Suchergebnisse

In this study we analyse the emerging patterns of regional collaboration for innovation projects in China, using official government statistics of 30 Chinese regions. We propose the use of Ordinal Multidimensional Scaling and Cluster analysis as a robust method to study regional innovation systems. Our results show that regional collaborations amongst organisations can be categorised by means of eight dimensions: public versus private organisational mindset; public versus private resources; innovation capacity versus available infrastructures; innovation input (allocated resources) versus innovation output; knowledge production versus knowledge dissemination; and collaborative capacity versus collaboration output. Collaborations which are aimed to generate innovation fell into 4 categories, those related to highly specialised public research institutions, public universities, private firms and governmental intervention. By comparing the representative cases of regions in terms of these four innovation actors, we propose policy measures for improving regional innovation collaboration within China.

Traditional geospatial information platforms are built, managed and maintained by the geoinformation agencies. They integrate various geospatial data (such as DLG, DOM, DEM, gazetteers, and thematic data) to provide data analysis services for supporting government decision making. In the era of big data, it is challenging to address the data- and computing- intensive issues by traditional platforms. In this research, we propose to build a spatiotemporal cloud platform, which uses HDFS for managing image data, and MapReduce-based computing service and workflow for high performance geospatial analysis, as well as optimizing auto-scaling algorithms for Web client users' quick access and visualization. Finally, we demonstrate the feasibility by several GIS application cases.

Traditional geospatial information platforms are built, managed and maintained by the geoinformation agencies. They integrate various geospatial data (such as DLG, DOM, DEM, gazetteers, and thematic data) to provide data analysis services for supporting government decision making. In the era of big data, it is challenging to address the data- and computing- intensive issues by traditional platforms. In this research, we propose to build a spatiotemporal cloud platform, which uses HDFS for managing image data, and MapReduce-based computing service and workflow for high performance geospatial analysis, as well as optimizing auto-scaling algorithms for Web client users' quick access and visualization. Finally, we demonstrate the feasibility by several GIS application cases.

As one of the common natural disasters, drought is extremely destructive to agriculture. Drought has risen in recent years, so how to effectively monitor drought has become the focus of attention of relevant government departments. When drought occurs, irrigation measures will be taken in the irrigation district for drought resistance and disaster reduction. At this time, drought monitoring based on meteorological data and remote sensing index has certain limitations. Based on the surface temperature data of MODIS, this paper extracted the irrigation area information in the irrigated district, compared the drought monitoring results of irrigated area with those based on precipitation anomaly percentage and vegetation anomaly index, and analysed the importance of agricultural drought monitoring based on remote sensing of irrigated area. This paper considers that it is necessary to consider the distribution of the irrigated area when conducting drought monitoring in the irrigation district, it can improve the accuracy of agricultural monitoring, and it can provide the basis for government and agriculture and other related departments to manage water resources and formulate drought-resistance policies.