Using Multiple Correspondence Analysis to Measure Multidimensional Poverty in Congo

The following analysis is based on a multidimensional understanding of poverty using a nonmonetary basic needs approach. It is ground on data from the first survey on household living conditions for poverty assessment, conducted by the National Institute of Statistics of Congo in 2005. Multiple Correspondence Analysis is applied to construct a composite indicator by aggregating several attributes likely to reflect the poverty of individuals or households. The application shows that Congolese households are not affected by the same type of poverty. Three types of non-monetary poverty are identified: infrastructure poverty, vulnerability of human existence and poverty of comfort. These households were then classified according to the composite indicator of Poverty. The results show that the incidence of poverty corresponds to the weight of poor class of about 70.67%.

Classification of subtypes of patients with eating disorders by correspondence analysis

BACKGROUND Grouping eating disorders(ED)patients into subtypes could help improve the establishment of more effective diagnostic and treatment strategies.AIM To identify clinically meaningful subgroups among subjects with ED using multiple correspondence analysis(MCA).METHODS A prospective cohort study was conducted of all outpatients diagnosed for an ED at an Eating Disorders Outpatient Clinic to characterize groups of patients with ED into subtypes according to sociodemographic and psychosocial impairment data,and to validate the results using several illustrative variables.In all,176(72.13%)patients completed five questionnaires(clinical impairment assessment,eating attitudes test-12,ED-short form health-related quality of life,metacognitions questionnaire,Penn State Worry Questionnaire)and sociodemographic data.ED patient groups were defined using MCA and cluster analysis.Results were validated using key outcomes of subtypes of ED.RESULTS Four ED subgroups were identified based on the sociodemographic and psychosocial impairment data.CONCLUSION ED patients were differentiated into well-defined outcome groups according to specific clusters of compensating behaviours.

Dealing with Multisource Information for Estuarine Flood Risk Appraisal in Two Western European Coastal Areas

Estuaries are usually affected by compound flooding triggers that cause diverse territorial damages.While fluvial flood risk assessment frameworks are well established in the literature,integrated management instruments that deal with estuarine flood risk remain incomplete and often lacking.This research presents a methodology to extract relevant information from multiple sources post-event and a database building process that is applied to two contrasting estuaries(the Tagus River estuary in Portugal,and the Shannon River estuary in Ireland)in the Western European coastal area.Overall,a total of 274 documents were analyzed and the information was stored in two databases.Multiple correspondence analysis was applied to extract the most informative and relevant estuarine flood indicators.An integrated estuarine flood risk assessment framework is presented and discussed based on the extracted indicators.The framework is driven by two distinct dimensions(oceanic and hydrographic)and revealed the transversal position of triggers of estuarine floods,reflecting the compounding effects usually present in these areas.The results also highlight two levels of flood risk mostly based on damage typology.

A Novel Web Video Event Mining Framework with the Integration of Correlation and Co-Occurrence Information

The massive web videos prompt an imperative demand on efficiently grasping the major events. However, the distinct characteristics of web videos, such as the limited number of features, the noisy text information, and the unavoidable error in near-duplicate keyframes （NDKs） detection, make web video event mining a challenging task. In this paper, we propose a novel four-stage framework to improve the performance of web video event mining. Data preprocessing is the first stage. Multiple Correspondence Analysis （MCA） is then applied to explore the correlation between terms and classes, targeting for bridging the gap between NDKs and high-level semantic concepts. Next, co-occurrence information is used to detect the similarity between NDKs and classes using the NDK-within-video information. Finally, both of them are integrated for web video event mining through negative NDK pruning and positive NDK enhancement. Moreover, both NDKs and terms with relatively low frequencies are treated as useful information in our experiments. Experimental results on large-scale web videos from YouTube demonstrate that the proposed framework outperforms several existing mining methods and obtains good results for web video event mining.