Spatial prediction of sparse events using a discrete global grid system; a case study of hate crimes in the USA

Abstract

Spatial prediction of any geographic phenomenon can be an intractable problem. Predicting sparse and uncertain spatial events related to many influencing factors necessitates the integration of multiple data sources. We present an innovative approach that combines data in a Discrete Global Grid System (DGGS) and uses machine learning for analysis. A DGGS provides a structured input for multiple types of spatial data, consistent over multiple scales. This data framework facilitates the training of an Artificial Neural Network (ANN) to map and predict a phenomenon. Spatial lag regression models (SLRM) are used to evaluate and rank the outputs of the ANN. In our case study, we predict hate crimes in the USA. Hate crimes get attention from mass media and the scientific community, but data on such events is sparse. We trained the ANN with data ingested in the DGGS based on a 50% sample of hate crimes as identified by the Southern Poverty Law Center (SPLC). Our spatial prediction is up to 78% accurate and verified at the state level against the independent FBI hate crime statistics with a fit of 80%. The derived risk maps are a guide to action for policy makers and law enforcement.

Keywords

• discrete global grid system
• geospatial data integration
• artificial neural network
• spatial prediction
• sparse events
• hates crimes