Extracting Computational Representations of Place with Social Sensing

Place-based GIS are at the forefront of GIScience research and characterized by textual descriptions, human conceptualizations as well as the spatial-semantic relationships among places. The concepts of places are difficult to handle in geographic information science and systems because of their intrinsic vagueness. They arise from the complex interaction of individuals, society, and the environment. The exact delineation of vague regions is challenging as their borders are vague and the membership within a region varies non-monotonically and as a function of context. Consequently, vague regions are difficult to handle computationally, e.g., in spatial analysis, cartography, geographic information retrieval, and GIS workflows in general. The emergence of big data brings new opportunities for us to understand the place semantics from large-scale volunteered geographic information and data streams, such as geotags, texts, activity streams, and GPS trajectories. The term "social sensing" describes such individual-level big geospatial data and the associated analysis methods. In this dissertation, I present a generalizable, data-driven framework that complements classical top-down approaches by extracting the representations of vague cognitive regions and function regions from bottom-up approaches using spatial statistics and machine learning techniques with various social sensing sources. I demonstrate how to derive crisp boundaries for cognitive and functional regions from points of interest data, and show how natural language processing techniques can enrich our understanding of places and form a foundation for the semantic characterization of place types and the generalization of regions. This work makes contributions to the development of computational methodologies for extracting vague cognitive regions and functional regions using data-driven approaches as well as the novel semantic generalization processing technique

Mining topological relations from the web

Topological relations between geographic regions are of interest in many applications. When the exact boundaries of regions are not available, such relations can be established by analysing natural language information from web documents. In particular we demonstrate how redundancy-based techniques can be used to acquire containment and adjacency relations, and how fuzzy spatial reasoning can be employed to maintain the consistency of the resulting knowledge base

An evaluation resource for geographic information retrieval

In this paper we present an evaluation resource for geographic information retrieval developed within the Cross Language Evaluation Forum (CLEF). The GeoCLEF track is dedicated to the evaluation of geographic information retrieval systems. The resource encompasses more than 600,000 documents, 75 topics so far, and more than 100,000 relevance judgments for these topics. Geographic information retrieval requires an evaluation resource which represents realistic information needs and which is geographically challenging. Some experimental results and analysis are reported

University of Twente at GeoCLEF 2006: geofiltered document retrieval

In this report we describe the approach of the University of Twente to the 2006 Geo-CLEF task. It is based on retrieval by content and the subsequent filtering by geographical relevance utilizing a gazetteer. The results do not show an improvement inretrieval performance when taking geographical information into account

GeoCLEF 2007: the CLEF 2007 cross-language geographic information retrieval track overview

GeoCLEF ran as a regular track for the second time within the Cross Language Evaluation Forum (CLEF) 2007. The purpose of GeoCLEF is to test and evaluate cross-language geographic information retrieval (GIR): retrieval for topics with a geographic specification. GeoCLEF 2007 consisted of two sub tasks. A search task ran for the third time and a query classification task was organized for the first. For the GeoCLEF 2007 search task, twenty-five search topics were defined by the organizing groups for searching English, German, Portuguese and Spanish document collections. All topics were translated into English, Indonesian, Portuguese, Spanish and German. Several topics in 2007 were geographically challenging. Thirteen groups submitted 108 runs. The groups used a variety of approaches. For the classification task, a query log from a search engine was provided and the groups needed to identify the queries with a geographic scope and the geographic components within the local queries

Generating approximate region boundaries from heterogeneous spatial information: an evolutionary approach

Spatial information takes different forms in different applications, ranging from accurate coordinates in geographic information systems to the qualitative abstractions that are used in artificial intelligence and spatial cognition. As a result, existing spatial information processing techniques tend to be tailored towards one type of spatial information, and cannot readily be extended to cope with the heterogeneity of spatial information that often arises in practice. In applications such as geographic information retrieval, on the other hand, approximate boundaries of spatial regions need to be constructed, using whatever spatial information that can be obtained. Motivated by this observation, we propose a novel methodology for generating spatial scenarios that are compatible with available knowledge. By suitably discretizing space, this task is translated to a combinatorial optimization problem, which is solved using a hybridization of two well-known meta-heuristics: genetic algorithms and ant colony optimization. What results is a flexible method that can cope with both quantitative and qualitative information, and can easily be adapted to the specific needs of specific applications. Experiments with geographic data demonstrate the potential of the approach