## 5 Results
### Event Detection and Classification
Figure 6 shows the average event severity distribution by weekday in logarithm scale, clarifying some general properties of Figure 5. Foremost, independent of the weekday, Mobility and Weather related events are the most frequent in any weekday. On the other hand, unbounded events are dominant over Social events on Wednesdays and Thursdays, indicating a clear centralization of impacting events during this workdays which may highly impacts the economy, traffic and behavior of essential services depending of the event location and scope. The results presented in Figures 5 and 6 depict an interesting behavior of modern cities (at least in New York), indicating the types of events that will happen along the time. Such kind of verification can provide static information that can support better designing of sensor-based monitoring systems. Figure 4: Schematic operation of the proposed approach over the considered tweets dataset. For the considered dataset, the average event priority by week computed by the Priority Computing Unit was also computed, as depicted in Figure 7, according to the proposed formulations to calculate a priority value for every detected event. In the presented results, the maximum average value is correspondent to Week 8. Compared with Figures 5 and 6 we perceived that, despite the slightly variation on the severity distribution which implicates in a small standard deviation (1.44), the weekly priority has a clear central tendency near to \(p=43.86\) due to the scope influence, which reinforces our initial city weekly routine hypothesis. Figure 5: Weekly Event Summary. Figure 6: Daily Event Summary. This relation of severity and scope in \(W_{NY}\) is studied on Figure 8. While mobility events covers almost the entire scope spectra, weather events are concentrated in very small scope values with more than 90% of them having a scope value inferior to 20. Social events are widely distributed, with 37.45% of its events having scope values between 30 and 70. Unbounded events have a similar distribution, with 48.19% of its events concentrated in the same scope interval. These characteristics show the essential role of the scope, since the events which would be unfairly associated with very similar priorities even with very different risks are now weighted by this factor. Figure 8: Scope in function of Severity. Figure 7: Average Priority per Week. In a different perspective, the positions of the detected events are checked for the city of New York. Figures 9 and 10 present heat maps of the detected events weighted by the respective priorities in different weeks, plotted using the Google Maps APIs [77]. Figure 9 depicts the events of Weeks 0 and 1, having a large number of unbounded events across Manhattan and few scattered events in Brooklyn, showing that specific regions (e.g., Battery Park neighborhood) may have more monitoring demand than momentarily quiet regions according to the events happening near it. The city of New York has a well-know uneven population distribution, where Brooklyn and Manhattan are the most populated regions. It is interesting to notice that New York's detected event distributions is slightly similar, where Manhattan and Brooklyn naturally generated more relevant events. In short, the Priority Computing Unit has very interesting results for densely populated regions, being directly linked to the population assiduity in Social Medias. Actually, the tests showed two different uses of _Twitter_-based detection of events of interest. First, the detection and classification of events of interest is an effective mechanism for priority computing and assignment for sensors in WSN applications, as proposed by the TwitterSensing approach. Second, if large tweets datasets are considered, statistical information can be used to support the decision of where to deploy sensors. By doing so, regions with historical occurrence of more relevant events may  
Figure 10: Priority distribution in Weeks 6 and 7.