Schröder Winfried
Environmental Sciences, University of Vechta, PO Box 1553, D-49264 Vechta, Germany.
Int J Med Microbiol. 2006 May;296 Suppl 40:23-36. doi: 10.1016/j.ijmm.2006.02.015. Epub 2006 Apr 4.
By the example of environmental monitoring, some applications of geographic information systems (GIS), geostatistics, metadata banking, and Classification and Regression Trees (CART) are presented. These tools are recommended for mapping statistically estimated hot spots of vectors and pathogens. GIS were introduced as tools for spatially modelling the real world. The modelling can be done by mapping objects according to the spatial information content of data. Additionally, this can be supported by geostatistical and multivariate statistical modelling. This is demonstrated by the example of modelling marine habitats of benthic communities and of terrestrial ecoregions. Such ecoregionalisations may be used to predict phenomena based on the statistical relation between measurements of an interesting phenomenon such as, e.g., the incidence of medically relevant species and correlated characteristics of the ecoregions. The combination of meteorological data and data on plant phenology can enhance the spatial resolution of the information on climate change. To this end, meteorological and phenological data have to be correlated. To enable this, both data sets which are from disparate monitoring networks have to be spatially connected by means of geostatistical estimation. This is demonstrated by the example of transformation of site-specific data on plant phenology into surface data. The analysis allows for spatial comparison of the phenology during the two periods 1961-1990 and 1991-2002 covering whole Germany. The changes in both plant phenology and air temperature were proved to be statistically significant. Thus, they can be combined by GIS overlay technique to enhance the spatial resolution of the information on the climate change and use them for the prediction of vector incidences at the regional scale. The localisation of such risk hot spots can be done by geometrically merging surface data on promoting factors. This is demonstrated by the example of the transfer of heavy metals through soils. The predicted hot spots of heavy metal transfer can be validated empirically by measurement data which can be inquired by a metadata base linked with a geographic information system. A corresponding strategy for the detection of vector hot spots in medical epidemiology is recommended. Data on incidences and habitats of the Anophelinae in the marsh regions of Lower Saxony (Germany) were used to calculate a habitat model by CART, which together with climate data and data on ecoregions can be further used for the prediction of habitats of medically relevant vector species. In the future, this approach should be supported by an internet-based information system consisting of three components: metadata questionnaire, metadata base, and GIS to link metadata, surface data, and measurement data on incidences and habitats of medically relevant species and related data on climate, phenology, and ecoregional characteristic conditions.
以环境监测为例，介绍了地理信息系统（GIS）、地统计学、元数据库以及分类与回归树（CART）的一些应用。推荐使用这些工具来绘制经统计估计的病媒和病原体热点区域。GIS被引入作为对现实世界进行空间建模的工具。建模可通过根据数据的空间信息内容对对象进行映射来完成。此外，这可以得到地统计和多元统计建模的支持。以对底栖生物群落的海洋栖息地和陆地生态区域进行建模为例进行了说明。这种生态区域划分可用于基于有趣现象（如医学相关物种的发病率）的测量值与生态区域相关特征之间的统计关系来预测现象。气象数据与植物物候数据的结合可以提高气候变化信息的空间分辨率。为此，必须将气象数据和物候数据进行关联。为实现这一点，来自不同监测网络的这两组数据集必须通过地统计估计在空间上进行连接。以将特定地点的植物物候数据转换为表面数据为例进行了说明。该分析允许对1961 - 1990年和1991 - 2002年这两个时期覆盖整个德国的物候进行空间比较。结果证明植物物候和气温的变化具有统计学意义。因此，它们可以通过GIS叠加技术进行合并，以提高气候变化信息的空间分辨率，并将其用于区域尺度上病媒发生率的预测。通过对促进因素的表面数据进行几何合并，可以确定此类风险热点区域的位置。以重金属在土壤中的迁移为例进行了说明。通过与地理信息系统相连的元数据库查询测量数据，可以凭经验验证预测的重金属迁移热点区域。推荐了一种在医学流行病学中检测病媒热点区域的相应策略。利用德国下萨克森州沼泽地区按蚊亚科的发病率和栖息地数据，通过CART计算出一个栖息地模型，该模型与气候数据和生态区域数据一起可进一步用于预测医学相关病媒物种的栖息地。未来，这种方法应由一个基于互联网的信息系统提供支持，该系统由三个组件组成：元数据问卷、元数据库以及用于连接元数据、表面数据以及医学相关物种的发病率和栖息地测量数据以及气候、物候和生态区域特征条件相关数据的GIS。
