The identification and zoning of areas having rural deteriorated textures in the Tehran province by using KDE and GIS

Abstract

Rural deteriorated textures (RDTs) are vulnerable against natural hazards (particularly earthquakes); thus, they need planning and coordinated intervention so that they can be organized. In this regard, identification and prioritization of rural deteriorated areas is one of the fundamental and basic actions. Hence, the present study seeks to identify and zone the areas having RDTs (with an emphasis on residential buildings) in Tehran province through the application of the kernel density estimation (KDE) and geographical information system (GIS). Based on this, the present study is applied and is investigated by library, documentary, and field studies. In this regard, using statistical data available for 655 villages and 14 counties in the region, as well as using a questionnaire, ideas of 16 elites and scientific experts were analyzed in order to discuss the criteria. The results obtained from a kernel density surface of deteriorating locations showed that the highest concentration of rural deterioration areas are located mainly on the Tehran metropolitan fringe. Also, the results showed that the overall accuracy for classification was 79.5% and Kappa statistics was 0.728. Hence, KDE is an appropriate method in the identification and zoning of areas with RDTs.

Keywords:

• deteriorated textures
• kernel density estimation
• kappa
• rural
• Tehran

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 Index measures age of buildings over 30 years: Residential units with more than 30 years of age to the entire residential block (or ratio of number of the buildings age with more than 30 years to total number of housing units) (Zebardast et al. 2013).

2 Or less than 100 m² (Zebardast et al. 2013).

3 Statistical data research criterions in the rural points of Tehran are related to the 2011 census; except for the criterion of measure of the buildings area (tendency to buildings with little area) (m²), where the census of 2006 (due to lack of access to information in 2011) is used.

4 The KDE model classified into five classes (very high, high, medium, low, and very low) (Figure 9).

5 It is reminded that the lack of statistical information on the blocks access to pathways with less than 6 meters has led to elimination of this criterion.