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Recently, India announced the ambitious project of “Smart Cities” that will improve the quality of life of the people living in the city as well as their standard of living. A list of 98 cities was announced where this project shall take place. Along with many other features one of the important features is applying smart solutions to infrastructure and services in area-based development. Being a Business Analyst, after reading about this I thought how Business Analytics can help in this and this blog is an effort to contribute towards the same.

With the increasing awareness of data and its impact on decision taken, Business Analytics is one category which is being highly beneficial by harnessing the power of data. Smart cities is one of the innovative ways of reshaping geographies at a granular level. While most cities are termed as cities since they meet a certain benchmark, it is important to make sure it has the usage of both human and business intelligence in its functioning. 

A smart city is smart due to certain components such as

  • Smart roadways
  • Smart usage of energy and oil
  • Smart restaurants
  • Smart crime control
  • Educational systems etc.

In this article I would like to emphasize on how can cities have smart ways to control the crime.

Business Analytics in Crime Control

Most cities these days are turning into Wi-Fi cities. In such a scenario, there could be a technological perspective of reducing crime in a city. Various police departments are now working with crime analysts to understand the past data and unleash patterns. There could be a mobile application which is available on the mobile of every police officer which enables them to look up the upcoming crime, its location and timing. This would enable the officer reach the location or take necessary actions. These KPI’s can be chosen based on the significant nodes that are obtained by performing statistical modeling on the data through non parametric methods. The data for a city Charlotte, North Carolina popped place of crime, time of crime and location of crime as the most significant factors for the occurrence of crime as the dependent variable in the equation. If the mobile application is able to predict these and provide an alert to the police officers and department, it would enable them to address the issue more intelligently based on the probability associated with the occurrence of crime. This sounds similar to the Uber mobile app but the alerts that are sent to the police officers would really enable them plan to address accordingly.

Statistical Analysis

Coming to the implementation of the above mentioned logic/algorithm, the entire data could be passed through non parametric methods such as
decision trees or random forests to understand the top significant nodes which split the data. Post this, a logistic regression could be performed to understand the probability of crime associated based on output and highlight the ones with greater probability on the mobile application through alerts. A good amount of past data is essential to process and analyze this scenario as this requires decent number of data points prior a decision has been made around them.

There could also be a model comparison in place prior choosing a technique among decision trees,
gradient boosting logistic regression and neural networks. Model evaluation is most used algorithm currently on various platforms like R and SAS before choosing the champion model which is statistically significant.

How this will help?

While this kind of a mobile application has various applications, it would help the smart city council of a certain city understand the percentage growth of crime post usage of the application. Business Analytics will play a huge role in the design and creation of the algorithm behind the mobile application. Digging deep into the data to understand various factors that contribute to the occurrence of crime and looking at the most frequent locations and timings will help the police department receive alerts. Since this mobile application is accessed only by the police officers, they have an edge over the availability of insights and caution the public indirectly in their preferred mode of communication. There is also a good scope of collaboration between the city police department and google maps in order to reroute the users to a safer route based on the alert received. This merge of information between two interfaces is a fantastic way to address issues and avoid them intelligently.

Such collaborations would not only benefit through the social cause but also prevent a huge monetary loss of the consumer and the government at the same time. This is pretty much similar to how the transportation leader Uber was planning to merge with dating and restaurant websites to give their customer a more continued and purpose driven experience. These collaboration multiply the role of business analytics in shaping a smart city.

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