Spatiotemporal pattern analysis provides a new dimension for data interpretation due to new trends in computer vision and big data analysis. The main aim of this study was to explore the recent advances in geospatial ...Spatiotemporal pattern analysis provides a new dimension for data interpretation due to new trends in computer vision and big data analysis. The main aim of this study was to explore the recent advances in geospatial technologies to examine the spatiotemporal pattern of COVID-19 at the Public Health Unit (PHU) level in Ontario, Canada. The spatial autocorrelation results showed that the incidence rate (no. of confirmed cases per 100,000 population–IR/100K) was clustered at the PHU level and found a tendency of clustering high values. Some PHUs in Southern Ontario were identified as hot spots, while Northern PHUs were cold spots. The space-time cube showed an overall trend with a 99% confidence level. Considerable spatial variability in incidence intensity at different times suggested that risk factors were unevenly distributed in space and time. The study also created a regression model that explains the correlation between IR/100K values and potential socioeconomic factors.展开更多
On May 5, 2014, an earthquake with a magnitude of Mw 6.1(the largest earthquake in Thailand so far) occurred in Chiang Rai of the Golden Triangle area in northern Thailand. We had an opportunity to conduct field surve...On May 5, 2014, an earthquake with a magnitude of Mw 6.1(the largest earthquake in Thailand so far) occurred in Chiang Rai of the Golden Triangle area in northern Thailand. We had an opportunity to conduct field survey immediately after the earthquake. Serious damage to buildings and casualties of lives were observed, and the estimated Maximum Mercalli Intensity(MMI) of the earthquake is Ⅷ(evaluated according to the MMI scale of the Chinese Standard). No long continuous ground ruptures were produced during the earthquake, but in the epicenter(commonly within MMI Ⅷ extent), massive small linear ruptures(usually several tens of meters long) developed and displayed intriguing structural features, offsetting many roads several centimeters left laterally on NE trending cracks or offsetting right laterally on NW trending ones. The focal mechanism solution of earthquake shows that this is a pure strike-slip event, and two nodal planes in NW and NE directions had the same motion senses respectively as those of breakage associated with the earthquake. The long axis of the isoseismals and aftershock distributions are in NE direction,which is consistent with the strike of Luang Namtha fault. The 230-km-long Luang Namtha fault which starts from the border of China and Laos, runs through northern Laos, and terminates at Chiang Rai of Thailand is predominated by left-lateral strike-slip and active in late Quaternary, and two earthquakes over Ms 6.0 occurred along the fault in 1925 and 2007 respectively. This Mw 6.1 earthquake occurred at the southwestern end of the fault. All related features such as evident structural rupturing, elongated orientation of MMI and aftershock distribution,as well as the location of the epicenter,suggest that the Luang Namtha fault may be responsible for the 2014 Northern Thailand earthquake.展开更多
One of the most important issues related to landscape ecology and ecosystem services is finding the pattern of habitat patches that offers the highest pollination in agricultural landscapes.In this regard,two processe...One of the most important issues related to landscape ecology and ecosystem services is finding the pattern of habitat patches that offers the highest pollination in agricultural landscapes.In this regard,two processes of habitat loss and fragmentation strongly affect the relationship between pollination and the pattern of habitat patches.In the present study,we aimed to examine the effects of habitat loss and fragmentation on pollination separately.For this purpose,first,we generated different simulated agricultural landscapes,including two habitats of forest and agriculture.Then,according to the Lonsdorf model,we estimated the potential of the simulated landscapes in providing pollination in different scenarios.Finally,using statistical models,we estimated the effects of habitat loss and fragmentation on pollination at the landscape and farm levels.Our results showed that the effects of habitat loss and fragmentation on pollination were completely different at the landscape and farm levels.At the landscape level,fragmentation negatively affected pollination,but at the farm level,the maximum pollination rate was observed in the landscapes with a high degree of fragmentation.Regarding the habitat loss effects,our results showed that pollination decreased linearly at the landscape level as habitat amount decreased,but at the farm level,it decreased exponentially.The present study considered the level of analysis(i.e.,landscape and farm levels)as a critical factor affecting pollination changes caused by fragmentation.We showed that using the Lonsdorf model could lead to confusing results for the landscape ecologists and alert farmers who want to reduce the adverse effects of fragmentation on their products by creating new forest patches.Therefore,agriculturalists and landscape ecologists should consider that the pollination rate at the landscape and farm levels is completely different according to the model and provide contradictory results about the process of habitat loss effects on pollination.展开更多
Background:The growing human population around the world is creating an increased demand for food.In agricultural landscapes,forests are cleared and turned into agricultural land to produce more food.Increasing the pr...Background:The growing human population around the world is creating an increased demand for food.In agricultural landscapes,forests are cleared and turned into agricultural land to produce more food.Increasing the productivity of agricultural land per unit area may prevent extreme forest degradation.Since many agricultural products are dependent on pollinators,it is possible to increase crop production by increasing the pollination rate in the agricultural landscapes.Pollinators are highly dependent on forest patches in agricultural landscapes.Therefore,by creating new forest patches around agricultural felds,we can increase the pollination rate,and thus the crop production.In this regard,estimating the efects of diferent scenarios of forest fragmentation helps us to fnd an optimized pattern of forest patches for increasing pollination in an agricultural landscape.Methods:To investigate the efect of diferent forest fragmentation scenarios on pollination,we used simulated agricultural landscapes,including diferent forest proportions and degrees of fragmentation.Using landscape metrics,we estimated the relationship between pollination and landscape structure for each landscape.Results:Our results showed that for increasing pollination,two signifcant factors should be considered:habitat amount and capacity of small patches to supply pollination.We found that when the capacity of small patches in supplying pollination was low,fragmented patterns of forest patches decreased pollination.With increasing capacity,landscapes with a high degree of forest fragmentation showed the highest levels of pollination.There was an exception for habitat amounts(the proportion of forest patches)less than 0.1 of the entire landscape where increasing edge density,aggregation,and the number of forest patches resulted in increasing pollination in all scenarios.Conclusion:This study encourages agriculturists and landscape planners to focus on increasing crop production per unit area by pollinators because it leads to biodiversity conservation and reduces socio-economic costs of land-use changes.We also suggest that to increase pollination in agricultural landscapes by creating new forest patches,special attention should be paid to the capacity of patches in supporting pollinators.展开更多
Cloud computing has been considered as the next-generation computing platform with the potential to address the data and computing challenges in geosciences.However,only a limited number of geoscientists have been ada...Cloud computing has been considered as the next-generation computing platform with the potential to address the data and computing challenges in geosciences.However,only a limited number of geoscientists have been adapting this platform for their scientific research mainly due to two barriers:1)selecting an appropriate cloud platform for a specific application could be challenging,as various cloud services are available and 2)existing general cloud platforms are not designed to support geoscience applications,algorithms and models.To tackle such barriers,this research aims to design a hybrid cloud computing(HCC)platform that can utilize and integrate the computing resources across different organizations to build a unified geospatial cloud computing platform.This platform can manage different types of underlying cloud infrastructure(e.g.,private or public clouds),and enables geoscientists to test and leverage the cloud capabilities through a web interface.Additionally,the platform also provides different geospatial cloud services,such as workflow as a service,on the top of common cloud services(e.g.,infrastructure as a service)provided by general cloud platforms.Therefore,geoscientists can easily create a model workflow by recruiting the needed models for a geospatial application or task on the fly.A HCC prototype is developed and dust storm simulation is used to demonstrate the capability and feasibility of such platform in facilitating geosciences by leveraging across-organization computing and model resources.展开更多
The advancements of sensing technologies,including remote sensing,in situ sensing,social sensing,and health sensing,have tremendously improved our capability to observe and record natural and social phenomena,such as ...The advancements of sensing technologies,including remote sensing,in situ sensing,social sensing,and health sensing,have tremendously improved our capability to observe and record natural and social phenomena,such as natural disasters,presidential elections,and infectious diseases.The observations have provided an unprecedented opportunity to better understand and respond to the spatiotemporal dynamics of the environment,urban settings,health and disease propagation,business decisions,and crisis and crime.Spatiotemporal event detection serves as a gateway to enable a better understanding by detecting events that represent the abnormal status of relevant phenomena.This paper reviews the literature for different sensing capabilities,spatiotemporal event extraction methods,and categories of applications for the detected events.The novelty of this review is to revisit the definition and requirements of event detection and to layout the overall workflow(from sensing and event extraction methods to the operations and decision-supporting processes based on the extracted events)as an agenda for future event detection research.Guidance is presented on the current challenges to this research agenda,and future directions are discussed for conducting spatiotemporal event detection in the era of big data,advanced sensing,and artificial intelligence.展开更多
文摘Spatiotemporal pattern analysis provides a new dimension for data interpretation due to new trends in computer vision and big data analysis. The main aim of this study was to explore the recent advances in geospatial technologies to examine the spatiotemporal pattern of COVID-19 at the Public Health Unit (PHU) level in Ontario, Canada. The spatial autocorrelation results showed that the incidence rate (no. of confirmed cases per 100,000 population–IR/100K) was clustered at the PHU level and found a tendency of clustering high values. Some PHUs in Southern Ontario were identified as hot spots, while Northern PHUs were cold spots. The space-time cube showed an overall trend with a 99% confidence level. Considerable spatial variability in incidence intensity at different times suggested that risk factors were unevenly distributed in space and time. The study also created a regression model that explains the correlation between IR/100K values and potential socioeconomic factors.
基金financially supported by National Institute of Natural Hazards,Ministry of Emergency Management of China(Grant No.ZDJ2019-21)the National Science Foundation of China(Grant No.41472204)。
文摘On May 5, 2014, an earthquake with a magnitude of Mw 6.1(the largest earthquake in Thailand so far) occurred in Chiang Rai of the Golden Triangle area in northern Thailand. We had an opportunity to conduct field survey immediately after the earthquake. Serious damage to buildings and casualties of lives were observed, and the estimated Maximum Mercalli Intensity(MMI) of the earthquake is Ⅷ(evaluated according to the MMI scale of the Chinese Standard). No long continuous ground ruptures were produced during the earthquake, but in the epicenter(commonly within MMI Ⅷ extent), massive small linear ruptures(usually several tens of meters long) developed and displayed intriguing structural features, offsetting many roads several centimeters left laterally on NE trending cracks or offsetting right laterally on NW trending ones. The focal mechanism solution of earthquake shows that this is a pure strike-slip event, and two nodal planes in NW and NE directions had the same motion senses respectively as those of breakage associated with the earthquake. The long axis of the isoseismals and aftershock distributions are in NE direction,which is consistent with the strike of Luang Namtha fault. The 230-km-long Luang Namtha fault which starts from the border of China and Laos, runs through northern Laos, and terminates at Chiang Rai of Thailand is predominated by left-lateral strike-slip and active in late Quaternary, and two earthquakes over Ms 6.0 occurred along the fault in 1925 and 2007 respectively. This Mw 6.1 earthquake occurred at the southwestern end of the fault. All related features such as evident structural rupturing, elongated orientation of MMI and aftershock distribution,as well as the location of the epicenter,suggest that the Luang Namtha fault may be responsible for the 2014 Northern Thailand earthquake.
文摘One of the most important issues related to landscape ecology and ecosystem services is finding the pattern of habitat patches that offers the highest pollination in agricultural landscapes.In this regard,two processes of habitat loss and fragmentation strongly affect the relationship between pollination and the pattern of habitat patches.In the present study,we aimed to examine the effects of habitat loss and fragmentation on pollination separately.For this purpose,first,we generated different simulated agricultural landscapes,including two habitats of forest and agriculture.Then,according to the Lonsdorf model,we estimated the potential of the simulated landscapes in providing pollination in different scenarios.Finally,using statistical models,we estimated the effects of habitat loss and fragmentation on pollination at the landscape and farm levels.Our results showed that the effects of habitat loss and fragmentation on pollination were completely different at the landscape and farm levels.At the landscape level,fragmentation negatively affected pollination,but at the farm level,the maximum pollination rate was observed in the landscapes with a high degree of fragmentation.Regarding the habitat loss effects,our results showed that pollination decreased linearly at the landscape level as habitat amount decreased,but at the farm level,it decreased exponentially.The present study considered the level of analysis(i.e.,landscape and farm levels)as a critical factor affecting pollination changes caused by fragmentation.We showed that using the Lonsdorf model could lead to confusing results for the landscape ecologists and alert farmers who want to reduce the adverse effects of fragmentation on their products by creating new forest patches.Therefore,agriculturalists and landscape ecologists should consider that the pollination rate at the landscape and farm levels is completely different according to the model and provide contradictory results about the process of habitat loss effects on pollination.
文摘Background:The growing human population around the world is creating an increased demand for food.In agricultural landscapes,forests are cleared and turned into agricultural land to produce more food.Increasing the productivity of agricultural land per unit area may prevent extreme forest degradation.Since many agricultural products are dependent on pollinators,it is possible to increase crop production by increasing the pollination rate in the agricultural landscapes.Pollinators are highly dependent on forest patches in agricultural landscapes.Therefore,by creating new forest patches around agricultural felds,we can increase the pollination rate,and thus the crop production.In this regard,estimating the efects of diferent scenarios of forest fragmentation helps us to fnd an optimized pattern of forest patches for increasing pollination in an agricultural landscape.Methods:To investigate the efect of diferent forest fragmentation scenarios on pollination,we used simulated agricultural landscapes,including diferent forest proportions and degrees of fragmentation.Using landscape metrics,we estimated the relationship between pollination and landscape structure for each landscape.Results:Our results showed that for increasing pollination,two signifcant factors should be considered:habitat amount and capacity of small patches to supply pollination.We found that when the capacity of small patches in supplying pollination was low,fragmented patterns of forest patches decreased pollination.With increasing capacity,landscapes with a high degree of forest fragmentation showed the highest levels of pollination.There was an exception for habitat amounts(the proportion of forest patches)less than 0.1 of the entire landscape where increasing edge density,aggregation,and the number of forest patches resulted in increasing pollination in all scenarios.Conclusion:This study encourages agriculturists and landscape planners to focus on increasing crop production per unit area by pollinators because it leads to biodiversity conservation and reduces socio-economic costs of land-use changes.We also suggest that to increase pollination in agricultural landscapes by creating new forest patches,special attention should be paid to the capacity of patches in supporting pollinators.
文摘Cloud computing has been considered as the next-generation computing platform with the potential to address the data and computing challenges in geosciences.However,only a limited number of geoscientists have been adapting this platform for their scientific research mainly due to two barriers:1)selecting an appropriate cloud platform for a specific application could be challenging,as various cloud services are available and 2)existing general cloud platforms are not designed to support geoscience applications,algorithms and models.To tackle such barriers,this research aims to design a hybrid cloud computing(HCC)platform that can utilize and integrate the computing resources across different organizations to build a unified geospatial cloud computing platform.This platform can manage different types of underlying cloud infrastructure(e.g.,private or public clouds),and enables geoscientists to test and leverage the cloud capabilities through a web interface.Additionally,the platform also provides different geospatial cloud services,such as workflow as a service,on the top of common cloud services(e.g.,infrastructure as a service)provided by general cloud platforms.Therefore,geoscientists can easily create a model workflow by recruiting the needed models for a geospatial application or task on the fly.A HCC prototype is developed and dust storm simulation is used to demonstrate the capability and feasibility of such platform in facilitating geosciences by leveraging across-organization computing and model resources.
基金supported by NSF[CNS 1841520 and ACI 1835507]NASA Goddard[80NSSC19P2033]the NSF Spatiotemporal I/UCRC IAB members.
文摘The advancements of sensing technologies,including remote sensing,in situ sensing,social sensing,and health sensing,have tremendously improved our capability to observe and record natural and social phenomena,such as natural disasters,presidential elections,and infectious diseases.The observations have provided an unprecedented opportunity to better understand and respond to the spatiotemporal dynamics of the environment,urban settings,health and disease propagation,business decisions,and crisis and crime.Spatiotemporal event detection serves as a gateway to enable a better understanding by detecting events that represent the abnormal status of relevant phenomena.This paper reviews the literature for different sensing capabilities,spatiotemporal event extraction methods,and categories of applications for the detected events.The novelty of this review is to revisit the definition and requirements of event detection and to layout the overall workflow(from sensing and event extraction methods to the operations and decision-supporting processes based on the extracted events)as an agenda for future event detection research.Guidance is presented on the current challenges to this research agenda,and future directions are discussed for conducting spatiotemporal event detection in the era of big data,advanced sensing,and artificial intelligence.
