In this paper the districts of Dortmund, a big German city, are ranked concerning their level of risk to be involved in an offence. In order to measure this risk the offences reported by police press reports in the year 2011 (Presseportal, http://www.presseportal.de/polizeipresse/pm/4971/polizei-dortmund?start=0, 2011) were analyzed and weighted by their maximum penalty corresponding to the German criminal code. The resulting danger index was used to rank the districts. Moreover, the socio-demographic influences on the different offences are studied. The most probable influences appear to be traffic density (Sierau, Dortmunderinnen und Dortmunder unterwegs—Ergebnisse einer Befragung von Dortmunder Haushalten zu Mobilität und Mobilitätsverhalten, Ergebnisbericht, Dortmund-Agentur/Graphischer Betrieb Dortmund 09/2006, 2006) and the share of older people. Also, the inner city parts appear to be much more dangerous than the outskirts of the city of Dortmund. However, can these results be trusted? Following the press office of Dortmund’s police, offences might not be uniformly reported by the districts to the office and small offences like pick-pocketing are never reported in police press reports. Therefore, this case could also be an example how an unsystematic press policy may cause an unintended bias in the public perception and media awareness.
Many published articles in automatic music classification deal with the development and experimental comparison of algorithms—however the final statements are often based on figures and simple statistics in tables and only a few related studies apply proper statistical testing for a reliable discussion of results and measurements of the propositions’ significance. Therefore we provide two simple examples for a reasonable application of statistical tests for our previous study recognizing instruments in polyphonic audio. This task is solved by multi-objective feature selection starting from a large number of up-to-date audio descriptors and optimization of classification error and number of selected features at the same time by an evolutionary algorithm. The performance of several classifiers and their impact on the pareto front are analyzed by means of statistical tests.
The MAGIC-telescopes on the canary island of La Palma are two of the largest Cherenkov telescopes in the world, operating in stereoscopic mode since 2009 (Aleksić et al., Astropart. Phys. 35:435–448, 2012). A major step in the analysis of MAGIC data is the classification of observations into a gamma-ray signal and hadronic background. In this contribution we introduce the data provided by the MAGIC telescopes, which has some distinctive features. These features include high class imbalance, unknown and unequal misclassification costs as well as the absence of reliably labeled training data. We introduce a method to deal with some of these features. The method is based on a thresholding approach (Sheng and Ling 2006) and aims at minimization of the mean square error of an estimator, which is derived from the classification. The method is designed to fit into the special requirements of the MAGIC data.
Hot deck methods impute missing values within a data matrix by using available values from the same matrix. The object from which these available values are taken for imputation is called the donor. Selection of a suitable donor for the receiving object can be done within imputation classes. The risk inherent to this strategy is that any donor might be selected for multiple value recipients. In extreme cases one donor can be selected for too many or even all values. To mitigate this donor over usage risk, some hot deck procedures limit the amount of times one donor may be selected for value donation. This study answers if limiting donor usage is a superior strategy when considering imputation variance and bias in parameter estimates.
It is argued that the determination of the best number of clusters k is crucially dependent on the aim of clustering. Existing supposedly "objective" methods of estimating k ignore this. k can be determined by listing a number of requirements for a good clustering in the given application and finding a k that fulfils them all. The approach is illustrated by application to the problem of finding the number of species in a data set of Australasian tetragonula bees. Requirements here include two new statistics formalising the largest within-cluster gap and cluster separation. Due to the typical nature of expert knowledge, it is difficult to make requirements precise, and a number of subjective decisions is involved.
