{"id":109,"date":"2015-11-16T16:22:22","date_gmt":"2015-11-16T23:22:22","guid":{"rendered":"https:\/\/blogs.ubc.ca\/advancedgis\/?page_id=109"},"modified":"2025-01-14T12:39:10","modified_gmt":"2025-01-14T20:39:10","slug":"spatial-analysis-2","status":"publish","type":"page","link":"https:\/\/blogs.ubc.ca\/advancedgis\/schedule\/slides\/spatial-analysis-2\/","title":{"rendered":"Spatial Analysis II: Interpolation"},"content":{"rendered":"

Transforming spatial data using methods such as spatial interpolation is a very important step in many GIS analyses. As such, I’ll present an in-depth review of the topic in this lecture. This topic is mentioned in Chapter 13.3.6<\/strong> of your text, as well in\u00a0Chapter 7<\/a> and Chapter 8<\/a> of GIS Basics, but unfortunately neither source provide much of an overview.\u00a0 This topic is also addressed in more detail in Geospatial Analysis, Chapter 6<\/a><\/p>\n

Spatial interpolation, one of the more important spatial data transformation techniques, is a necessary element in many GIS-based analyses. Often you obtain data in one form (e.g., pH values at wells, elevation values at spot heights, PM 2.5 values from air quality stations) and need to conduct analyses with surfaces or other point data sets (i.e., transform the data from one spatial data type to another). Creating surfaces from points is a very complex process–both from a conceptual point-of-view (knowing which method is most appropriate) and from a process point-of-view (the mathematics can be complex). As such, spending some time learning about spatial interpolation is beneficial.<\/p>\n

View Spatial Interpolation explanatory examples >><\/a>
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Learning Objectives<\/a><\/h3>
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