GEOG 371 Lecture Notes - Lecture 4: Local Analysis, Positive-Definite Kernel, Edge Effects
15 Feb 2018
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Document Summary
Measuring spatial dispersion/clustering of points: standard distance (sd, measures dispersion/clustering for a set of n points, radius of a circle centered on mean center (xm, ym) = coordinates of mean center n i . A = size of area: sd adjusted to area of study, standard deviational ellipse. Incorporates orientation: better conforms to actual distribution of points, better than using circle to calculate points. Global and local spatial analysis methods: global methods, overall characteristics, how dispersed, where is the middle. Local methods: differences in points pattern w/in study area. Local analysis with point data: many methods, kernel density estimation. If your area is larger/ has more data, then bigger bandwidth is more useful: ex to show there is a major problem, study area size + # of points affects bandwidth. Issues in point data analysis: edge effects, makes area less dense missing points from study area x x.
