INTRODUCTION
1.1 Societal Applications  
1.2 Input Spatial Data  
1.3 Formal Problem Definition  
1.4 Challenges  
1.5 Comparison with Existing Surveys  
1.6 Scope and Outline  

SPATIAL STATISTICAL FOUNDATIONS
2.1 Spatial Autocorrelation (Dependency)  
2.2 Spatial Heterogeneity  

A TAXONOMY OF SPATIAL PREDICTION METHODS
3.1 Spatial Autocorrelation (Dependency)  
3.2 Spatial Heterogeneity  
3.3 Limited Ground Truth  
3.4 Multiple Scales and Resolutions  
3.5 Spatiotemporal Extensions to Prediction Methods  

FUTURE RESEARCH OPPORTUNITIES
4.1 Prediction for Heterogeneous Spatial Data  
4.2 Data Fusion of Multi-scale Spatial Data from Different Sources  
4.3 Deep Learning Methods for Spatial Prediction  
4.4 Spatial Big Data Prediction  

CONCLUSION