Using Jupyter Notebooks for viewing and analyzing geospatial data: Two examples for emotional maps and educational data
2021-10-01, 10:30–11:00, Academic

This research 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.

Python tools and libraries for spatial data analysis have been developed along with the open science trend in the last ten years. There was also a widespread adoption of computational notebooks, as Jupyter and Google Colab, enabling researchers of many areas to become not just code users but also creators, contributing to making science even more open. A Jupyter notebook is a browser-based tool that works together with python libraries, which supports acquiring, transforming, exploring, describing, and visualising non-spatial and spatial data. These notebooks allow the interaction between data and users once it is possible to create widgets, connect with spatial databases and develop interactive maps. Similarly, the Google Colab is a Python environment executed in a browser using Google Cloud and is also a tool to share the codes developed, an alternative to Github or NBviewer. In this context, we developed two applications in Google Colab, one for analysis and visualisation of collaborative emotional mapping in urban mobility in Curitiba, Brasil, and another for spatial query visualisation of educational data from Paraná state, Brazil. The first applications are in collaborative emotional mapping, which allows the representation of the emotions or sentiments experienced in a specific location of the space that occurs according to the individual's emotional bond generating information with the environment's experience. Thus, a case study was developed with participants of an intermodal challenge in the city of Curitiba, the capital of Parana state Brazil, for emotional mapping in the context of urban mobility, through the collection and representation of data using emojis. A Jupyter notebook was developed for data analysis and visualisation through thematic maps, which represent emotions with emojis. In the notebook, it is possible to consult the database through widgets and visualise which emotions were associated with a specific street or make a route and see what emotions were attributed to the route's streets. The second application is mapping educational data, aiming to support analysis of the demand for initial and continuing teacher education. This task is part of MAPFOR project, which aims to ensure the educational quality offered in all stages of primary education in the State of Paraná. Using higher education institutions' location and the number of teachers by the number of vacancies in higher education courses was developed as a thematic map with proportional symbols. The user of Jupyter notebook can choose the parameters through the widgets. The selected data is automatically sorted by Jenks method using the Jenkspy library. This platform was chosen to elaborate the maps instead of a more specialised system, such as QGIS, so there could be better communication with the interdisciplinary team, mainly in the development area. For the development of both applications, we used python libraries, namely, folium, psycopg2, json, among others. Using these tools, it was possible to create a method for spatial queries through widgets, allowing users to explore and analyse the data without specific knowledge.

Authors and Affiliations

Gabriele Silveira Camara
Federal University of Parana, Brasil. Graduate Program in Geodetic Science.
Silvana Philippi Camboim
Federal University of Parana, Brasil. Graduate Program in Geodetic Science.
João Vitor Meza Bravo
Federal University of Uberlandia, Brasil. Institute of Geography, Graduate Program in Geography and Graduate Program in Agriculture and Geospatial Information.

Requirements for the Attendees




Open and Reproducible Science


2 - Basic. General basic knowledge is required.

Language of the Presentation