Advanced Gis Exam 1

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Geographic Objects

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in a GIS, a representation of a spatial or non spatial entity. usually belongs to a class of objects with same basic values

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Geographic Attributes

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non spatial information about an object in a GIS, usually stored in a table and linked to a feature by a unique identifier. Ex: attributes of a river might include sediment load, length and name.

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Planar Coordinates

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a two dimensional measurement system that locates features based on their distance from origin (0,0) along a perpendicular axis.

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Absolute Distance

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the measure of geographic distance between coordinates with units like length (mile/kilometer)

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Relative Distance

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measure of distance using things other than actual measurement, where something is relative to something else. ex: jimmy johns is located on the square right next to the bike shop.

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Geographic data models: Vector (object based)

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a coordinate based data model that uses points, lines and polygons. each point feature is a coordinate pair while line and polygons are ordered lists of vertices. attributes are represented by each vector feature while rasters are grid cells.

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Geographic data models: Topology vs. Spaghetti models

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Spaghetti: vector data composed of simple lines.

- No topology

- usually no attributes

- spaghetti lines may cross

- no intersections are created at crossings

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contind... : Topology

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in geodatabases the the arrangement that constrains how points lines and polygons share geometry. For example, street centerlines and census blocks share geometry, and adjacent soil polygons share geometry.

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Raster (location based)

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a data model that defines space as an array of equally sized cells arranged in rows & columns and composed of single or multiple bands. each cell contains attribute value and location coordinates.groups of cells that share the same value represent the same attribute.

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Spatial Measurement levels: Nominal

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the value of a nominal variable are just different names. ex: eye color, {male,female}, zip codes.

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Spatial Measurement levels: Ordinal

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the value of an ordinal object provides enough to rank or order objects. ex: {good, better, best}, Grades, street numbers.

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Spatial Measurement levels: Interval

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the values are meaningful and a unit of measurement exists but there is no set beginning value to the scale. ex: calender dates, temperature in farenheit or celsius.

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Spatial Measurement Levels: Ratio

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both differences and ratios are meaningful, and there exists and absolute beginning value of the scale. ex: temp in kelvin, monetary values, age, mass, length

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Vector Examples: Points

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a geographic element defined by a pair of x,y coordinates

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Vector examples: Lines

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on a map a shape defined by the connection of unique x and y coordinates, a line may be straight or curved.

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Vector examples: Polygons

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a closed shape connected by a sequence of x,y coordinate pairs where the first and last coordinate pair are the same and the others are unique

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Continuous vs. Discrete data

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continuous data: can take any value within a range. represents real world with finite number of variables, each one defined at every possible position (continuous is raster)

Discrete data: can only take certain values. objects can be counted, represents the geographic world as objects with well defined boundaries in otherwise empty space (discrete is vector)

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Scale: Types of measurements

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absolute (1" = 1 mile), Bar (graphic), representative fraction (1:24,000)

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scale: Large vs Small

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scale vs magnitude: large scale is zoomed in and close up (imagine zoomed in gps on san marcos)

small scale is zoomed out like a world map.

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Resolution:

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Spatial: Pixel Size

Spectral: how many wavelength bands measured

temporal: how many times or how often data is updated

radiometric: precision we measure

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Datum:

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Reference or base for other measurements

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Map Projection:

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means of representing a spherical surface on a flat surface

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Families of Projections:

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Shape (conformal)

Distance(equidistant)

Direction (azimuthal)

Area (equal area)

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Coordinate System:

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a grid system from which we make location measurements

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3 main coordinate systems:

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- latitude/longitude: spherical coordinate system

- UTM (not good for polars)

- state plane: every state in the us and also puerto rico. varies by state but is very accurate for those.

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latitude and longitude: significant parallels

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Longitude and latitude

- degrees, minutes, seconds

- decimal degrees

- prime meridian (0 degrees longitude)

- equator (0 degrees latitude)

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Universal Trans Mercator: UTM

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how many zones? 60

zones are 6 degrees wide

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where do the zones begin?

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donyt

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Projection type:

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df

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Geographic patterns:

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Distance: Absolute and Relative

Direction

Arrangement: regular, random, clustered

orientation

scale: absolute, bar, representative fraction

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Basic and advanced (topology)

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patterns can be random or clustered

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Topology:

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Adjacency: how the shapes sit next to each other

connectivity: how the shapes connect to one another

containment:

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Geographic process: Dispersion

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spread of attribute from one area to another (not distribution- that is a pattern)

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Types of dispersion:

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contagious expansion diffusion: rapid spread like flu

hierchical expansion diffusion: spread by authority figures

relocation diffusion: spread by migration

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Spatial Covariation:

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the study of two or more geographic distributions which vary over the same area (such as crime and unemployment)

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Time distance decay:

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applies to all types of diffusion, as time and distance increase presence of spatial attributes decrease.

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Patterns and processes of amazonia:

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na

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GIS operations: OVERLAY

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a series of registered data layers overlaying each other arguably the most important GIS analysis function of geoprocessing (clip, intersect, union , merge)

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Buffer:

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proximity function that works with distance. a buffer is a region that is less than or equal to a specified distance from a feature.

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Dissolve:

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removes boundaries between polygons or nodes between arcs, features with same attributes are dissolved. Combines features on a common attribute.

Advantages: removes unnecesary borders and simplifies info.

Disadvantages: loses some information

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Needs Analysis:

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what do you want to do and how are you going to do it, what questions do you ask? what do you need to do to answer those questions?

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Primary and secondary data sources:

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Primary data is collected by you

- Raster: digital or remote sensing images and digital aerial photos

-Vector: GPS measurement or field survey

Secondary data is data that is found that was collected by others.

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OpenGIS consortium:

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protects standards of data and how it should be provided.

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Metadata:

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Metadata is data about the data, meaning that it describes the data, quality, condition, origin and source of for data or other peices of information. metadata could be what projection something is, the source of where it came from, the scale, resolution etc.

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FGDC standards:

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the official comittee that determines standards of metadata and how it should be presented.

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Scanning - types of secondary data:

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1. Flatbed

2. Drum (fixed width but not length)

3. line (vectors)

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Digitizing

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the process of converting geographic features on an analogue map into digital format using a tablet or on screen program (using mouse),

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How to digitize:

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1. Needs analysis

2. obtain data sources

3. capture raster data

4. georeference data

5. digitize me captain

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Nodes vs. Vertices:

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nodes are points beginning and ends, vertices are the points in between.

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differences between data, information, and knowledge:

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na

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Descriptive statistics:

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Mean:

Median:

Mode:

Normal dist:

skew:

Kurtosis:

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Low order objects:

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points, lines, polygons

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high order objects:

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object that is derived from info we already have. a centroid doesnt exist until we create it.

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point patterns:

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used to identify whether occurences or events are interrelated looking at other points to determine a pattern

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creation of voronoi diagrams:

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a partition of space into areas, or cells that surround a geometric objects, (usually points). voronoi diagrams are used to delineate areas of influence around geographic features.

( polygon represents an area closest to a known value)

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thiessen polygons:

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polygons generated from a set of sample points, each polygon defines an area of influence around its sample point so that any location inside the polygon is closer to that point than any of the other sample points.

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Centroids:

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the geographic center of a feature. for line, polygon or 3d figures its the center of mass (center of grav). the weight for a point feature is 1, for a line feature it is length, and for a polygon feature it is the area.

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Networks:

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Model the flow of goods and services, (radial or looped). an interconnected set of points that represent possible routes from one location to the other. ex: a set of lines that represent a city streets layer is a network.

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Fragmentation:

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a set of metrics to describe the landscape. higher order polygon object.

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Nearest Neighbor Analysis:

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point pattern analysis based on the nearest point. (calculates the the nearest neighbor based on the average distance from each feature to its nearest neighbor)

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Steps and interpretation of NNA:

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1. find the nearest neighbor

2. add the distance between the NN

3. average the NN distance

4. calculate for the R value

R= 0 clustered (because there is no distance between)

R= 1 is random

R=2.14 Uniform

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Hoover Index:

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how evenly population is distributed throughout an area.

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calculation of hoover index:

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the sum of the absolute value of the area minus the absolute value of a population divided by 2. (the closer to 0 the more evenly distributed the population is)

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Tessellation

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the division of a two dimensional area into polygon tiles, or a three dimensional area into polyhedral blocks in a way so that no polygon overlaps and there are no gaps.

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most common tessellation:

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regularly spaced matrix, each cell is the same size and shape and are most commonly squares.

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Statistical surface definition:

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x,y,z rasters can represent natural or cultural phenomena - ex: elevation, temp, population, income.

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Slope and Aspect:

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Slope: rise over run

Aspect: direction surface is facing

Contour: elevation lines usually spaced in 10's (10,20,30)

hillshade: shadows that are cast from a specific light angle to show the hillshade of a map

viewshed: locations visible from specified lines or points on the map

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Tolbers first law of geography:

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everything is related to everything else, but near things are more related than far things.

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autocorrelation

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the measure of the degree to which a set of spatial features and their associated data values tend to be clustered together in space (positive spatial autocorrelation) or dispersed (negative spatial autocorrelation). data exhibits autocorrelation when values measured nearby are more similar than values measured far away.

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Spatial interpolation:

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creation of surfaces from point data and other attributes.

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types of interpolation:

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Global: uses all known sample points to estimate a value at an unsampled location.

Local average: uses local neighborhood data to estimate value ex: closest number of points or within a given search radius

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interpolation:

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the estimation of surface values at unsampled points based on known surface values of surrounding points. can be used to estimate elevation, rainfall etc

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extrapolation:

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using known or observed data to infer or calculate values for unobserved times,

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trend surface analysis:

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na

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Spline:

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an interpolation method in which cell values are estimated using a mathematical function that minimizes overall surface curvature resulting in a smooth surface that passes exactly through the input points.

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IDW:

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an interpolation technique that estimates cell value in a raster from a set of sample points that have been weighted so that the farther a sampled point is from the cell being evaluated the less weight it has in the calculation of the cells value.

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Kriging:

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an interpolation technique in which the surrounding measured values are weighted to derive a predicted value for an unmeasured location. (provides an easy method for characterizing the variance of predictions. (uses distance, probability and neighbors)

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kinds of kriging:

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ordinary kriging: no trend

universal kriging: trend without model parameters

simple kriging: trend with known model parameters,

indicator kriging: binary prediction surface

cokriging: multiple inputs