Regional bias of landslide data in generating susceptibility maps using logistic regression: Case of Hong Kong Island

Abstract

On the basis of 1,834 landslide data for Hong Kong Island (HKI), landslide susceptibility maps were generated using logistic regression and GIS. Regional bias of the landslide inventory is examined by dividing the whole HKI into a southern and a northern region, separated by an east-west trending water divide. It was found that the susceptibility map of southern HKI generated by using the southern data differs significantly from that generated by using northern data, and similar conclusion can be drawn for the northern HKI. Therefore, a susceptibility map of HKI was established based on regional data analysis, and it was found to reflect closely the spatial distributions of historical landslides. Elevation appears to be the most dominant factor in controlling landslide occurrence, and this probably reflects that human developments are concentrated at certain elevations on the island. Classification plot, goodness of fit, and occurrence ratio were used to examine the reliability of the proposed susceptibility map. The size of landslide susceptible zones varies depending on the data sets used, thus this demonstrates that the historical landslide data may be biased and affected by human activities and geological settings on a regional basis. Therefore, indiscriminate use of regional-biased data should be avoided.