# A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics +
## 4 Experiments and Results
### Experimental Evaluation
#### 4.2.2 Qualitative Results
Figure 5: Empirically and collaboratively constructed ground truth of vernacular regions within the center of the city of Athens. Figure 6: Bar charts for users’ answers on Q1, for the sets of regions that are illustrated in Figure 4.  
From all results, it is clear that users strongly preferred regions that resulted upon tile size \(W_{R}=200\) m. Descriptive statistics of user evaluations, empirical observations on bar charts and also on the produced regions that were shown to the users vs. the ground truth, indicate that best results according to the users' opinion were those of Figure 4h,i. Also, we could argue that among the three aforementioned heuristics that we applied in order to select the top tags, the occurrences of the tag within the given region denoted by (T) was the one that users favoured. Upon careful observation on the evaluation results and brief interviews with the users, we feel that generally the proposed approach succeeds to satisfy its goals both towards the extraction of vernacular regions and their underlying semantics. Furthermore, in Figures 7 and 8 we present two cases of the tags contained into each tile of two geo-clusters. The first corresponds to an area known as \"Filopappou hill\", ([[https://en.wikipedia.org/wiki/Philopapppos_Monument](https://en.wikipedia.org/wiki/Philopapppos_Monument)]([https://en.wikipedia.org/wiki/Philopapppos_Monument](https://en.wikipedia.org/wiki/Philopapppos_Monument))) which offers views of a large part of Athens and Attica in general, some ancient monuments (ruins) and provides excellent view to the Acropolis, preferred by tourists for taking photos. The latter, known as \"Koukaki\" ([[https://en.wikipedia.org/wiki/Koukaki](https://en.wikipedia.org/wiki/Koukaki)]([https://en.wikipedia.org/wiki/Koukaki](https://en.wikipedia.org/wiki/Koukaki))) is a rather popular neighborhood mainly due to its proximity to the Acropolis, its excellent connection to all means of transportation along with its bars and restaurants. Moreover, in Figure 9 we illustrate the tag clouds that describe in total each of the aforementioned geo-clusters. It is clear that the most important tags (i.e., those depicted in red and in larger fonts) are rather useful for describing each area. Finally, in Figure 10 we illustrate the top-3 tags resulting upon the application of TF-IDF to the clustering depicted in Figure 4i. A reader which is familiar to the city of Athens, may easily perceive the usefulness of these tags both to describe the semantics of an area and to discover its name. The aforementioned qualitative evaluation was similarly performed. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{\(W_{R}=50\) m} & \multicolumn{4}{c}{\(W_{R}=100\) m} & \multicolumn{4}{c}{\(W_{R}=200\) m} \\ \hline  & **0.85/5** & **0.85/10** & **0.9/10** & **0.85/5** & **0.85/5** & **0.85/5** & **0.9/10** & **0.9/20** & **0.85/5** & **0.85/5** & **0.85/5** & **0.85/10** \\  & **(T)** & **(T)** & **(T)** & **(T)** & **(U)** & **(TU)** & **(T)** & **(T)** & **(T)** & **(U)** & **(TU)** & **(T)** \\ \hline name (\%) & 0.18 & 0.18 & 0.27 & 0.31 & 0.33 & 0.31 & 0.40 & 0.49 & **0.87** & 0.78 & 0.82 & 0.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The percentage of users which felt that the name of the vernacular regions was successfully included within the top-3 extracted tags for the results depicted in Figure 4.