The demand for reproducible research is on the rise in disciplines concerned with data analysis and computational methods. Therefore, we reviewed current recommendations for reproducible research and translated them into criteria for assessing the reproducibility of articles in the field of geographic information science (GIScience). Using this criteria, we assessed a sample of GIScience studies from the Association of Geographic Information Laboratories in Europe (AGILE) conference series, and we collected feedback about the assessment from the study authors. Results from the author feedback indicate that although authors support the concept of performing reproducible research, the incentives for doing this in practice are too small. Therefore, we propose concrete actions for individual researchers and the GIScience conference series to improve transparency and reproducibility. For example, to support researchers in producing reproducible work, the GIScience conference series could offer awards and paper badges, provide author guidelines for computational research, and publish articles in Open Access formats.

The spatial analysis functionalities of geographic information systems (GIS) are increasingly used across the web. Interface specifications of geoprocessing web services define the syntactic properties of the services (number and type of parameters) and provide textual descriptions of the operations. The discovery and reuse of web services based on these syntactic properties is restricted and has led to the quest for extended operation descriptions. A number of extended operation descriptions have been proposed and are reviewed in this article. The reviewed descriptions focus on particular steps of the workflow development process. In this article we analyse all phases of the development process of a spatial analysis workflow regarding the requirements of operation descriptions. These requirements are translated into a knowledge base that contains information about spatial analysis operations for the operations’ discovery, selection and composition. The knowledge base also foresees the automated discovery of operations in well-defined application contexts such as the transformation of data types or coordinate reference systems. The knowledge base combines the geooperator approach with elements of ontology-based approaches. The geooperator browser is the implementation of the discovery and selection of geoprocessing operations based on the knowledge base. A draft formalization of the knowledge base demonstrates its use and the support it provides during the composition of operations.

This article details the process of integrating models to answer a specific policy-driven question: ‘What could be the impact of proposed Natural Water Retention Measures (NWRMs) on Europe’s Green Infrastructure (GI)?’ It describes the new Land Use Modelling Platform (LUMP), now enabling a high spatial scale (100-m) and large coverage (pan-European), whereby several sector-specific models contribute to assessing the impact of regional-level policy on a given spatial topic of concern. The configuration (land claims and land allocations modules) and calibration (accessibility and biophysical suitability) of the LUMP are explained. Four NWRM scenarios (riparian areas, afforestation, grassland and baseline scenario) are configured to run the simulations. For the reference: year 2006, the spatial representation of GI is based on land-use features of a refined version of the CORINE Land Cover (CLC), and resumed as connected components made of nodes and links. Mathematical morphological image processing and network graph theory model, available from the free software package GUIDOS (the Joint Research Center of the European Commission), enabled the measurement of the GI connectivity and identified most critical links. Results show that the competition for land claimed by different economic sectors, combined with policy-driven rule-sets for the implementation of different NWRMs, yields very different results for the 2030 land-use projections, and subsequently for the morphology of GI. Three indicators associated with the morphology of GI are computed in order to assess the model outputs for 2030. The indicators are computed to answer the following questions: (1) How is the quantity of GI affected by each of the NWRM, and what proportion of that GI is most valuable? (2) What is the location of the most critical nodes and connectors of GI, and what land-use conversions occur under these? (3) Are the average components getting larger or smaller? Whereas the grassland measure results in the largest net increase of GI, the afforestation measure results in the overall largest number of hectares of key nodes and links within the network. Land conversions occur under the critical GI nodes and links, with a large increase in agricultural areas, especially for the riparian measure under critical nodes and the grassland measure under critical links. Also predominant is the swapping of land from pasture to forest under critical links with the afforestation measure. The riparian measure most increases the average size of GI components, and all three measures contribute to bridging two large components which were divided in the 2006 land-use map, thus increasing the size of the largest component by more than 50%.

Traditional geographic information system models for map representation use superposition of layers to model physical reality, neglecting the integrity of the environment and limiting the ability to express interactions between features in complex phenomenon. This results in limitations regarding dynamic simulation and geographic causality reasoning. In this paper, we extend the framework of the geographic scene by formalizing the relationship between geographic processes and events to construct a dynamic data model: the process-event-centred dynamic data model. The key element of this data model is relationships between processes, events, and states of the natural or man-made phenomenon of interest. The identified relationships can be translated into a network of hierarchical, developmental, and causal graphs and realized in the Neo4j graph database. The implementation in the graph database supports spatio-temporal reasoning in geographic scenes and achieves an organizational framework for simulating spatio-temporal dynamics and complex calculations. The example of a 2019 mega-typhoon process is used to demonstrate the introduced process-event-centred model and its implementation in the graph database. A series of queries to the graph database show the capabilities of the data model for spatial reasoning and dynamic modeling.