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Open Science in the Geospatial Domain

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A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (20 December 2019) | Viewed by 41271

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Prof. Dr. Massimiliano Cannata

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Institute of Earth Sciences, University of Applied Sciences and Arts of Southern Switzerland, Campus Trevano, CH-6952 Canobbio, Switzerland
Interests: GIS; geomatics; open source; open science; environmental resource monitoring, modelling and management; geospatial web services; natural hazards

Special Issue Information

Dear Colleagues,

Open Science is being adopted by several research foundations and agencies worldwide as a core strategy to foster knowledge diffusion. This represents a new approach to research and educational processes based on OPEN data, sources, methodologies, reviews, access, and educational resources. While benefits are widely recognized, it is not always simple to apply this approach in practice: available resources, set strategies and existing common practices may be obstacles. Despite the fact that in the geospatial domain several open science practices are already widely adopted (see for example the success of Open Source Software at FOSS4G conferences, the number of Open Standards approved by OGC and the diffusion of Open Data created by OpenStreetMap), there are still underestimated and not yet properly addressed factors that limits the full realization of open science. In fact, while using the open standard and producing open source software and data is certainly important, it is not sufficient and different challenges are still to be addressed at a large scale. First and foremost, there are socio-cultural aspects such as the lack of recognition and rewards of open science practices, the overload for opening data, and unwillingness to change defined working procedures. Then, there are technological barriers, such as unintuitive tools for supporting open science, and missing political endorsements, not only by national or international bodies, but also by universities that ultimately implement strategies and policies that require economic investment in the short term to fulfill technical and organizational aspects. Last but not least, there are legal barriers, such as not always clear legal frameworks and lack of licenses understandable by scientists.

This Special Issue aims to show the state-of-the-art in the geospatial domain with respect to the application of Open Science. Contributions on experiences that implement the Open Science approach, or any of the specific Open Science pillars (data, code, paper, review, standards ?), in the geospatial domain, and that discuss principles, barriers and solutions are important to identify future challenges and best practices. This will help scientists harnessing the full potential of this new paradigm and put in practice Open Science, contributing to the future benefit of humanity.

Prof. Dr. Massimiliano Cannata

Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Open Science
Open Source Software
Open Education Material
Open Data
Open Standard
Open Access
Open Methodology

Published Papers (10 papers)

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22 pages, 1070 KiB

Open AccessArticle

Leveraging Container Technologies in a GIScience Project: A Perspective from Open Reproducible Research

by Benito M. Zaragozí, Sergio Trilles and José T. Navarro-Carrión

ISPRS Int. J. Geo-Inf. 2020, 9(3), 138; https://doi.org/10.3390/ijgi9030138 - 25 Feb 2020

Cited by 10 | Viewed by 3321

Abstract

Scientific reproducibility is essential for the advancement of science. It allows the results of previous studies to be reproduced, validates their conclusions and develops new contributions based on previous research. Nowadays, more and more authors consider that the ultimate product of academic research is the scientific manuscript, together with all the necessary elements (i.e., code and data) so that others can reproduce the results. However, there are numerous difficulties for some studies to be reproduced easily (i.e., biased results, the pressure to publish, and proprietary data). In this context, we explain our experience in an attempt to improve the reproducibility of a GIScience project. According to our project needs, we evaluated a list of practices, standards and tools that may facilitate open and reproducible research in the geospatial domain, contextualising them on Peng’s reproducibility spectrum. Among these resources, we focused on containerisation technologies and performed a shallow review to reflect on the level of adoption of these technologies in combination with OSGeo software. Finally, containerisation technologies proved to enhance the reproducibility and we used UML diagrams to describe representative work-flows deployed in our GIScience project. Full article

(This article belongs to the Special Issue Open Science in the Geospatial Domain)

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20 pages, 7812 KiB

Open AccessArticle

Adaptive Choropleth Mapper: An Open-Source Web-Based Tool for Synchronous Exploration of Multiple Variables at Multiple Spatial Extents

by Su Yeon Han, Sergio Rey, Elijah Knaap, Wei Kang and Levi Wolf

ISPRS Int. J. Geo-Inf. 2019, 8(11), 509; https://doi.org/10.3390/ijgi8110509 - 11 Nov 2019

Cited by 4 | Viewed by 4725

Abstract

Choropleth mapping is an essential visualization technique for exploratory spatial data analysis. Visualizing multiple choropleth maps is a technique that spatial analysts use to reveal spatiotemporal patterns of one variable or to compare the geographical distributions of multiple variables. Critical features for effective exploration of multiple choropleth maps are (1) automated computation of the same class intervals for shading different choropleth maps, (2) dynamic visualization of local variation in a variable, and (3) linking for synchronous exploration of multiple choropleth maps. Since the 1990s, these features have been developed and are now included in many commercial geographic information system (GIS) software packages. However, many choropleth mapping tools include only one or two of the three features described above. On the other hand, freely available mapping tools that support side-by-side multiple choropleth map visualizations are usually desktop software only. As a result, most existing tools supporting multiple choropleth-map visualizations cannot be easily integrated with Web-based and open-source data visualization libraries, which have become mainstream in visual analytics and geovisualization. To fill this gap, we introduce an open-source Web-based choropleth mapping tool called the Adaptive Choropleth Mapper (ACM), which combines the three critical features for flexible choropleth mapping. Full article

(This article belongs to the Special Issue Open Science in the Geospatial Domain)

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21 pages, 728 KiB

Open AccessArticle

An Empirical Evaluation of Data Interoperability—A Case of the Disaster Management Sector in Uganda

by Allan Mazimwe, Imed Hammouda and Anthony Gidudu

ISPRS Int. J. Geo-Inf. 2019, 8(11), 484; https://doi.org/10.3390/ijgi8110484 - 26 Oct 2019

Cited by 5 | Viewed by 3974

Abstract

One of the grand challenges of disaster management is for stakeholders to be able to discover, access, integrate and analyze task-appropriate data together with their associated algorithms and work-flows. Even with a growing number of initiatives to publish data in the disaster management sector using open principles, integration and reuse are still difficult due to existing interoperability barriers within datasets. Several frameworks for assessing data interoperability exist but do not generate best practice solutions to existing barriers based on the assessment they use. In this study, we assess interoperability for datasets in the disaster management sector in Uganda and identify generic solutions to interoperability challenges in the context of disaster management. Semi-structured interviews and focus group discussions were used to collect qualitative data from sector stakeholders in Uganda. Data interoperability was measured to provide an understanding of interoperability in the sector. Interoperability maturity is measured using qualitative methods, while data compatibility metrics are computed from identifiers in the RDF-triple model. Results indicate high syntactic and technical interoperability maturity for data in the sector. On the contrary, there exists considerable semantic and legal interoperability barriers that hinder data integration and reuse in the sector. A mapping of the interoperability challenges in the disaster management sector to solutions reveals a potential to reuse established patterns for managing data interoperability. These include; the federated pattern, linked data patterns, broadcast pattern, rights and policy harmonization patterns, dissemination and awareness pattern, ontology design patterns among others. Thus a systematic approach to combining patterns is critical to managing data interoperability barriers among actors in the disaster management ecosystem. Full article

(This article belongs to the Special Issue Open Science in the Geospatial Domain)

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25 pages, 5737 KiB

Open AccessArticle

Performance Testing of istSOS under High Load Scenarios

by Massimiliano Cannata, Milan Antonovic, Daniele Strigaro and Mirko Cardoso

ISPRS Int. J. Geo-Inf. 2019, 8(11), 467; https://doi.org/10.3390/ijgi8110467 - 23 Oct 2019

Cited by 3 | Viewed by 2212

Abstract

In the last 20 years, a mainstream in Earth information and decision making has been drawn by the vision of the digital earth that calls for 3D representation, interoperability and modelling. In this context, the time dimension is essential but despite its importance, not many open standards and implementations are available. The Sensor Observation Service from the Open Geospatial Consortium is one of them and was specifically designed to collect, store and share timeseries of observations from sensors. To better understand the performance and limitation of one software implementation of this standard in real cases, this study executed a load testing of the istSOS application under a high load condition, characterized by a high number of concurrent users, in three cases mimicking existing monitoring networks. The results, in addition to providing reference values for future similar tests, show the general capacity of istSOS in meeting the INSPIRE quality of service requirements and in offering good performance with less than 500 concurrent users. When the number of concurrent users increases to 1000 and 2000, only 80% of the response times are below 30 seconds, performance that is unsatisfactory in most modern usages. Full article

(This article belongs to the Special Issue Open Science in the Geospatial Domain)

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13 pages, 41711 KiB

Open AccessArticle

Comparison of FOSS4G Supported Equal-Area Projections Using Discrete Distortion Indicatrices

by Luís Moreira de Sousa, Laura Poggio and Bas Kempen

ISPRS Int. J. Geo-Inf. 2019, 8(8), 351; https://doi.org/10.3390/ijgi8080351 - 09 Aug 2019

Cited by 9 | Viewed by 5131

Abstract

This study compares the performance of five popular equal-area projections supported by Free and Open Source Software for Geo-spatial (FOSS4G)—Sinusoidal, Mollweide, Hammer, Eckert IV and Homolosine. A set of 21,872 discrete distortion vindicatrices were positioned on the ellipsoid surface, centred on the cells of a Snyder icosahedral equal-area grid. These indicatrices were projected on the plane and the resulting angular and distance distortions computed, all using FOSS4G. The Homolosine is the only projection that manages to minimise angular and distance distortions simultaneously. It yields the lowest distortions among this set of projections and clearly outclasses when only land masses are considered. These results also indicate the Sinusoidal and Hammer projections to be largely outdated, imposing too large distortions to be useful. In contrast, the Mollweide and Eckert IV projections present trade-offs between visual expression and accuracy that are worth considering. However, for the purposes of storing and analysing big spatial data with FOSS4G the superior performance of the Homolosine projection makes its choice difficult to avoid. Full article

(This article belongs to the Special Issue Open Science in the Geospatial Domain)

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25 pages, 1849 KiB

Open AccessArticle

Recommendation of Heterogeneous Cultural Heritage Objects for the Promotion of Tourism

by Landy Rajaonarivo, André Fonteles, Christian Sallaberry, Marie-Noëlle Bessagnet, Philippe Roose, Patrick Etcheverry, Christophe Marquesuzaà, Annig Le Parc Lacayrelle, Cécile Cayèré and Quentin Coudert

ISPRS Int. J. Geo-Inf. 2019, 8(5), 230; https://doi.org/10.3390/ijgi8050230 - 17 May 2019

Cited by 8 | Viewed by 3693

Abstract

The cultural heritage of a region, be it a highly visited one or not, is a formidable asset for the promotion of its tourism. In many places around the world, an important part of this cultural heritage has been catalogued by initiatives backed by governments and organisations. However, as of today, most of this data has been mostly unknown, or of difficult access, to the general public. In this paper, we present research that aims to leverage this data to promote tourism. Our first field of application focuses on the French Pyrenees. In order to achieve our goal, we worked on two fronts: (i) the ability to export this data from their original databases and data models to well-known open data platforms; and (ii) the proposition of an open-source algorithm and framework capable of recommending a sequence of cultural heritage points of interests (POIs) to be visited by tourists. This itinerary recommendation approach is original in many aspects: it not only considers the user preferences and popularity of POIs, but it also integrates different contextual information about the user as well as the relevance of specific sequences of POIs (strong links between POIs). The ability to export the cultural heritage data as open data and to recommend sequences of POIs are being integrated in a first prototype. Full article

(This article belongs to the Special Issue Open Science in the Geospatial Domain)

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10 pages, 2835 KiB

Open AccessArticle

The Light Source Metaphor Revisited—Bringing an Old Concept for Teaching Map Projections to the Modern Web

by Magnus Heitzler, Hans-Rudolf Bär, Roland Schenkel and Lorenz Hurni

ISPRS Int. J. Geo-Inf. 2019, 8(4), 162; https://doi.org/10.3390/ijgi8040162 - 28 Mar 2019

Cited by 2 | Viewed by 4526

Abstract

Map projections are one of the foundations of geographic information science and cartography. An understanding of the different projection variants and properties is critical when creating maps or carrying out geospatial analyses. The common way of teaching map projections in text books makes use of the light source (or light bulb) metaphor, which draws a comparison between the construction of a map projection and the way light rays travel from the light source to the projection surface. Although conceptually plausible, such explanations were created for the static instructions in textbooks. Modern web technologies may provide a more comprehensive learning experience by allowing the student to interactively explore (in guided or unguided mode) the way map projections can be constructed following the light source metaphor. The implementation of this approach, however, is not trivial as it requires detailed knowledge of map projections and computer graphics. Therefore, this paper describes the underlying computational methods and presents a prototype as an example of how this concept can be applied in practice. The prototype will be integrated into the Geographic Information Technology Training Alliance (GITTA) platform to complement the lesson on map projections. Full article

(This article belongs to the Special Issue Open Science in the Geospatial Domain)

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14 pages, 4522 KiB

Open AccessArticle

A Knowledge-Based Filtering Method for Open Relations among Geo-Entities

by Li Yu, Peiyuan Qiu, Jialiang Gao and Feng Lu

ISPRS Int. J. Geo-Inf. 2019, 8(2), 59; https://doi.org/10.3390/ijgi8020059 - 28 Jan 2019

Cited by 4 | Viewed by 3038

Abstract

Knowledge graphs (KGs) are crucial resources for supporting geographical knowledge services. Given the vast geographical knowledge in web text, extraction of geo-entity relations from web text has become the core technology for construction of geographical KGs; furthermore, it directly affects the quality of geographical knowledge services. However, web text inevitably contains noise and geographical knowledge can be sparsely distributed, both of which greatly restrict the quality of geo-entity relationship extraction. We propose a method for filtering geo-entity relations based on existing knowledge bases (KBs). Accordingly, ontology knowledge, fact knowledge, and synonym knowledge are integrated to generate geo-related knowledge. Then, the extracted geo-entity relationships and the geo-related knowledge are transferred into vectors, and the maximum similarity between vectors is the confidence value of one extracted geo-entity relationship triple. Our method takes full advantage of existing KBs to assess the quality of geographical information in web text, which is helpful to improve the richness and freshness of geographical KGs. Compared with the Stanford OpenIE method, our method decreased the mean square error (MSE) from 0.62 to 0.06 in the confidence interval [0.7, 1], and improved the area under the receiver operating characteristic (ROC) curve (AUC) from 0.51 to 0.89. Full article

(This article belongs to the Special Issue Open Science in the Geospatial Domain)

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16 pages, 6094 KiB

Open AccessArticle

Distributed Geoscience Algorithm Integration Based on OWS Specifications: A Case Study of the Extraction of a River Network

by Xicheng Tan, Liping Di, Yanfei Zhong, Nengcheng Chen, Fang Huang, Jinchuan Wang, Ziheng Sun and Yahya Ali Khan

ISPRS Int. J. Geo-Inf. 2019, 8(1), 12; https://doi.org/10.3390/ijgi8010012 - 28 Dec 2018

Cited by 3 | Viewed by 3232

Abstract

To understand and solve various natural environmental problems, geoscience research activities are becoming increasingly dependent on the integration of knowledge, data, and algorithms from scientists at different institutes and with multiple perspectives. However, the facilitation of these integrations remains a challenge because such scientific activities require gathering numerous geoscience researchers to provide data, knowledge, algorithms, and tools from different institutes and geographically distributed locations. The pivotal issue that needs to be addressed is the identification of a method to effectively combine geoscience algorithms in a distributed environment to promote cooperation. To address this issue, in this paper, a scheme for building a distributed geoscience algorithm integration based on the Open Geospatial Consortium web service (OWS) specifications is proposed. The architecture of the geoscience algorithm integration, algorithm service management mechanism, XML description method for algorithm integration, and integrated model execution strategy are designed and implemented. The experiment implements the integration of geoscience algorithms in a distributed cloud environment and evaluates the feasibility and efficiency of the integrated geoscience model. The proposed method provides a theoretical basis and practical guidance for promoting the integration of distributed geoscience algorithms; this approach can help to aggregate the distributed geoscience capabilities to address natural challenges. Full article

(This article belongs to the Special Issue Open Science in the Geospatial Domain)

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14 pages, 7425 KiB

Open AccessTechnical Note

pyjeo: A Python Package for the Analysis of Geospatial Data

by Pieter Kempeneers, Ondrej Pesek, Davide De Marchi and Pierre Soille

ISPRS Int. J. Geo-Inf. 2019, 8(10), 461; https://doi.org/10.3390/ijgi8100461 - 17 Oct 2019

Cited by 6 | Viewed by 6571

Abstract

A new Python package, pyjeo, that deals with the analysis of geospatial data has been created by the Joint Research Centre (JRC). Adopting the principles of open science, the JRC strives for transparency and reproducibility of results. In this view, it has been decided to release pyjeo as free and open software. This paper describes the design of pyjeo and how its underlying C/C++ library was ported to Python. Strengths and limitations of the design choices are discussed. In particular, the data model that allows the generation of on-the-fly data cubes is of importance. Two uses cases illustrate how pyjeo can contribute to open science. The first is an example of large-scale processing, where pyjeo was used to create a global composite of Sentinel-2 data. The second shows how pyjeo can be imported within an interactive platform for image analysis and visualization. Using an innovative mechanism that interprets Python code within a C++ library on-the-fly, users can benefit from all functions in the pyjeo package. Images are processed in deferred mode, which is ideal for prototyping new algorithms on geospatial data, and assess the suitability of the results created on the fly at any scale and location. Full article

(This article belongs to the Special Issue Open Science in the Geospatial Domain)

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