Leveraging Container Technologies in a GIScience Project: A Perspective from Open Reproducible Research
"> Figure 1
<p>The spectrum of scientific reproducibility, between a scientific publication and full replication (based on [<a href="#B3-ijgi-09-00138" class="html-bibr">3</a>]).</p> "> Figure 2
<p>An Open Reproducible Research spectrum in the field of GISciences. Note: the relationships between the elements in the core of the diagram are not defined, so these will vary according to the needs of each project.</p> "> Figure 3
<p><span class="html-italic">Docker</span> Hub ecosystem and packaging of compiled containers.</p> "> Figure 4
<p>Computational experience in the SIOSE-INNOVA project [<a href="#B23-ijgi-09-00138" class="html-bibr">23</a>].</p> "> Figure 5
<p>UML activity diagram showing all the tasks automated in the SIOSE-splitter solution.</p> "> Figure 6
<p>UML components diagram showing the containers and configurations used in the SIOSE-splitter solution.</p> ">
Abstract
:1. Introduction
1.1. The SIOSE-INNOVA Project
1.2. Objectives
2. Open Reproducible Research in the Geospatial Domain
2.1. Collaborative Development and Code Sharing
- Python. This is the language with the greatest presence in the GIS realm. This is due to its use as a scripting language in the best-known GIS desktop applications, such as QGIS [38].
- R. This language is used for statistical computing, data visualisation, data science and geospatial analysis. It can also be used to access other GIS or inside GIS tools such as QGIS via connectors. The most relevant and useful R libraries are collected into the Spatial Task View (https://cran.r-project.org/view=Spatial).
- SQL. For many years, it has been used to query geospatial databases. SQL is at the heart of many GIS operations in spatial queries. It is used in software such as PostGIS [41], Spatialite and QGIS.
2.2. Geospatial Data Availability
- ESRI Shapefile [55]. This is a traditional and widely used vector data format. Normally, a Shapefile encloses at least three or four different files (.shp, .dbf, .shx and .prj) and defines a single type of geometry, which implies that each Shapefile can only store points, lines or polygons. This format, although widely used, was not intended as an open format when it was conceived. However, any GIScientist should know this format given the large amount of legacy data stored in this format and users that still use it.
- GeoCSV [56]. Comma-Separated Values (CSV) is a vector file format and is extensively used in any discipline. The CSV format allows work to be carried out using the most usual programs, such as Excel or Notepad. CSV is a plain text file where columns are separated using commas and rows by lines. More specifically, an optional geometry extension (GeoCSV) has been created that can store points (latitude and longitude) and Well-Known Text (WKT) standard geometries.
- GeoJSON [57]. This is an extension of JavaScript Object Notation (JSON) adding geospatial data. It offers the advantage of being a lightweight, easy-to-read notation for web applications. It is usually used as output for APIs and is a vector format.
- Spatialite/SQLite [58]. SpatiaLite is an open source library that extends the SQLite core with Spatial SQL capabilities. This is conceptually similar to the combination of PostgreSQL/PostGIS, but in this case, Spatialite is meant for embedded and portable geodatabases.
- NetCDF [61]. This is a data model for array-oriented scientific data that can also store both, vector and raster data. It is a recommended format to encode and distribute multi-dimensional and gridded geospatial data.
2.3. Linked Solutions in the Geospatial Context
3. A Brief Look at the Adoption of Containerisation in GISc Projects
- Selection of OSGeo tools. There are currently sixty-three different projects indexed on the OSGeo website, including OSGeo projects and community projects. Following the naming convention used on this website in determining featured tools that appears in the main menu, twenty-one tools have been selected from the seven categories defined in Table 1.
- Definition of searches. Searches were defined using the names of the OSGeo tools as query terms. Each search returned the list of images stored in Docker Hub, ordered by descending number of downloads (pulls). A filter was applied to set a maximum of 1000 results in order to ignore residual Docker images and build the analysis on those that are widely used.
- Execution of searches. Searches were executed by using the above-mentioned Docker Registry API. More specifically, we used a PowerShell module called PSDockerHub (https://github.com/beatcracker/PSDockerHub). The results were saved in a CSV file for each OSGeo project. From all the images, we obtained the name, description and number of downloads (pulls).
- Manual selection of results. After the search step, a manual phase is performed to detect possible Docker images not related to OSGeo tools. To reach this goal, the returned image results have been verified using the description field to check whether an OSGeo tool is used.
- Aggregation and final results. In the last step, an aggregation is performed on each tool to obtain a total number of downloads.
4. Enabling Research Reproducibility in the SIOSE-INNOVA Project
4.1. Benchmarking SQL and NoSQL Database Models
4.2. An ETL Process Using Containerisation
- The user launches the workflow using several docker run commands or one single execution of the deployment software (docker-compose up).
- Docker starts all the necessary services sequentially (see Figure 6).
- A PostgreSQL/PostGIS server is started which already contains a preloaded SIOSE database.
- SQL scripts are executed from the Makefile, creating spatial grids and statistics that will be used to split the database into GIS portable files. GNU Make will ensure that all files are created, and it will prevent any unnecessary overwrite operations from being carried out on any file.
- Then, the process loops over the cells of several spatial grids at different mapping scales.
- In every iteration, a spatial query is executed and intersects the SIOSE database with the corresponding area of interest (cell grid).
- Using the GDAL/OGR library, the selected SIOSE polygons together with a selection of LU/LC attributes are written into a new Geopackage file.
- This new Geopackage is compressed and stored in the working directory.
- Then the user can inspect the database or check the produced outputs using a containerised version of PGadmin4 or QGIS.
- When the user stops the process, Docker stops all services and frees all the unused computing resources.
5. Concluding Remarks
Author Contributions
Funding
Conflicts of Interest
References
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Category | Tools |
---|---|
Content Management Systems | GeoNode |
Desktop Applications | Marble, gvSIG Desktop, QGIS Desktop and GRASS GIS |
Geospatial Libraries | GeoTools, Orfeo ToolBox, GDAL/OGR and GEOS |
Metadata Catalogues | GeoNetwork and pycsw |
Spatial Databases | PostGIS |
Web Mapping | MapServer, MapFish, Deegree, OpenLayers, GeoMoose, Mapbender, PyWPS and GeoServer |
Category | Tool | Downloads |
---|---|---|
Content Management Systems | GeoNode | 873,869 |
Desktop Applications | GRASS GIS | 317 |
gvSIG | 0 | |
Marble | 0 | |
QGIS | 392,882 | |
Geospatial Libraries | GDAL/OGR | 1,653,481 |
GEOS | 101,165 | |
GeoTools | 2437 | |
Orfeo Toolbox | 1736 | |
Metadata Catalogues | GeoNetwork | 2,983,271 |
pycsw | 482,912 | |
Spatial Databases | PostGIS | 35,267,083 |
Web Mapping | geomoose | 0 |
Degree | 67 | |
Geoserver | 1,731,538 | |
MapBender | 986 | |
MapFish | 0 | |
MapServer | 1,170,538 | |
OpenLayers | 320,234 | |
pywps | 1162 |
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Zaragozí, B.M.; Trilles, S.; Navarro-Carrión, J.T. Leveraging Container Technologies in a GIScience Project: A Perspective from Open Reproducible Research. ISPRS Int. J. Geo-Inf. 2020, 9, 138. https://doi.org/10.3390/ijgi9030138
Zaragozí BM, Trilles S, Navarro-Carrión JT. Leveraging Container Technologies in a GIScience Project: A Perspective from Open Reproducible Research. ISPRS International Journal of Geo-Information. 2020; 9(3):138. https://doi.org/10.3390/ijgi9030138
Chicago/Turabian StyleZaragozí, Benito M., Sergio Trilles, and José T. Navarro-Carrión. 2020. "Leveraging Container Technologies in a GIScience Project: A Perspective from Open Reproducible Research" ISPRS International Journal of Geo-Information 9, no. 3: 138. https://doi.org/10.3390/ijgi9030138
APA StyleZaragozí, B. M., Trilles, S., & Navarro-Carrión, J. T. (2020). Leveraging Container Technologies in a GIScience Project: A Perspective from Open Reproducible Research. ISPRS International Journal of Geo-Information, 9(3), 138. https://doi.org/10.3390/ijgi9030138