Digital mapping and predicting the urban growth: integrating scenarios into cellular automata-Markov chain modeling
Yazarlar (3)
Doç. Dr. Öznur IŞINKARALAR Kastamonu Üniversitesi, Türkiye
Cigdem Varol
Gazi Üniversitesi, Türkiye
Gazi Üniversitesi, Türkiye
Dilara Yilmaz
Kastamonu Üniversitesi, Türkiye
Kastamonu Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(ESCI dergilerinde yayınlanan tam makale)
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| Dergi Adı | Applied Geomatics | ||
| Dergi ISSN | 1866-9298 Dergi Bilgileri (2022) | ||
| Dergi Tarandığı Indeksler | Emerging Source Citation Index | ||
| Makale Dili | İngilizce | Basım Tarihi | 12-2022 |
| Kabul Tarihi | 17-08-2022 | Yayınlanma Tarihi | 02-09-2022 |
| Cilt / Sayı / Sayfa | 14 / 4 / 695–705 | DOI | 10.1007/s12518-022-00464-w |
| Makale Linki | https://link.springer.com/article/10.1007/s12518-022-00464-w | ||
| UAK Araştırma Alanları |
Planlamada Sürdürülebilirlik, Dayanıklılık, Ekoloji ve Enerji
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| Özet |
| Predictive modeling and land use/land cover change studies in complex systems are well advanced. Cellular automata (CA)-Markov chain (MC) can be defined as one frequently preferred method for this purpose. This paper aims to adapt the CA-MC model to the simulation of residential areas in the city. The proposed method was tested in the city center of Kastamonu, Türkiye, using four time periods: 1985, 2011, 2015, and 2021. Spatio-temporal change maps were produced using ArcGIS 10.0 software. Land use simulation of the urban center, including residence units for 2031 and 2057, was performed using the integrated CA-MC technique. The method’s suitability was demonstrated with the Kappa index of agreement values (Kstandart: 0.93; Klocation: 0.98; Kno: 0.98; and KlocationStrata: 0.95). Within the scope of the study, two different scenarios were designed as short term (S1) and long term (S2). According … |
| Anahtar Kelimeler |
| Geographic information | Growth modeling | Kappa statistic | Land degradation | LULCC | Spatial analysis |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 24 |
| Scopus | 23 |
| Google Scholar | 34 |