کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7472036 | 1485143 | 2018 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Spatio-temporal population modelling as improved exposure information for risk assessments tested in the Autonomous Province of Bolzano
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
فیزیک زمین (ژئو فیزیک)
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Spatio-temporal population modelling as improved exposure information for risk assessments tested in the Autonomous Province of Bolzano Spatio-temporal population modelling as improved exposure information for risk assessments tested in the Autonomous Province of Bolzano](/preview/png/7472036.png)
چکیده انگلیسی
Population data is commonly available for administrative units referring to the year of the last census. That level of aggregation and the static character of the information pose particular difficulties for spatial analysis in applications such as disaster management or spatial planning, for which much more time-sensitive population distributions are required. In this study, a flexible model to create dynamic gridded population data with a spatial resolution of 100Â m is implemented for the mountainous, hazard-prone and highly touristic region of the Autonomous Province of Bolzano, based on the integration of multiple data sources within an explicit spatio-temporal modelling framework. It is argued that dynamic gridded population information provides an improvement to the existing regional datasets. Our study shows that integrating daily and seasonal changes to the distribution of population improves exposure information for risk assessments especially in highly touristic areas.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Disaster Risk Reduction - Volume 27, March 2018, Pages 470-479
Journal: International Journal of Disaster Risk Reduction - Volume 27, March 2018, Pages 470-479
نویسندگان
Kathrin Renner, Stefan Schneiderbauer, Fabio PruÃ, Christian Kofler, David Martin, Samantha Cockings,