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by: Adrain Lebsack


Adrain Lebsack
GPA 3.54


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Class Notes
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This 3 page Class Notes was uploaded by Adrain Lebsack on Wednesday September 9, 2015. The Class Notes belongs to GEOG 460 at University of Washington taught by Staff in Fall. Since its upload, it has received 21 views. For similar materials see /class/192241/geog-460-university-of-washington in Geography at University of Washington.




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Date Created: 09/09/15
Lecture 15 Neighborhoods and Neighborhood Operations Learning Objectives 151 Describe the spatial component of neighborhoods for raster and vector structured spaces How do those neighborhoods relate to the concept of surfaces 152 What rules are used to combine attribute data values 153 Why is a crosstabulation framework consisting of levels of data measurement and combination rule useful for understanding combinations of neighborhood attribute data values A buffer spreads a distance measurement zone outwards to include more space This transformation makes a new data layer that can be used in subsequent overlay analysis Buffers are the simplest kind of neighborhood operation focused on neighborhoods defined by Euclidean distance The attribute of the center spatial object is simply moved outward it dominates its neighbors This can be extended to other treatment of attributes Surfaces have properties such as slope composed of gradient amp aspect that are estimated from neighboring values This process is a special case of neighborhood operations as well Exploring 615 Chapter 7 presents a scheme for these operations using the same groupings of procedures found in overlay operations First geometric part assemble the neighboring values Note there are problems on edge of layer Near neighbors immediate39 focal39 to approximate an incremental step extended39 neighbors beyond local is in chapter 8 Options 1 amp 2 are pretty much the same although Tomlin s Map Algebra and ESRI s Spatial Analyst separates them 1 immediate neighbors39 in grid 39roving window39 3X3 kernel39 etc 2 fixed radius of Euclidean distance 39Focal39 with variants like nested holes etc 3 adjacent objects topologically connected in vector eg next subway stop Distance relationships in a raster grid structured space Figure 77 see lecture handout shows grid distance and count of cells at that distance The spatial neighborhood component is developed in discrete increments of next to Distance relationships in a vector structured space Multitude of computations from points polylines areas The spatial neighborhood component can be devised by next to but the spatial neighborhood is usually computed based on distanced Now the attribute part Attribute combination produces an output value by applying a rule that uses the neighboring data values The rules can be grouped by combination method and by level of measurement Exploring GIS Table 71 Operations on Near Neighbors Examples of operations on near neighbors M Level Of Dominance Contributory Interaction easurement Voting tabulation BU er majority filter Edge detectors Nomlnal Drop llne aggregatlon I I proportlon dIversIty ExplICIt comblnatlon dlssolve examples below Ordinal at least Maxmin of neighbors Percentile Pm le dramage example below lContinuous aspatial lMaXmin neighbor Sumaverage Edge detectors Continuous attribute with horizontal measures l Slope lMaximum slope Best t plane l None usually l Distance weighting lNone usually Smoothing filters Autocorrelation V I Amazon precipitation an Spllnes see below None usually None usually I Ianlmated glf Examples for Table 71 Nominal data measurement using contributory rule Holdridge vegetation map in 1 degree LongLat cells o Majority filter most frequent category in neighborhood Figure 78 0 Proportion ratio number of a total Figure 79 0 Diversity count of different categories in neighborhood Figure 710 Diversity measurement is important for habitat analysis at micro and macro scales diversity in a wetland bat diversity in Washington State Ordinal data measurement using interaction rule 0 Drainage Continuous aspatial measurement using contributory rule 0 Population density Continuous with horizontal measure 0 Slope data measurement using contributory rule Best t plane 0 Distance weighting using contributory rule filteringsmoothing detecting aspens in IR photography using max and min filters distance filter applied to cesium contamination 0 Distance weighting using interaction rule hillshading example


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