Showing papers by "Simon Scheider published in 2019"

Finding and sharing GIS methods based on the questions they answer

Abstract: Geographic information has become central for data scientists of many disciplines to put their analyzes into a spatio-temporal perspective. However, just as the volume and variety of data sources on the Web grow, it becomes increasingly harder for analysts to be familiar with all the available geospatial tools, including toolboxes in Geographic Information Systems (GIS), R packages, and Python modules. Even though the semantics of the questions answered by these tools can be broadly shared, tools and data sources are still divided by syntax and platform-specific technicalities. It would, therefore, be hugely beneficial for information science if analysts could simply ask questions in generic and familiar terms to obtain the tools and data necessary to answer them. In this article, we systematically investigate the analytic questions that lie behind a range of common GIS tools, and we propose a semantic framework to match analytic questions and tools that are capable of answering them. To support the matching process, we define a tractable subset of SPARQL, the query language of the Semantic Web, and we propose and test an algorithm for computing query containment. We illustrate the identification of tools to answer user questions on a set of common user requests.

Distinguishing extensive and intensive properties for meaningful geocomputation and mapping

Abstract: A most fundamental and far-reaching trait of geographic information is the distinction between extensive and intensive properties. In common understanding, originating in Physics and Chemistry, ext...

Deeply integrating Linked Data with Geographic Information Systems

Abstract: The realization that knowledge often forms a densely interconnected graph has fueled the development of graph databases, Web-scale knowledge graphs and query languages for them, novel visualization and query paradigms, as well as new machine learning methods tailored to graphs as data structures. One such example is the densely connected and global Linked Data cloud that contains billions of statements about numerous domains including life science and geography. While Linked Data has found its way into everyday applications such as search engines and question answering systems, there is a growing disconnect between the classical ways in which GIS are still used today and the open-ended, exploratory approaches used to retrieve and consume data from knowledge graphs such as Linked Data. In this work, we conceptualize and prototypically implement a Linked Data connector framework as a set of toolboxes for Esri’s ArcGIS to close this gap and enable the retrieval, integration, and analysis of Linked Data from within geographic information systems. We discuss how to connect to Linked Data endpoints, how to use ontologies to probe data and derive appropriate GIS representations on-the-fly, how to make use of reasoning, how to derive data that is ready for spatial analysis out of RDF triples, and, most importantly, how to utilize the link structure of Linked Data to enable analysis. The proposed Linked Data connector framework can also be regarded as the first step towards a guided geographic question answering system over geographic knowledge graphs.

Tracing Tourism Geographies with Google Trends: A Dutch Case Study.

Abstract: Search engines make information about places available to billions of users, who explore geographic information for a variety of purposes. The aggregated, large-scale search behavioural statistics provided by Google Trends can provide new knowledge about the spatial and temporal variation in interest in places. Such search data can provide useful knowledge for tourism management, especially in relation to the current crisis of tourist (over)crowding, capturing intense spatial concentrations of interest. Taking the Amsterdam metropolitan area as a case study and Google Trends as a data source, this article studies the spatial and temporal variation in interest in places at multiple scales, from 2007 to 2017. First, we analyze the global interest in the Netherlands and Amsterdam, comparing it with hotel visit data. Second, we compare interest in municipalities, and observe changes within the same municipalities. This interdisciplinary study shows how search data can trace new geographies between the interest origin (what place users search from) and the interest destination (what place users search for), with potential applications to tourism management and cognate disciplines.

Tracing tourism geographies with Google Trends: a Dutch case study

Abstract: Search engines make information about places available to billions of users, who explore geographic information for a variety of purposes. The aggregated, large-scale search behavioural statistics provided by Google Trends can provide new knowledge about the spatial and temporal variation in interest in places. Such search data can provide useful knowledge for tourism management, especially in relation to the current crisis of tourist (over)crowding, capturing intense spatial concentrations of interest. Taking the Amsterdam metropolitan area as a case study and Google Trends as a data source, this article studies the spatial and temporal variation in interest in places at multiple scales, from 2007 to 2017. First, we analyze the global interest in the Netherlands and Amsterdam, comparing it with hotel visit data. Second, we compare interest in municipalities, and observe changes within the same municipalities. This interdisciplinary study shows how search data can trace new geographies between the interest origin (what place users search from) and the interest destination (what place users search for), with potential applications to tourism management and cognate disciplines.