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Approaches to and challenges in analysing spatial data. Specific techniques will include geographical regression, point pattern analysis, interpolation, and newer geocomputation and machine learning methods. Students will gain an advanced knowledge of spatial analysis. An understanding equivalent to GISCI 242 will be assumed.

Approaches to and challenges in analysing spatial data. Specific techniques will include geographical regression, point pattern analysis, interpolation, and newer geocomputation and machine learning methods. Students will gain an advanced knowledge of spatial analysis. An understanding equivalent to GISCI 242 will be assumed.

Approaches and challenges to analysing spatial data. Specific techniques will include spatial autocorrelation, geographical regression, point pattern analysis, interpolation, overlay analysis, and newer geocomputation methods. Students will gain an advanced knowledge of spatial analysis and be well-prepared for postgraduate research or professional practice. No formal prerequisite but an understanding equivalent to GEOG 318 will be assumed.

Approaches and challenges to analysing spatial data. Specific techniques will include spatial autocorrelation, geographical regression, point pattern analysis, interpolation, overlay analysis, and newer geocomputation methods. Students will gain an advanced knowledge of spatial analysis and be well-prepared for postgraduate research or professional practice. No formal prerequisite but an understanding equivalent to GEOG 318 will be assumed.

Approaches and challenges to analysing spatial data. Specific techniques will include spatial autocorrelation, geographical regression, point pattern analysis, interpolation, overlay analysis, and newer geocomputation methods. Students will gain an advanced knowledge of spatial analysis and be well-prepared for postgraduate research or professional practice. No formal prerequisite but an understanding equivalent to GEOG 318 will be assumed.