Geographical data related to image processing, environmental monitoring and urban planning are beings collected and stored in various databases. Processing and managing these voluminous multidimensional data have become an important requirement and gained the researchers attention. For any application dealing with the multidimensional data analysis, efficient and effective data processing techniques are required to produce best results from these geographical data sets. The processing of these datasets in timed manner using appropriate techniques is the ultimate requirement while dealing with multidimensional data. There are number of optimization methods available but the Nature-inspired algorithms are among the most powerful algorithms for optimization. We proposed Multi Objective Algorithm (MOA) which is the combination of Dragon Fly (DF) Optimization and Cuckoo Search (CS) Algorithm for Visualization & Data Analysis of Geospatial database. We have compared the various parameters from MOA algorithm with the existing K-nearest neighbors (KNN) algorithm. Results indicate that the MOA algorithm is producing better output in term of classification compare to existing algorithm. Finally, the proposed algorithm provides a framework where image classification and interpretation can be possible for various types of GIS images.

Data Analysis, Cuckoo Search, Dragon Fly Optimization, Optimization, Levy flight, Visualization, K-Nearest Neighbour

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