Using location-based social network data for activity intensity analysis: A case study of New York City
Location-based social networks (LBSN) are social media sites where users check-in at venues and share content linked to their geo-locations. LBSN, considered to be a novel data source, contain valuable information for urban planners and researchers. While earlier research efforts focused either on d...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of Minnesota Libraries Publishing
2019-10-01
|
| Series: | Journal of Transport and Land Use |
| Online Access: | https://www.jtlu.org/index.php/jtlu/article/view/1470 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850038096430628864 |
|---|---|
| author | Haluk Laman Shamsunnahar Yasmin Naveen Eluru |
| author_facet | Haluk Laman Shamsunnahar Yasmin Naveen Eluru |
| author_sort | Haluk Laman |
| collection | DOAJ |
| description | Location-based social networks (LBSN) are social media sites where users check-in at venues and share content linked to their geo-locations. LBSN, considered to be a novel data source, contain valuable information for urban planners and researchers. While earlier research efforts focused either on disaggregate patterns or aggregate analysis of social and temporal attributes, no attempt has been made to relate the data to transportation planning outcomes. To that extent, the current study employs LBSN service-based data for an aggregate-level transportation planning exercise by developing land-use planning models. Specifically, we employ check-in data aggregated at the census tract level to develop a quantitative model for activity intensity as a function of land use and built-environment attributes for the New York City (NYC) region. A statistical exercise based on clustering of census tracts and negative binomial regression analyses are adopted to analyze the aggregated data. We demonstrate the implications of the estimated models by presenting the spatial aggregation profiling based on the model estimates. The findings provide insights on relative differences of activity engagements across the urban region. The proposed approach thus provides a complementary analysis tool to traditional transportation planning exercises. |
| format | Article |
| id | doaj-art-e30c20f0b3794530b62fb26a1c21aef1 |
| institution | DOAJ |
| issn | 1938-7849 |
| language | English |
| publishDate | 2019-10-01 |
| publisher | University of Minnesota Libraries Publishing |
| record_format | Article |
| series | Journal of Transport and Land Use |
| spelling | doaj-art-e30c20f0b3794530b62fb26a1c21aef12025-08-20T02:56:40ZengUniversity of Minnesota Libraries PublishingJournal of Transport and Land Use1938-78492019-10-0112110.5198/jtlu.2019.1470Using location-based social network data for activity intensity analysis: A case study of New York CityHaluk Laman0Shamsunnahar Yasmin1Naveen Eluru2University of Central FloridaUniversity of Central FloridaUniversity of Central FloridaLocation-based social networks (LBSN) are social media sites where users check-in at venues and share content linked to their geo-locations. LBSN, considered to be a novel data source, contain valuable information for urban planners and researchers. While earlier research efforts focused either on disaggregate patterns or aggregate analysis of social and temporal attributes, no attempt has been made to relate the data to transportation planning outcomes. To that extent, the current study employs LBSN service-based data for an aggregate-level transportation planning exercise by developing land-use planning models. Specifically, we employ check-in data aggregated at the census tract level to develop a quantitative model for activity intensity as a function of land use and built-environment attributes for the New York City (NYC) region. A statistical exercise based on clustering of census tracts and negative binomial regression analyses are adopted to analyze the aggregated data. We demonstrate the implications of the estimated models by presenting the spatial aggregation profiling based on the model estimates. The findings provide insights on relative differences of activity engagements across the urban region. The proposed approach thus provides a complementary analysis tool to traditional transportation planning exercises.https://www.jtlu.org/index.php/jtlu/article/view/1470 |
| spellingShingle | Haluk Laman Shamsunnahar Yasmin Naveen Eluru Using location-based social network data for activity intensity analysis: A case study of New York City Journal of Transport and Land Use |
| title | Using location-based social network data for activity intensity analysis: A case study of New York City |
| title_full | Using location-based social network data for activity intensity analysis: A case study of New York City |
| title_fullStr | Using location-based social network data for activity intensity analysis: A case study of New York City |
| title_full_unstemmed | Using location-based social network data for activity intensity analysis: A case study of New York City |
| title_short | Using location-based social network data for activity intensity analysis: A case study of New York City |
| title_sort | using location based social network data for activity intensity analysis a case study of new york city |
| url | https://www.jtlu.org/index.php/jtlu/article/view/1470 |
| work_keys_str_mv | AT haluklaman usinglocationbasedsocialnetworkdataforactivityintensityanalysisacasestudyofnewyorkcity AT shamsunnaharyasmin usinglocationbasedsocialnetworkdataforactivityintensityanalysisacasestudyofnewyorkcity AT naveeneluru usinglocationbasedsocialnetworkdataforactivityintensityanalysisacasestudyofnewyorkcity |