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Orientador(es)
Resumo(s)
Geographic Information Systems (GIS) and spatial analysis are considered to be
a science in their own right, with a solid theoretical and methodological basis. The
science behind GIS and spatial analysis has been coined as geoinformatics, which
is defined as the application of Geographic Information Science (GISc) to solve
problems in earth and environmental sciences. Geoinformatics involves the collection,
storage, processing, analysis, visualization, and dissemination of geographic
information.
Spatial analysis is a fundamental aspect of geoinformatics and is used to study the
distribution and relationship between geographic objects and events. Spatial analysis
involves the use of statistical, mathematical, and computational techniques to explore
patterns and trends in geographic data. It also allows users to create spatial models
and make predictions based on different scenarios.
The science behind spatial analysis involves the application of mathematical,
statistical, and computational methods to analyze and interpret spatial patterns
and relationships between geographic objects and events. It draws on a variety of
disciplines such as geography, mathematics, statistics, computer science, and remote
sensing to provide a comprehensive understanding of spatial data.
The theoretical basis of spatial analysis includes concepts such as spatial autocorrelation,
spatial heterogeneity, and spatial dependence, which helps to explain
the spatial patterns and relationships observed in geographic data. Spatial analysis
methods can be broadly categorized into descriptive, exploratory, and inferential
techniques, which are used to visualize, explore, and test spatial data.
Some common spatial analysis techniques include spatial interpolation, spatial
regression, spatial clustering, spatial smoothing, and spatial econometrics. These
methods can be applied to a wide range of spatial data, including point data, areal
data, and network data.
Spatial analysis has become increasingly important in many fields such as public
health, environmental studies, urban planning, and criminology, among others. It
provides a powerful tool to study spatial problems and make informed decisions
based on spatial data. Advances in technology have also led to the development of
new spatial analysis methods, such as machine learning and deep learning, which are
being applied to address complex spatial problems.
Descrição
Palavras-chave
GIS Spatial Analysis
Contexto Educativo
Citação
Viana, C. M., Boavida-Portugal, I., Gomes, E. & Rocha, J. (2023). GIS and Spatial Analysis: introductory chapter. In: J. Rocha, E. Gomes, I. Boavida-Portugal, C. M. Viana, L. Truong-Hong, & A. T. Phan (Eds.). GIS and Spatial Analysis (pp. 3-7). IntechOpen. https://doi.org/10.5772/intechopen.111735
Editora
IntechOpen