change Models.

Authors:  Youjung Kim; Galen Newman; Burak Güneralp
Journal:  Land (Basel)       Date:  2020-07-27

4.  Exploring an Ecologically Sustainable Scheme for Landscape Restoration of Abandoned Mine Land: Scenario-Based Simulation Integrated Linear Programming and CLUE-S Model.

Authors:  Liping Zhang; Shiwen Zhang; Yajie Huang; Meng Cao; Yuanfang Huang; Hongyan Zhang
Journal:  Int J Environ Res Public Health       Date:  2016-03-24       Impact factor: 3.390

  4 in total

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