A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A Geospatial Analysis of Tweets During Post-circuit Breaker in Singapore
Since 19 July 2020, Singapore entered Phase 2 of re-opening after one and half month’s “Circuit Breaker” measures to curb the spread of COVID 19. Although most businesses and public places have resumed operation at a reduced capacity, individuals were strongly advised to practice social distancing and avoid crowds. Both implicit and explicit measures to prevent overcrowding had impacted on how people visit places in Singapore. The current study used geotagged Twitter data between September to October in 2020 to examine the spatial and temporal patterns of residents’ locations in Singapore and explored the service amenities which remain “attractive” to residents. Random Forest Supervised Machine Learning Model was used to train and predict spatial distribution of activities during off-work recreational hours using service amenities point of interests (POIs) and land use merge. Five explanatory variables used were parks, public links between parks and malls, taxi stands, residential areas, and shopping malls which had the strongest influence in driving the model prediction of spatial distribution of activities in off-work recreational hours. While distinct temporal patterns of tweets were expected during office hour, this analysis revealed no such statistically significant clusters. The regression analysis showed that distances to service amenities did not provide strong explanations for tweeting patterns.
A Geospatial Analysis of Tweets During Post-circuit Breaker in Singapore
Since 19 July 2020, Singapore entered Phase 2 of re-opening after one and half month’s “Circuit Breaker” measures to curb the spread of COVID 19. Although most businesses and public places have resumed operation at a reduced capacity, individuals were strongly advised to practice social distancing and avoid crowds. Both implicit and explicit measures to prevent overcrowding had impacted on how people visit places in Singapore. The current study used geotagged Twitter data between September to October in 2020 to examine the spatial and temporal patterns of residents’ locations in Singapore and explored the service amenities which remain “attractive” to residents. Random Forest Supervised Machine Learning Model was used to train and predict spatial distribution of activities during off-work recreational hours using service amenities point of interests (POIs) and land use merge. Five explanatory variables used were parks, public links between parks and malls, taxi stands, residential areas, and shopping malls which had the strongest influence in driving the model prediction of spatial distribution of activities in off-work recreational hours. While distinct temporal patterns of tweets were expected during office hour, this analysis revealed no such statistically significant clusters. The regression analysis showed that distances to service amenities did not provide strong explanations for tweeting patterns.
A Geospatial Analysis of Tweets During Post-circuit Breaker in Singapore
Advances in 21st Century Human Settlements
Kundu, Sandeep Narayan (editor) / Yuting, Xu (author) / Zhu An, Lim (author) / Loh Wei, Sherie (author) / Yong Xin, Phang (author)
2022-01-03
22 pages
Article/Chapter (Book)
Electronic Resource
English
Geospatial Analysis of Grab Trips in Singapore
| Springer Verlag | 2022
Generator Circuit Breaker Retrofit
| British Library Conference Proceedings | 1995