Spatial Data Management, Analysis, and Modeling in GIS: Principles and Applications

Abstract

Recent advances in spatial data development and management in the framework of geographic information system (GIS) have created a new era in the field of land resource management. Spatial database generally refers to any set of data describing the semantic and spatial properties of real-world phenomena. Manual digitization of paper maps, existing digital datasets, satellite remote-sensing imageries, global positioning system (GPS), field surveys, internet, etc. are the promising data input sources for spatial data development in GIS. Geographic information, attribute information, and display information are the three important types of information embedded in digital maps. Relational database management system (RDBMS) is the most effective and efficient data storage and management model in spatial database generation and management in GIS. Geographic analysis in GIS facilitates the study of various processes by developing and applying suitable models. The objective of geographic analysis is to transform data into useful information to satisfy the requirements or objectives of decision-makers. GIS provides tools and methods for the integration of different data into a format to be compared and analyzed. Analysis models comprise simple user-defined views to complex stochastic models, like suitability analysis, network analysis, optimization, allocation, etc. Geometric modeling has immense potential in generating buffers, calculating areas and perimeters, and calculating distances between features. Spatial analysis in GIS helps to identify trends on the data, create new relationships from the data, and view complex relationships between datasets to make better decisions. Vector and raster overlay operations are two different types of overlays in GIS depending upon data structures. GIS provides different ways in which the information can be presented once it is analyzed and processed by GIS.