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.

摘要

任何地理现象的空间预测都可能是一个棘手的问题。预测与许多影响因素有关的稀疏和不确定的空间事件，需要集成多个数据源。我们提出了一种创新的方法，该方法将数据集成到离散全球网格系统（DGGS）中，并使用机器学习进行分析。DGGS为多种类型的空间数据提供了结构化的输入，并在多个尺度上保持一致。该数据框架促进了人工神经网络（ANN）的训练，以绘制和预测现象。空间滞后回归模型（SLRM）用于评估和排序ANN的输出。在我们的案例研究中，我们预测了美国的仇恨犯罪。仇恨犯罪受到大众媒体和科学界的关注，但有关此类事件的数据很少。我们根据南部贫困法中心（SPLC）确定的50％仇恨犯罪样本，利用DGGS中提取的数据对ANN进行了训练。我们的空间预测准确率高达78％，并在州一级针对独立的FBI仇恨犯罪统计数据进行了验证（拟合度为80％）。得出的风险图为政策制定者和执法人员提供了行动指南。