GPHY384 Midterm review
GPHY384 Midterm review GPHY 384
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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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