Decomposing malaria mosquito aquatic habitat data into spatial autocorrelation eigenvectors in a SAS/GIS module.
Decomposing malaria mosquito aquatic habitat data into spatial autocorrelation eigenvectors in a SAS/GIS module.
复制标题
DOI：
10.1111/j.1467-9671.2008.01104.x
复制
发表时间：
2008-01-01
影响因子：
2.4
通讯作者：
Novak, R. J.
中科院分区：
文献类型：
--
作者：
Jacob, B. G.;Griffith, D. A.;Novak, R. J.
我们提出了一种地统计学的方法，占空间自相关的疟疾蚊子水生栖息地在两个东非城市环境。QuickBird 0.61 m数据，包括可见光波段和近红外（NIR）波段，被选择来合成冈比亚按蚊s.l.肯尼亚基苏穆和马林迪的水生生境。现场采样数据。冈比亚公司水生生境被用来确定哪些生态协变量与安。冈比亚有限公司幼虫栖息地发育。一个SAS/GIS空间数据库被用来计算单变量统计，相关性和泊松回归分析的An。冈比亚公司水生生境数据集。在ArcGIS中生成半变异函数和全局自相关统计。空间依赖模型表明An的分布。冈比亚公司水生生境在两个研究地点都表现出弱的正自相关性，具有相似对数幼虫计数的水生生境在空间上倾向于聚集。个别按蚊栖息地进一步评估其与空间自相关回归候选空间滤波器特征向量的协变。这涉及到分解的莫兰的我统计成正交和不相关的地图模式组件使用负二项回归。该程序生成的合成地图模式的潜在空间相关性代表的地理配置的一个。冈比亚有限公司每个研究地点的水生栖息地位置。高斯近似空间滤波模型在生态数据集中约占13%至32%的冗余位置信息。在SAS/GIS模块中生成的空间统计数据可以捕获对现场和远程采样An的泊松回归分析的平均响应项的空间依赖性影响。冈比亚有限公司水生生境数据。
We present a geostatistical approach that accounts for spatial autocorrelation in malaria mosquito aquatic habitats in two East African urban environments. QuickBird 0.61 m data, encompassing visible bands and the near infra-red (NIR) bands, were selected to synthesize images of Anopheles gambiae s.l. aquatic habitats in Kisumu and Malindi, Kenya. Field sampled data of An. gambiae s.l. aquatic habitats were used to determine which ecological covariates were associated with An. gambiae s.l. larval habitat development. A SAS/GIS spatial database was used to calculate univariate statistics, correlations and perform Poisson regression analyses on the An. gambiae s.l. aquatic habitat datasets. Semivariograms and global autocorrelation statistics were generated in ArcGIS. The spatially dependent models indicate the distribution of An. gambiae s.l. aquatic habitats exhibits weak positive autocorrelation in both study sites, with aquatic habitats of similar log-larval counts tending to cluster in space. Individual anopheline habitats were further evaluated in terms of their covariations with spatial autocorrelation by regressing them on candidate spatial filter eigenvectors. This involved the decomposition of Moran's I statistic into orthogonal and uncorrelated map pattern components using a negative binomial regression. The procedure generated synthetic map patterns of latent spatial correlation representing the geographic configuration of An. gambiae s.l. aquatic habitat locations in each study site. The Gaussian approximation spatial filter models accounted for approximately 13% to 32% redundant locational information in the ecological datasets. Spatial statistics generated in a SAS/GIS module can capture spatial dependency effects on the mean response term of a Poisson regression analysis of field and remotely sampled An. gambiae s.l. aquatic habitat data.