Neighborhood Rough-Sets-Based Spatial Data Analytics

Background

Mining spatial data is useful in fields like weather forecasting, natural calamity prediction, crime management, transmission and spread of infectious diseases and others. This calls for expertise in these areas and the nature of spatial data. For example, representing topology in spatial data modelling is inherent to dealing with uncertainties. And, Rough Sets have been used to deal with uncertainty in spatial data mining. Pawlak’s (1982) Rough Set Theory (RST) has been used to model spatial regions with unclear boundaries. Beaubouef and Petry (1994) have demonstrated the use of rough sets have been used to query crisp data in relational databases. The Region Connection Calculus (RCC) proposed by Randell & Cohn (1992) and Egg-Yolk models by Cohn and Gotts (1996) have been blended with the approximation concepts of RST to identify vague region boundaries. Rough sets have been used by Bai et. al (2010) to identify villages with birth defects, Ahlqvist (2005) for spatial classification and analysis, Leung et. al. (2007) for discovering classification rules in remote sensor data, Øhrn A (1999) for disease diagnosis and outcome prediction and Thangavel and Pethalakshmi (2006) for dimensionality reduction.

Figure 1.

A sample geographic region