Documenting the heterogeneity of rainfall regimes is a prerequisite for water resources management, mitigation of risks associated to extremes weather events and for impact studies. In this paper, we present a method for regionalization of rainfall over the Peruvian Pacific slope and coast, which is the main economic zone of the country and concentrates almost 50% of the population. Our approach is based on a two‐step process based on k‐means clustering followed by the regional vector method (RVM) applied to a network of 145 rainfall stations covering the period 1964–2011. The advantage of combining cluster analysis and RVM is demonstrated compared with just applying each of these methods. Nine homogeneous regions are identified that depict the salient features of the rainfall variability over the study area. A detailed characterization of the rainfall regime in each of the identified regions is presented in response to climate variability at seasonal and interannual timescale. They are shown to grasp the main modes of influence of the El Niño Southern Oscillation (ENSO), that is, increased rainfall over downstream regions in northern Peru during extreme El Niño events and decreased rainfall over upstream regions along the Pacific slope during central Pacific El Niño events. Overall our study points to the value of our two‐step regionalization procedure for climate impact studies.

This exploratory study identifies spatial patterns of crimes and their associations with the index of Unsatisfied Basic Needs (UBN), with Communitarian Policy Units (CPU) density, as well as with population density. The case study is the Metropolitan District of Quito. Correlation analyses were applied between number of registers of each type of crime, and the UBN index, CPU density and population density measures. The spatial autocorrelation index of Getis-Ord Gi* was calculated to identify hotspots of the different types of crime. Ordinary least squares regressions and geographically weighted regressions considering types of crime as dependent variables, were calculated. Larceny and robbery were found to be the predominant crimes in the study area. An inverse relationship between the UBN index and number of crimes was identified for each type of crime, while positive relationships were found between crimes and CPU density, and between crimes and population density. Significant hotspots of fraud, homicide, larceny, murder, rape and robbery were found in all urban parishes. Additionally, crime hotspots were identified in eastern rural parishes adjacent to urban parishes. This study provides important implications for crime prevention in the Metropolitan District of Quito (MDQ), and the obtained results contribute to the ecology of crime research in the study area.