scholarly journals Multi-Temporal Analysis of Forest Fire Probability Using Socio-Economic and Environmental Variables

2019 ◽  
Vol 11 (1) ◽  
pp. 86 ◽  
Author(s):  
Sea Jin Kim ◽  
Chul-Hee Lim ◽  
Gang Sun Kim ◽  
Jongyeol Lee ◽  
Tobias Geiger ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Random Forest ◽  
South Korea ◽  
Human Activity ◽  
Forest Fire ◽  
Roc Curve ◽  
Temporal Analysis ◽  
Economic Factors ◽  
Multi Temporal ◽  
Socio Economic Factors

As most of the forest fires in South Korea are related to human activity, socio-economic factors are critical in estimating their probability. To estimate and analyze how human activity is influencing forest fire probability, this study considered not only environmental factors such as precipitation, elevation, topographic wetness index, and forest type, but also socio-economic factors such as population density and distance from urban area. The machine learning Maximum Entropy (Maxent) and Random Forest models were used to predict and analyze the spatial distribution of forest fire probability in South Korea. The model performance was evaluated using the receiver operating characteristic (ROC) curve method, and models’ outputs were compared based on the area under the ROC curve (AUC). In addition, a multi-temporal analysis was conducted to determine the relationships between forest fire probability and socio-economic or environmental changes from the 1980s to the 2000s. The analysis revealed that the spatial distribution was concentrated in or around cities, and the probability had a strong correlation with variables related to human activity and accessibility over the decades. The AUC values for validation were higher in the Random Forest result compared to the Maxent result throughout the decades. Our findings can be useful for developing preventive measures for forest fire risk reduction considering socio-economic development and environmental conditions.

Multi-Temporal Analysis of Urbanization in Mountain Environment: a Case Study of Chongqing, China

2012 ◽  
Vol 468-471 ◽  
pp. 2155-2164
Author(s):  
Jing Dong Jiang ◽  
Jing Nan Huang ◽  
Ling Tian ◽  
Yong Liu
Keyword(s):  
Temporal Analysis ◽  
Underlying Mechanism ◽  
Rapid Urbanization ◽  
The Past ◽  
Natural Factors ◽  
Multi Temporal ◽  
Mountain Environment ◽  
Urbanization Pattern ◽  
Socio Economic Factors

Urbanization in Southwest China, an extensive mountainous region, has been unprecedentedly rapid in the past two decades, particularly with the implementation of “Great West Development”. However, most present studies on urbanization of Chinese cities are limited to coastal area. Little is known about the urbanization pattern and underlying mechanism in this region. The main purpose of this research is to analyze the process of rapid urbanization and its impact on mountain environment, using Chongqing, the well known “mountain city” in China, as an example. Four time-series satellite images were employed to extract the landscape data. The result was assessed by several “landscape metrics”. The research also investigated how complex natural factors as well as socio-economic factors exerted influence on the urbanization. Based on examination of present landscape, a planning model which was believed suitable for mountain urban development was proposed.

Shaping and Reshaping Care and Migration in East and Southeast Asia

2018 ◽  
Vol 44 (7-8) ◽  
pp. 1117-1132 ◽  
Author(s):  
Ito Peng
Keyword(s):  
Hong Kong ◽  
South Korea ◽  
Conceptual Framework ◽  
Cultural Norms ◽  
Economic Factors ◽  
Policy Feedback ◽  
Care Workers ◽  
East And Southeast Asia ◽  
And Migration ◽  
Socio Economic Factors

This article examines how culture, institution, and social policies interact to shape national approaches to care and the use of migrant care workers. I compare Japan, South Korea, Taiwan, Hong Kong and Singapore to show variations in approaches to care and migration despite their cultural similarities. Through a conceptual framework that intersects culture, institution and policy I identify a spectrum of approaches that are evident across East Asia, ranging from highly regulated institutional to very liberal market oriented. The analysis shows that cultural, institutional and socio-economic factors are continuously interacting with each other to shape national understandings of care and the use of foreign care workers, and that different policies interact with each other referentially as they develop and affect social and cultural norms through policy feedback.

scholarly journals Analysis of lockdown perception in the United States during the COVID-19 pandemic

2021 ◽  
Author(s):  
Francesco Vincenzo Surano ◽  
Maurizio Porfiri ◽  
Alessandro Rizzo
Keyword(s):  
United States ◽  
Temporal Analysis ◽  
The United States ◽  
Economic Effects ◽  
Economic Factors ◽  
Political Orientations ◽  
Containment Measures ◽  
Spatio Temporal ◽  
Different Parts ◽  
Socio Economic Factors

AbstractContainment measures have been applied throughout the world to halt the COVID-19 pandemic. In the United States, several forms of lockdown have been adopted in different parts of the country, leading to heterogeneous epidemiological, social, and economic effects. Here, we present a spatio-temporal analysis of a Twitter dataset comprising 1.3 million geo-localized Tweets about lockdown, from January to May 2020. Through sentiment analysis, we classified Tweets as expressing positive or negative emotions about lockdown, demonstrating a change in perception during the course of the pandemic modulated by socio-economic factors. A transfer entropy analysis of the time series of Tweets unveiled that the emotions in different parts of the country did not evolve independently. Rather, they were mediated by spatial interactions, which were also related to socio-ecomomic factors and, arguably, to political orientations. This study constitutes a first, necessary step toward isolating the mechanisms underlying the acceptance of public health interventions from highly resolved online datasets.

scholarly journals Development of a global 30-m impervious surface map using multi-source and multi-temporal remote sensing datasets with the Google Earth Engine platform

2020 ◽  
Author(s):  
Xiao Zhang ◽  
Liangyun Liu ◽  
Changshan Wu ◽  
Xidong Chen ◽  
Yuan Gao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Human Activity ◽  
Impervious Surface ◽  
Google Earth ◽  
Impervious Surfaces ◽  
Sar Images ◽  
Training Samples ◽  
Multi Temporal ◽  
Google Earth Engine

Abstract. The amount of impervious surface is an important indicator in the monitoring of the intensity of human activity and environmental change. The use of remote sensing techniques is the only means of accurately carrying out global mapping of impervious surfaces covering large areas. Optical imagery can capture surface reflectance characteristics, while synthetic aperture radar (SAR) images can be used to provide information on the structure and dielectric properties of surface materials. In addition, night-time light (NTL) imagery can detect the intensity of human activity and thus provide important a priori probabilities of the occurrence of impervious surfaces. In this study, we aimed to generate an accurate global impervious surface map at a resolution of 30-m for 2015 by combining Landsat-8 OLI optical images, Sentinel-1 SAR images and VIIRS NTL images based on the Google Earth Engine (GEE) platform. First, the global impervious and non-impervious training samples were automatically derived by combining the GlobeLand30 land-cover product with VIIRS NTL and MODIS enhanced vegetation index (EVI) imagery. Then, based on global training samples and multi-source and multi-temporal imagery, a random forest classifier was trained and used to generate corresponding impervious surface maps for each 5°×5° cell of a geographical grid. Finally, a global impervious surface map, produced by mosaicking numerous 5°×5° regional maps, was validated by interpretation samples and then compared with three existing impervious products (GlobeLand30, FROM_GLC and NUACI). The results indicated that the global impervious surface map produced using the proposed multi-source, multi-temporal random forest classification (MSMT_RF) method was the most accurate of the maps, having an overall accuracy of 96.6 % and kappa coefficient of 0.903 as against 92.5 % and 0.769 for FROM_GLC, 91.1 % and 0.717 for GlobeLand30, and 87.43 % and 0.585 for NUACI. Therefore, it is concluded that a global 30-m impervious surface map can accurately and efficiently be generated by the proposed MSMT_RF method based on the GEE platform. The global impervious surface map generated in this paper are available at https://doi.org/10.5281/zenodo.3505079 (Zhang et al., 2019).

Climate of the Reformation: droughts and anomalous weather in the 1500s-1510s in Europe

2020 ◽  
Author(s):  
Andrea Kiss ◽  
Mariano Barriendos ◽  
Rudolf Brázdil ◽  
Chantal Camenisch ◽  
Silvia Enzi ◽  
...  
Keyword(s):  
Western Europe ◽  
Weather Conditions ◽  
Temporal Analysis ◽  
Economic Consequences ◽  
Economic Factors ◽  
Documentary Evidence ◽  
The Reformation ◽  
Rapid Spread ◽  
Spatial Temporal Analysis ◽  
Socio Economic Factors

<p>In the 1500s-1510s an unusually high number of significant droughts in Central and Western, and partly in Southern Europe; the years 1502-1504, 1506-1507, 1513-1514 and 1516-1518 were dry particularly in Central and Western Europe. Droughts, interspersed with wet years marked even by significant floods and other weather-related extremes, and frequent hard winters were mainly responsible for the reduced or poor crop and hay harvests in multiple years. These circumstances, in combination with other socio-economic factors, contributed to the increased social tension of the period, manifesting itself in major peasant uprisings, and might have acted as a catalyst in the timing and rapid spread of the Reformation.</p><p>The first part of the presentation is concentrated on the reconstruction and spatial-temporal analysis of the droughts (and hard winters) using documentary evidence – in comparison with the tree-ring based hydroclimate reconstruction (OWDA: Cook et al. 2015) and the multiproxy-based reconstruction of Central European precipitation (Pauling et al. 2006).</p><p>The most significant groups of socio-economic consequences are analysed in the second part of the presentation, with special emphasis on discussing the possible cumulative effects of the anomalous weather conditions during the period on the ongoing transformation of the late-medieval society and economy and the Reformation itself.</p>

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