City Walking Tour Videos(CWTVs)are a novel source of Volunteered Geographic Information providing street-level imagery through video sharing platforms such as YouTube.We demonstrate that these videos contain rich info...City Walking Tour Videos(CWTVs)are a novel source of Volunteered Geographic Information providing street-level imagery through video sharing platforms such as YouTube.We demonstrate that these videos contain rich information for urban analytical applications,by conducting a mobility study.We detect transport modes with a focus on active(pedestrians and cyclists)and motorised mobility(cars,motorcyclists and trucks).We chose the City of Paris as our area of interest given the rapid expansion of the bicycle network as a response to the Covid-19 pandemic and compiled a video corpus encompassing more than 66 hours of video footage.Through the detection of street names in the video and placename containing timestamps we extracted and georeferenced 1169 locations at which we summarise the detected transport modes.Our results show high potential of CWTVs for studying urban mobility applications.We detected significant shifts in the mobility mix before and during the pandemic as well as weather effects on the volumes of pedestrians and cyclists.Combined with the observed increase in data availability over the years we suggest that CWTVs have considerable potential for other applications in the field of urban analytics.展开更多
基金supported by the Swiss National Science Foundation project EV A-VGI 2[grant number 186389].
文摘City Walking Tour Videos(CWTVs)are a novel source of Volunteered Geographic Information providing street-level imagery through video sharing platforms such as YouTube.We demonstrate that these videos contain rich information for urban analytical applications,by conducting a mobility study.We detect transport modes with a focus on active(pedestrians and cyclists)and motorised mobility(cars,motorcyclists and trucks).We chose the City of Paris as our area of interest given the rapid expansion of the bicycle network as a response to the Covid-19 pandemic and compiled a video corpus encompassing more than 66 hours of video footage.Through the detection of street names in the video and placename containing timestamps we extracted and georeferenced 1169 locations at which we summarise the detected transport modes.Our results show high potential of CWTVs for studying urban mobility applications.We detected significant shifts in the mobility mix before and during the pandemic as well as weather effects on the volumes of pedestrians and cyclists.Combined with the observed increase in data availability over the years we suggest that CWTVs have considerable potential for other applications in the field of urban analytics.
