  Automated characterization of spatial data is a kind of critical geographical
intelligence. As an emerging technique for characterization, Spatial
Representation Learning (SRL) uses deep neural networks (DNNs) to learn
non-linear embedded features of spatial data for characterization. However, SRL
extracts features by internal layers of DNNs, and thus suffers from lacking
semantic labels. Texts of spatial entities, on the other hand, provide semantic
understanding of latent feature labels, but is insensible to deep SRL models.
How can we teach a SRL model to discover appropriate topic labels in texts and
pair learned features with the labels? This paper formulates a new problem:
feature-topic pairing, and proposes a novel Particle Swarm Optimization (PSO)
based deep learning framework. Specifically, we formulate the feature-topic
pairing problem into an automated alignment task between 1) a latent embedding
feature space and 2) a textual semantic topic space. We decompose the alignment
of the two spaces into: 1) point-wise alignment, denoting the correlation
between a topic distribution and an embedding vector; 2) pair-wise alignment,
denoting the consistency between a feature-feature similarity matrix and a
topic-topic similarity matrix. We design a PSO based solver to simultaneously
select an optimal set of topics and learn corresponding features based on the
selected topics. We develop a closed loop algorithm to iterate between 1)
minimizing losses of representation reconstruction and feature-topic alignment
and 2) searching the best topics. Finally, we present extensive experiments to
demonstrate the enhanced performance of our method.
