## 3 Dataset Analysis
### Sources Analysis  
The analysis of this study in relation to the top 20 most representative sources for the topic under discussion highlights the diversification that exists among the research considered, as it has been published in various journals, as shown in Figure 4. In Figure 4, the number inside the dots represent the number of papers published in each source, while the color and the size of the dots provide a visually representation of this contribution (e.g. larger and darker dots represent a higher contribution of the journals to the field). The most significant source is represented by _Remote Sensing_, with 16 articles, emphasizing the importance of the usage of AI applications within local and regional studies, especially their usage in facilitating our day-to-day lives. The second-ranked source is _Sustainability_, with 15 articles, highlighting the positive impact that studies related to the subject matter have, covering topics such as applied sciences, economics, or engineering. Amongst other notable sources, there are _PLOS ONE_, with eight articles, _Mathematical Problems in Engineering_, with seven articles, followed by _Computational Intelligence and Neuroscience_, _IEEE Access_, _Sensors_, and _Soft Computing_, each of them being described by four articles. Consequently, given the existing journal variation that exists within the analyzed topic, it is evident that the discussed topic is a complex one, covering the impact that AI applications have over the global context, whether it is an economic, engineering, or theoretical one, so there is a need for research that addresses the use of AI applications in local and regional studies. Figure 4: Top 20 most relevant journals. \begin{table}
\begin{tabular}{c c} \hline \hline
**Main Information about Authors** & **Results** \\ \hline Single-authored docs & 23 \\ Co-authors per doc & 4.41 \\ International co-authorships (\%) & 30.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Collaboration of the authors. Using Figure 5, the grouping of sources based on [PERSON]'s law can be seen, as they have been categorized into separate areas, given the distribution of articles. [PERSON]'s law is a theory in bibliometrics that classifies scientific journals into three zones based on the relevance and frequency of article appearances. Zone 1, known as the core of highly productive journals, includes those most frequently cited in specialized literature. Zone 2 represents journals with a moderate number of citations, while Zone 3 consists of journals that are less frequently cited [74; 75]. Figure 5 shows all journals that have a high number of citations; thus, they are placed into Zone 1 according to [PERSON]'s law. The first zone described by [PERSON]'s law consists of sources that have the highest productivity, contributing the most to the research field. In our scenario, the first zone is characterized by journals such as _Remote Sensing_, _Sustainability_, _PLOS ONE_, _Mathematical Problems in Engineering_, _Computational Intelligence and Neuroscience_, _Ecological Informatics_, and _Applied Energy_. The aforementioned journals are only a few examples of highly important journals in the context of our study, proving that the usage of AI applications in local and regional studies can be covered from very wide ranges, although most of them are mainly focused on technological computation. Journals' local impact, underscored by the H-index, emphasizes how impactful various journals are. The H-index is described by the number of articles from a specific source, each of them having more than H citations. The H-index (Hirsch index) is a measure used to assess the productivity and citation impact of a researcher, journal, or institution. It reflects both the number of publications (productivity) and the number of citations each publication receives (impact) [76]. Figure 6 puts this into perspective, with the journal _Remote Sensing_ having an H-index of 6, implying the fact that this journal contains six articles that have at least six citations each.