GIS Investigation of Crime Prediction with an Operationalized Tweet Corpus
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 artifactfused with traditional environmental criminology theory and social mediaerects 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. Poster Download: http:/scholarworks.umass.edu/foss4g/vol17/iss1/21 *Corresponding author Email address: acorso@calbaptist.edu (Anthony J. Corso) Submitted to FOSS4G 2017 Conference Proceedings, Boston, USA. September 20, 2017 Social media (e.g. tweets) are the de facto communication channel to disseminate one's diurnal self revelations. This profound phenomenon contains doubletalk, peculiar insight, and contextual data or information about real world events. Amid such complex and personal expose, natural language processing (NLP) techniques uncover both obvious and latent knowledge claims published within. A geographical information system is capable of large scale data analysis and possesses methods that enable dataset processing, evaluation, and spatial visualization. When fused with traditional research theory such an artifact defines guidelines, algorithms, and models for substantive and predictive investigation. Introduction With a novel NLP pipeline tweets were processed and used to measure the change in performance of an ArcGIS 10.4.1 artifact. A 1,000 tweet sample was hand tagged and compared to a baseline model, and to an innovative social media grammar applied by a rule based social media NLP pipeline. GIS evaluation tools answer the question, prior to content analysis of a tweet, does a method exist to support identifying a tweet as "useful" for subsequent GIS processing? Indeed, "useful" tweet identification via NLP returned precision of 0.9256, recall of 0.6590, and F measure of 0.7699; consequently, exploratory GIS processing of a social media variable increased 0.2194 over baseline. Predictive capability potential of a GIS artifact implementing social media's latent behavior attributes is vast. Yes, preliminary results are encouraging but future research is important and needs to identify its value. References Alonso, O. Marshall, C. C. Najork, M. (2013) Are some tweets more interesting than others? hardquestion. 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[journal article] Social Network Analysis and Mining, 4(1) 1 12. Contact Dr. Corso holds Ph.D. in Information Systems and Technology from Claremont Graduate University. Since 2007, he is an Associate Professor in the Gordon and Jill Bourns College of Engineering at California Baptist University. E mail: acorso@calbaptist.edu Despite a tweet's sparse content, NLP makes their use in a predictive GIS artifact feasible. For example, subsequent to processing, useful tweets are able to: Predict the validity of a real world event only recorded by observation of social media eyewitness; or Predict real time trends by amalgamating social media with traditional social behavior variables. California Baptist University Anthony J. Corso, Ph.D. GIS Investigation of Crime Prediction with an Operationalized Tweet Corpus