The recently introduced collaborative nonnegative matrix factorization (CoNMF) algorithm was conceived to simultaneously estimate the number of endmembers, the mixing matrix, and the fractional abundances from hyperspectral linear mixtures. This paper introduces R-CoNMF, which is a robust version of CoNMF. The robustness has been added by a) including a volume regularizer which penalizes the distance to a mixing matrix inferred by a pure pixel algorithm; and by b) introducing a new proximal alternating optimization (PAO) algorithm for which convergence to a critical point is guaranteed. Our experimental results indicate that R-CoNMF provides effective estimates both when the number of endmembers are unknown and when they are known.Index Terms-Hyperspectral imaging, spectral unmixing, endmember extraction, collaborative nonnegative matrix factorization (CoNMF).

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The literature shows that offenders' subsequent crime location choices are affected by their prior crime location choices. However, the published studies have focused on the influence of time and place of a previous crime, without testing the impact of whether the offender was arrested during the act of the prior crime. On the basis of the literature, this study further examines the influence of the prior robbery experiences on the subsequent street robbery location choices, by testing explicit hypotheses on how the time, place, and being arrested in the act of previous robberies affect a robber's subsequent decisions of where to commit robberies. The data set used in this study includes 1262 detected robberies committed by 527 street robbers from the ZG City Public Security Bureau in China. Results of a mixed logit model demonstrate that prior street robbery experiences have a strong effect on subsequent street robbery location choices. A shorter time interval and less possibility of being arrested in the act of a prior street robbery significantly increase the likelihood of a robber returning to the previous location. However, the impact of distance of journey to prior crime location is not statistically significant.

Immigrants and natives are generally comparable in committing violent crimes in many Western cities. However, little is known about spatial differences between internal migrant offenders and native offenders in committing violence in contemporary urban China. To address this gap, this research aims to explore spatial variation in violent crimes committed by migrant and native offenders, and examine different effects of ambient population, crime attractors, crime generators, and offender anchor points on these crimes. Offender data, mobile phone data, and points-of-interest (POI) data are combined to explain the crime patterns of these offenders who committed offenses and were arrested from 2012 to 2016 in a large Chinese city by using box maps and negative binomial regression models. It is demonstrated that migrant and native violent crimes vary enormously across space. Ambient population is only positively related to migrant violent crimes. Crime attractors and generators have more significant and stronger correlations with migrant violent crimes, while offender anchor points have a stronger association with native violent crimes. The results reveal that migrant offenders tend to be attracted by larger amounts of people and more affected by crime attractors and generators than native offenders.

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