An interactive 4D spatio-temporal visualization system for hydrometeorological data in natural disasters

2019 ◽  
Vol 13 (11) ◽  
pp. 1258-1278 ◽  
Author(s):  
Xuequan Zhang ◽  
Mingda Zhang ◽  
Liangcun Jiang ◽  
Peng Yue
Keyword(s):  
Natural Disasters ◽  
Visualization System ◽  
Spatio Temporal

scholarly journals Spatio-temporal variations of the major meteorological disasters and its response to climate change in Henan Province during the past two millennia

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12365
Author(s):  
Xiang Li ◽  
Hui Lu ◽  
Zhaokang Zhang ◽  
Wei Xing
Keyword(s):  
Low Temperature ◽  
Correlation Analysis ◽  
Natural Disasters ◽  
Temporal Variations ◽  
Henan Province ◽  
Historical Documents ◽  
The Past ◽  
Meteorological Disaster ◽  
Spatio Temporal ◽  
Meteorological Disasters

In China, historical documents have recorded large quantities of information related to natural disasters, and these disasters have had long-lasting effects on economic and social activities. Understanding the occurrence of the natural disasters and their spatio-temporal variation characters is crucial for sustainable of our society. Therefore, based on the collection and collation of historical documents, and adopting mathematical statistics, Kriging interpolation, correlation analysis and other methods, we systematically explored the meteorological disasters in Henan Province during the past two millennia in analyzing their spatio-temporal distribution characters and driving forces. The results demonstrate that there were five major types of meteorological disasters in Henan Province, including drought, flood, hails, low temperature and frost and insect pests, which presented obvious spatio-temporal variations and have occurred frequently during the past two millennia. According to the historical documents, the major meteorological disasters occurred 1,929 times in Henan from 221 BCE to 2000 CE. On the whole, the disaster frequency show that the occurrence cycle of the meteorological disasters has obvious changes, which mainly occurred in the middle and late stages during the past two millennia, especially after 1300 CE. Furthermore, we also find that the variation of meteorological disaster events is consistent with the variation of temperature in eastern China and the frequency of meteorological disaster increases in the cold period, but decreases in the warm period. In addition, there are obvious differences in the spatial distribution of the major meteorological disaster, which were mainly distributed in the northwest and southern part region of the Henan Province before 1911 CE. While after 1911 CE, the northern and southeastern parts were the meteorological disaster-prone areas in this region during this period. Spatial correlation analysis of each meteorological disaster before and after 1911 CE points out the droughts disaster frequency-occurring district has transferred in different periods, while the hail and low temperature and frost disasters just have a smaller transferred during these two periods. Conversely, the frequency-occurring districts of floods and insect pest disasters have no obviously transferred in different periods. These results can provide an important scientific basis for governmental decision makers and local people to prevent and mitigate meteorological disaster in the future.

scholarly journals AtmoVis: Visualization of Air Quality Data

2021 ◽  
Author(s):  
◽  
Benjamin Powley
Keyword(s):  
Air Quality ◽  
User Study ◽  
Poor Quality ◽  
Quality Data ◽  
Temporal Data ◽  
Multiple Sources ◽  
Web Based ◽  
Visualization System ◽  
Spatio Temporal ◽  
Temporal Data Analysis

<p>Air quality has an adverse impact on the health of people living in areas with poor quality air. Monitoring is needed to understand the effects of poor air quality. It is difficult to compare measurements to find trends and patterns between different monitoring sites when data is contained in separate data stores. Data visualization can make analyzing air quality more effective by making the data more understandable. The purpose of this research is to design and build a prototype for visualizing spatio-temporal data from multiple sources related to air quality and to evaluate the effectiveness of the prototype against criteria by conducting a user study. The prototype web based visualization system, AtmoVis, has a windowed layout with 6 different visualizations: Heat calendar, line plot, monthly rose, site view, monthly averages and data comparison. A pilot study was performed with 11 participants and used to inform the study protocol before the main user study was performed on 20 participants who were air quality experts or experienced with Geographic Information Systems (GIS). The results of the study demonstrated that the heat calendar, line plot, site view, monthly averages and monthly rose visualizations were effective for analyzing the air quality through AtmoVis. The line plot and the heat calendar were the most effective for temporal data analysis. The interactive web based interface for data exploration with a window layout, provided by AtmoVis, was an effective method for accessing air quality visualizations and inferring relationships among air quality variables at different monitoring sites. AtmoVis could potentially be extended to include other datasets in the future.</p>

scholarly journals AtmoVis: Visualization of Air Quality Data

2021 ◽  
Author(s):  
◽  
Benjamin Powley
Keyword(s):  
Air Quality ◽  
User Study ◽  
Poor Quality ◽  
Quality Data ◽  
Temporal Data ◽  
Multiple Sources ◽  
Web Based ◽  
Visualization System ◽  
Spatio Temporal ◽  
Temporal Data Analysis

<p>Air quality has an adverse impact on the health of people living in areas with poor quality air. Monitoring is needed to understand the effects of poor air quality. It is difficult to compare measurements to find trends and patterns between different monitoring sites when data is contained in separate data stores. Data visualization can make analyzing air quality more effective by making the data more understandable. The purpose of this research is to design and build a prototype for visualizing spatio-temporal data from multiple sources related to air quality and to evaluate the effectiveness of the prototype against criteria by conducting a user study. The prototype web based visualization system, AtmoVis, has a windowed layout with 6 different visualizations: Heat calendar, line plot, monthly rose, site view, monthly averages and data comparison. A pilot study was performed with 11 participants and used to inform the study protocol before the main user study was performed on 20 participants who were air quality experts or experienced with Geographic Information Systems (GIS). The results of the study demonstrated that the heat calendar, line plot, site view, monthly averages and monthly rose visualizations were effective for analyzing the air quality through AtmoVis. The line plot and the heat calendar were the most effective for temporal data analysis. The interactive web based interface for data exploration with a window layout, provided by AtmoVis, was an effective method for accessing air quality visualizations and inferring relationships among air quality variables at different monitoring sites. AtmoVis could potentially be extended to include other datasets in the future.</p>

Application of map and file information visualization system to comprehensive division of natural disasters

2001 ◽  
Vol 11 (4) ◽  
pp. 326-335 ◽  
Author(s):  
Bao-qing Hu ◽  
Jiang Mei-xin ◽  
Jun Su-lan ◽  
Zeng Qiao-song
Keyword(s):  
Natural Disasters ◽  
Information Visualization ◽  
Visualization System

scholarly journals Impact of Climate on Food Security in Mainland China: A New Perspective Based on Characteristics of Major Agricultural Natural Disasters and Grain Loss

2019 ◽  
Vol 11 (3) ◽  
pp. 869 ◽  
Author(s):  
Jingpeng Guo ◽  
Kebiao Mao ◽  
Yinghui Zhao ◽  
Zhong Lu ◽  
Lu Xiaoping
Keyword(s):  
Food Security ◽  
Low Temperature ◽  
Natural Disasters ◽  
Natural Disaster ◽  
Mainland China ◽  
Intensity Index ◽  
Mk Test ◽  
Geographical Regions ◽  
Spatio Temporal ◽  
Grain Losses

Under the background of global warming, China has experienced frequent natural disasters that have seriously affected grain production in recent decades. Based on historical documents from 1978-2014, we explored the spatio-temporal variation of five major kinds of natural disasters and grain losses in China using statistical techniques: the Mann-Kendall (MK) test, social network analysis (SNA), and geographic information system (GIS) tools. The disaster intensity index (Q) clearly showed the variation of natural disasters; all of China experienced a significant increasing trend at an annual scale, reaching its peak (27.77%) in 2000. The step change points in floods, droughts, hail, and low-temperature events began to occur in 1983, 1988, 1988, 1992, respectively, while no obvious trend was detected for typhoon activity from 2001 to 2014. Drought and flood were the most serious types of disaster over the last four decades, accounting for more than 50% of total grain losses. Eight major provinces were identified with severe grain losses: Heilongjiang, Shandong, Henan, Hebei, Anhui, Sichuan, Jiangsu, Hunan, and Hubei. Five studied natural disaster types were identified throughout the seven physical geographical regions. Spatial distribution for the different disaster types showed significant geographical distribution characteristics. Natural disasters gradually became more diverse from north to south. Droughts, hail, and low-temperature disasters were randomly distributed throughout China; flood and typhoon disasters exhibited significant spatial auto-correlation and clustering patterns. Finally, in accordance with the intensity of natural disaster, the annual grain losses at the provincial scale initially increased (ranging from 0.14 million to 3.26 million tonnes in 1978-2000), and then decreased after 2000 (ranging from 3.26 million to 1.58 million tonnes in 2000-2014). The center of gravity of grain losses gradually moved northward. These results emphasize that developing different strategies for disaster prevention and mitigation programs in the major grain producing areas (e.g., Heilongjiang, Shandong, and Henan) are critical and important to China's food security.

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