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Prediction models and testing of resilience in regions
- Source :
- Region: the journal of ERSA
- Publication Year :
- 2023
- Publisher :
- European Regional Science Association, 2023.
-
Abstract
- A significant amount of research has been conducted regarding the resilience of the regions and the factors that contribute to allow them to face challenges, crises, or disasters. The rise of promising sectors like Machine learning (ML) and Artificial Intelligence (AI) can enhance this research using computing power in regional economic, social, and environmental data analysis to find patterns and create prediction models. Through Machine Learning, the following research introduces the use of models that can predict the performance of a region in disasters. A case study of the performance of USA Counties during the Covid19 first wave period of the pandemic and the related restrictions that were applied by the authorities was used in order to reveal the obvious or hidden parameters and factors that affected their resilience, in particular their economic response, and other interesting patterns between all the involved attributes. This paper aims to contribute to a methodology and to offer useful guidelines in how regional factors can be translated and processed by data and ML/AI tools and techniques. The proposed models were evaluated on their ability to predict the economic performance of each county and in particular the difference of its unemployment rate between March and June of 2020. The former is based on several economic, social, and environmental data -up to that point in time- using classifiers like neural networks and decision trees. A comparison of the different models' execution was performed, and the best models were further analyzed and presented. Further execution results that identified patterns and connections between regional data and attributes are also presented. The main results of this research are i) a methodological framework of how regional status can be translated into digital models and ii) related examples of predictive models in a real case. An effort was also made to decode the results in terms of regional science to produce useful and meaningful conclusions, thus a decision tree is also presented to demonstrate how these models can be interpreted. Finally, the connection between this work and the strong current trend of regional and urban digitalization towards sustainability is established.
- Subjects :
- Digitalisierung
economic impact
unemployment
Economics and Econometrics
analysis
Raumplanung und Regionalforschung
Geography, Planning and Development
Prognose
Arbeitslosigkeit
Epidemie
Modell
United States of America
digitalization
epidemic
wirtschaftliche Folgen
Resilienz
Covid-19
Machine Learning
Prediction Models
Counties
Restrictions
ddc:710
resilience
USA
künstliche Intelligenz
Landscaping and area planning
Städtebau, Raumplanung, Landschaftsgestaltung
model
Nachhaltigkeit
Area Development Planning, Regional Research
regional factors
Analyse
artificial intelligence
sustainability
Daten
data
regionale Faktoren
prognosis
Subjects
Details
- ISSN :
- 24095370
- Volume :
- 10
- Database :
- OpenAIRE
- Journal :
- REGION
- Accession number :
- edsair.doi.dedup.....5da377b8b5200b4e07aa66226bb7468f