# Spatiotemporal Analysis of Web News Archives for Crime Prediction
## 1 Introduction  
Crimes are the most common social issues nowadays, affecting the economic growth, quality of life, and economy of any country. Crimes affect the reputation of a country on an international scale and affect the economy of the country by placing a financial burden on the government in hiring additional police forces. For the eradication of crimes, the government needs to adopt some optimized strategy [1] and sustainable e-goverance information systems. Algorithms that predict the occurrence of crimes based on time and location can help the government to deploy law enforcement in highly dangerous areas [2]. Internet-based news resources, such as online newspapers and news channel archives, have been tremendously increased in number, volume, and coverage, and they contain useful as well as authentic data [3]. Nevertheless, the data of the archives are not so arranged and categorized, so it can be quite challenging to extract useful information about specific or interesting crime events [4; 5; 6]. According to the Pakistan Bureau of Statistics, the crime rate of Pakistan is increasing constantly, and among all the crimes, the rate of murder, kidnapping, robbery, accidents, and blasts is high. News archives provide a valuable source of information. It contains rich and purposeful content which is recorded carefully by specialists and it portrays some principal aspects related to the specific article [7]. The most popularand authentic newspaper's archives of Pakistan are Dawn News, Dunya News, Ary News, The News, Daily Times, Pakistan Press Foundation, The Nations, and Journalism Pakistan [8]. The purpose of this research work is to utilize free of cost data available in news archives and perform the spatiotemporal analysis for crime prediction. NLP is an efficient mechanism to extract the keywords as representative of the whole text of the news body and researchers have used different Natural Language Processing (NLP) techniques for mining the data of news web archives [7]. Similarly, geostatistical-based approaches have been used by different researchers to identify the high-risk regions [1; 2; 9]. The development in Geographical Information Systems (GIS) approaches has enabled the analysis of spatial data in different domains. GIS-based approaches provide the visualization and exploration of incidences by creating map layers as visualization of spatial data which can help detect the patterns and trends of criminal networks. Hence, the spatial distribution of crime data for the prediction of future crime events using data mining and machine learning on the spatial dataset can provide accurate distribution of crimes [10]. These types of novel methods for crime mapping can be helpful in many aspects of society, such as decreasing the probability of accidents, crime ratio, and murder cases. Moreover, it can secure the nation from blasts, kidnappers, and murders [11]. This study focuses on information retrieval from the news archives, extraction of attributes from the news headlines, and applying spatial analysis as well as machine learning to predict future crimes. Crime-solving is a complex task that requires human efforts and intelligence for the processing of criminal data. Therefore, data mining can assist researchers in crime identification problems [12; 13; 14]. Researchers have done extensive research on the usage of data mining and machine learning techniques in the identification and prediction of crime events and criminal networks [15]. There are many data mining and machine learning tools available for researchers. Weka is one of the tools which can assist researchers in mining the data and applying certain machine learning algorithms [16]. It has the capabilities of performing preprocessing, feature selection, clustering, and classification on data [17]. In this study, the algorithm of KNN predicted the crime type with 92% accuracy. In today's world, security is the most promising aspect which should be provided by the government to their citizens.