## 4 Discussion and Conclusions  
The unprecedented growth in the amount of data related with 911 events has increased the need for agile processing and visualization. When large amounts of data are available, even simple statistical, temporal, and geographical descriptions are necessary and strongly informative. In this work, we propose a system for temporal analysis, including seasonal representations and nonparametric CIs, together with spatial representations, including heat maps, in terms of Parzen estimations. In the case study of 911 events in Quito, temporal analysis allowed us to divide the FRIs into three groups according to the periodicity of the registered events, namely, STPIs, LTPIs, and NTPIs. More than 1 million emergencies were analyzed for this research in terms of relatively simple statistical descriptions. For periodically behaved institutions, seasonal patterns were identified separately from trends and atypical events. From the geographical-analysis point of view, the emergencies could be readily seen over a referenced Google Map, and their spatial dynamics could be observed and quantified; moreover, FRIs could be scrutinized from different scales. The proposed system can be used to provide 911 managers with statistical, spatial, and temporal analysis of the emergencies registered in a PSAP. The temporal and geographical visualization is a necessary tool, but we would like to stress that the proposed system is not just a visualization tool. Statistical processing provides us with powerful analysis tools and relevant information, which cannot fully be extracted from raw data, as in Figure 2. The use of bootstrap resampling and Parzen windowing is quite an operative statistical approach to these data, as far as these nonparametric methods are easy to use in order to deal with statistical tests and density estimations under conditions for which the underlying statistical distributions cannot be assumed as known. The efficient management of resources requires an agile and natural time-space representation supporting system and an adequate statistical analysis to convert the set of events into meaningful and solidly estimated dynamics. Whereas some of the results presented here can seem trivial (i.e., a larger number of events in the weekend), a solid quantification is the basis for establishing resource allocation. From this point, we can move now towards the design of systems used for resource allocation, by combining the resource information with the quantified data model, as well as towards more advanced and improved estimation and data processing tools. For instance, more advanced data analysis algorithms, prediction systems, or parametric statistical analysis can now be pursued. The role from the other recorded variables in the DB certainly remains to be exploited. Moreover, data quality considerations are also to be taken into account, as is usual in problems based on storing large amounts of data. In fact, by using the information obtained with the developed tool, the institutions do not depend only on the empirical experience of the dispatchers. Usually, this kind of experience takes several months to be learned for people that work in the dispatching area. The spatial dynamics for each FRI are even more complicated in terms of being abstracted by the dispatchers. We note that the present work does not only present a temporal representation of the emergencies, but also a geographical representation in which \"hot points\" can be identified. Hence, through our proposal, the occurrence of different emergency types at different locations can be characterized, hence allowing the saving of time when resources are preallocated according to the type and approximate number of expected events. Moreover, economical resources can be saved when units for emergency attendance are not constantly moved or travel only short distances. This is also very relevant when an opportune response may save lives as a result of a technical planning method.