# Spatiotemporal Analysis of Web News Archives for Crime Prediction
## 8 Conclusions  
The usage of digital information archives is a cost-effective way of predicting crime events occurring in a country. Data related to crimes extracted through automated tools such as Python can be converted into useful information for the prediction of fruitful results. The location-based geo-coded data, processed through GIS-based software, i.e., ArcMap provided locations based statistical information, which helped in the identification of the patterns, trends, and relationships between crime features. Furthermore, the hotspots analysis assisted in identifying the areas and regions of high susceptibility. Such types of research can be quite helpful to law-enforcement agencies to monitor highly sensitive areas and to remain in high alert in terms of security. KNN and the Random Forest algorithm concluded that Pakistan has the worst condition in a robbery as compared to other crimes. Such types of a robust method can be an effective way to keep an eye on risk-prone areas. In conclusion, such types of automated processes can open new ways of handling a peaceful and sustainable society in eradicating crimes for the developing and under-developed countries having a paucity of financial resources.  
Due to limitations of time, availability of data, and lack of resources, we were only be able to extract limited datasets, i.e., 900 crime records at the city level. There is a possibility of uncertainty in the number of crime cases because the data has been extracted from particular news archives. Adding other electronic resources, mainly from the local language, can increase the accuracy of the dataset. Moreover, the unpredictability and uncertainty in the crime rate is still a challenge for researchers and decision-makers. This is because various other factors affect the crime rate simultaneously such as criminal mental state, poverty, low income, unemployment, illiteracy, family pressure, bad company, etc. [47; 48]. By adding socioeconomic data, precise locations of crimes, and data from other electronic resources, a useful prediction model can be developed. In addition to that, the demographic data (population density) of Pakistan can help us to improve the crime prediction. It can show how the population distribution is associated with the crime rate in Pakistan. Similarly, some other potential biases such as information bias can produce more fruitful results for crime prediction.  
Conceptualization, A.U., M.S.S., U.H., and M.H.U.; Data curation, A.U., M.S.S., and M.H.U.; Formal analysis, A.U., M.S.S., and M.M.; Funding acquisition, M.A. and M.M.; Investigation, A.U., M.A., M.H.U., and M.M.; Methodology, A.U.; Supervision, M.S.S., M.A., and U.H.; Validation, A.U., M.S.S., M.A., and U.H.; Visualization, A.U., M.S., and U.H.; Writing--original draft, A.U., M.S.S., M.A., U.H., M.H.U., and M.M.; Writing--review and editing, A.U., M.S.S., M.A., U.H., M.H.U., and M.M. All authors have read and agreed to the published version of the manuscript.  
This research received funding from a research project under national research program for universities, No: 10537/Federal/NRPU/R&D/HEC/2017. This research was partially supported by Innopolis University, Innopolis, Russia.  
The authors declare no conflict of interest.