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Intro. To GIS Midterm Review March 8 th, 2013

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Reminders Lab on next Monday Try to catch up on homework assignments

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Why Georeferencing?

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Georeferencing – The process of converting a map or an image (or scanned map) from one coordinate system to another by using a set of control points and a transformation equation. Two steps – Coordinate transformation (scaling, rotating, skew) – Resamping Georeferencing

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Coordinate Transformation Methods – First-order polynomial (Affine) – 2 nd Order polynomial – 3 rd order polynomial – Spline 2 nd order 1 st order >>Control points are used to estimate the coefficients (a0,b0,…)<<

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Transformation types: Affine The affine transformation function is: x’ = Ax + By + C y’ = Dx + Ey + F where x and y are coordinates of the input layer and x’ and y’ are the transformed coordinates. The C and F parameters control shift in origin (translation) A, B, D, E control scale and rotation their values are determined by comparing the location of source and destination control points. Scales, skews, rotates, and translates 6 unknowns( A,B,C,D,E,F) so a minimum of three “displacement links” required Little benefit from more than 18-30 links The most common choice

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Example: Transformation Let’s do a simple example – We would like to calculate new coordinates for point A(x=1, y=1), i.e., we want to convert coordinate system (x,y) to (x’,y’). – We assume a 1 st order (affine) transformation works fine – All the six coefficients (for affine transformation) are given (a0=1, a1=1.1, a2=0.4 and b0=0.2,b1=1.8,b2=0.8) – x’ and y’ are the new coordinates for (x,y) in the new coordinate system – Continue on next Slide >>>>

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Resampling coordinate 68 65 70 80 Pixel value x x’ 78 7378 74 69 y 1 1 2 3 2 3 1 2 3 1 2 3 y’ e.g., Average of 80 and 68 would be the pixel’s new value

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A bit of clarification on Optical RS The end result is surface reflectance/temperature or a thematic map (classified map)

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Orthophoto Vs. Aerial photos (or Remotely sensed Imagery)

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Midterm Overview Based primarily on lecture and homework/book Good knowledge of lab exercises helps! Closed notes, closed book, no computers Basic calculators Question types will include: – Multiple choice – True-False – Short answer – Few long answer

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Vector Data and Topology Topology – The arrangement for how point, line, and polygon features share geometry – Or knowledge about relative spatial positioning Two types of vector models exist in a GIS – Geo-relational Vector Model Arc Coverage (has topology) >>> format: binay Shape files (no topology) >>>> format: *.shp, *.shx, *dbf, etc. – Object-based Vector Model Includes classes and geodatabases >>> format: *.mdb

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Topology Concepts – Adjacency – Enclosure – Connectivity Terms to be defined – Node – Arc – Polygon

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Query A query is a “question” posed to a database (attribute data) Examples: – Mouse click on a map symbol (e.g. road) may mean What is the name of road pointed to by mouse cursor ? – Typing a keyword in a search engine (e.g. google, yahoo) means Which documents on web contain given keywords? – SELECT ‘FROM Senator S’ WHERE S.gender = ‘F’ means Which senators are female?

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Organizing Attribute Data Flat Files – Spreadsheets (e.g. excel spreadsheet)

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Hierarchical Organizing Attribute Data

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Relational ( What is commonly used in GIS ) – Various tables (databases) are “linked” through unique identifiers Organizing Attribute Data

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Query: Making Selections Usually interested in some subset of the data Selections can be made in two primary ways: – Select by Attribute – specify matching criteria – Select by Location – based on spatial proximity

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Select by Attribute Tips Be careful with case sensitivity and spaces Use parentheses to carefully construct a query Use “Boolean” Operators (AND, OR, NOT, LIKE) – AND means both criteria, OR means either – NOT allows you to exclude some criteria – LIKE lets you be more flexible, use wildcard characters (_ for one character, % for many) – Verify your expression to make sure it works

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Selection Criteria (#8.8) CountryPopulation (millions) Energy Use (barrels of oil per capita) Infant Mortality (per 1000) Life Expectancy (per 1000) Car Theft (%) Australia19.95,668479.22.2 England59.35,945577.52.6 Finland5.26,456478.00.5 France59.74,350479.21.8 Japan127.24,071381.60.1 Netherlands16.25,993578.30.5 Norway4.66,019478.91.5 South Africa45.33,7035246.52.4 Spain41.12,945578.30.5 United States291.08,066777.30.5 Per capita energy use > 4,000 AND population < 20,000,000

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Selection Criteria (#8.8) CountryPopulation (millions) Energy Use (barrels of oil per capita) Infant Mortality (per 1000) Life Expectancy (per 1000) Car Theft (%) Australia19.95,668479.22.2 England59.35,945577.52.6 Finland5.26,456478.00.5 France59.74,350479.21.8 Japan127.24,071381.60.1 Netherlands16.25,993578.30.5 Norway4.66,019478.91.5 South Africa45.33,7035246.52.4 Spain41.12,945578.30.5 United States291.08,066777.30.5 [Per capita energy use 40,000,000)] AND (car theft <1)

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Selection Criteria (#8.8) CountryPopulation (millions) Energy Use (barrels of oil per capita) Infant Mortality (per 1000) Life Expectancy (per 1000) Car Theft (%) Australia19.95,668479.22.2 England59.35,945577.52.6 Finland5.26,456478.00.5 France59.74,350479.21.8 Japan127.24,071381.60.1 Netherlands16.25,993578.30.5 Norway4.66,019478.91.5 South Africa45.33,7035246.52.4 Spain41.12,945578.30.5 United States291.08,066777.30.5 Population 1.5

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GOOD LUCK ON YOUR MIDTERM!

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