Twitter is a well-known microblogging platform for rapid diffusion of views,ideas,and information.During disasters,it has widely been used to communicate evacuation plans,distribute calls for help,and assist in damage...Twitter is a well-known microblogging platform for rapid diffusion of views,ideas,and information.During disasters,it has widely been used to communicate evacuation plans,distribute calls for help,and assist in damage assessment.The reliability of such information is very important for decision-making in a crisis situation,but also difficult to assess.There is little research so far on the transferability of quality assessment methods from one geographic region to another.The main contribution of this research is to study Twitter usage characteristics of users based in different geographic locations during disasters.We examine tweeting activity during two earthquakes in Italy and Myanmar.We compare the granularity of geographic references used,user profile characteristics that are related to credibility,and the performance of Naive Bayes models for classifying Tweets when used on data from a different region than the one used to train the model.Our results show similar geographic granularity for Myanmar and Italy earthquake events,but the Myanmar earthquake event has less information from locations nearby when compared to Italy.Additionally,there are significant and complex differences in user and usage characteristics,but a high performance for the Naive Bayes classifier even when applied to data from a different geographic region.This research provides a basis for further research in credibility assessment of users reporting about disasters.展开更多
User-Generated Content(UGC)provides a potential data source which can help us to better describe and understand how places are conceptualized,and in turn better represent the places in Geographic Information Science(G...User-Generated Content(UGC)provides a potential data source which can help us to better describe and understand how places are conceptualized,and in turn better represent the places in Geographic Information Science(GIScience).In this article,we aim at aggregating the shared meanings associated with places and linking these to a conceptual model of place.Our focus is on the metadata of Flickr images,in the form of locations and tags.We use topic modeling to identify regions associated with shared meanings.We choose a grid approach and generate topics associated with one or more cells using Latent Dirichlet Allocation.We analyze the sensitivity of our results to both grid resolution and the chosen number of topics using a range of measures including corpus distance and the coherence value.Using a resolution of 500 m and with 40 topics,we are able to generate meaningful topics which characterize places in London based on 954 unique tags associated with around 300,000 images and more than 7000 individuals.展开更多
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
文摘Twitter is a well-known microblogging platform for rapid diffusion of views,ideas,and information.During disasters,it has widely been used to communicate evacuation plans,distribute calls for help,and assist in damage assessment.The reliability of such information is very important for decision-making in a crisis situation,but also difficult to assess.There is little research so far on the transferability of quality assessment methods from one geographic region to another.The main contribution of this research is to study Twitter usage characteristics of users based in different geographic locations during disasters.We examine tweeting activity during two earthquakes in Italy and Myanmar.We compare the granularity of geographic references used,user profile characteristics that are related to credibility,and the performance of Naive Bayes models for classifying Tweets when used on data from a different region than the one used to train the model.Our results show similar geographic granularity for Myanmar and Italy earthquake events,but the Myanmar earthquake event has less information from locations nearby when compared to Italy.Additionally,there are significant and complex differences in user and usage characteristics,but a high performance for the Naive Bayes classifier even when applied to data from a different geographic region.This research provides a basis for further research in credibility assessment of users reporting about disasters.
基金funded by the Swiss National Science Foundation Project PlaceGen[grant number 200021_149823].
文摘User-Generated Content(UGC)provides a potential data source which can help us to better describe and understand how places are conceptualized,and in turn better represent the places in Geographic Information Science(GIScience).In this article,we aim at aggregating the shared meanings associated with places and linking these to a conceptual model of place.Our focus is on the metadata of Flickr images,in the form of locations and tags.We use topic modeling to identify regions associated with shared meanings.We choose a grid approach and generate topics associated with one or more cells using Latent Dirichlet Allocation.We analyze the sensitivity of our results to both grid resolution and the chosen number of topics using a range of measures including corpus distance and the coherence value.Using a resolution of 500 m and with 40 topics,we are able to generate meaningful topics which characterize places in London based on 954 unique tags associated with around 300,000 images and more than 7000 individuals.
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.
