A software framework for classification models of geographical data

Authors:
Yu Liu;Qinghua Guo;Yuan Tian
Affiliations:
Institute of Remote Sensing and Geographical Information Systems, Peking University, Beijing 100871, China;School of Engineering, University of California, Merced, CA 95343, USA;Institute of Remote Sensing and Geographical Information Systems, Peking University, Beijing 100871, China
Venue:
Computers & Geosciences
Year:
2012

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Abstract

With the advances of GIS (Geographical Information System), GPS (Global Positioning System) and remote sensing, spatial data has become increasingly available. A significant amount of such data is related to point localities, such as locations of landslides, species occurrences, disease cases, and transportation accidents. There is a great need to predict the potential distribution of these geographical events given their localities and influencing features. In this study, we present a framework that can integrate a range of classification algorithms to predict the geographical distribution of a specific event. The proposed framework is unique in its implementation of a number of procedures that support a variety of geographical data types such as presence-only data, two-class data, and multi-class data. The framework is developed in C++ and based on object-oriented polymorphism, which enables us to add new classifiers to the framework by implementing a number of predefined interfaces.