Environmental Data Acquisition, Elaboration and Integration： Preliminary Application to a Vulnerable Mountain Landscape and Village （Novalesa, NW Italy）

Climate conditions play a crucial role in the survival of mountain communities, whose survival already critically depends on socioeconomic factors. In the case of montane areas that are prone to natural haz-ards, such as alpine slope failure and debris flows, climatic factors exert a major influence that should be considered when creating appropriate sustainable scenarios. In fact, it has been shown that climate change alters the availability of ecosystem services （ES）, thus increasing the risks of declining soil fertility and reduced water availability, as well as the loss of grassland, potential shifts in regulatory services （e.g., protection from natural hazards）, and cultural services. This study offers a preliminary discussion on a case study of a region in the Italian Alps that is experiencing increased extreme precipitation and erosion, and where an isolated and historically resilient community directly depends on a natural resource econ- omy. Preliminary results show that economic factors have influenced past population trends of the Novalesa community in the Piemonte Region in northwest Italy. However, the increasing number of rock fall and debris flow events, which are triggered by meteo-climatic factors, may further influence the livelihood and weflbeing of this community, and of other similar communities around the world, Therefore, environmental monitoring and data analysis will be important means of detecting trends in landscape and climate change and choosing appropriate planning options. Such analysis, in turn, would ensure the survival of about 10% of the global population, and would also represent a possibility for future economic development in critical areas prone to poverty conditions.

Influence of environmental data of different sources on marine species habitat modeling:A case study for Ommastrephes bartramii in the Northwest Pacific Ocean

The quality of environmental data and its possible impact on the marine species habitat modelling are often overlooked while the sources for these data are increasing.This study selected sea surface temperature(SST)from two commonly used sources,the NOAA Ocean Watch and IRI/LDEO Climate Data Library,and then constructed habitat suitability index model to evaluate the influences of SST from the two sources on the outcomes of Ommastrephes bartramii habitat models for the months of July–October in the Northwest Pacific Ocean during 1996–2012.This study examined the differences in the amount of estimated unfavourable/favourable habitat area when the SST used for model building and inference were the same or different.Dynamics in suitable habitat area calculated from SST was insensitive to the two different SST products.In the fishing season of O.bartramii,the changes of magnitude and trend of monthly suitable habitat area in August and September were similar over time,whereas there were large differences for July and October.Importantly,there is a substantial lack of consistency in the O.bartramii habitat distribution based on SST of two sources.This study considered the sources of environmental data for habitat modelling and then inferred species habitat distribution whether by the same or different data source.

Models for estimating the leaf NDVI of japonica rice on a canopy scale by combining canopy NDVI and multisource environmental data in Northeast China

Remote sensing of rice traits has advanced significantly with regard to the capacity to retrieve useful plant biochemical,physiological and structural quantities across spatial scales.The rice leaf NDVI(normalized difference vegetation index)has been developed and applied in monitoring rice growth,yield prediction and disease status to guide agricultural management practices.This study combined rice canopy NDVI and environmental data to estimate rice leaf NDVI.The test site was a japonica rice experiment located in the eastern city of Shenyang,Liaoning Province,China.This paper describes(1)the use of multiple linear regression to establish four periods of rice leaf NDVI models with good accuracy(R2=0.782–0.903),and(2)how the key point of the rice growth period based on these models was determined.The techniques for modeling leaf NDVI at the point of remote canopy sensing were also presented.The results indicate that the rice leaf NDVI has a high correlation with the canopy NDVI and multisource environmental data.This research can provide an efficient method to detect rice leaf growth at the canopy scale in the future.

Application of Bayesian Analysis Based on Neural Network and Deep Learning in Data Visualization

This study aims to explore the application of Bayesian analysis based on neural networks and deep learning in data visualization.The research background is that with the increasing amount and complexity of data,traditional data analysis methods have been unable to meet the needs.Research methods include building neural networks and deep learning models,optimizing and improving them through Bayesian analysis,and applying them to the visualization of large-scale data sets.The results show that the neural network combined with Bayesian analysis and deep learning method can effectively improve the accuracy and efficiency of data visualization,and enhance the intuitiveness and depth of data interpretation.The significance of the research is that it provides a new solution for data visualization in the big data environment and helps to further promote the development and application of data science.

Web-based spatiotemporal visualization of marine environment data

With long-term marine surveys and research,and especially with the development of new marine environment monitoring technologies,prodigious amounts of complex marine environmental data are generated,and continuously increase rapidly.Features of these data include massive volume,widespread distribution,multiple-sources,heterogeneous,multi-dimensional and dynamic in structure and time.The present study recommends an integrative visualization solution for these data,to enhance the visual display of data and data archives,and to develop a joint use of these data distributed among different organizations or communities.This study also analyses the web services technologies and defines the concept of the marine information gird,then focuses on the spatiotemporal visualization method and proposes a process-oriented spatiotemporal visualization method.We discuss how marine environmental data can be organized based on the spatiotemporal visualization method,and how organized data are represented for use with web services and stored in a reusable fashion.In addition,we provide an original visualization architecture that is integrative and based on the explored technologies.In the end,we propose a prototype system of marine environmental data of the South China Sea for visualizations of Argo floats,sea surface temperature fields,sea current fields,salinity,in-situ investigation data,and ocean stations.An integration visualization architecture is illustrated on the prototype system,which highlights the process-oriented temporal visualization method and demonstrates the benefit of the architecture and the methods described in this study.

An Overview of Big Data Industry in China

The year of 2011 is considered the first year of big data market in China.Compared with the global scale,China's big data growth will be faster than the global average growth rate,and China will usher in the rapid expansion of big data market in the next few years.This paper presents the overall big data development in China in terms of market scale and development stages,enterprise development in the industry chain,the technology standards,and industrial applications.The paper points out the issues and challenges facing big data development in China and proposes to make polices and create support approaches for big data transactions and personal privacy protection.

Addressing the Security Challenges of Big Data Analytics in Healthcare Research

Big data and associated analytics have the potential to revolutionize healthcare through the tools and techniques they offer to manage and exploit the large volumes of heterogeneous data being collected in the healthcare domain. The strict security and privacy constraints on this data, however, pose a major obstacle to the successful use of these tools and techniques. The paper first describes the security challenges associated with big data analytics in healthcare research from a unique perspective based on the big data analytics pipeline. The paper then examines the use of data safe havens as an approach to addressing the security challenges and argues for the approach by providing a detailed introduction to the security mechanisms implemented in a novel data safe haven. The CIMVHR Data Safe Haven (CDSH) was developed to support research into the health and well-being of Canadian military, Veterans, and their families. The CDSH is shown to overcome the security challenges presented in the different stages of the big data analytics pipeline.

Research on the Mass structured Data Storage and Sorting Algorithm and Methodology for SQL Database under the Big Data Environment

In this paper, we research on the research on the mass structured data storage and sorting algorithm and methodology for SQL database under the big data environment. With the data storage market development and centering on the server, the data will store model to data- centric data storage model. Storage is considered from the start, just keep a series of data, for the management system and storage device rarely consider the intrinsic value of the stored data. The prosperity of the Internet has changed the world data storage, and with the emergence of many new applications. Theoretically, the proposed algorithm has the ability of dealing with massive data and numerically, the algorithm could enhance the processing accuracy and speed which will be meaningful.

Projecting distributions of Argentine shortfin squid(Illex argentinus)in the Southwest Atlantic using a complex integrated model

We developed an approach that integrates generalized additive model（GAM） and neural network model（NNM）for projecting the distribution of Argentine shortfin squid（Illex argentinus）. The data for this paper was based on commercial fishery data and relevant remote sensing environmental data including sea surface temperature（SST）, sea surface height（SSH） and chlorophyll a（Chl a） from January to June during 2003 to 2011. The GAM was used to identify the significant oceanographic variables and establish their relationships with the fishery catch per unit effort（CPUE）. The NNM with the GAM identified significant variables as input vectors was used for predicting spatial distribution of CPUE. The GAM was found to explain 53.8% variances for CPUE. The spatial variables（longitude and latitude） and environmental variables（SST, SSH and Chl a） were significant. The CPUE had nonlinear relationship with SST and SSH but a linear relationship with Chl a. The NNM was found to be effective and robust in the projection with low mean square errors（MSE） and average relative variances（ARV）.The integrated approach can predict the spatial distribution and explain the migration pattern of Illex argentinus in the Southwest Atlantic Ocean.

Comprehensive evaluation method of urban air quality statistics based on environmental monitoring data and its application

Air quality monitoring is effective for timely understanding of the current air quality status of a region or city.Currently,the huge volume of environmental monitoring data,which has reasonable real-time performance,provides strong support for in-depth analysis of air pollution characteristics and causes.However,in the era of big data,to meet current demands for fine management of the atmospheric environment,it is important to explore the characteristics and causes of air pollution from multiple aspects for comprehensive and scientific evaluation of air quality.This study reviewed and summarized air quality evaluation methods on the basis of environmental monitoring data statistics during the 13th Five-Year Plan period,and evaluated the level of air pollution in the Beijing-Tianjin-Hebei region and its surrounding areas(i.e.,the“2+26”region)during the period of the three-year action plan to fight air pollution.We suggest that air quality should be comprehensively,deeply,and scientifically evaluated from the aspects of air pollution characteristics,causes,and influences of meteorological conditions and anthropogenic emissions.It is also suggested that a threeyear moving average be introduced as one of the evaluation indexes of long-term change of pollutants.Additionally,both temporal and spatial differences should be considered when removing confounding meteorological factors.

A procedure to assess the spatial variability in the importance of abiotic factors affecting distributions： the case of world freshwater fishes

Understanding the factors shaping species＇ distributions is a key longstanding topic in ecology with unresolved issues. The aims were to test whether the relative contribution of abiotic factors that set the geographical range of freshwater fish species may vary spatially and/or may depend on the geographical extent that is being considered. The relative contribution of factors, to discriminate between the conditions prevailing in the area where the species is present and those existing in the considered extent, was estimated with the instability index included in the R pack- age SPEDInstabR. We used 3 different extent sizes： 1） each river basin where the species is present （local）; 2） all river basins where the species is present （regional）; and 3） the whole Earth （global）. We used a data set of 16,543 freshwater fish species with a total of 845,764 geographical records, together with bioclimatic and topographic variables. Factors associated with tempera- ture and altitude show the highest relative contribution to explain the distribution of freshwater fishes at the smaller considered extent. Altitude and a mix of factors associated with temperature and precipitation were more important when using the regional extent. Factors associated with precipitation show the highest contribution when using the global extent. There was also spatial variability in the importance of factors, both between species and within species and from region to region. Factors associated with precipitation show a clear latitudinal trend of decreasing in importance toward the equator.

Geographic analysis-oriented Virtual Geographic Environment:Framework,structure and functions

Previous research on the Virtual Geographic Environment （VGE） has focused mainly on representation rather than geographic analysis. However, geographic analysis plays a significant role in modem geography. To address this issue, this paper systematically examines theories and implementing VGE techniques that support geographical analysis and simulation. Based on its framework, VGE can be divided into four subtypes. These are the data environment, modeling environment, expression environment, and collaborative environment. The functions and key techniques of each are examined, and some case studies are discussed. This study provides direction for necessary new developments of advanced VGE platforms.

Regional inequality, spatial spillover effects, and the factors influencing city-level energy-related carbon emissions in China

Data show that carbon emissions are increasing due to human energy consumption associated with economic development. As a result, a great deal of attention has been focused on efforts to reduce this growth in carbon emissions as well as to formulate policies to address and mitigate climate change. Although the majority of previous studies have explored the driving forces underlying Chinese carbon emissions, few have been carried out at the city-level because of the limited availability of relevant energy consumption statistics. Here, we utilize spatial autocorrelation, Markov-chain transitional matrices, a dynamic panel model, and system generalized distance estimation(Sys-GMM) to empirically evaluate the key determinants of carbon emissions at the city-level based on Chinese remote sensing data collected between 1992 and 2013. We also use these data to discuss observed spatial spillover effects taking into account spatiotemporal lag and a range of different geographical and economic weighting matrices. The results of this study suggest that regional discrepancies in city-level carbon emissions have decreased over time, which are consistent with a marked spatial spillover effect, and a ‘club' agglomeration of high-emissions. The evolution of these patterns also shows obvious path dependence, while the results of panel data analysis reveal the presence of a significant U-shaped relationship between carbon emissions and per capita GDP. Data also show that per capita carbon emissions have increased in concert with economic growth in most cities, and that a high-proportion of secondary industry and extensive investment growth have also exerted significant positive effects on city-level carbon emissions across China. In contrast, rapid population agglomeration, improvements in technology, increasing trade openness, and the accessibility and density of roads have all played a role in inhibiting carbon emissions. Thus, in order to reduce emissions, the Chinese government should legislate to inhibit the effects of factors that promote the release of carbon while at the same time acting to encourage those that mitigate this process. On the basis of the analysis presented in this study, we argue that optimizing industrial structures, streamlining extensive investment, increasing the level of technology, and improving road accessibility are all effective approaches to increase energy savings and reduce carbon emissions across China.