Geosemantic surveillance and profiling of abduction locations and risk hotspots using print media reports.

29 Jul 2023 (modified: 07 Dec 2023)DeepLearningIndaba 2023 Conference SubmissionEveryoneRevisionsBibTeX
Keywords: Crime location identification, Natural Language processing (NLP), Geospatial Analysis
TL;DR: This paper presents a data-driven solution utilizing NLP techniques and geospatial analysis to address the issue of kidnapping in Nigeria.
Abstract: Kidnapping is a significant social risk in Nigeria which often lack adequate intervention due to the unavailability of local crime data, underreporting of cases due to fear of retaliation from suspected perpetrators or involvement of security operatives. In response, we have developed a data-driven solution by generating a reliable dataset of crime locations and entities in Nigeria. Our approach involves geoparsing newspaper-reported crime locations and entities using NLP techniques and Google geocoder, as well as clustering and geospatial analysis of identified social risk hotspots. We have designed an algorithm that geoparse locations in unstructured raw text. Our research aims to provide insights and solutions for combating the menace posed by kidnappers to Nigeria.
Submission Category: Machine learning algorithms
Submission Number: 46
Loading