Predicting the exact urban places where crime is most likely to occur is one of the greatest interests for Police Departments. Therefore, the goal of the research presented in this paper is to identify specific urban areas where a crime could happen in Manhattan, NY for every hour of a day. The outputs from this research are the following: (i) predicted land uses that generates the top three most committed crimes in Manhattan, by using machine learning (random forest and logistic regression), (ii) identifying the exact hours when most of the assaults are committed, together with hot spots during these hours, by applying time series and hot spot analysis, (iii) built hourly prediction models for assaults based on the land use, by deploying logistic regression. Assault, as a physical attack on someone, according to criminal law, is identified as the third most committed crime in Manhattan. Land use (residential, commercial, recreational, mixed use etc.) is assigned to every area or lot in Manhattan, determining the actual use or activities within each particular lot. While plotting assaults on the map for every hour, this investigation has identified that the hot spots where assaults occur were ‘moving’ and not confined to specific lots within Manhattan. This raises a number of questions: Why are hot spots of assaults not static in an urban environment? What makes them ‘move’—is it a particular urban pattern? Is the ‘movement’ of hot spots related to human activities during the day and night? Answering these questions helps to build the initial frame for assault prediction within every hour of a day. Knowing a specific land use vulnerability to assault during each exact hour can assist the police departments to allocate forces during those hours in risky areas. For the analysis, the study is using two datasets: a crime dataset with geographical locations of crime, date and time, and a geographic dataset about land uses with land use codes for every lot, each obtained from open databases. The study joins two datasets based on the spatial location and classifies data into 24 classes, based on the time range when the assault occurred. Machine learning methods reveal the effect of land uses on larceny, harassment and assault, the three most committed crimes in Manhattan. Finally, logistic regression provides hourly prediction models and unveils the type of land use where assaults could occur during each hour for both day and night.

Through its cloud-based source-to-pay business network, SAP Ariba provides an integrated platform and interface to help companies manage their supply chain. In this chapter, the authors examine how Ariba, a small startup company formed during the internet boom of the '90s, was able to overcome hardships, survive market and industry downturns, and continue to thrive and survive in such a competitive industry. The authors also review major events, innovations, and decisions that helped the company to grow and succeed rather than to fail like its many competitors.

The dispersion of talent within the United States is not uniform. There is sufficient statistical evidence to suggest that there is an interstate brain drain phenomenon occurring within the country. The authors set out to examine this by first determining whether states could be classified into four broad categories of talent: ‘repel', ‘loyal', ‘magnet' and ‘boring'. To do this they observed the relative pull or push of talent and looked at the results relative to which states tended to retain or lose their native talent, as well as which states tended to attract a large or small number of the migratory student population seeking education outside of their own respective home states. Once they completed this categorization, the authors attempted to see whether within these groupings, a set of randomly selected independent attributes or themes could be statistically significant to support these categorizations.

Finding the costs and risks associated with highway traffic routes would allow companies and people alike to find routes that offer a comfortable amount of risk. With the amount of traffic data being collected at a more granular level the ability to find costs and risks associated with traffic routes given real time circumstances is plausible. Weighing these data and finding the areas that are most accident-prone allows for an assessment of the probability that an accident happens and what the cost of that accident would be for any given route. This information is very valuable for both safety and cost saving for drivers and insurance companies.

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