# Spatiotemporal Analysis of Web News Archives for Crime Prediction
## 2 Related Work  
With the advancement in technology, criminal behavior is becoming more and more channeled and complex [4]. For crime control, the nature of crime must be understood [18]. The spatial analysis helps to decode the spatial behavior of criminal activities [18] and assist law enforcement in making predictions about future crimes that may occur [1]. Many crime prediction approaches have been proposed earlier by different researchers. [PERSON] et al. proposed the framework of crime prediction in which crime analysis is performed on crime datasets by k-means clustering using a rapid miner tool [4]. However, there is a need to apply machine learning. [PERSON] et al. proposed a new framework for clustering and crime prediction in which they used the Genetic Algorithm (GA) for the detection of outliers. Their main focus was to classify the crime cases based on the frequency of crime occurrence during different years [19]. [PERSON] et al. used the tools of R such as Rgoogle maps [1], googleVis [1], ggplot2 [1], and ggmap for visualization of criminal data. They used the K-Nearest Neighbor (KNN) algorithms and Naive Bayes algorithms to help the prediction of crimes [1], however, they can use more advanced methods of machine learning and apply spatial analysis as well. [PERSON] et al. proposed the method of crime prediction using land-use data with the help of machine learning algorithms. They identified the exact hours of crime occurring using hotspot analysis by using logistic regression and determining the precise time of the next crime [2], but the prediction results can be enhanced using advanced methods of machine learning. [PERSON] et al. proposed the model of crime prediction using data mining techniques. The model consisted of data cleaning, data clustering, classification, and outlier detection [12]. In [20], [PERSON] et al. used GIS to visualize the spatial distribution of accidents along with the road networks. They identified the spatial patterns of road-side accidents along with its occurrence in different moments such as hours, days, seasons, and years, etc. [PERSON] et al. used kernel density estimation and kriging for identifying the hotspot of crashing incidents and estimating the collision frequency, respectively [9]. [PERSON] used kernel density for estimation of concentration at a given point in space [21]. [PERSON] et al. proposed the method of spatial analysis with a latent decision for crime prediction. They designed two different spatial models for crime prediction such as uniform and distinct. Both models helped in understanding the spatial pattern of crimes and criminal behaviors [11]. In [22] the authors design a deep neural network for the crime prediction by utilizing the New York crime dataset while in [23] [PERSON] et al. predicted the crime suspect location using spatiotemporal analysis. [PERSON] et al. designed a Bayesian model for urban crime prediction based on regional statistics [24]. [PERSON] used a random forest algorithm to predict criminal activities by offenders having a mental illness. This approach can be helpful not only for the judiciary but also for designing new strategies for risk management [25]. [PERSON] et al. found crime patterns using decision trees and Naive Bayesian classifiers. They predicted the future crime events in a particular location (latitude, longitude) within a specific time interval. They combined demographic information with the findings of the crime dataset of cities and then estimated which factor is affecting the neighbors the most [26]. The crime hotspot and spatial analysis can help to identify the spatial crime patterns. [PERSON] et al. compared the prediction rate and accuracy of KNN with the Naive Bayes over the crime dataset. They used the previous scenarios of KNN over crime prediction and compared with their proposed scenario of Naive Bayes and found out that both the techniques showed different accuracy rates. [PERSON] et al. reported that the accuracy of Naive Bayes is higher than the KNN algorithm.