Spatial Thinking in Planning Practice: An Introduction to GIS

47 boidal dimensions. However, the problem is larger than a simple reformation of the square pixel. Indeed, the reprojection of a raster image dataset from one projection to another brings change to pixel values that may, in turn, signi!cantly alter the output information (Seong 2003) 3 . "e !nal disadvantage of using the raster data model is that it is not suitable for some types of spatial analy- ses. For example, di&culties arise when attempting to overlay and analyze multiple raster graphics produced at di#ering scales and pixel resolutions. Combining information from a raster image with 10 m spatial resolution with a raster image with 1 km spatial resolution will most likely produce nonsensical output information as the scales of analysis are far too disparate to result in meaningful and/or interpretable conclusions. In addition, some network and spatial analyses (i.e., determining directionality or geocoding) can be problematic to perform on raster data. SINGLE LAYER ANALYSIS Reclassifying, or recoding, a dataset is commonly one of the !rst steps undertaken during raster analysis. Re- classi!cation is basically the single layer process of assigning a new class or range value to all pixels in the dataset based on their original values. For example, an elevation grid commonly contains a di#erent value for nearly ev- ery cell within its extent. "ese values could be simpli!ed by aggregating each pixel value in a few discrete classes (i.e., 0–100 = “1,” 101–200 = “2,” 201–300 = “3,” etc.). "is simpli!cation allows for fewer unique values and cheaper storage requirements. In addition, these reclassi!ed layers are o$en used as inputs in secondary analyses. In vector analysis, bu#ering is the process of creating an output dataset that contains a zone (or zones) of a spec- i!ed width around an input feature. In the case of raster datasets, these input features are given as a grid cell or a group of grid cells containing a uniform value (e.g., bu#er all cells whose value = 1). Bu#ers are particularly suited for determining the area of in'uence around features of interest. Whereas bu#ering vector data results in a precise area of in'uence at a speci!ed distance from the target feature, raster bu#ers tend to be approximations represent- ing those cells that are within the speci!ed distance range of the target (Figure 9.2). Figure 9.1 Raster Reclassi!cation. http://2012books.lardbucket.org/books/geographic-information-system-ba- sics/s12-geospatial-analysis-ii-raster-.html 3 Seong, J. C. 2003. “Modeling the Accuracy of Image Data Reprojection.” International Journal of Remote Sensing 24 (11): 2309–21. Chapter 9: Raster Data Models