Replication Data for: Do All Roads Lead to Sapporo? The Role of Linking and Bridging Ties in Evacuation Decisions
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Why do some communities evacuate more than others after disaster, and how do they choose whether to go? This mixed-methods study uses a new dataset of long-distance evacuation rates after Hokkaido’s Eastern Iburi Earthquake in September 2018, aggregated to the city level from geolocated Facebook user movement. We found that communities with stronger linking and bridging social capital tended to see much lower evacuation rates to distant towns. We used statistical models, social network visualization, fieldwork, and content analysis of 12 interviews, finding that despite rumors on social media, communities with bridging and linking social networks got better quality information and decided to stay in local evacuation shelters. Meanwhile, residents with less diverse networks may have believed rumors of extreme water, food, and power short' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["disaster","evacuation","Japan","policy","resilience","social capital"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Why do some communities evacuate more than others after disaster, and how do they choose whether to go? This mixed-methods study uses a new dataset of long-distance evacuation rates after Hokkaido’s Eastern Iburi Earthquake in September 2018, aggregated to the city level from geolocated Facebook user movement. We found that communities with stronger linking and bridging social capital tended to see much lower evacuation rates to distant towns. We used statistical models, social network visualization, fieldwork, and content analysis of 12 interviews, finding that despite rumors on social media, communities with bridging and linking social networks got better quality information and decided to stay in local evacuation shelters. Meanwhile, residents with less diverse networks may have believed rumors of extreme water, food, and power short' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Why do some communities evacuate more than others after disaster, and how do they choose whether to go? This mixed-methods study uses a new dataset of long-distance evacuation rates after Hokkaido’s Eastern Iburi Earthquake in September 2018, aggregated to the city level from geolocated Facebook user movement. We found that communities with stronger linking and bridging social capital tended to see much lower evacuation rates to distant towns. We used statistical models, social network visualization, fieldwork, and content analysis of 12 interviews, finding that despite rumors on social media, communities with bridging and linking social networks got better quality information and decided to stay in local evacuation shelters. Meanwhile, residents with less diverse networks may have believed rumors of extreme water, food, and power short
disaster; evacuation; Japan; policy; resilience; social capital