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...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.展开更多
全球离散格网(Discrete Global Grid,DGG)模型是数字地球及空间信息格网的基础,不同的建模方法不但影响空间数据的存储和管理效率,而且影响全球GIS的操作功能。该文介绍了DGG的评价标准,将DGG的建模方法归纳为3种类型:经纬度格网模型、...全球离散格网(Discrete Global Grid,DGG)模型是数字地球及空间信息格网的基础,不同的建模方法不但影响空间数据的存储和管理效率,而且影响全球GIS的操作功能。该文介绍了DGG的评价标准,将DGG的建模方法归纳为3种类型:经纬度格网模型、自适应格网模型和正多面体格网模型,重点分析了不同类型球面离散格网模型的几何结构、单元特征和应用模式。最后,提出了DGG在Global GIS中亟待解决的基本问题,包括编码、精度、应用、误差、整合和定位问题。展开更多
文摘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.
文摘全球离散格网(Discrete Global Grid,DGG)模型是数字地球及空间信息格网的基础,不同的建模方法不但影响空间数据的存储和管理效率,而且影响全球GIS的操作功能。该文介绍了DGG的评价标准,将DGG的建模方法归纳为3种类型:经纬度格网模型、自适应格网模型和正多面体格网模型,重点分析了不同类型球面离散格网模型的几何结构、单元特征和应用模式。最后,提出了DGG在Global GIS中亟待解决的基本问题,包括编码、精度、应用、误差、整合和定位问题。
