## 3 Dataset Analysis
### Analysis of Literature
#### 3.4.3 Words Analysis  
In order to provide a method that is efficient when investigating the language or the existing content within scientific documents, an analysis of the keywords was conducted. This analysis not only provides valuable insights into the research area, but also offers valuable information regarding the trends or characteristics that are present within specific domains. Studying the keywords is an essential step in order to better understand the key concepts and terminologies that are used in the research field, proving to be really helpful when it comes to being able to comprehend the definition of some specific terms, or in scenarios when some clarification is needed. Additionally, by inspecting the group of words that often appear together in documents, groups of words or clusters where each word is associated with the others can be easily identified. Furthermore, topics that possess a high relevance within the research area can also be determined with the help of the number of citations that their specific keywords have. Therefore, with the help of this kind of analysis, not only is a rundown of the main themes or subdomains provided, but subjects that are impactful on the field of study can also be discovered. With the help of Table 8, the top 10 most frequent words that have occurred under Keywords Plus are presented. It can be observed that the word with the most occurrences is \"model\", implying the fact that, in order to make use of the AI applications within local and regional studies correctly, researchers should first develop an AI model that is suitable to their own needs, to correctly analyze its output. More than this, \"classification\" also registers a high number of occurrences, with 19, emphasizing the fact that the data used in the research need to be first locally or regionally classified. Another frequently used term is \"prediction\", indicating that the articles often rely on prediction models, as they prove to give robustness to the quantitative models used in various regional contexts. Thus, the words with the most occurrences for the Keywords Plus section suggest that there is a strong focus on using an AI model, such as random forest or Linear Regression, based on the locally or regionally classified data in order to correctly predict the output in various economic contexts. Furthermore, the appearances of other words, such as \"impact\", \"gis\", \"index\", \"performance\", and \"support vector machine\" also indicate some specific areas which the researchers are paying attention to in the area of AI applications within local and regional studies. With regard to the top 10 most frequent words in authors' keywords, these are described with the help of Table 9. The recurring terms serve to illustrate the dominant themes, as these are the focus within the research. Amongst the most frequent words that have been used in authors' keywords, terms such as \"machine learning\", \"deep learning\", \"artificial intelligence\", and \"random forest\" are present, underscoring the fact that these concepts represent focal points in the context of scientific research. Collectively, these groups of words indicate a major interest in the usage of AI applications, especially in local and regional studies, as suggested by the appearance of other terms that can be seen in the table below, like \"air pollution\" or \"regional economy\". Thus, the focus of our research can be determined by the word frequency analysis, as this analysis is able to provide a very solid perspective on the key themes and areas present in our article, offering details about the predominant methodologies and topics in the area of study. The high frequency of the keyword \"machine learning\" indicates that this technology is central to current research on the applications of artificial intelligence at the local and regional levels. This suggests that many studies focus on the use of machine learning algorithms to solve problems specific to the analyzed areas.