Abstract
In geographic information systems (GIS), analysts answer questions by designing workflows that transform a certain type of data into a certain type of goal. Semantic data types help constrain the application of computational methods to those that are meaningful for such a goal. This prevents pointless computations and helps analysts design effective workflows. Yet, to date it remains unclear which types would be needed in order to ease geo-analytical tasks. The data types and formats used in GIS still allow for huge amounts of syntactically possible but nonsensical method applications. Core concepts of spatial information and related geo-semantic distinctions have been proposed as abstractions to help analysts formulate analytic questions and to compute appropriate answers over geodata of different formats. In essence, core concepts reflect particular interpretations of data which imply that certain transformations are possible. However, core concepts usually remain implicit when operating on geodata, since a concept can be represented in a variety of forms. A central question therefore is: Which semantic types would be needed to capture this variety and its implications for geospatial analysis? In this article, we propose an ontology design pattern of core concept data types that help answer geo-analytical questions. Based on a scenario to compute a liveability atlas for Amsterdam, we show that diverse kinds of geo-analytical questions can be answered by this pattern in terms of valid, automatically constructible GIS workflows using standard sources.
Highlights
It is still common for geospatial analysts to capture their tasks in the language of their favorite tools, such as QGIS, ArcGIS, or R [51]
It is crucial to find out about the different ways geodata can be interpreted in terms of core concepts, and how this can be made explicit in a semantic type system
We start with typical geo-analytical questions that can be handled within a geographic information systems (GIS)
Summary
It is still common for geospatial analysts to capture their tasks in the language of their favorite tools, such as QGIS, ArcGIS, or R [51]. It is crucial to find out about the different ways geodata can be interpreted in terms of core concepts, and how this can be made explicit in a semantic type system This would allow us to add the missing conceptual detail in current data descriptions, in order to assess whether geo-analytical questions are answerable using given GIS methods, by synthesizing them into executable workflows. We formalize the diversity of ways how core concepts can be represented with common geodata types, and how they can be transformed to enable the answering of geo-analytical questions in terms of automated workflows. Using OWL class definitions, semantic distinctions are combined into an ontology design pattern which is used to add semantic type signatures to common GIS operations and data sources for workflow synthesis Using a prototype we test how well our type signatures enable us to synthesize answers in terms of valid GIS workflows
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
