Data Science_ Placement of traffic enforcement cameras by the correlation of red light violations wi

Data Science_ Placement of traffic enforcement cameras by the correlation of red light violations wi

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NHSJS The National High School Journal of Science
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Data Science: Placement of tra ffi c enforcement cameras by the correlation of red light violations with crime data ! January 31, 2018 / " Admin / # 2017 Issue, Winter 2017 Issue Data Science: Placement of tra c enforcement cameras by the correlation of red light violations with crime data By Karuna Kumar Abstract Tra c enforcement cameras, also known as red light cameras or road safety cameras, are devices installed on roads (particularly at intersections) or in enforcement vehicles to detect tra c regulation violations, such as speeding and red light violations. They are usually linked to an automated ticketing system that works in tandem with the latest automatic number plate recognition system. In metropolitan centers, tra c enforcement cameras are mounted throughout the city due to the presence of immense road networks and the high population density. But, due to the existence of widespread diversity in big cities, tra c enforcement e ff orts may not always be e ff ective to their full potential. Some sections of a city may require more tra c supervision
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whereas some neighborhoods might have little to no prevalence of tra c violations. Therefore, there is a need for the placement of tra c enforcement cameras depending on where they are most needed. But, this requirement is yet to be met in most cities as tra c enforcement is ine ff ectively concntrated throughout di ff erent neighborhoods. For this research paper, we used data science to evaluate the factors causing tra c regulation violations by using Spearman rank-order correlation, so that tra c enforcement cameras can be placed appropriately thus ensuring more focused e ff orts for crime management and control. Introduction Data science is an interdisciplinary fi eld concerning processes and systems. It employs techniques and theories drawn from many fi elds including mathematics, statistics, operations research, information science, and computer science to extract insights from data. Data science can be used to determine the correlation between variables, that is, to detect and measure the interdependency of di ff erent variables or quantities. The measurement of interdependency between two di ff erent variables is called the correlation coe cient. A correlation coe cient measures the extent to which two variables tend to change together. It describes both the strength and the direction of the relationship and is a number between 1 and ?1 calculated so as to represent the interdependence of two variables or sets of data. The two main analytical methods used to measure correlation are Pearson product moment correlation and Spearman rank-order correlation. Pearson product moment correlation is used to measure the linear interdependency between two variables, whereas Spearman rank-order correlation measures the monotonic interdependency between two variables. In a monotonic
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  • Spring '17
  • Correlation and dependence, Spearman's rank correlation coefficient, traffic enforcement cameras

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