As spatial autocorrelation latent in georeferenced data increases, the amount of duplicate information contained in these data also increases. This property suggests the research question asking what the number of independent observations, ay n*, is that is equivalent o the sample size, n, of a data set. This is the notion of effective sample size. Intuitively speaking, when zero spatial autoco...

The concentrations of As, Hg, Co, Cr and Cd were tested for each soil sample, and their spatial patterns were analyzed by the semivariogram approach of geostatistics and geographical information system technology. Multivariate statistic approaches (principal component analysis and cluster analysis) were used to identify heavy metal sources and their spatial pattern. Principal component analysis...

The impact of violence on the population's health profile in large Brazilian cities is a real concern and has been incorporated into the country's public health agenda. Although violent crimes affect the entire population, they are distributed unequally in the city. In this context, analytical tools to evaluate the risk and spatial distribution of homicide are important for surveillance and pre...

The characterization of spatial dependence is an important component of a spatial modeling exercise. For reasons of convenience, model parsimony, or computational efficiency, the spatial covariance structure is often assumed to be isotropic (direction-invariant), completely symmetric, or reflection symmetric (the latter two being forms of directional invariance somewhat weaker than isotropy). W...

Hyperspectral remotely sensed data are useful for studying ecosystem processes and patterns. However, spatial characterization of such remotely sensed images is needed to optimize sampling procedures and address scaling issues. We have investigated spatial scaling in ground-based and airborne hyperspectral data for canopyto watershed-level ecosystem studies of southern California chaparral and ...

Most studies on urban land values focus on the determinants, using hedonic land price models with the consideration of the physical and socio-economic factors. This article employs geostatistical methods to analyze urban land values. Through a case study of Milwaukee, we set up semivariogram models and 3-D TIN surface models for urban land values, and explore anisotropy characteristics and the ...

There are many phenomena in nature, such as earthquakes, landslides, floods, and large-scale mineralization that are characterized by singular functions exhibiting scale invariant properties. A local singularity analysis based on multifractal modeling was developed for detection of local anomalies for mineral exploration. An iterative approach is proposed in the current paper for improvement of...

Soybean is currently the most cultivated oilseed in world. The objective of this study was to analyze chemical attributes Oxisol correlated with soybean productivity. evaluated were grain productivity, soil properties organic matter contents, phosphorous, pH, potassium, calcium, magnesium, hydrogen + aluminum, sum bases, cation exchange capacity (CEC), base saturation, liming requirement; penet...

This paper presents an algorithm for generating realizations of the spatial distribution of an environmental variable; these realizations must minimize the local error variance in addition to the traditional constraints of reproduction of histogram and semivariogram. The optimization technique is performed by simulated annealing and requires a prior determination of the conditional probability ...