  Multi-source spatial point data prediction is crucial in fields like
environmental monitoring and natural resource management, where integrating
data from various sensors is the key to achieving a holistic environmental
understanding. Existing models in this area often fall short due to their
domain-specific nature and lack a strategy for integrating information from
various sources in the absence of ground truth labels. Key challenges include
evaluating the quality of different data sources and modeling spatial
relationships among them effectively. Addressing these issues, we introduce an
innovative multi-source spatial point data prediction framework that adeptly
aligns information from varied sources without relying on ground truth labels.
A unique aspect of our method is the 'fidelity score,' a quantitative measure
for evaluating the reliability of each data source. Furthermore, we develop a
geo-location-aware graph neural network tailored to accurately depict spatial
relationships between data points. Our framework has been rigorously tested on
two real-world datasets and one synthetic dataset. The results consistently
demonstrate its superior performance over existing state-of-the-art methods.
