## 3 Dataset Analysis
### Analysis of Literature
#### 3.4.3 Words Analysis
This can also be observed in the word cloud in Figure 16B. Likewise, the emergence of \"deep learning\", \"artificial intelligence\", and \"random forest\" indicates that many studies focus on advanced methods to improve the accuracy and efficiency of artificial intelligence models in a regional context. Figure 16A shows the word cloud based on the keywords used in the analyzed research. We observe a high frequency of occurrence of the following words: model, \begin{table}
\begin{tabular}{c c} \hline \hline
**Words** & **Occurrences** \\ \hline model & 24 \\ classification & 19 \\ prediction & 18 \\ logistic-regression & 17 \\ impact & 15 \\ random forest & 15 \\ gis & 11 \\ index & 10 \\ performance & 10 \\ support vector machine & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Top 10 most frequent words in Keywords Plus. \begin{table}
\begin{tabular}{c c} \hline \hline
**Words** & **Occurrences** \\ \hline machine learning & 48 \\ deep learning & 22 \\ artificial intelligence & 18 \\ random forest & 12 \\ air pollution & 9 \\ remote sensing & 8 \\ regional economy & 7 \\ gis & 6 \\ artificial neural network & 5 \\ internet of things & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Top 10 most frequent words in authors’ keywords. classification, prediction, optimization, index, quality, performance, and climate change. The high frequency of the word \"model\" indicates that a large part of the researched research focuses on the development and application of artificial intelligence models. These models are used to simulate and analyze various phenomena and processes at the local and regional levels. \"Classification\" suggests the use of classification algorithms in AI to sort and organize data into distinct categories. This is essential in many applications, such as risk analysis, pattern identification, and geographic data segmentation. The word \"prediction\" reflects the importance of predictions in regional and local studies. AI models are often used to predict future trends, such as climate change, regional economic developments, or environmental risks. \"Optimization\" highlights the use of optimization techniques to improve the efficiency and performance of the systems under analysis. This term is relevant in the context of resource management, urban planning, and optimization of transport networks. \"Index\" indicates the development and use of indices to measure and compare different aspects of performance and quality of life in the regions under study. Indices may include measures of air quality, economic development, or environmental sustainability. \"Quality\" suggests that research focuses on assessing and improving quality in a variety of areas, from air and water quality to the quality of public services and infrastructure. The word \"performance\" emphasizes the interest in measuring and evaluating performance in various contexts. This term can be associated with economic performance, infrastructure performance, or the performance of resource management systems. \"Climate change\" emphasizes concern about the impact of climate change on the regions under study. The research focuses on how AI can be used to monitor, predict, and manage the effects of climate change. Tables 10 and 11 give an overview of the most frequent bigrams and trigrams in the titles and abstracts of the analyzed articles. These tables highlight the main themes and concepts that are frequently discussed in the literature, emphasizing the predominant research directions. Regarding Table 10, the most frequent bigrams in the abstracts are \"machine learning\", with 173 occurrences, and \"artificial intelligence\", with 96 occurrences, emphasizing the importance of these technologies in local and regional research. Also, the high frequency of the key concepts \"regional economic\" and \"local authorities\" indicates an emphasis on the economic impact and the role of local authorities in the deployment and use of these technologies.