A Research & Development Model of Resource Mapping for Livelihood Planning

Abstract

The envisioned R&D Model of Resource Mapping for Livelihood Planning is based on the experience gained in a survey-based research work on resource mapping for livelihood planning, conducted in the backward district of Purulia (West Bengal, India) in 2018-20.

The research work identified some parameters and attributes associated in the villagelevel resource mapping work, and highlighted the scope of the application of information technology in the work process through analysis of geo-referenced data – to render an extra edge in real time analysis. The research work produced a combined database having 67 baselevel attributes, containing spatial and non-spatial data of the area of study comprising of 606 mouzas. It was felt that such database, when powered with GIS-enabled search engine and WebGIS infrastructure, may generate huge volume of strategic just-in-time information, which when linked with e-governance mechanism of the government, may help promote livelihood at the village level through optimal utilization of resources.

Technological tools like Geographical Information System (GIS) and Remote Sensing Data Analysis help us to develop spatially connected databases. Such database when combined with strategic non-spatial data-sets may produce a combined database, which once made up to village level, can provide a tremendous boost in the planning and monitoring of activities of social projects of the governments and NGOs, as well as may provide efficient and quality service to the entrepreneurs by way of providing just-in-time information.

In livelihood resource mapping work, criteria analysis of resource mapping data can help in extracting relevant information in super-imposed form from multi-layered datasets, through multiple object-based inter-related queries.

Four case studies are illustrated in this paper that offer examples of the ways to generate criteria maps for strategic information (along with its dataset) from combined database of GIS, in response to multi-level queries. Such criteria maps provide information on the existing and other socio-economic conditions of the region. Thousands of such multi-level queries may generate numerous Resource Planning Maps and Participatory Planning Maps, useful for livelihood planning and its implementation.

Livelihood planning and its implementation at the grass-root level need collaborative efforts at various levels – both the government and non-government institutions may join hands in the fields of survey works, compilation of combined databases, analysis of the data, generating strategic information for livelihood planning, and most important, in the task of livelihood cluster development works at the grass-root level. A participative planning roadmap is given in the concluding note.