Natural disasters,such as wildfires,earthquakes,landslides,or floods,lead to an increase in topical information shared on social media and in increased mapping activities in volunteered geographic information(VGI)plat...Natural disasters,such as wildfires,earthquakes,landslides,or floods,lead to an increase in topical information shared on social media and in increased mapping activities in volunteered geographic information(VGI)platforms.Using earthquakes in Nepal and Central Italy as case studies,this research analyzes the effects of natural disasters on short-term(weeks)and longer-term(half year)changes in OpenStreetMap(OSM)mapping behavior and tweet activities in the affected regions.An increase of activities in OSM during the events can be partially attributed to those focused OSM mapping campaigns,for example,through the Humanitarian OSM Team(HOT).Using source tags in OSM change-sets,it was found that only a small portion of external mappers actually travels to the affected regions,whereas the majority of external mappers relies on desktop mapping instead.Furthermore,the study analyzes the spatio-temporal sequence of posted tweets together with keyword filters to identify a subset of users who most likely traveled to the affected regions for support and rescue operations.It also explores where,geographically,earthquake information spreads within social networks.展开更多
In 2016,Niantic Labs released Pokémon Go,an augmented reality smartphone game that attracted millions of users worldwide.This game allows users to“catch”Pokémons through their mobile cameras in different g...In 2016,Niantic Labs released Pokémon Go,an augmented reality smartphone game that attracted millions of users worldwide.This game allows users to“catch”Pokémons through their mobile cameras in different geographic locations that often correspond to prominent places.This paper analyzes the distribution of PokéStops,Pokémon gyms,and spawnpoints in selected urban areas of South Florida and Boston.It identifies which socioeconomic variables and landuse categories affect the density of PokéStops,and how PokéStops and gyms cluster relative to each other.Using nearest neighbor analysis,this paper assesses also how actual PokéStop locations are reflected in Yelp’s“PokéStop nearby”attribute.Results show that black and Hispanic neighborhoods are disadvantaged when it comes to crowd-sourced data coverage,that PokéStops occur more frequently in commercial,recreational and touristic sites and around universities,and that PokéStops tend to cluster around gyms.The latter suggests that these point sets were generated by a similar location selection process.To mitigate geographically linked biases,future versions of augmented reality and geo-games should aim to make them equally accessible in all areas,for example by placing extra resources,such as points of interest,in neighborhoods that are currently underrepresented in data coverage.展开更多
文摘Natural disasters,such as wildfires,earthquakes,landslides,or floods,lead to an increase in topical information shared on social media and in increased mapping activities in volunteered geographic information(VGI)platforms.Using earthquakes in Nepal and Central Italy as case studies,this research analyzes the effects of natural disasters on short-term(weeks)and longer-term(half year)changes in OpenStreetMap(OSM)mapping behavior and tweet activities in the affected regions.An increase of activities in OSM during the events can be partially attributed to those focused OSM mapping campaigns,for example,through the Humanitarian OSM Team(HOT).Using source tags in OSM change-sets,it was found that only a small portion of external mappers actually travels to the affected regions,whereas the majority of external mappers relies on desktop mapping instead.Furthermore,the study analyzes the spatio-temporal sequence of posted tweets together with keyword filters to identify a subset of users who most likely traveled to the affected regions for support and rescue operations.It also explores where,geographically,earthquake information spreads within social networks.
文摘In 2016,Niantic Labs released Pokémon Go,an augmented reality smartphone game that attracted millions of users worldwide.This game allows users to“catch”Pokémons through their mobile cameras in different geographic locations that often correspond to prominent places.This paper analyzes the distribution of PokéStops,Pokémon gyms,and spawnpoints in selected urban areas of South Florida and Boston.It identifies which socioeconomic variables and landuse categories affect the density of PokéStops,and how PokéStops and gyms cluster relative to each other.Using nearest neighbor analysis,this paper assesses also how actual PokéStop locations are reflected in Yelp’s“PokéStop nearby”attribute.Results show that black and Hispanic neighborhoods are disadvantaged when it comes to crowd-sourced data coverage,that PokéStops occur more frequently in commercial,recreational and touristic sites and around universities,and that PokéStops tend to cluster around gyms.The latter suggests that these point sets were generated by a similar location selection process.To mitigate geographically linked biases,future versions of augmented reality and geo-games should aim to make them equally accessible in all areas,for example by placing extra resources,such as points of interest,in neighborhoods that are currently underrepresented in data coverage.
