Geographic feature mining: framework and fundamental tasks for geographic knowledge discovery from user-generated data

We live in a data-rich environment where massive amounts of data such as text messages, articles, images, and search queries are continuously generated by users. In this environment, new opportunities to discover and utilize knowledge about the real-world arise, such as the extraction and description of places and events from social media records, the organization of documents by spatio-temporal topics, and the prediction of epidemics by search engine queries. Major challenges addressed in these data- and application-specific works arise from the unstructured and complex nature of the data, and the high level of uncertainty and sparsity of the attributes. Despite the evident progress in utilizing specific data sources for different applications, there remains a lack of common concepts and techniques on how to exploit the data as high-quality sensors of geographic space in a general manner. However, such a general point of view allows to address the common challenges and to define fundamental building blocks to deal with problems in fields like information retrieval, recommender systems, market research, health surveillance, and social sciences. In this thesis, we develop concepts and techniques to utilize various kinds of user-generated data as a steady source of information about geographic processes and entities (together called geographic phenomena). For this, we introduce a novel conceptual data mining framework, called geographic feature mining, that provides the foundation to discover and extract highly informative and discriminative dimensions of geographic space in a unifying and systematic fashion. This is achieved by representing the qualitative and geographic information in the records as geographic feature signals, each constituting a potential dimensions to describe geographic space. The mining process then determines highly informative features or feature combinations from the candidate sets that can be used as a steady source of auxiliary information for domain-specific applications. In developing the framework, we make contributions to several fundamental problems: (1) We introduce a novel probabilistic model to extract high-quality geographic feature signals. The signals are robust to noise and background distributions, and the model allows to exploit diverse kinds of qualitative and geographic information in the records. This flexibility is achieved by utilizing a Bayesian network model and the robustness by choosing appropriate prior distributions. (2) We address the problem of categorizing and selecting geographic features based on their spatio-temporal type, such as feature signals having landmark, regional, or global semantics. For this, we introduce representations of the signals by interaction characteristics and evaluate their performance in clustering and data summarization tasks. (3) To extract a small number of highly informative feature combinations that reflect geographic phenomena, we introduce a model that extracts latent geographic features from the candidate signals using dimensionality reduction. We show that this model outperforms document-centric topic models with respect to the informativeness of the extracted phenomena, and we exhaustively evaluate how different statistical properties of the approaches affect the characteristics of the resulting feature combinations.