EGU2020-14433
https://doi.org/10.5194/egusphere-egu2020-14433
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

A text-mining approach to assess impacts and benefits of Nature-Based Solutions

Leydy Alejandra Castellanos Diaz1, Pierre Antoine Versini1, Ioulia Tchiguirinskaia1, and Olivier Bonin2
Leydy Alejandra Castellanos Diaz et al.
  • 1HM&Co, Ecole des Ponts ParisTech, Champs-sur-Marne, France (leydy.castellanos@enpc.fr)
  • 2LVMT, Ecole des Ponts ParisTech, Champs-sur-Marne, France (olivier.bonin@enpc.fr)

Worldwide, research community has studied the benefits of green and blue spaces implementation in urban areas, generating a great amount of literature regarding this topic. Since these solutions are of interest to face climate change impacts in cities, the European Commission (EC) has funded several projects to make an extensive review of the available literature. Three of these projects were especially studied here, namely EKLIPSE, Mapping Assessment of Ecosystem and their Services- Urban Ecosystem (MAES: Urban Ecosystems), and NATure-based URban innoVATION (NATURVATION). They all aim to identify the physical and social impacts, benefits and trade-offs of Nature-Based Solutions (NbS).

To objectively compare findings presented in the deliverable reports, a text-mining approach was carried out. This methodology coupled with a data visual representation allowed to convert the EC projects reports (corpus) into a meaningful structured analysis. As a result, a graphical representation was created, making possible to recognize concepts, patterns and attributes addressed by each text, as well as stakeholders and their position with respect to the topic.

 The text mining analysis was implemented through Gargantex Blue Jasmine Version (an open source software developed by ISC-PIF). Gargantex results permitted to recover a list of key-terms from each corpus based in their co-occurrence in the whole text. These terms were used to elaborate a visual representation or network, placing the words strongly related close to each other and characterizing the obtained clusters by a similar color.

This approach underlined the specific focus of each project: the conciliation between urbanisation and urban ecosystems (MAES), or the economic valuation and monetisation of NbS (NATURVATION) for instance. Moreover, it demonstrated that despite the different literature review methodologies of each report/project, there are some common trends exhibited by the obtained graphical networks and their statistical attributes. For instance, the need to assess the NbS performance with some adapted indicators; and the important EC supporting role in the implementation of NbS. Similarly, some regulating (e.g. water quality or temperature reduction) and cultural (e.g. recreation or health benefits) services are more addressed.  

This analysis can be applied to all kind of corpus, which makes it easy to understand different and similar concepts and approaches of a set of text data. A text-mining analysis can be conducted over the direct references of NbS benefits, on a collection of publications of a research database like Scopus or Science Direct. 

How to cite: Castellanos Diaz, L. A., Versini, P. A., Tchiguirinskaia, I., and Bonin, O.: A text-mining approach to assess impacts and benefits of Nature-Based Solutions , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14433, https://doi.org/10.5194/egusphere-egu2020-14433, 2020

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Display material version 1 – uploaded on 05 May 2020
  • CC1: Comment on EGU2020-14433, Sam Illingworth, 05 May 2020

    This is such cool research! How wasy would it be to apply this methodology to another topic?

    • AC1: Reply to CC1, Leydy Alejandra Castellanos Diaz, 06 May 2020

      Thank you Sam! This method could be used for any kind of topic and documents. It is a method that objectively facilities the interpretation of guidelines or public documents, based on the occurrence of terms in them. For example, in 2017 this methodology was used to characterize the themes exposed in the French presidential speech.