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Social and Behavioral Sciences Commons™
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Articles 1 - 3 of 3
Full-Text Articles in Social and Behavioral Sciences
Mining Urban Perceptions From Social Media Data, Yu Liu, Yihong Yuan, Fan Zhang
Mining Urban Perceptions From Social Media Data, Yu Liu, Yihong Yuan, Fan Zhang
Journal of Spatial Information Science
This vision paper summaries the methods of using social media data (SMD) to measure urban perceptions. We highlight two major types of data sources (i.e., texts and imagery) and two corresponding techniques (i.e., natural language processing and computer vision). Recognizing the data quality issues of SMD, we propose three criteria for improving the reliability of SMD-based studies. In addition, integrating multi-source data is a promising approach to mitigating the data quality problems.
Methosm: A Methodology For Computing Composite Indicators Derived From Openstreetmap Data, Dumitru Roman, Tatiana Tarasova, Javier Paniagua
Methosm: A Methodology For Computing Composite Indicators Derived From Openstreetmap Data, Dumitru Roman, Tatiana Tarasova, Javier Paniagua
Journal of Spatial Information Science
The task of computing composite indicators to define and analyze complex social, economic, political, or environmental phenomena has traditionally been the exclusive competence of statistical offices. Nowadays, the availability of increasing volumes of data and the emergence of the open data movement have enabled individuals and businesses affordable access to all kinds of datasets that can be used as valuable input to compute indicators. OpenStreetMap (OSM) is a good example of this. It has been used as a baseline to compute indicators in areas where official data is scarce or difficult to access. Although the extraction and application of OSM …
A Grounding-Based Ontology Of Data Quality Measures, Franz-Benjamin Mocnik, Amin Mobasheri, Luisa Griesbaum, Melanie Eckle, Clemens Jacobs, Carolin Klonner
A Grounding-Based Ontology Of Data Quality Measures, Franz-Benjamin Mocnik, Amin Mobasheri, Luisa Griesbaum, Melanie Eckle, Clemens Jacobs, Carolin Klonner
Journal of Spatial Information Science
Data quality and fitness for purpose can be assessed by data quality measures. Existing ontologies of data quality dimensions reflect, among others, which aspects of data quality are assessed and the mechanisms that lead to poor data quality. An understanding of which source of information is used to judge about data quality and fitness for purpose is, however, lacking. This article introduces an ontology of data quality measures by their grounding, that is, the source of information to which the data is compared to in order to assess their quality. The ontology is exemplified with several examples of volunteered geographic …