## 3 Experiments and Results
### Geographical and Spatiotemporal Analysis
In this case, by using Parzen-KDD, we could easily identify the points of higher concentration of police events. This accuracy is very useful when the responder institutions are planning the distribution of their units across the city for a faster response or to prevent incidents in the future. As described in Section 2, the spatiotemporal analysis results from the combined analysis of spatial and temporal information of the emergencies registered in our DB. This combination allows us to better understand the occurrence of emergencies associated to each FRI. In other words, when we represent each confidence area in terms of 3D coordinates (latitude, longitude, and time), it is possible to obtain a useful visualization of the events [39]. In Figure 9, we represent the 95% confidence areas for each day in December 2014 (only 11 days are represented for ease of visualization on paper), corresponding to the police emergencies in the city of Quito. We note the temporal variations in the position of the slices with time, as well as the changes in shape and area. It is noteworthy to emphasize that this kind of variation remains hidden if we only scrutinize the temporal representations of the number of events. In order to further analyze STPIs and LTPIs, we compared the geographical Parzen estimation during the day of the maximum and minimum number of emergencies in 2014 and for each FRI. For the police service, the maximum peak of events was registered on Saturday 6 December, with 3090 emergencies, and the minimum was on Thursday 2 January, with 630 emergencies, as illustrated in Figure 10a. We note that this represents a noticeable number of emergencies, even for the minimum peak. The same figure depicts the Parzen distribution both in a red gradient over the map and its corresponding bidimensional PDF, for better visualization purposes. It is interesting to note that regions with intense activity for this service tended to be quite similar, as seen in the heat maps; however, their relative intensity was different over these days, which is more evident in the PDF representation. This points out that the modal regions with police activity were repetitive, whereas the intensity within these regions tended to be different over different time periods. Figure 10: _Cont._  
For health services, the maximum peak of events was registered on Wednesday 1 January, with 1437 emergencies, and the minimum was on Wednesday 6 August, with 307 emergencies. Figure 11 shows a similar representation for these, making evident that in this case the dynamics were different, as far as some modal regions were repeated (in the south and along roads to the east and south-east), but other modal areas were very different, which was made more evident in the PDF representations. Therefore, health emergencies were more variable in terms of their geographical support, and not only in the intensity of events. Figure 11: _Cont._  
Figure 10: Analysis of police emergencies. (**a**) Events during 2014, with maximum and minimum peaks; (**b,c**) Parzen distribution in red gradient over the map and bidimensional probability density function (PDF) representation for maximum peak on December 6; (**d,e**) The same for minimum peak on 2 January. For transit services, the maximum peak was on Saturday 20 December, with 1177 emergencies, and the minimum was on Thursday 2 January, with 199 emergencies, as Figure 12a shows. On weekends, transit emergencies were distributed in a wider area that included sectors and small towns around Quito, compared to the weekdays. The visible distribution modes allowed us to identify concentrations in Carcelen (A), Tanda (B), Luluncoto (C), Conocoto (D), and Sangolqui (E), which correspond to the city periphery (see Figure 12b). During weekdays, transit emergencies were mainly concentrated in the urban area of the city, as clearly seen in Figure 12c. Figure 11: Analysis of health emergencies.