HomeIAMURE International Journal of Ecology and Conservationvol. 36 no. 1 (2021)

Poverty Mapping Using Clustering Algorithm and Geographical Information System: A Basis for Rural Planning




Poverty is a multidimensional problem that does not have a standard definition for all countries of the world. Data mining technique was used in this study to analyze the poverty data, while the GIS was used to map the location of every household. The data set that was used in the study was the survey result of the Community Based Monitoring System (CBMS). The data sets were from the eight basic needs of CBMS that consist of 14 indicators and the household size. A recommender system was developed to help the Local Government Unit to visualize the extent of poverty in their place and to craft strategic poverty reduction policies and programs. With the aid of poverty mapping using clustering algorithm and GIS, it is easy to understand who the poor are, where the poor are found. The visualization of data and the recommended list of poverty alleviation programs in the system can be a great help to the Local Government Unit. It may serve as a basis for rural planning, especially in poverty reduction programs.