District Level Planning In Kerala And Karnataka - A Comparative Study

When we consider Kerala and Karnataka States according to their levels of decentralisation. Kerala is at the beginning of the scale of decentralisation whereas Kamataka has moved far ahead along this scale. Therefore I in order to conduct a comparative study of the SUbject under analysis t Kamataka has been selected owing to the fact that it is in an advanced stage in the experience of district planning compared to Kerala , Karnataka could successfully implement district planning and it is me of the pioneering states in this regard. But Kerala has not gained much experience in the field of decentralised district planning till now. Furthermore Kerala and Kamataka states are selected for the present study due to operational reasons I besides the author I s familiarity with the socia-economic conditions of these states. Thus. an analysis of the district planning experience of Kamataka will provide constructive and valuable information. which will be of great importance to Kerala State, which is now aspiring to introduce ful.I-f'Iedge district planning by constituting elected District Coancils in every district of Kerala. Moreover. the findings and policy implications of the present study will be of immense help to planners, politicians. administrators, academicians and people at large.

Detection of Flock Movement in Spatio-Temporal Database using Clustering Techniques - an Experience

In this paper, moving flock patterns are mined from spatio- temporal datasets by incorporating a clustering algorithm. A flock is defined as the set of data that move together for a certain continuous amount of time. Finding out moving flock patterns using clustering algorithms is a potential method to find out frequent patterns of movement in large trajectory datasets. In this approach, SPatial clusteRing algoRithm thrOugh sWarm intelligence (SPARROW) is the clustering algorithm used. The advantage of using SPARROW algorithm is that it can effectively discover clusters of widely varying sizes and shapes from large databases. Variations of the proposed method are addressed and also the experimental results show that the problem of scalability and duplicate pattern formation is addressed. This method also reduces the number of patterns produced