Multiple Correspondence Analysis of Emergencies Attended by Integrated Security Services

Abstract

A public safety answering point (PSAP) receives thousands of security alerts and attends a similar number of emergencies every day, and all the information related to those events is saved to be post-processed and scrutinized. Visualization and interpretation of emergency data can provide fundamental feedback to the first-response institutions, to managers planning resource distributions, and to all the instances participating in the emergency-response cycle. This paper develops the application of multiple correspondence analysis (MCA) of emergency responses in a PSAP, with the objective of finding informative relationships among the different categories of registered and attended events. We propose a simple yet statistically meaningful method to scrutinize the variety of events and recorded information in conventional PSAPs. For this purpose, MCA is made on the categorical features of the available report forms, and a statistical description is achieved from it by combining bootstrap resampling and Parzen windowing, in order to provide the user with the most relevant factors, their significance, and a meaningful representation of the event grouping trends in a given database. We analyzed the case of the 911-emergency database from Quito, Ecuador, which includes 1,078,846 events during 2014. Individual analysis of the first-response institutions showed that there are groups with very related categories, whereas their joint analysis showed significant relationships among several types of events. This was the case for fire brigades, military, and municipal services attending large-scale forest fires, where they work in a combined way. Independence could be established among actions in other categories, which was the case for specific police events (as drug selling and distribution) or fire brigades events (as fire threats). We also showed that a very low number of factors can be enough to accurately represent the dynamics of frequent events.

Text Analytics and Mixed Feature Extraction in Ovarian Cancer Clinical and Genetic Data

“…Correspondence Analysis (CA) is a technique for exploration of two categorical variables (with several categories for each of the two variables) used with a two-way contingency table [13]. This technique is also known as Simple CA [14].…”

A Big Data Approach to Customer Relationship Management Strategy in Hospitality Using Multiple Correspondence Domain Description

“…The main difference between them is that the former represents a variance orthonormalization, whereas the later represents a probability-space orthonormalization. We follow some of the principles pointed out in previous works on MCA (see, e.g., [42]), but with emphasis here on the behavior and adaptability of the method to summarize categorical features together with the description of a number of cases, as is usually the case of the client view in CRMs in hospitality. The identification of relations among features and samples is not straightforward, specially in datasets consisting of categorical variables.…”

“…Correspondence analysis (CA) is a well-known exploratory bivariate technique for analyzing contingency or count tables both visually and numerically [17]. In contrast, MCA can be seen as a generalization of CA, which allows us to study relationships among categorical variables [18,42].…”

“…Note that, since Z is a symmetric matrix, r and c are equal. Therefore, P and Q are the same matrices, being V = P = Q the eigenvector matrix, and thus F is also equal to G. Using these eigenvector and eigenvalue matrices, MCA results can be analyzed [42]. In this way, normalized eigenvalues can be used to represent the data dispersion surrounding the center of gravity, according to each category, and this yields a straightforward interpretation that is comparable to the variance criteria often used in PCA for metric vectors.…”

Bootstrap Analysis of the Production Processes Capability Assessment

“…The bootstrap method can be used for what-if studies. It is used in many different areas, including in simulation models analyzing medical data [7][8][9][10][11][12], in financial analyzes [13][14][15], in solving problems in the area of logistics and distribution [16][17][18], in environmental protection [19][20][21][22], safety sciences [23], automotive [24], risk management [25,26] and in classic queuing models [27].…”