Data Mining and Predictive Policing

This paper focuses on the operation and utilization of predictive policing software that generates spatial and temporal hotspots. There is a literature review that evaluates previous work surrounding the topics branched from predictive policing. It dissects two different crime datasets for San Francisco, California and Chicago, Illinois. Provided, is an in depth comparison between the datasets using both statistical analysis and graphing tools. Then, it shows the application of the Apriori algorithm to re-enforce the formation of possible hotspots pointed out in a actual predictive policing software. To further the analysis, targeted demographics of the study were evaluated to create a snapshot of the factors that have attributed to the safety of the neighborhoods. The results of this study can be used to create solutions for long term crime reduction by adding green spaces and community planning in areas with high crime rates and heavy environmental neglect

Association Rule Mining for Improvement of IT Project Management

In this research we extract knowledge from human resources data, accumulated in IT companies for the right selection of teams to work on software projects. We are looking for interesting and unknown dependencies and connections in the data, based on which managers can form more cohesive and professionally working project teams. The proposed approach to improve the selection of teams working on IT projects is based on association rule mining and can be used by IT managers to select the members of the teams. The approbation of the proposed approach is made using the software product RapidMiner

Crime Prediction with Historical Crime and Movement Data of Potential Offenders Using a Spatio-Temporal Cokriging Method

Crime prediction using machine learning and data fusion assimilation has become a hot topic. Most of the models rely on historical crime data and related environment variables. The activity of potential offenders affects the crime patterns, but the data with fine resolution have not been applied in the crime prediction. The goal of this study is to test the effect of the activity of potential offenders in the crime prediction by combining this data in the prediction models and assessing the prediction accuracies. This study uses the movement data of past offenders collected in routine police stop-and-question operations to infer the movement of future offenders. The offender movement data compensates historical crime data in a Spatio-Temporal Cokriging (ST-Cokriging) model for crime prediction. The models are implemented for weekly, biweekly, and quad-weekly prediction in the XT police district of ZG city, China. Results with the incorporation of the offender movement data are consistently better than those without it. The improvement is most pronounced for the weekly model, followed by the biweekly model, and the quad-weekly model. In sum, the addition of offender movement data enhances crime prediction, especially for short periods

ADACOP - Analytics

El presente proyecto, realizado de la mano con el Centro de Excelencia y Apropiación en Big Data y Data Analytics(CAOBA), se focaliza en el desarrollo de una arquitectura de Big Data que soporte la extracción, almacenamiento, procesamiento y exploración analítica sobre los datos abiertos del gobierno colombiano, inicialmente relacionados con la contratación estatal, con el finde mejorarlos procesos de transparencia y brindar una mayor cercanía hacia el ciudadano, ya que se debe trabajar en la consistencia, estandarización de los datos y en particular en las propiedades que generen valor y aporten a la toma de decisiones. Este documento describe la implementación de la arquitectura, el prototipo resultante y provee los resultados de las pruebas TAM (technology acceptance model) de aceptación a usuarios interesados.The present project was carried out within the Center of Excellence and Appropriation in Big Data and Data Analytics (CAOBA),in order to develop a Big Data architecture for extracting, storing, processing and analytical exploration of the Colombian government open data, initially including public contracts; the goal is to improve the transparency and usefulness that citizens can extract from such data, which implies work on the consistency, data standardization and highlighting certain properties that can generate value and contribute to decision making. This document describes the implementation of the architecture, the resulting prototype and provides the results of TAM (technology acceptance model) of acceptance with potential expert users.Magíster en Analítica para la Inteligencia de NegociosMaestrí