Community recovery from a major natural hazard-related disaster can be a long process, and rebuilding likely does not occur uniformly across space and time. Spatial and temporal clustering may be evident in certain da...Community recovery from a major natural hazard-related disaster can be a long process, and rebuilding likely does not occur uniformly across space and time. Spatial and temporal clustering may be evident in certain data types that can be used to frame the progress of recovery following a disaster. Publically available building permit data from the city of Joplin, Missouri, were gathered for four permit types, including residential, commercial, roof repair, and demolition. The data were used to(1) compare the observed versus expected frequency(chi-square) of permit issuance before and after the EF5 2011 tornado;(2), determine if significant space-time clusters of permits existed using the SaTScan^(TM) cluster analysis program(version 9.7);and(3) fit any emergent cluster data to the widely-cited Kates 10-year recovery model. All permit types showed significant increases in issuance for at least 5 years following the event,and one(residential) showed significance for nine of the 10years. The cluster analysis revealed a total of 16 significant clusters across the 2011 damage area. The results of fitting the significant cluster data to the Kates model revealed that those data closely followed the model, with some variation in the residential permit data path.展开更多
An ever-increasing number of sensor resources are being exposed via the World Wide Web to become part of the Digital Earth.Discovery,selection and use of these sensors and their observations require a robust sensor in...An ever-increasing number of sensor resources are being exposed via the World Wide Web to become part of the Digital Earth.Discovery,selection and use of these sensors and their observations require a robust sensor information model,but the consistent description of sensor metadata is a complex and difficult task.Currently,the only available robust model is SensorML,which is intentionally designed in a very generic way.Due to this genericness,interoperability can hardly be achieved without the definition of application profiles that further constrain the use and expressiveness of the root language.So far,such SensorML profiles have only been developed up to a limited extent.This work describes a new approach for defining sensor metadata,the Starfish Fungus Language(StarFL)model.This language follows a more restrictive approach and incorporates concepts from the recently published Semantic Sensor Network Ontology to overcome the key issues users are experiencing with SensorML.StarFL defines a restricted vocabulary and model for sensor metadata to achieve a high level of interoperability and a straightforward reusability of sensor descriptions.展开更多
文摘Community recovery from a major natural hazard-related disaster can be a long process, and rebuilding likely does not occur uniformly across space and time. Spatial and temporal clustering may be evident in certain data types that can be used to frame the progress of recovery following a disaster. Publically available building permit data from the city of Joplin, Missouri, were gathered for four permit types, including residential, commercial, roof repair, and demolition. The data were used to(1) compare the observed versus expected frequency(chi-square) of permit issuance before and after the EF5 2011 tornado;(2), determine if significant space-time clusters of permits existed using the SaTScan^(TM) cluster analysis program(version 9.7);and(3) fit any emergent cluster data to the widely-cited Kates 10-year recovery model. All permit types showed significant increases in issuance for at least 5 years following the event,and one(residential) showed significance for nine of the 10years. The cluster analysis revealed a total of 16 significant clusters across the 2011 damage area. The results of fitting the significant cluster data to the Kates model revealed that those data closely followed the model, with some variation in the residential permit data path.
文摘An ever-increasing number of sensor resources are being exposed via the World Wide Web to become part of the Digital Earth.Discovery,selection and use of these sensors and their observations require a robust sensor information model,but the consistent description of sensor metadata is a complex and difficult task.Currently,the only available robust model is SensorML,which is intentionally designed in a very generic way.Due to this genericness,interoperability can hardly be achieved without the definition of application profiles that further constrain the use and expressiveness of the root language.So far,such SensorML profiles have only been developed up to a limited extent.This work describes a new approach for defining sensor metadata,the Starfish Fungus Language(StarFL)model.This language follows a more restrictive approach and incorporates concepts from the recently published Semantic Sensor Network Ontology to overcome the key issues users are experiencing with SensorML.StarFL defines a restricted vocabulary and model for sensor metadata to achieve a high level of interoperability and a straightforward reusability of sensor descriptions.
