OpenUAS: Embeddings of Cities in Japan with Anchor Data for Cross-city Analysis of Area Usage Patterns
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916477758603264 |
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| author | Tamura, Naoki Shoji, Kazuyuki Katayama, Shin Urano, Kenta Yonezawa, Takuro Kawaguchi, Nobuo |
| author_facet | Tamura, Naoki Shoji, Kazuyuki Katayama, Shin Urano, Kenta Yonezawa, Takuro Kawaguchi, Nobuo |
| contents | We publicly release OpenUAS, a dataset of area embeddings based on urban usage patterns, including embeddings for over 1.3 million 50-meter square meshes covering a total area of 3,300 square kilometers. This dataset is valuable for analyzing area functions in fields such as market analysis, urban planning, transportation infrastructure, and infection prediction. It captures the characteristics of each area in the city, such as office districts and residential areas, by employing an area embedding technique that utilizes location information typically obtained by GPS. Numerous area embedding techniques have been proposed, and while the public release of such embedding datasets is technically feasible, it has not been realized. One reason for this is that previous methods could not embed areas from different cities and periods into the same embedding space without sharing raw location data. We address this issue by developing an anchoring method that establishes anchors within a shared embedding space. We publicly release this anchor dataset along with area embedding datasets from several periods in eight major Japanese cities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_19872 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | OpenUAS: Embeddings of Cities in Japan with Anchor Data for Cross-city Analysis of Area Usage Patterns Tamura, Naoki Shoji, Kazuyuki Katayama, Shin Urano, Kenta Yonezawa, Takuro Kawaguchi, Nobuo Machine Learning We publicly release OpenUAS, a dataset of area embeddings based on urban usage patterns, including embeddings for over 1.3 million 50-meter square meshes covering a total area of 3,300 square kilometers. This dataset is valuable for analyzing area functions in fields such as market analysis, urban planning, transportation infrastructure, and infection prediction. It captures the characteristics of each area in the city, such as office districts and residential areas, by employing an area embedding technique that utilizes location information typically obtained by GPS. Numerous area embedding techniques have been proposed, and while the public release of such embedding datasets is technically feasible, it has not been realized. One reason for this is that previous methods could not embed areas from different cities and periods into the same embedding space without sharing raw location data. We address this issue by developing an anchoring method that establishes anchors within a shared embedding space. We publicly release this anchor dataset along with area embedding datasets from several periods in eight major Japanese cities. |
| title | OpenUAS: Embeddings of Cities in Japan with Anchor Data for Cross-city Analysis of Area Usage Patterns |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.19872 |