Chapter Unsupervised spatial data mining for the development of future scenarios: a Covid-19 application
In the context of Futures Studies, the scenario development process permits to make assumptions on what the futures can be in order to support better today decisions. In the initial stages of the scenario building (Framing and Scanning phases), the process requires much time and efforts to scanning...
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| Үндсэн зохиолчид: | , |
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| Формат: | Online |
| Хэл сонгох: | англи |
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Firenze University Press
2022
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| Нөхцлүүд: | |
| Онлайн хандалт: | ONIX_20220601_9788855184618_570 |
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Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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| _version_ | 1869514937744752640 |
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| author | Calleo, Yuri Di Zio, Simone |
| author_browse | Calleo, Yuri Di Zio, Simone |
| author_facet | Calleo, Yuri Di Zio, Simone |
| author_sort | Calleo, Yuri |
| collection | Directory of Open Access Books |
| description | In the context of Futures Studies, the scenario development process permits to make assumptions on what the futures can be in order to support better today decisions. In the initial stages of the scenario building (Framing and Scanning phases), the process requires much time and efforts to scanning data and information (reading of documents, literature review and consultation of experts) to understand more about the object of the foresight study. The daily use of social networks causes an exponential increase of data and for this reason here we deal with the problem of speeding up and optimizing the Scanning phase by applying a new combined method based on the analysis of tweets with the use of unsupervised classification models, text-mining and spatial data mining techniques. For the purpose of having a qualitative overview, we applied the bag-of-words model and a Sentiment Analysis with the Afinn and Vader algorithms. Then, in order to extrapolate the influence factors, and the relevant key factors (Kayser and Blind, 2017; 2020) the Latent Dirichlet Allocation (LDA) was used (Tong and Zhang, 2016). Furthermore, to acquire also spatial information we used spatial data mining technique to extract georeferenced data from which it was possible to analyse and obtain a geographic analysis of the data. To showcase our method, we provide an example using Covid-19 tweets (Uhl and Schiebel, 2017), upon which 5 topics and 6 key factors have been extracted. In the last instance, for each influence factor, a cartogram was created through the relative frequencies in order to have a spatial distribution of the users discussing each particular topic. The results fully answer the research objectives and the model used could be a new approach that can offer benefits in the scenario developments process. |
| format | Online |
| id | doab-20.500.12854ir-83811 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Firenze University Press |
| publisherStr | Firenze University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-838112022-06-02T04:39:03Z Chapter Unsupervised spatial data mining for the development of future scenarios: a Covid-19 application Calleo, Yuri Di Zio, Simone text-mining spatial analysis scenario development georeferenced textual data covid-19 In the context of Futures Studies, the scenario development process permits to make assumptions on what the futures can be in order to support better today decisions. In the initial stages of the scenario building (Framing and Scanning phases), the process requires much time and efforts to scanning data and information (reading of documents, literature review and consultation of experts) to understand more about the object of the foresight study. The daily use of social networks causes an exponential increase of data and for this reason here we deal with the problem of speeding up and optimizing the Scanning phase by applying a new combined method based on the analysis of tweets with the use of unsupervised classification models, text-mining and spatial data mining techniques. For the purpose of having a qualitative overview, we applied the bag-of-words model and a Sentiment Analysis with the Afinn and Vader algorithms. Then, in order to extrapolate the influence factors, and the relevant key factors (Kayser and Blind, 2017; 2020) the Latent Dirichlet Allocation (LDA) was used (Tong and Zhang, 2016). Furthermore, to acquire also spatial information we used spatial data mining technique to extract georeferenced data from which it was possible to analyse and obtain a geographic analysis of the data. To showcase our method, we provide an example using Covid-19 tweets (Uhl and Schiebel, 2017), upon which 5 topics and 6 key factors have been extracted. In the last instance, for each influence factor, a cartogram was created through the relative frequencies in order to have a spatial distribution of the users discussing each particular topic. The results fully answer the research objectives and the model used could be a new approach that can offer benefits in the scenario developments process. 2022-06-02T04:39:02Z 2022-06-02T04:39:02Z 2022-06-01T12:21:20Z 2021 chapter ONIX_20220601_9788855184618_570 2704-5846 https://library.oapen.org/handle/20.500.12657/56385 9788855184618 https://directory.doabooks.org/handle/20.500.12854/83811 eng Proceedings e report open access image/jpeg Attribution 4.0 International https://library.oapen.org/bitstream/20.500.12657/56385/1/26251.pdf Firenze University Press 10.36253/978-88-5518-461-8.33 10.36253/978-88-5518-461-8.33 2ec4474d-93b1-4cfa-b313-9c6019b51b1a 9788855184618 6 Florence open access |
| spellingShingle | text-mining spatial analysis scenario development georeferenced textual data covid-19 Calleo, Yuri Di Zio, Simone Chapter Unsupervised spatial data mining for the development of future scenarios: a Covid-19 application |
| title | Chapter Unsupervised spatial data mining for the development of future scenarios: a Covid-19 application |
| title_full | Chapter Unsupervised spatial data mining for the development of future scenarios: a Covid-19 application |
| title_fullStr | Chapter Unsupervised spatial data mining for the development of future scenarios: a Covid-19 application |
| title_full_unstemmed | Chapter Unsupervised spatial data mining for the development of future scenarios: a Covid-19 application |
| title_short | Chapter Unsupervised spatial data mining for the development of future scenarios: a Covid-19 application |
| title_sort | chapter unsupervised spatial data mining for the development of future scenarios a covid 19 application |
| topic | text-mining spatial analysis scenario development georeferenced textual data covid-19 |
| topic_facet | text-mining spatial analysis scenario development georeferenced textual data covid-19 |
| url | ONIX_20220601_9788855184618_570 |
| work_keys_str_mv | AT calleoyuri chapterunsupervisedspatialdataminingforthedevelopmentoffuturescenariosacovid19application AT diziosimone chapterunsupervisedspatialdataminingforthedevelopmentoffuturescenariosacovid19application |