  The next point-of-interest (POI) prediction is a significant task in
location-based services, yet its complexity arises from the consolidation of
spatial and semantic intent. This fusion is subject to the influences of
historical preferences, prevailing location, and environmental factors, thereby
posing significant challenges. In addition, the uneven POI distribution further
complicates the next POI prediction procedure. To address these challenges, we
enrich input features and propose an effective deep-learning method within a
two-step prediction framework. Our method first incorporates remote sensing
data, capturing pivotal environmental context to enhance input features
regarding both location and semantics. Subsequently, we employ a region
quad-tree structure to integrate urban remote sensing, road network, and POI
distribution spaces, aiming to devise a more coherent graph representation
method for urban spatial. Leveraging this method, we construct the QR-P graph
for the user's historical trajectories to encapsulate historical travel
knowledge, thereby augmenting input features with comprehensive spatial and
semantic insights. We devise distinct embedding modules to encode these
features and employ an attention mechanism to fuse diverse encodings. In the
two-step prediction procedure, we initially identify potential spatial zones by
predicting user-preferred tiles, followed by pinpointing specific POIs of a
designated type within the projected tiles. Empirical findings from four
real-world location-based social network datasets underscore the remarkable
superiority of our proposed approach over competitive baseline methods.
