Mining Volunteered Geographic Information datasets with heterogeneous spatial reference

Abstract

When the information created online by users has a spatial reference, it is known as Volunteered Geographic Information (VGI). The increased availability of spatiotemporal data collected from satellite imagery and other remote sensors provides opportunities for enhanced analysis of Spatiotemporal Patterns. This area can be defined as efficiently discovering interesting patterns from large data sets. The discovery of hidden periodic patterns in spatiotemporal data could provide unveiling important information to the data analyst. In many applications that track and analyze spatiotemporal data, movements obey periodic patterns; the objects follow the same routes (approximately) over regular time intervals. However, these methods cannot directly be applied to a spatiotemporal sequence because of the fuzziness of spatial locations in the sequence. In this paper, we define the problem of mining VGI datasets with our already established bottom up algorithm for spatiotemporal data.