Site Suitability Analysis for Road in Mountainous Terrain Region of Papua New Guinea (A Case Study of Salt Noname Karimui District of Simbu Province)

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N. Poi
S. Samanta
T. Sekac

Abstract

Road infrastructure developments in rural Papua New Guinea (PNG) have not picked up pace due to mountainous and difficult geographical landscape. Hence road accessibility in rural Salt Nomane Karimui (SNK) District of Simbu province is emerged as a serious concern particularly with regard to impact of poor accessibility and mobility on agricultural produce and ability to access public amenities. Governing agencies have plans in place for road connectivity in Karimui region and other parts of SNK district but their attempts remain unsuccessful due to difficult geography and lack of technical data. This study utilized Geographical Information System (GIS) and multi-criteria evaluation (MCE) techniques through Analytic Hierarchy Process (AHP) to develop site suitability model to evaluate mountainous terrain and forest road connectivity in SNK district.  The approach looking at in this research is to classify suitability factors into two principle classes’ i.e. Geophysical and Geotechnical influence as principle factors. Relatively important geophysical factors influencing road suitability sites  including altitude, slope, river network, road and rainfall data are integrated to generate alternatively suitability map one. Geotechnical factors influencing road suitability sites including Lithology, Soil Texture and Landform are integrated to generate alternatively suitability map two. The final suitability map is produced by integrating the thematic layers of two principle factors and classified into five suitability classes i.e. less suitable, marginally less suitable, moderately suitable, suitable and highly suitable.

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How to Cite
Poi, N., Samanta, S., & Sekac, T. (2021). Site Suitability Analysis for Road in Mountainous Terrain Region of Papua New Guinea (A Case Study of Salt Noname Karimui District of Simbu Province). International Journal of Geoinformatics, 17(2), 9–20. https://doi.org/10.52939/ijg.v17i2.1751
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