Unsupervised text encoding models have recently fueled substantial progress in Natural Language Processing (NLP). The key idea is to use neural networks to convert words in texts to vector space representations (embeddings) based on word positions in a sentence and their contexts. We see a strikingly similar situation in spatial analysis, which focuses on incorporating both absolute positions and spatial contexts of geographic objects such as Points of Interest (POIs) into models. A general space encoding method is valuable for a multitude of tasks. However, no such general model exists to date beyond simply applying discretizing or feed forward nets to coordinates, and little effort has been put into jointly modeling distributions with vastly different characteristics, which commonly emerges from GIS data. Meanwhile, Nobel Prize-winning Neuroscience research shows that grid cells in mammals provide a multi-scale periodic representation that functions as a metric for encoding space and are critical for recognizing places and for path-integration. Therefore, we propose a representation learning model called Space2vec to encode the absolute positions and spatial relationships of places. We conduct experiments on two real world geographic data 1) predict types of POIs 2) image classification leveraging their locations. Results show that because of its multi-scale representations Space2vec outperforms well established ML approaches such as RBF kernels, multi-layer feed forward nets, and tile embedding approaches for the Location Modeling tasks and image classification tasks. Detailed analysis shows that all baselines can at most well handle distribution at one scale but show poor performances in other scales. In contrast, Space2vec 's multi-scale representation can handle distributions at different scales.  (Qi et al., 2017), POI type similarity reasoning , land cover classification (Kussul et al., 2017), and geographic question answering (Mai et al.,  2019). Developing general models for vector space representation of any point in space would pave the way to all these applications.
However, existing models often utilize specific methods to deal with geographic information and often disregards geographic coordinates. For example, Place2Vec  converts the coordinates of POIs into spatially collocated POI pairs within certain distance bins, and does not preserve information about distance and direction between POIs. Li et al. (2017) propose DCRNN for traffic forecasting in which the traffic sensor network is converted to a distance weighted graph which necessarily forfeits information about the spatial layout of sensors. There is, however, no general 1 link to repository redacted during double-blind review.
The paper introduces Space2Vec, a space representation learning model. The work is motivated by the biological grid cell’s multi-scale periodic representations and the success of representation learning of NLP. So, the key idea behind the model is two-fold. On one hand, utilize the position information and the context associated with the position. On the other hand, the authors build a multiscale point space encoder based on Theorem 1 (in the paper), which was previously proved by Gao et al. (2019).
The multi-scale point feature encoder is novel. The experimental results turn out that the whole model is good at predicting features using only location information but does not outperform the RBF kernel (on validation) in terms of using spatial context modeling.
One core selling point of this paper is dealing with location distributions with very different characteristics. This is very well motivated at the beginning. More analysis/statistics would help better understand how the model "theory" wins Table one. See comments on experiments.  
I have some comments/questions about model architecture and also experimental results/analysis.
1. Regarding Contextual Embedding
-- The encoder of this paper is not doing much “contextual embedding”.  The encoder typically encodes features of each position independently thus lead to very local embedding. 
-- The location decoder would reconstruct the same (or similar) type of point features given embeddings of locations of the same type. As the distributions of different types of locations are very different, an encoder capable to deal with multi-scale data is crucial here. 
-- The Spatial context decoder, like a context-dependent language model, would reconstruct the current position features, given the neighboring information.
-- Overall, unlike many existing pre-training models in NLP with deep encoders, the full model of this paper is with very local encoder, while the decoder does the most work of “gathering contextual information". 
As you claim your model to be "a general-purpose space representation model", can you describe/specify how you would use your model for other tasks? Will you take some intermediate output of decoders to be representations? Or will you fine-tune the whole encoder-decoder?
2. For experiments:
a.	I do not prefer saying your method outperforms RBF in the “spatial context modeling” task when you getting worse validation set performance. It is interesting that RBF is stronger in terms of validation.
b.	Is it possible for the authors to do some statistics on the different types of locations? For your first task “location modeling”, should we expect to see that your model does not have very bad performance on certain types of locations while other non-multi-scale approaches do? This is trying to provide better support to one of your core contributions.
c.    Again, to claim the model can be widely applied, try more tasks?
To clarify my "experience assessment", I mean I read many related representation learning papers rather than specific papers related to GIS data.
My actual rating for the paper is between weak reject to weak accept (but the system does not have intermediate choices). I would like to hear the author's feedback to further revise the rating.