The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XLVI-4/W2-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W2-2021, 17–24, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W2-2021-17-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W2-2021, 17–24, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W2-2021-17-2021

  19 Aug 2021

19 Aug 2021

USING JUPYTER NOTEBOOKS FOR VIEWING AND ANALYSING GEOSPATIAL DATA: TWO EXAMPLES FOR EMOTIONAL MAPS AND EDUCATION DATA

G. S. Camara1, S. P. Camboim1, and J. V. M. Bravo2 G. S. Camara et al.
  • 1Graduate Program in Geodetic Science, Federal University of Parana, Brazil
  • 2Institute of Geography, Graduate Program in Geography and Graduate Program in Agriculture and Geospatial Information, Federal University of Uberlandia, Brazil

Keywords: Open Science, Jupyter Notebooks, Google Colab, Education Data, Emotional Maps

Abstract. This article presents two applications developed using Jupyter Notebook in the Google Colab, combining several Python libraries that enable an interactive environment to query, manipulate, analyse, and visualise spatial data. The first application is from an educational context within the MAPFOR project, aiming to elaborate an interactive map of the spatial distributions of teachers with higher education degrees or pedagogical complementation per vacancies in higher education courses. The Jupyter solutions were applied in MAPFOR to better communicate within the research team, mainly in the development area. The second application is a framework to analyse and visualise collaborative emotional mapping data in urban mobility, where the emotions were collected and represented through emojis. The computational notebook was applied in this emotional mapping to enable the interaction of users, without a SQL background, with spatial data stored in a database through widgets to analyse and visualise emotional spatial data. We developed these different contexts in a Jupyter Notebook to practice the FAIR principles, promote the Open Science movement, and Open Geospatial Resources. Finally, we aim to demonstrate the potential of using a mix of open geospatial technologies for generating solutions that disseminate geographic information.