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As survey methods adapt to technological and societal changes, a growing body of research seeks to understand the tradeoffs associated with various sampling methods and administration modes. We show how the NSF-funded 2022 Collaborative Midterm Survey (CMS) can be used as a dynamic and transparent framework for evaluating which sampling approaches - or combination of approaches - are best suited for various research goals. The CMS is ideally suited for this purpose because it includes almost 20,000 respondents interviewed using two administration modes (phone and online) and data drawn from random digit dialing, random address-based sampling, a probability-based panel, two nonprobability panels, and two nonprobability marketplaces. The analysis considers three types of population benchmarks (election data, administrative records, and large government surveys) and focuses on the national-level estimates as well as oversamples in three states (California, Florida, and Wisconsin). In addition to documenting how each of the survey strategies performed, we develop a strategy to assess how different combinations of approaches compare to different population benchmarks in order to guide researchers combining sampling methods and sources. We conclude by providing specific recommendations to public opinion and election survey researchers and demonstrating how our approach could be applied to a large government survey conducted at regular intervals to provide ongoing guidance to researchers, government, businesses, and nonprofits regarding the most appropriate survey sampling and administration methods.

The linked open data (LOD) paradigm has emerged as a promising approach to structuring and sharing geospatial information. One of the major obstacles to this vision lies in the difficulties found in the automatic integration between heterogeneous vocabularies and ontologies that provides the semantic backbone of the growing constellation of open geo-knowledge bases. In this article, we show how to utilize WordNet as a semantic hub to increase the integration of LOD. With this purpose in mind, we devise Voc2WordNet, an unsupervised mapping technique between a given vocabulary and WordNet, combining intensional and extensional aspects of the geographic terms. Voc2WordNet is evaluated against a sample of human-generated alignments with the OpenStreetMap (OSM) Semantic Network, a crowdsourced geospatial resource, and the GeoNames ontology, the vocabulary of a large digital gazetteer. These empirical results indicate that the approach can obtain high precision and recall., Comment: 21 pages, 1 figure

In geographic information science and semantics, the computation of semantic similarity is widely recognised as key to supporting a vast number of tasks in information integration and retrieval. By contrast, the role of geo-semantic relatedness has been largely ignored. In natural language processing, semantic relatedness is often confused with the more specific semantic similarity. In this article, we discuss a notion of geo-semantic relatedness based on Lehrer's semantic fields, and we compare it with geo-semantic similarity. We then describe and validate the Geo Relatedness and Similarity Dataset (GeReSiD), a new open dataset designed to evaluate computational measures of geo-semantic relatedness and similarity. This dataset is larger than existing datasets of this kind, and includes 97 geographic terms combined into 50 term pairs rated by 203 human subjects. GeReSiD is available online and can be used as an evaluation baseline to determine empirically to what degree a given computational model approximates geo-semantic relatedness and similarity., Comment: GeoInformatica 2014

Over the past decade, rapid advances in web technologies, coupled with innovative models of spatial data collection and consumption, have generated a robust growth in geo-referenced information, resulting in spatial information overload. Increasing 'geographic intelligence' in traditional text-based information retrieval has become a prominent approach to respond to this issue and to fulfill users' spatial information needs. Numerous efforts in the Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the Linking Open Data initiative have converged in a constellation of open knowledge bases, freely available online. In this article, we survey these open knowledge bases, focusing on their geospatial dimension. Particular attention is devoted to the crucial issue of the quality of geo-knowledge bases, as well as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic Network, is outlined as our contribution to this area. Research directions in information integration and Geographic Information Retrieval (GIR) are then reviewed, with a critical discussion of their current limitations and future prospects.

Computational measures of semantic similarity between geographic terms provide valuable support across geographic information retrieval, data mining, and information integration. To date, a wide variety of approaches to geo-semantic similarity have been devised. A judgment of similarity is not intrinsically right or wrong, but obtains a certain degree of cognitive plausibility, depending on how closely it mimics human behavior. Thus selecting the most appropriate measure for a specific task is a significant challenge. To address this issue, we make an analogy between computational similarity measures and soliciting domain expert opinions, which incorporate a subjective set of beliefs, perceptions, hypotheses, and epistemic biases. Following this analogy, we define the semantic similarity ensemble (SSE) as a composition of different similarity measures, acting as a panel of experts having to reach a decision on the semantic similarity of a set of geographic terms. The approach is evaluated in comparison to human judgments, and results indicate that an SSE performs better than the average of its parts. Although the best member tends to outperform the ensemble, all ensembles outperform the average performance of each ensemble's member. Hence, in contexts where the best measure is unknown, the ensemble provides a more cognitively plausible approach., Comment: Special feature on Semantic and Conceptual Issues in GIS (SeCoGIS)

The two main theorems of this paper provide a characterization of hyperbolic affine iterated function systems defined on Rm. Atsushi Kameyama (Distances on Topological Self-Similar Sets, Proceedings of Symposia in Pure Mathematics, Volume 72.1, 2004) asked the following fundamental question: given a topological self-similar set, does there exist an associated system of contraction mappings? Our theorems imply an affirmative answer to Kameyama's question for self-similar sets derived from affine transformations on Rm.