to test for “randomness” in spatial point patterns, we propose two test statistics that are obtained by “reducing” two-dimensional point patterns to the one-dimensional one. also the exact and asymptotic distribution of these statistics are drawn.

A Geographical Information System (GIS) provides a powerful collection of tools for the management and visualization of spatial data. These tools can be even more powerful when they are integrated with methods for spatial data analysis. In this context, we provide several examples that show the power of exploratory spatial data analysis (ESDA) within a GIS and how this can provide the foundatio...

At the moment we realize that the world will face major changes in land use in the coming decades. Agricultural production will need to meet the needs of the world population which is expected to double to 12 billion, and to meet the increasing demand as a result of continued economic growth and consumer demands. Agriculture needs to meet this rising demand on less land, with less resources suc...

a Department of Finance & Economics, Texas State University, United States b School of Public Health, University of Minnesota, United States c Institute for Economic Geography and GIScience, Vienna University of Economics and Business Administration, Austria d Centre for Statistics, Queen Mary, University of London, United Kingdom e Department of Geography, Queen Mary, University of London, Uni...

Functional Data Analysis is a relatively new branch in Statistics. Experiments where a complete function is observed for each individual give rise to functional data. In this work we focus on the case of functional data presenting spatial dependence. The three classic types of spatial data structures (geostatistical data, point patterns and areal data) can be combined with functional data as it...

The spatial scan statistic has been widely used in spatial disease surveillance and spatial cluster detection for more than a decade. However, overdispersion often presents in real-world data, causing not only violation of the Poisson assumption but also excessive type I errors or false alarms. In order to account for overdispersion, we extend the Poisson-based spatial scan test to a quasi-Pois...

In the sections below we review basic motivations for spatial statistical analysis, review three general categories of data structure and associated inferential questions, and describe Bayesian methods for achieving inference. Our goal is to highlight similarities across spatial analytic methods, particularly with regards to how hierarchical probability structures often link approaches develope...

In the analysis of spatial data, the inverse of the covariance matrix needs to be calculated. For example, the inverse is needed for best linear unbiased prediction or kriging, and is repeatedly calculated in the maximum likelihood estimation or the Bayesian inferences. Since the spatial sample size can be quite large, operations on the large covariance matrix can be a numerical challenge if no...