 GPS coordinates and other location indicators are fine-grained location indicators that are difficult to be effectively utilized by machine learning models in Geo-aware applications. Previous location embedding methods are mostly tailored for specific problems that are taken place within areas of interest. When it comes to the scale of the entire cities , existing approaches always suffer from extensive computational cost and signigicant information loss. An increasing amount of location-based service (LBS) data are being accumulated and released to the public and enables us to study urban dynamics and human mobility. This study learns vector representations for locations using the large-scale LBS data. Different from existing studies, we propose to consider both spatial connection and human mobility , and jointly learn the representations from a flow graph and a spatial graph through a GCN-aided skip-gram model named GCN-L2V. This model embeds context information in human mobility and spatial information. By doing so, GCN-L2V is able to capture relationships among locations and provide a better notion of semantic similarity in a spatial environment. Across quantitative experiments and case studies, we empirically demonstrate that the representations learned by GCN-L2V are effective.  GCN-L2V  can be applied in a complementary manner to other place embedding methods and down-streaming Geo-aware applications.
An increasing amount of location-based service (LBS) data is being accumulated and helps to study urban dynamics and human mobility. GPS coordinates and other location indicators are normally low dimensional and only representing spatial proximity, thus difficult to be effectively utilized by machine learning models in Geo-aware applications. Existing location embedding methods are mostly tailored for specific problems that are taken place within areas of interest. When it comes to the scale of a city or even a country , existing approaches always suffer from extensive computational cost and significant data sparsity. Different from existing studies, we propose to learn representations through a GCN-aided skip-gram model named GCN-L2V by considering both spatial connection and human mobility . With a flow graph and a spatial graph , it embeds context information into vector representations. GCN-L2V is able to capture relationships among locations and provide a better notion of similarity in a spatial environment. Across quantitative experiments and case studies, we empirically demonstrate that representations learned by GCN-L2V are effective. As far as we know, this is the first study that provides a fine-grained location embedding at the city level using only LBS records. GCN-L2V is a general-purpose embedding model with high flexibility and can be applied in down-streaming Geo-aware applications.
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An increasing amount of location-based service (LBS) data is being accumulated and helps to study urban dynamics and human mobility.
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fine-grained location indicators that are
normally low dimensional and only representing spatial proximity, thus
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the entire cities
a city or even a country
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signigicant information loss. An increasing amount of location-based service (LBS) data are being accumulated and released to the public and enables us to study urban dynamics and human mobility. This study learns vector representations for locations using the large-scale LBS data.
significant data sparsity.
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consider
learn representations through a GCN-aided skip-gram model named GCN-L2V by considering
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through a GCN-aided skip-gram model named GCN-L2V. This model
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in human mobility and spatial information. By doing so,
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semantic
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the
A
As far as we know, this is the first study that provides a fine-grained location embedding at the city level using only LBS records.
A
is a general-purpose embedding model with high flexibility and
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a complementary manner to other place embedding methods and