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GPHY384 Midterm review

by: Sarah Massar

GPHY384 Midterm review GPHY 384

Sarah Massar
GPA 3.9

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Here is a review for the test
Advanced GIS and Spatial Analysis
Stuart Challenger
Study Guide
50 ?




Popular in Advanced GIS and Spatial Analysis

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This 7 page Study Guide was uploaded by Sarah Massar on Wednesday March 2, 2016. The Study Guide belongs to GPHY 384 at Montana State University taught by Stuart Challenger in Fall 2015. Since its upload, it has received 61 views. For similar materials see Advanced GIS and Spatial Analysis in Earth Sciences at Montana State University.


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Date Created: 03/02/16
GPHY384 Midterm Review SCALE:  +5 different measurement scales beyond qualitative and quantitative:  1. Nominal­ categorical/qualitative  ­land use (range or urban)  2. Ordinal­ ranked order  ­low  high  3. Interval­ relative location on a linear scale, no point of origin  ­temperature, years 4. Ratio­ relative on a linear scale with a fixed point of origin  ­elevation (sea­level)  5. Cyclic­ direction  + 3 ways to represent scale  ­Representative fraction­ what we use in GIS  ­ 1/24,000 or 1:24,000 ­ Unity ­ numerator always 1  ­Units – same units in numerator and denominator  ­Verbal­ what architects use  ­ 1”= 2,000’  ­Units are not the same  ­Graphic­ what is printed on maps    +Large Scale­ smaller area, zoomed in  ­local, high resolution  +Small Scale­ larger area, zoomed out  ­regional, low resolution  < 1: 30,000 –large scale    1: 30,000   1:300,000 – intermediate scale  >1: 300,000 – small scale  ­RMSE­Root Mean Square Error ­equation used to find error between two datasets  2 2 2 2 ­  √ (e 1 e +2e + 3 )  n e=error         n n= number of measurements  ­Use RMSE to find the appropriate scale:  ­ex. if RMSE 27 meters: (1/x) = (1/50”)/(27 m)  (1/x) = (.0005 m)/(27 m)  .0005x = 27  1:54,000 COLOR MODELS: 1. RGB­ red, green, blue 2. HSV­ hue (color), saturation (amount of color 1­100%), value (amount of white or black  mixed in 1­100%)  3. CMYK­ cyan, magenta, yellow, black ­reflected light, what is printed  MAP COMPOSTION:  +Applied Design Principles­ What a well­designed map needs to have  1. Readability­ easy to read and decipher what is being mapped  2. Hierarchy­ most important map elements are where the eye is drawn a. Top left is highest level, bottom right has the lowest level 3. Balance­ if the map was hung on a string, would it hang straight? Or does one end appear “heavier/more crowded” than the other?  4. Contrast­ text pops out of background  a. Add a halo around text 5. Repetition­ if have a duplicate image make sure they are the same orientation and scale 6. Alignment­ keep everything in straight lines 7. Proximity­ “like” things on the map should be grouped a. North arrow, scale bar, and credits should all be in the same area, normally the  bottom right  PROJECTIONS: important  +Coordinate Systems (CS) 1. Geographic­ Geographic CS­ unprojected, just shows the graticule (latitude and                         longitude) ­conceptualized with national data ­non­projected so units are degrees (degrees, minutes, and seconds)  ­keeps direction constant, but area, shape, and distance are distorted 2. State Plane­State Plane­ projected, runs E­W ­Montana Library Clearinghouses ­projection is Lambert conformal conic  ­Used to map Montana  3. Universal Transverse Mercator­ Universal Transverse Mercator (UTM) ­ projected, runs N­S  and divides Earth into 60 zones ­Gallatin County Clearinghouse  ­projection is transverse Mercator  ­Used to map Gallatin County  +Projection Families­ How globe is projected onto a flat map (where standard line is placed) 1. Azimuthal (planar)­ flat plane touches globe in one place a. Best for polar mapping  b. Touches at poles (polar aspect) creates small circles, except at equator where it is  a great circle  2. Cylindrical­ projected on a cylinder a. Best for equatorial mapping  b. Equatorial (up and down cylinder) or transverse (cylinder on its side)  3. Conic­ projected on a cone  a. Best for mid­latitude mapping  b. Good for E­W mapping  4. Mathematical­ not a real projection, do math and change it up  a. Ex. Pseudocylindrical  +Ways to reduce Distortions  1. Equal Area­ reduces area distortion 2. Conformal­ shape preserved (Mercator)  3. Equidistant­ distance on certain line reduced, often along a great circle (all lines of  longitude and the equator)  4. Azimuthal­ direction preserved 5. ­Combine CS, Projection family, and distortions, to define a maps overall projection:  ­ex. Lambert Conformal Conic (used for Montana)  ­ex. Universal Transverse Mercator zone 12 (used for Gallatin County)  +Datum­ basis for coordinate system  ­NAD27, NAD83, and WGS84  ­In Geographic Coordinate System­ NAD27 and NAD83 62 m apart  ­In UTM Coordinate System­ NAD27 and NAD83 220 m apart TOPOLOGY: important for test  +Data Models­ ways to represent Geography  ­Raster­ geography represented as cell matrix’s that store numerical values  ­Vector­ geography represented a points, lines, and polygons ­Various types of vector data models  1. Spaghetti data structure­ looks like spaghetti  2. Arc/Node data structure ­Nodes­ points ­Ties­ min and max coordinates for the data set 3. Topological data structure  4. Object oriented data structures  +Topology­ a set of rules or behaviors which defines spatial relationships between vector  geographic features ­it integrates feature data so points, lines, and polygons are all in the same table  ­detects error well ­great for managing spatial relationships  ­Integrated feature management  ­Can edit multiple geoclasses in one step ­edited in database ­What the rules of Topology dictate:  ­Connectivity­ arcs connected at nodes ­Area Definition­ polygons defined by arcs ­Contiguity­ arcs store left/right polygons ­Since points, lines, and polygons are in the same space, certain relationship rules have to be  followed ­Polygon Rules:  ­Must not overlap ­Line Rules:  ­Must not overlap ­Point Rules:  ­Must be within a polygon ­Must be at the end of a line  ­Topology ensures that buffers don’t overlap ­Use topology if the rules would be helpful in managing the data  CONVERTING: Degrees, minutes, and seconds (DMS)  Decimal degrees (DD) +/­ [D + M/60 + S/3600]  DMS  Decimal minutes (DM) +/­ [D, M + S/60]  DATA CONVERSION:  +Geocoding­conversion of spatial data into digital form  ­Different ways to attain data:  1. Primary Data Capture­ direct measurement   ­Raster ­via. remote sensing from satellites  ­Vector ­surveying  ­GPS  2. Secondary Data Capture­ conversion from hard copies  ­Raster ­scanning  ­photogrammetry­manipulating raster data to make it more accurate  ­Vector ­digitizing ­COGO –Coordinate Geometry­ where cadastral data comes from  3. Other Sources  SQL: important for test   +SQL­ Structure query language ­used to define and select certain attributes in a dataset ­need to have three words  ­Select, From, Where Example: SELECT <column> FROM <table> WHERE <Boolean expression>  +Boolean expression­ something that is either “on/off” or “yes/no” or “true/false”  ­2 types of stings: text and numeric ­Text strings are surrounded by single quotes, numeric is not ­ex. WHERE “Stream_NAME” = ‘Bozeman Creek’ or “Stream_NAME” = ‘E Gallatin’   GIS:  ­Can think of GIS standing for two things:  1. Geographic Information Systems a. System­ All of the components of things we are working with i. hardware, software, data, warmware 2. Geographic Information Sciences   a. Science­ knowledge that requires study  b. GIS has many supporting sciences  i. Geodesy, Computer Science, Cartography, Geography, Math/Stats UNCERTAINTY­ use actual words on test to describe it ­Examples of error:  ­accuracy/inaccuracy ­ambiguity ­when an attribute may be appropriately assigned two or more different values ­the shoreline: a point may be dry land at one point in the day and  underwater at a different time  ­Direct measurement­ crop yield  ­Indirect measurement­ fertility of the soil  ­vagueness  ­used to describe indefinite boundaries that are constantly changing  ­the area of land that acts as a habitat for animals  ­fuzziness ­boundary is blurred  ­line between soil types  ­There are several stages in our projects where we can begin to expect uncertainty/errors  ­Real World  Observations/Conception Measurements/compilation Analysis  +Observations/Conception: ex. of error ­are units the same throughout the entire polygon?  ­where is the input exactly?  ­the line between soil types is blurred +Measurements/Compilation: ex. of error ­raster vs. vector­ choose one  ­scale and accuracy  ­measurement error ­recreational grade GPS: 25 ft. accuracy ­resource grade GPS: in between  ­survey grade GPS: centimeter accuracy  ­warmware +Analysis: ex. of error ­in inaccuracies in output data will come from inaccuracies in input data ­if two polygons have 80% of their points within the accuracy reading, then when  they are combined, 64% of the resulting points will be within the accuracy  reading  ­ (.8) x (.8) = .64 = 64% +MAUP­ Modifiable Areal Unit Problem­ A problem that occurs during the spatial  analysis of aggregated data in which the results differ when the same analysis is applied  to the same data, but different aggregation schemes are used ­This happens when artificial boundaries on imposed on spatial phenomenon ­Cadastral data ­changes every 10 years  ­example of: Ecological Fallacy­ defining individual data from aggregate data ­selection of data matters ­watershed vs. township ­is phenomenon evenly distributed?  SPATIAL OVERLAY:  +Union­ outputs have everything from inputs whether they intersect or not  ­will have ‘null’ values +Intersect­ preforms a clip, only have outputs where both inputs overlap +Identity­ output contains where both overlap and all the inputs from only one ­will have ‘null’ values  OTHER STUFF:  +Distance ­Euclidean­a straight line, Pythagoreans theorem ­Network­linear networks like roads or rivers ­Cost Surface­ cost weighted difference, add in elevation and how much it would be to  cover the distance not only horizontally, but vertically as well  ­review insect trap and soil data in class exercises to understand 1:1, 1:m, and m:1 relationships 


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