The GARP modelling system: problems and solutions to automated spatial prediction
Abstract
This paper is concerned with the problems and solutions to reliable analysis of arbitrary datasets. Our approach is to describe components of a system called the GARP Modelling System (GMS) which we have developed for automating predictive spatial modelling of the distribution of species of plants and animals. The essence of the system is an underlying generic spatial modelling method which filters out potential sources of errors. The approach is generally applicable however, as the statistical problems arising in arbitrary spatial data analysis potentially apply to any domain. For ease of development, GMS is integrated with the facilities of existing database and visualization tools, and Internet browsers. The GMS is an example of a class of application which has been very successful for providing spatial data analysis in a simple to use way via the Internet.
Read the paper
Related studies
- Understanding the factors influencing fall armyworm (Spodoptera frugiperda J.E. Smith) damage in African smallholder maize fields and quantifying its impact on yield. A case study in Eastern Zimbabwe
- Alarming situation of emerging H5 and H7 avian influenza and effective control strategies
- Livestock and food security: vulnerability to population growth and climate change
- Atmosphere–ocean oxygen and productivity dynamics during early animal radiations
- Characterization of the global distribution and diversified plasmid reservoirs of the colistin resistance gene mcr-9
- The global seroprevalence of Toxoplasma gondii in pigs: A systematic review and meta-analysis
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.