Since populations in the developing world have been rapidly increasing, accurately determining the population distribution is becoming more critical for many countries. One of the most widely used population density estimation methods is dasymetric mapping. This can be defined as a precise method for areal interpolation between different spatial units. In most applications of dasymetric mapping, land use and land cover data have been considered as ancillary data for the areal disaggregation process. This research presents an alternative dasymetric approach using area specific ancillary data for hilly area population mapping in a GIS environment. Specifically, we propose a Hilly Area Dasymetric Mapping (HDM) technique by combining topographic variables and land use to better disaggregate hilly area population distribution at fine-grain division of ancillary units. Empirical results for Sri Lanka’s highest mountain range show that the combined dasymetric approach estimates hilly area population most accurately because of the significant association that is found to exist between topographic variables and population distribution within this setting. This research is expected to have significant implications for national and regional planning by providing useful information about actual population distributions in environmentally hazardous and sparsely populated areas.

Social support networks have been become one of the mostly influential metaphors in many areas around the world, especially in terms of mitigating disaster consequences and revivifying disaster affected livelihoods. More importantly, reciprocal supports ties are more powerful in healing disaster wounds of communities. Especially social capital legacies enrich by reciprocal support networks by the ways in which making may hopes among disaster victims. In this context, the objective of this study is to investigate the social support network behaviors in supporting 16 flood-affected households in the mass flooding event occurred in 2017. Particularly, the study researched social support network behaviors at different flood inundation phases such as before, during, and after. This study collected primary data (mainly social network data) using household survey and filed observations. The study used the Social Network Analysis (SNA) method for the network data analysis. This article shows that flood affected households have received social network supports in different magnitudes at before, during, and after flood inundation phases. More importantly, provision of foods, water and basic needs, sheltering, clearing contaminated households, and emotional supports have mobilized and reciprocated among victims in reviving their livelihoods. The social support network legacies have evolved at different flood inundation phases. This study fills the gabs in the flood disaster discourse on Sri Lankan context.

Mass flooding events are predominant hydrometeorological disasters in the modern world. This situation has worsened due to the accelerating climate change scenarios. This is because social vulnerability to flood disasters has become one of the greatest challenges in the world. Similarly, Sri Lanka as a middle-income country in the developing world, has been experiencing mass flooding events for decades. Many emerging studies have paid their upmost attention to model the flood disaster vulnerability. But a little has been explored about the understanding of the “Spatiality” or “Geographies” of social vulnerability and resilience status and distributional patterns. In light of the existing body of literature, this study aims to demonstrate a new mechanism for mapping the spatial patterns of social vulnerability and resilience for flooding called “Social Vulnerability Contours (SVCs)”. This study occupied with the multi-facet composite social vulnerability index (MFCSVI) in order to measure the values of each and every surveyed household for generating SVCs. The 213 flood affected households situated in eight Grama Niladari Divisions (GNDs) were considered for the empirical data collection. General normalization procedure was used to scale up all the variable values with different measures to a single scale. Study occupied with ArcGIS 10.4 for generating SVCs were based upon households’ MFCSVI values. The resultant SVCs revealed the significant spatial distribution patterns of social vulnerability and resilience through sampled GNDs. A good consistency of data distribution can be expected with increasing the sample size among GNDs.