SpacetimeAI and GeoAI are currently hot topics,applying the latest algorithms in computer science,such as deep learning,to spatiotemporal data.Although deep learning algorithms have been successfully applied to raster...SpacetimeAI and GeoAI are currently hot topics,applying the latest algorithms in computer science,such as deep learning,to spatiotemporal data.Although deep learning algorithms have been successfully applied to raster data due to their natural applicability to image processing,their applications in other spatial and space-time data types are still immature.This paper sets up the proposition of using a network(&graph)-based framework as a generic spatial structure to present space-time processes that are usually represented by the points,polylines,and polygons.We illustrate network and graph-based SpaceTimeAI,from graph-based deep learning for prediction,to space-time clustering and optimisation.These applications demonstrate the advantages of network(graph)-based SpacetimeAI in the fields of transport&mobility,crime&policing,and public health.展开更多
The coronavirus pandemic that started in 2019 has had wide-ranging impacts on many aspects of people’s daily lives.At the peak of the outbreak,lockdown measures and social distancing changed the ways in which cities ...The coronavirus pandemic that started in 2019 has had wide-ranging impacts on many aspects of people’s daily lives.At the peak of the outbreak,lockdown measures and social distancing changed the ways in which cities function.In particular,they had profound impacts on urban transportation systems,with public transport being shut down in many cities.Bike share systems(BSS)were widely reported as having experienced an increase in demand during the early stages of the pandemic before returning to pre-pandemic levels.However,the studies published to date focus mainly on the first year of the pandemic,when various waves saw continual relaxing and reintroductions of restrictions.Therefore,they fall short of exploring the role of BSS as we move to the post-pandemic period.To address this gap,this study uses origin-destination(O-D)flow data from London’s Santander Cycle Hire Scheme from 2019-2021 to analyze the changing use of BSS throughout the first two years of the pandemic,from lockdown to recovery.A Gaussian mixture model(GMM)is used to cluster 2019 BSS trips into three distinct clusters based on their duration and distance.The clusters are used as a reference from which to measure spatial and temporal change in 2020 and 2021.In agreement with previous research,BSS usage was found to have declined by nearly 30%during the first lockdown.Usage then saw a sharp increase as restrictions were lifted,characterized by longer,less direct trips throughout the afternoon rather than typical peak commuting trips.Although the aggregate number of BSS trips appeared to return to normal by October 2020,this was against the backdrop of continuing restrictions on international travel and work from home orders.The period between July and December 2021 was the first period that all government restrictions were lifted.During this time,BSS trips reached higher levels than in 2019.Spatio-temporal analysis indicates a shift away from the traditional morning and evening peak to a more diffuse pattern of working hours.The results indicate that the pandemic may have had sustained impacts on travel behavior,leading to a“new normal”that reflects different ways of working.展开更多
基金UK Research and Innovation Council (UKRI) Funding(Nos.EP/R511683/1,EP/J004197/1,ES/L011840/1)UCL Dean Prize and China Scholarship Council(No.201603170309)。
文摘SpacetimeAI and GeoAI are currently hot topics,applying the latest algorithms in computer science,such as deep learning,to spatiotemporal data.Although deep learning algorithms have been successfully applied to raster data due to their natural applicability to image processing,their applications in other spatial and space-time data types are still immature.This paper sets up the proposition of using a network(&graph)-based framework as a generic spatial structure to present space-time processes that are usually represented by the points,polylines,and polygons.We illustrate network and graph-based SpaceTimeAI,from graph-based deep learning for prediction,to space-time clustering and optimisation.These applications demonstrate the advantages of network(graph)-based SpacetimeAI in the fields of transport&mobility,crime&policing,and public health.
文摘The coronavirus pandemic that started in 2019 has had wide-ranging impacts on many aspects of people’s daily lives.At the peak of the outbreak,lockdown measures and social distancing changed the ways in which cities function.In particular,they had profound impacts on urban transportation systems,with public transport being shut down in many cities.Bike share systems(BSS)were widely reported as having experienced an increase in demand during the early stages of the pandemic before returning to pre-pandemic levels.However,the studies published to date focus mainly on the first year of the pandemic,when various waves saw continual relaxing and reintroductions of restrictions.Therefore,they fall short of exploring the role of BSS as we move to the post-pandemic period.To address this gap,this study uses origin-destination(O-D)flow data from London’s Santander Cycle Hire Scheme from 2019-2021 to analyze the changing use of BSS throughout the first two years of the pandemic,from lockdown to recovery.A Gaussian mixture model(GMM)is used to cluster 2019 BSS trips into three distinct clusters based on their duration and distance.The clusters are used as a reference from which to measure spatial and temporal change in 2020 and 2021.In agreement with previous research,BSS usage was found to have declined by nearly 30%during the first lockdown.Usage then saw a sharp increase as restrictions were lifted,characterized by longer,less direct trips throughout the afternoon rather than typical peak commuting trips.Although the aggregate number of BSS trips appeared to return to normal by October 2020,this was against the backdrop of continuing restrictions on international travel and work from home orders.The period between July and December 2021 was the first period that all government restrictions were lifted.During this time,BSS trips reached higher levels than in 2019.Spatio-temporal analysis indicates a shift away from the traditional morning and evening peak to a more diffuse pattern of working hours.The results indicate that the pandemic may have had sustained impacts on travel behavior,leading to a“new normal”that reflects different ways of working.
