Understanding metro station areas’ functional characteristics via embedding representation: A case study of shanghai
Abstract As crucial transportation hubs for urban travel, metro stations catalyze the transformation of their surrounding areas into highly prominent locations where many activities converge. Uncovering the functional attributes of station areas holds immense significance in comprehending citizens’...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-87336-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832585812545372160 |
|---|---|
| author | Heping Jiang Ruihua Liu Shijia Luo Disheng Yi Jing Zhang |
| author_facet | Heping Jiang Ruihua Liu Shijia Luo Disheng Yi Jing Zhang |
| author_sort | Heping Jiang |
| collection | DOAJ |
| description | Abstract As crucial transportation hubs for urban travel, metro stations catalyze the transformation of their surrounding areas into highly prominent locations where many activities converge. Uncovering the functional attributes of station areas holds immense significance in comprehending citizens’ activity demands, thereby offering valuable insights for regional development and planning in proximity to metro stations. This study introduces a framework that improves the process of accurately representing station areas. On the basis of the semantic vectors of point of interests (POI) categories trained by the GloVe model, the partition smooth inverse frequency (P-SIF) model and affinity propagation (AP) are employed to generate the embedding representations of station areas and categorize. Finally, we classify the station areas into 9 functional groups: and analyse the spatial distribution characteristics of each group. It is found that most of the station areas in Shanghai show the characteristics of mixed type, in which the characteristics of residential type and commercial type are obvious. In terms of spatial, the stations with commercial characteristics are mainly distributed in the central area of the city, while those with residential and working characteristics are scattered. |
| format | Article |
| id | doaj-art-df4d1c8d65fb48d3bbac4609e9de46d2 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-df4d1c8d65fb48d3bbac4609e9de46d22025-01-26T12:29:00ZengNature PortfolioScientific Reports2045-23222025-01-0115111910.1038/s41598-025-87336-6Understanding metro station areas’ functional characteristics via embedding representation: A case study of shanghaiHeping Jiang0Ruihua Liu1Shijia Luo2Disheng Yi3Jing Zhang4Chengdu Center, China Geological Survey (Southwest China Center for Geoscience Innovation)The High School Affiliated to Beijing International Studies UniversityBeijing Guangqumen Middle SchoolCollege of Resources Environment and Tourism, Capital Normal UniversityCollege of Resources Environment and Tourism, Capital Normal UniversityAbstract As crucial transportation hubs for urban travel, metro stations catalyze the transformation of their surrounding areas into highly prominent locations where many activities converge. Uncovering the functional attributes of station areas holds immense significance in comprehending citizens’ activity demands, thereby offering valuable insights for regional development and planning in proximity to metro stations. This study introduces a framework that improves the process of accurately representing station areas. On the basis of the semantic vectors of point of interests (POI) categories trained by the GloVe model, the partition smooth inverse frequency (P-SIF) model and affinity propagation (AP) are employed to generate the embedding representations of station areas and categorize. Finally, we classify the station areas into 9 functional groups: and analyse the spatial distribution characteristics of each group. It is found that most of the station areas in Shanghai show the characteristics of mixed type, in which the characteristics of residential type and commercial type are obvious. In terms of spatial, the stations with commercial characteristics are mainly distributed in the central area of the city, while those with residential and working characteristics are scattered.https://doi.org/10.1038/s41598-025-87336-6Metro station areaFunctional characteristicsText representationPOI data |
| spellingShingle | Heping Jiang Ruihua Liu Shijia Luo Disheng Yi Jing Zhang Understanding metro station areas’ functional characteristics via embedding representation: A case study of shanghai Scientific Reports Metro station area Functional characteristics Text representation POI data |
| title | Understanding metro station areas’ functional characteristics via embedding representation: A case study of shanghai |
| title_full | Understanding metro station areas’ functional characteristics via embedding representation: A case study of shanghai |
| title_fullStr | Understanding metro station areas’ functional characteristics via embedding representation: A case study of shanghai |
| title_full_unstemmed | Understanding metro station areas’ functional characteristics via embedding representation: A case study of shanghai |
| title_short | Understanding metro station areas’ functional characteristics via embedding representation: A case study of shanghai |
| title_sort | understanding metro station areas functional characteristics via embedding representation a case study of shanghai |
| topic | Metro station area Functional characteristics Text representation POI data |
| url | https://doi.org/10.1038/s41598-025-87336-6 |
| work_keys_str_mv | AT hepingjiang understandingmetrostationareasfunctionalcharacteristicsviaembeddingrepresentationacasestudyofshanghai AT ruihualiu understandingmetrostationareasfunctionalcharacteristicsviaembeddingrepresentationacasestudyofshanghai AT shijialuo understandingmetrostationareasfunctionalcharacteristicsviaembeddingrepresentationacasestudyofshanghai AT dishengyi understandingmetrostationareasfunctionalcharacteristicsviaembeddingrepresentationacasestudyofshanghai AT jingzhang understandingmetrostationareasfunctionalcharacteristicsviaembeddingrepresentationacasestudyofshanghai |