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Social media as the de facto communication channel is being used to disseminate one’s diurnal self-revelations. This profound discovery often contains double-talk, peculiar insights, or contextual information about real-world events. Natural language processing is regularly used to uncover both obvious and latent knowledge claims within disclosures published amid the complex environment. For example, a perpetrator with first-hand knowledge of their criminal incident uses social media to post critical information about it. A geographic information system (GIS) is capable of large-scale point data analysis and possesses methods that enable dataset processing, evaluation, and automatic spatial visualization. Such an artifact—fused with traditional environmental criminology theory and social media—erects guidelines, tools, and models for substantive construction and evaluation of GIS crime analysis solutions. Provided the social media stream is timely and correctly processed, corrective action can be taken. The construction of a natural language processing social media annotation pipe identifies latent indicators extracted from a social media corpus and is an integral part of societal mishap prediction. Spatial visualizations and regression analyses were used to describe and evaluate project artifacts. As a result, a social media corpus was operationalized, and subsequently used as a proxy for a traditional environmental criminology risk layer in construction of a social media GIS crime analysis artifact. Using such multi-domain collaboration, the artifact was able to increase the predictive crime incident outcome with an overall R-squared increase of 21.94%. This result is the state-of-the-art; there are no other results to compare it to.





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