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Techniques of Spatial Analysis GEOG 2105

by: Ivana Szwejkowski

Techniques of Spatial Analysis GEOG 2105 Geog 2015

Marketplace > George Washington University > Geography > Geog 2015 > Techniques of Spatial Analysis GEOG 2105
Ivana Szwejkowski
GPA 3.4

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About this Document

These notes cover extended concepts of central tendency, variability, and corresponding map projections.
Techniques of Spacial Analysis
Qin Yu
Class Notes
GIS, Statistics
25 ?




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This 3 page Class Notes was uploaded by Ivana Szwejkowski on Friday September 16, 2016. The Class Notes belongs to Geog 2015 at George Washington University taught by Qin Yu in Fall 2016. Since its upload, it has received 4 views. For similar materials see Techniques of Spacial Analysis in Geography at George Washington University.


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Date Created: 09/16/16
Spatial Analysis week 3 9/13 Box Plots 1. Center 2. Spread 3. Skewness 4. Outliers Using measure of Center and Spread: the box plot Divides the data into 4 sets containing an equal number of measurements Constructing a box plot; Calculate the median, Q1, Q3, isolate extreme values Q1= .25(n+1) Q3= .75(n+1) Interpreting Box Plots - Midian Line in center of the box and whiskers equal length- symmetric distribution - Median Line left of center and long right whisker- skewed right - Median Line right of center and long left whicker- skewed left How to display data poorly - Unknown value - Incorrect scale - Clarity for separate units Some rules for Displaying Data badly 1. Show as little data as possible 2. Hide what data you do show 3. Ignore the visual metaphor altogether 4. Only order matters 5. Graph data out of context 6. Change the scales Displaying Data well 1. Be accurate and clear 2. Let the data speak 3. Science not scales 4. In tables, every digit should be meaningful 9/15 Spatial Distributions Distances, Points, Polygons, problems with Spatial Data Distance: Distance on a sphere, Projections Cylindrical Projection, Conical Surface, Planal Projection Surface Cartographer’s Dilemma; all maps introduce distortion -direction -distance; Euclid, Manhattan -shape -size Descriptive Statistics for Point Data -also called Geostatistics -Used to describe point data including the center of the points and dispersion of the points Summary Measures; Central Tendency and Variability Arithmetic Mean Center- average location of a set of points “center of gravity” Manhattan Median Center Euclidian Median Center Mean Center- minimized squared distances, easy to calculate, but will be affected by all points. Manhattan Median- minimizes absolute deviations, shortest distances when traveling only N-S and or E-W, easy to calculate, no exact solution for an even number of points Euclidian Median- True shortest path, harder to calculate (and no exact solution) Measuring Dispersion- Range, variance (xi-mean), STDEV, Standard Distance- shows how points deviate from the mean center, analogous standard deviation, represented graphically as circles on a 2-D scatter plot. Weighted Standard Distance- tells you percentage of population inside area from mean center Standard Deviational Ellipse -Directional Deviation Short axis(minor) Long axis(major) Rotation Descriptive Statistics for Areal Data -location quotient -Coefficient of localization -Lorenze curve -Gini coefficient Location Quotient- a measure of the proportion of an activity in a particular area compared to the proportion of some base measurement


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