Reconstruction of residential land cover and spatial analysis of population in bursa region (Turkey) in the mid-nineteenth century

USTAOĞLU E., Kabadayı M. E.

Land, cilt.10, sa.10, 2021 (SSCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 10 Sayı: 10
  • Basım Tarihi: 2021
  • Doi Numarası: 10.3390/land10101077
  • Dergi Adı: Land
  • Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), Scopus, CAB Abstracts, Veterinary Science Database, Directory of Open Access Journals
  • Anahtar Kelimeler: historic reconstruction, residential land cover, census data, socio-economic and physical factors, accessibility, natural amenities, population mapping, Bursa region, Turkey
  • Abdullah Gül Üniversitesi Adresli: Evet

Özet

The historic reconstruction of residential land cover is of significance to uncover the hu-man-environment relationship and its changing dynamics. Taking into account the historical census data and cadastral maps of seven villages, this study generated residential land cover maps for the Bursa Region in the 1850s using a model based on natural constraints, land zoning, socio-economic factors and residential suitability. Two different historical reconstructions were generated; one based on a high density residential model and another based on a low density model. The simulated landcover information was used as an ancillary data to redistribute aggregated census counts to fine scale raster cells. Two different statistical models were developed; one based on probability maps and the other applying regression models including Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models. The regression models were validated with historical census data of the 1840s. From regression models, socio-economic and physical characteristics, accessibility and natural amenities showed significant impacts on the distribution of population. Model validation analysis revealed that GWR is more accurate than OLS models. The generated residential land cover and gridded population datasets can provide a basis for the historical study of population and land use.