Governance and social development (12 articles)

Survey assessment for decision support using self-organizing maps profile characterization with an odds and cluster heat map: application to children’s perception of urban school environments

Abarca-Alvarez, F. J., Campos-Sánchez, F. S., & Mora-Esteban, R. (2019). Entropy, 21(9), 916.

The aim of this article is to build a decision support system for opinion and satisfaction survey data. Viscovery maps and clustering are a major component of this system and are complemented by a heat map of odds ratios on the Viscovery clusters. This system is applied to a dataset containing survey data on the perception of children in Granada, Spain about their school environment.

An AI based approach to multiple census data analysis for feature selection

Shanmuganathan, S., & Li, Y. (2016). Journal of Intelligent & Fuzzy Systems, 31(2), 859-872.

Building upon prior work on sociodemographic clustering of Beppu City, Japan, with data from the year 2000 census, this paper analyzes the sociodemographic development of the city. Viscovery SOMine clustering shows that, although there are some new sociodemographic trends, local districts in 2010 largely cluster in the same way they did in the year 2000.

Assessing regional wellbeing in Italy: an application of Malmquist–DEA and self-organizing map neural clustering

Carboni, O. A., & Russu, P. (2015). Social indicators research, 122(3), 677-700.

Social and economic well-being of all 20 Italian regions in the period of 2005–2011 is analyzed. The explorative analysis conducted with Viscovery SOMine reveals a North–South disparity. The North is not only richer but also exhibits better performance with respect to well-being within this time frame.

Citizens as consumers: profiling e-government services’ users in Egypt via data mining techniques

Mostafa, M. M., & El-Masry, A. A. (2013). International Journal of Information Management, 33(4), 627-641.

The dependence of Egyptian citizens' use of e-government services from various demographic and psychographic variables is researched. Viscovery SOMine is used alongside various other machine learning methods to classify citizens into users, non-users and potential users.

Typhoon damage scale forecasting with self-organizing maps trained by selective presentation learning

Kohara, K., & Sugiyama, I. (2013, July). In International Workshop on Machine Learning and Data Mining in Pattern Recognition (pp. 16-26). Springer, Berlin, Heidelberg.

A self-organizing map model is used as a classifier for the severity of damages caused by typhoons. Viscovery SOMine is used to create a model based on emergence and early life data of a typhoon to predict the extent of expected damage.

Mathematical clustering integrated with SWOT analysis as a tool for design of sustainable development strategies

Nondek, L., & Smutný, M. (2012). International Journal of Sustainable Development and Planning, 7(4), 397-411.

This paper aims at creating a draft for a sustainable-development strategy framework in the Czech Republic. Viscovery SOMine is used to analyze statements with respect to their meaning for sustainability. By applying SWOT analyses to the resulting clusters, specific interventions are defined for building a basis for the development strategy.

Community health assessment using self-organizing maps and geographic information systems

Basara, H. G., & Yuan, M. (2008). International journal of health geographics, 7(1), 67.

This study analyzes 511 communities from New York State with respect to social, demographic, economic and environmental background to reveal connections between these variables and occurrences of particular diseases. Viscovery SOMine found clusters of similar communities and significant different incidence rates for diseases, such as hepatitis, tuberculosis, asthma, chronic obstructive pulmonary disease (COPD), diabetes, influenza and atherosclerosis.

Toward optimal calibration of the SLEUTH land use change model

Dietzel, C., & Clarke, K. C. (2007). Transactions in GIS, 11(1), 29-45.

SLEUTH is a computational simulation model that uses adaptive cellular automata to simulate the way cities grow and change their surrounding land uses. Viscovery SOMine is used to generate a self-organizing map for reducing data, to pursue the isolation of the best parameter sets and to indicate which of the existing 13 calibration metrics used in SLEUTH are necessary to arrive at the optimum. A new metric is proposed for increasing the value in future SLEUTH applications.

Social area analysis using SOM and GIS: a preliminary research

Li, Y., & Shanmuganathan, S. (2007). Ritsumeikan Center for Asia Pacific Studies (RCAPS) Working paper.

Local units of the Japanese city Beppu are analyzed with respect to their sociodemographic and economic properties. Viscovery SOMine is used to obtain clusters of similar local units to enable city planners to understand local properties and take them into account for planning decisions.

Development of an ecologically derived environmental health model using geographic information systems

Basara, H. G. (2006).

This dissertation studies environmental influences on public health. Viscovery SOMine is used to cluster environmental, socioeconomic and health data for 569 communities in 5 counties of New York State.

Towards fair ranking of Olympics achievements: the case of Sydney 2000

Churilov, L., & Flitman, A. (2006). Computers & Operations Research, 33(7), 2057-2082.

An objective, impartial system for analyzing Olympic results, which the majority of participating countries could agree upon, is analyzed by discussing different ways of ranking the performance of participating countries at the Sydney 2000 Olympic Games. The unsupervised data mining technique of self-organizing maps is used to group the participating countries into homogenous clusters. A model based on data-envelopment analysis is then used for producing a new ranking of participating teams that could be accepted as “fair” by the majority of participants.

Operationalising multidimensional concepts of chronic poverty: an exploratory spatial analysis

Mehta, A.K., Panigrahi, R., & Sivramkrishna, S. (2004). CPRC-IIPA Working Paper 18. New Delhi: Chronic Poverty Research Centre, University of Manchester and Indian Institute of Public Administration.

Viscovery SOMine analysis is used to detect spatial inequalities in social development for Indian regions and districts. The nature and extent of these inequalities varies with choice of indicator and geographical space over which comparisons are made.