Techniques of Spacial Analysis week 1
Techniques of Spacial Analysis week 1 Geog 2015
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This 3 page Class Notes was uploaded by Ivana Szwejkowski on Friday September 2, 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 2 views. For similar materials see Techniques of Spacial Analysis in Geography at George Washington University.
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Date Created: 09/02/16
Techniques of Spatial Analysis 2105 Geography: Physical Geography + Human Geography Geographic tools (Quantitative analysis, Cartography, Remote sensing, GIS) Statistics the science of collecting, organizing, analyzing, and interpreting data to make decisions. Geographers Use Stats to... describe and summarize spatial data Make generalizations about spatial patterns estimate the likelihood of certain events at certain locations use a limited sample to make an inference about a larger population Determine if Spatial patterns support hypothesized pattern, and also how a pattern varies from one place to another. Geostatistics :can be regarded as a collection of numerical techniques that deal with the characterization of spatial attributes :deals with spatially auto collected data Components of Geographic Data Thematic Space Time Basic Concepts: Variables and Data A variable is a characteristic that changes or varies over time Experimental Unit an experimental unit is the individual or object on which a variable is measured (Restaurant, CNN fans, Students) a measurement results when a variable is actually measured on an experimental unit. A set of measurements, called data, can be either a sample or a population. Variables can be: Univariate, Bivariate, and Multivariate on a single experimental unit Types of variables; quantitative & qualitative (categorical data vs. numerical data) Quantitative can be discrete or continuous (finite or infinite) Levels of Measurement 1. Nominal Scale Categories, names, labels 2. Ordinal Scale Data can be compared in order, one data entry is greater than another. (for example; tv ratings) 3. Interval Data can be ordered and differences between 2 entries can be calculated, but there is no inherent zero, it is arbitrary. (for example, temperature, year or birth.) 4. Ratio There is an inherent zero, data can be ordered, differences found, one data value is a multiple of another, zero is a natural starting point or nonarbitrary. (for example, height, weight, age) Summary; levels of measurement 1. Nominal (A=B) 2. Ordinal (A>B) 3. Interval (AB) 4. Ratio (AxB, A/B, A+B, AB) Precision vs. Accuracy Precision: the degree of closeness of repeated measurements to each other degree of variability of the variables (exactness) Accuracy: how close the observed measurements to the unobserved true value bias Systematic error large= low accuracy Random error large= low precision Measurement error model Y=X+E Y=unknown true value X=recorded value E=unknown amount of departure (error) Gross error mistake committed by person collecting or recording the measurement Systematic error (accuracy) error of method consistently higher or lower, instrument oriented Random error (precision) inconsistent error SPACE; Dimensions of Geometric Elements Point (0d) Line(1d) Aerial (2d) Surface(3d) Discrete/Finite features; Points, Lines, Areas Continuous or infinite features: Annual Mean Temperatures, Precipitation, Solar radiation, Discrete; Settlements , Continuous; population density Special considerations for Spatial Data The modifiable areal unit problem (MAUP) Zonal configurations/scale Boundary Problems Spatial sampling procedures Spatial autocorrelation or spatial dependence