Social capital and disasters: How does social capital shape post-disaster conditions in the Philippines?

ABSTRACT

Social capital is a common feature among disaster-resilient communities. This research aims to define how social capital shapes the post-disaster conditions in the 2011 Typhoon Washi-affected communities of Cagayan de Oro and Iligan City in Region 10 Philippines. Qualitative analysis was used in analyzing the data gathered through purposive sampling and semi-structured interviews. Thirty typhoon survivors and 14 focal persons of the government and non-government agencies were chosen based on their active involvement in the community. The findings revealed that the solidarity among typhoon-affected communities contributed to the recovery of the survivors. The findings also highlighted that the solidarity in the typhoon-affected communities is part of the normative structure of the society where bonding and linking social capital are nurtured. Further, the community remains to believe that their respective local officials can be trusted and are capable of helping them in times of need despite the shortcomings during the 2011 Typhoon Washi. We argue that social capital in the community is not easily diminished over a crisis and therefore must be nurtured towards effective community-based disaster resilience mechanisms.     

Robust Multivariate Outlier Labeling

A criterion for robust estimation of location and covariance matrix is considered, and its application in outlier labeling is discussed. This method, unlike the methods based on MVE and MCD, is applicable to large and high-dimension data sets. The method proposed here is also robust and has the same breakdown point as the MVE- and MCD-based methods. Furthermore, the computational complexity of the proposed method is significantly smaller than that of other methods.     

Multivariate Process Variability Monitoring

Understanding multivariate variability is a difficult task because there is no single measure that can be properly used. This article presents a new measure that features good properties. If this measure is simultaneously used with generalized variance, it will give a better understanding of multivariate variability. It can also efficiently be used for large data sets with high dimensions. Furthermore, when it is used for constructing a Shewhart-type chart to monitor multivariate variability, the resulting chart has a much better out-of-control ARL than the generalized variance chart. An example illustrates its advantage.