# Risk and Pattern Analysis of Pakistani Crime Data Using Unsupervised Learning Techniques
## 2 Literature Review
The main aim of this study is to help law enforcement agencies in predicting and analysing criminal activities Weak Ahead Electricity Power and Price Forecasting using Improved DenseNet-121 Method n addition, our objective is to reduce criminal activities before they occur by analysing crime patterns and risky crime locations. Research in [22] has been conducted on two UK neighbourhoods to address organised crimes. The police record the effect of organised crime to analyse organised crime and its impact on communities. According to the findings, the planned crime groups are accountable for a much more extensive range of harm than uttered by the national police insights mechanism. This paper only focused on the impact of crime, whereas our primary focus is the reduction of crime rates by predicting them before happening in a particular location. To analyse and map criminal activities in Pakistan, [23] has investigated the role of Geospatial Technologies. Crime data of the Bhakkar district in the year 2017 have been collected from eleven police stations. Different types of Geographic Information System (GIS) analyses are performed on the data using ArcGIS software. Hot Spot analysis is performed to identify better patrolling areas for the police officers. Moreover, violent and risky crime locations are also determined using the spatial and temporal distribution of the crime data. However, patterns of criminal activities are unaddressed by the paper, which is considered in our contribution. The author in [24] has analysed neighbourhood relations in Kyoto to test their already developed model for preventing social capital and community-based crimes. The theoretical Structural Equation Modelling (SEM) was used to measure the factors for preventing the selected crimes. The survey data and police records for street crimes and residential burglary suggest that specific efforts by community residents can enhance social capital and lead to security and community safety. Social capital has a great impact on minimising the ratio of street crime. However, the analysis indicated that social capital decreases crime anxiety and enhances awareness in families and others. The study in [25] applied different data mining techniques to the San Francisco crimes dataset. The dataset includes information regarding the date, crime type, location, etc. The crime analysis reported in the paper is performed for San Francisco to help their analyst solve crime cases by analysing crime patterns. Two classification algorithms, K nearest neighbour (KNN) and Naive Bays were applied to the crime dataset to predict crimes at a particular time in a specified location. For testing purposes, K fold cross-validation was performed. The paper reports an accuracy of 70%, which is the result of Naive Bayes and K fold cross-validation. However, a grouping of similar crime event locations and frequent crime pattern identification is the limitation of this research work which is addressed in our paper. In [26], data on reported crimes and accidents in Denver, USA are collected from 2011 to 2015. The trends and patterns of crime and accident-related activities are detected and highlighted. Multiple classification algorithms are applied to the dataset to get the desired results. The results, TP Rate, FP Rate, Precision, Recall, and F-Measure values were computed. Additionally, year-wise crime reports of the city were analysed to find out the months when maximum crimes and accidents occurred. The authors in [27] tried to predict the crime rate by monitoring human behaviour, while in the previous research, criminal activities and crime patterns were indicated using historical data. The crime dataset used by the authors contained reported crimes in the United Kingdom. The crime dataset contained information about the month, year, location, crime type, etc. Additionally, another dataset was created, which includes the demographic data of UK citizens.