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Articulating Uneven Regional Development: Artificial intelligence as a tool in development planning

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  • Sashi Sivramkrishna
  • Ramakrushna Panigrahi

Abstract

There is an urgent need for more efficient and effective design, targeting and implementation of interventions to reduce regional imbalances in development. To do so, development agencies and practitioners need to articulate uneven regional development as regional inequalities in, and patterns of, development. The widespread popularity of composite indices like the Human Development Index has led to the acceptance of regional inequalities as a basis for intervention. However, in computing composite indices of development like the Human Development Index, information that could be of great utility to planners is lost. This is especially important when planners work on smaller spaces and several indicators of development. There is then a need to also articulate patterns of development for optimal intervention. Unfortunately, the conventional statistical methods to discern patterns in development are complex and have not found widespread acceptance like composite indices. Artificial intelligence, in particular the Kohonen Self-Organizing Map, is a user-friendly tool for development planners and practitioners to explore patterns in development. An application with several indicators over 399 Indian districts illustrates the need to study development patterns. This paper also makes clear the versatility of the Kohonen Self-Organizing Map technique in exploring these regional patterns of development.

Suggested Citation

  • Sashi Sivramkrishna & Ramakrushna Panigrahi, 2003. "Articulating Uneven Regional Development: Artificial intelligence as a tool in development planning," Journal of Human Development and Capabilities, Taylor & Francis Journals, vol. 4(3), pages 437-456.
    • Handle: RePEc:taf:jhudca:v:4:y:2003:i:3:p:437-456
    DOI: 10.1080/1464988032000125782
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    Cited by:

    1. Goletsis, Y. & Chletsos, M., 2011. "Measurement of development and regional disparities in Greek periphery: A multivariate approach," Socio-Economic Planning Sciences, Elsevier, vol. 45(4), pages 174-183, December.
    2. Omrani, Hashem & Valipour, Mahsa & Jafari Mamakani, Saeid, 2019. "Construct a composite indicator based on integrating Common Weight Data Envelopment Analysis and principal component analysis models: An application for finding development degree of provinces in Iran," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    3. Omrani, Hashem & Fahimi, Pegah & Mahmoodi, Abdollah, 2020. "A data envelopment analysis game theory approach for constructing composite indicator: An application to find out development degree of cities in West Azarbaijan province of Iran," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).

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