By Tonny J. Oyana, Florence Margai
An introductory textual content for the subsequent new release of geospatial analysts and information scientists, Spatial research: data, Visualization, and Computational equipment focuses at the basics of spatial research utilizing conventional, modern, and computational equipment. Outlining either non-spatial and spatial statistical options, the authors current sensible purposes of geospatial information instruments, concepts, and techniques in geographic experiences. they provide a problem-based studying (PBL) method of spatial analysis―containing hands-on problem-sets that may be labored out in MS Excel or ArcGIS―as good as unique illustrations and diverse case stories.
The publication allows readers to:
- Identify kinds and signify non-spatial and spatial data
- Demonstrate their competence to discover, visualize, summarize, examine, optimize, and obviously current statistical information and results
- Construct testable hypotheses that require inferential statistical analysis
- Process spatial information, extract explanatory variables, behavior statistical assessments, and clarify results
- Understand and interpret spatial info summaries and statistical tests
Spatial research: statistics, Visualization, and Computational Methods
contains conventional statistical equipment, spatial facts, visualization, and computational equipment and algorithms to supply a concept-based problem-solving studying method of learning functional spatial research. issues lined comprise: spatial descriptive tools, speculation checking out, spatial regression, scorching spot research, geostatistics, spatial modeling, and knowledge science.
Read or Download Spatial analysis : statistics, visualization, and computational methods PDF
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Additional resources for Spatial analysis : statistics, visualization, and computational methods
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J. M. MacEachren. 2008. Geovisual analytics to enhance spatial scan statistic interpretation: an analysis of US cervical cancer mortality. 1186/1476-072X-7-57. J. C. Evans. 1954. Distance to nearest neighbor as a measure of spatial relationships in populations. Ecology 35: 445–453. J. C. Evans. 1955. On some aspects of spatial pattern in biological populations. Science 121: 397–398. , M. Gahegan, and J. Macgill. 2005. A genetic approach to detecting clusters in point data sets. Geographical Analysis 37(3): 286–314.
At the conceptual level, we can take a philosophical view that considers representation of the world through spatial reasoning, spatiotemporal reasoning, and temporal reasoning. We can also reason beyond the two-dimensional (2D) perspective by thinking about representation in terms of three or more dimensions. At the logical level, we have a GIS data model. This enables us to utilize a set of mathematical constructs to describe, formalize, and represent selected aspects of the real world in a computer.