Crime Against Women: Analysis And Prediction

Crime Against Women: Analysis And Prediction

Purvi Prasad Computer Science Engineering SRM Institute of Science and

Technology Chennai, India

Amrita Nair Computer Science Engineering SRM Institute of Science and

Technology Chennai, India

Dr. S. Godfrey Winster Computer Science Engineering SRM Institute of Science and Technology

Chennai, India

Abstract-In recent times, the crimes occurring against women is increasing at a rapid rate. It has become a major social issue not only in India, but across the world. Many attempts have been carried out with serious measures being taken in order to prevent such crimes. Every year, a massive amount of data is produced of different kinds of crimes being reported from different parts of the world. This knowledge can be very helpful in understanding and detecting violence, as well as assisting us in combating it to some degree. Analyzing such datasets can play a major role in identifying the crime patterns and occurrence of it. Here, data mining plays a huge role as it enables us to analyze, visualize and predict the different crimes which are occurring in a particular region. In this paper, we use Huber regression to analyze the data and visualize it using time series algorithm, based on different states of India and predicting out the particular crime occurring in a particular state.

KeywordsHuber Regression, Time Series, Analyzing, Visualization

1. INTRODUCTION

   In India, women are worshipped. They are given the status of goddesses. But the reality is completely opposite to it. With the passage of time, safety of women has started to become an alarming concern. With each passing day, the rate of crimes is increasing at a rapid rate. It is now considered to be a global issue, with many countries trying to bring in reforms, to curb down the crime rate. India too, is not far behind. With the data showed by the National Crime Records Bureau, the records of crime being reported against women has turned out to be higher than the past few years. Cases of rapes, murder, abduction and trafficking are occurring much frequent now. The government is trying to implement stricter laws and serious measures to prevent such crimes and ensure the safety of women. A huge amount of data is generated every year related to such different crimes in different regions of India. Analyzing such huge data records may seem a very tedious task. With the emerging technologies and methodologies, Data mining plays a huge role in analyzing of such large number of records with displaying accurate results and discovering out the patterns. The outcome of such analysis cant be exactly equal to the perceived outcome; but it gives out an adequate rough figure of crimes which will be occurring in a particular state in the coming years. The main challenge in this prediction is to reduce out the losses and bring out the resultant number of a particular crime, say rape in a particular state like Andhra Pradesh in the coming years to the actual figure. The challenges that we are facing this are:

   • Analyzing data of different types of crimes based on each 28 states and 7 union territories.

   • Reducing the loss to the minimum for each type of crime.

   • Obtaining more datasets with more sets of crimes from various crime departments.

   An ideal crime analysis tool should be able to identify crime patterns quickly and in an efficient manner for future crime pattern detection and action [ efficient approach]. This will enable the law officials and the governments of various states to enforce serious measures to reduce such crimes and to provide a safe place for women to live in. Previously, many data mining techniques like clustering have been used to predict a certain crime based on a single region with producing more accurate results. The present work proposed is to use Huber regression to analyze different types of crimes based on different states and territories and to predict out the number of that particular crime which will occur in that particular state/union territory and to visualize the data in a graphical form using time series to identify the number of particular crimes which will occur in that region. Also, this will indicate which type of crime is dominant in a certain region.

2. STATE OF THE ART (LITERATURE SURVEY) Researchers compared many data mining algorithms

   using a variety of real-world applications with some related works. Apart from those one of them is Crime against Women (CAW) Analysis and Prediction in Tamil Nadu Police Using Data Mining Techniques where they used Clustering in WEKA utensils, Euclidean distance calculation by S. Lavanyaa, D. Akila [1] which states that Clustering in WEKA utensils, Euclidean distance calculation gives improved exactness in the metropolitan urban areas violations rate to decrease and predict, according to the investigation. Analysis and Prediction of Crimes using Clustering, Classification and General algorithm by Rasoul Kiani, Siamak Mahdavi, Amin Keshavarzi [2] where the main objective of occurrence frequency during different years. We used a theoretical model focused on data mining techniques including clustering and classification to analyse a real crime dataset collected by the police in England and Wales between 1990 and 2011. In this certain kind of weights are assigned so as to refine the model which is being used and eliminating the lesser values. Using the RapidMiner tool, the Genetic Algorithm (GA) is used to optimise the Outlier Detection operator parameters. Crime Analysis using K-Means Clustering on crime dataset using rapid miner tool by Jyoti Agarwal ,Renuka Nagpal, Rajni Sehgal [3] where this project is based on the concept of crime analysis by using clustering algorithm on the obtained data set using a rapid miner tool. The analysis is done by taking

   in a particular type of crime that is homicide and presenting it in a graphical form, matching it with to the particular year it happened. The outcome derived was, that the rate of homicide is decreasing from year 1990 to 2011. Crimes Against Women in India: Analysis of Trends Using Regression and Visualization by R. Devakunchari, Bhowmick S, Bhutada S P, Shishodia Y [4] where Detection technologies improves identifying of the incidents and make use of public safety equipment as soon as possible. With faster identification of incidents, this helps to improve the response time and thus in this the accuracy and reliability of incident response and reporting, as well as the distribution of investigative resources, can all be improved with technology. This can also help in boosting up the clearing rates.

3. PROPOSED WORK

   The data which used for doing the analysis plays a key role in finding out the patterns, especially in crime analysis. In this, the dataset is obtained from Kaggle which contains different types of crimes which are occurring against women such as rape, kidnapping, dowry death, assault on women, cruelty against women, importation of young girls, insult to modesty and immoral traffic. This time series-data is converted into supervised data, in order for predicting out the number of crimes that may take place in future. For analysing these different crimes, Huber Regression is used. It determines the loss score, that is how much the difference is going to be between the actual and predicted value. It may be not hat accurate as the prediction is done based on the number of crimes that happened in the past years. There is no constant increase/ decrease in the crimes taking place. Based on that, the predicted values are broughtcloser to the actual value in order to reduce the loss. The predictions can be visualized in a bar- graph form, displaying the top states/union territories where the particular listed crimes are at the peak. It will also enable us to identify, which type of crime is dominant in a particular state.

4. IMPLEMENTATION

   A. Methods Used Huber Regression

   The Huber Regressor optimizes the squared loss for samples where |(y – X'w) / sigma| is less than epsilon, and the absolute loss for samples where |(y – X'w) / sigma| is greater than epsilon, where w and sigma are the parameters to be optimised. The sigma parameter ensures that if y is scaled up or down by a certain factor, epsilon does not need to be rescaled to maintain the same level of robustness.

   Time Series

   Time series is a chain of data points which is mostly in chronological sequence, with gathering in regular intervals. This analysis can be applied to any number of variables that changes over time, generally data points that are closer together are more similar in nature than those further apart. Here we used time series algorithm to predict future number of crimes in each state that will happen. We can show the graph of rape in Andhra Pradesh as predicted in fig 1.

   Fig. 1. Graph of Andhra Pradesh crime

   Time Series

   Time series is a chain of data points which is mostly in chronological sequence, with gathering in regular intervals. This analysis can be applied to any number of variables that changes over time, generally data points that are closer together are more similar in nature than those further apart. Here we used time series algorithm to predict future number of crimes in each state that will happen. We can show the graph of rape in Andhra Pradesh as predicted in fig 1.

   Fig. 1. Graph of Andhra Pradesh crime

   1. Dataset

      sklearn.linear_model.HuberRegressor

      sklearn.linear_model.HuberRegressor

      Here in dataset in fig 2, we have 8 different crimes such as rape, kidnapping and abduction, dowry death, insult to modesty of women, cruelty by husband or relatives, assault on women with intent to outrage her modesty, immoral traffic, indecent representation of women. These are depicted from 2007 to 2018 and we will be predicting crime number for the year 2019.

      Fig. 2. Crime Dataset

      For example, for a particular state like Andhra Pradesh, the below dataset showcases all the different crimes in that particular region.

      Fig. 3. Andhra Pradesh dataset

      Here in dataset in fig 2, we have 8 different crimes such as rape, kidnapping and abduction, dowry death, insult to modesty of women, cruelty by husband or relatives, assault on women with intent to outrage her modesty, immoral traffic, indecent representation of women. These are depicted from 2007 to 2018 and we will be predicting crime number for the year 2019.

      Fig. 2. Crime Dataset

      For example, for a particular state like Andhra Pradesh, the below dataset showcases all the different crimes in that particular region.

      Fig. 3. Andhra Pradesh dataset

      Here in our project, we used Huber regressor as

      follows:

      model_rape = linear_model.HuberRegressor().fit(X_train_ rape,y_train_rape)

      model_rape = linear_model.HuberRegressor().fit(X_train_ rape,y_train_rape)

   2. Converting time series data into context-aware supervised learning

      where s is score and p is prediction.

      It can be also specified as and can be shown in fig 4 and fig 5,

      scores[i],predictions[i] = get_results_for_state(i) number,state = get_final_results('cruelty',l)

      scores[i],predictions[i] = get_results_for_state(i) number,state = get_final_results('cruelty',l)

      We'll use the Pandas library to import DataFrame and concat and then convert using the function below.
      	def series_to_supervised(data, n_in=1, n_out=1, dropnan= True)

      We'll use the Pandas library to import DataFrame and concat and then convert using the function below.
      	def series_to_supervised(data, n_in=1, n_out=1, dropnan= True)

      ,

      Fig. 4. Score and Prediction of state wise

      Fig. 5. Max cruelty number and state

      Fig. 4. Score and Prediction of state wise

      Fig. 5. Max cruelty number and state

      where we define series to supervised(), a new Python function that converts a multivariate time series into a supervised learning dataset.

      The role takes four arguments in this case:

      • data: This is a set of observations in the form of a list or a 2D NumPy array.

      • n_in: This is a collection of lag observations that is used as an input (X). Values will range from [1 to len(data)] and are completely optional. The default value can be set to 1

      • n_out: This is a set of observations that is used as an output (y). Values will range from [0 to len(data)-1] and are completely optional. The default value can be set to 1.

        For getting scores and prediction we have divided the dataset into groups or context of years by specifying it into variables such as var1 for rape, var2 for kidnapping and abduction etc. We have done this for predicting number for the year 2019 by specifying var(t-2) as 2007 to 2016, var(t-1) as 2008 to 2017 and var(t) as 2018 and var(t+1) as 2019.

        For getting scores and prediction we have divided the dataset into groups or context of years by specifying it into variables such as var1 for rape, var2 for kidnapping and abduction etc. We have done this for predicting number for the year 2019 by specifying var(t-2) as 2007 to 2016, var(t-1) as 2008 to 2017 and var(t) as 2018 and var(t+1) as 2019.

      • dropnan: This is a boolean form that indicates whether or not rows with NaN values should be dropped. It's also optional, and if it's present, it'll be set to True.

        Then we have done Train test split where for training it is 40% and testing is 60% and have done model fitting using linear model Huber regressor then from that we will get out scores and prediction.

        Then we have done Train test split where for training it is 40% and testing is 60% and have done model fitting using linear model Huber regressor then from that we will get out scores and prediction.

   3. Iterating to obtain scores and predictions for every state

   4. Visualization

   state_data = series_to_supervised(values, 2,2) state_rape =

   state_data[['var1(t-2)','var1(t- 1)','var1(t)','var1(t+1)']]

   state_data = series_to_supervised(values, 2,2) state_rape =

   state_data[['var1(t-2)','var1(t- 1)','var1(t)','var1(t+1)']]

   Fig.6. Graph with most rapes

   Fig.6. Graph with most rapes

   Here we get the prediction of states with most rapes, assault, dowry etc., as shown in following below figures 6,7,8.

   Here we are predicting the scores and predict the rape, assault individually using this function and get our result where we will get scores in positive or negative value which is compared with the previous year and the predicted year if the crime number is more then the score goes positive and if the crime is less in number then the score goes negative. And also, we can find number and state of a particular crime and max number of a particular crime and in which state through which police department can take necessary steps to decrease that type of crime in their state. Such as for Maharashtra:

   s,p = get_results_for_state('Maharashtra')

   Fig. 7. Map of number of assaults in different states

   Fig. 8. Map with color to depict highest number of assaults in a state

5. CONCLUSION

Using Huber regression for analyzing different type of crimes for each state the predicted outcome is accurately close to the actual number of crimes which may occur in the frequent years. The predicted outcome is determined by the number of crimes taken from the previous years. Time series enabled the data to be visualized in a graphical form forecasting the increase/decrease of the crimes which may occur in a particular region in India. With the outcome of this whole program, the results help us to identify what type of crime is of dominance in a particular state and rough figures of different crimes in different states and union territories of India. This will help the law enforcement officials and government to implement stricter laws in order to curb down the crime rate and make a safer place for women to live in.

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