Spatial analysis : statistics, visualization, and by Tonny J. Oyana, Florence Margai

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.

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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.

Show description

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

Example text

Edwards. 1990. Spatial clustering for inhomogeneous populations. Journal of the Royal Statistical Society, Series B 52(1): 73–104. J. 1990. A point process modeling approach to raised incidence of a rare phenomenon in the vicinity of a prespecified point. Journal Royal Statistical Society, Series A (Statistics in Society) 153: 349–362. J. S. Rowlingson. 1994. A conditional approach to point process modeling of elevated risk. Journal Royal Statistical Society, Series A (Statistics in Society) 157: 433–440.

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.

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