gis final Flashcards
vector data model
uses discrete objects to represent spatial features on the earth’s surface
how vector data are prepared
- classify spatial features into points/lines/etc over an empty space using x/y coordinates to show location
- structures properties and spatial relationships of objects in a logical framework
- codes and stores vector data into digital data files
buffering
creating an output polygon layer containing a zone of specified width around an input point, line, or polygon feature
constant width buffers
require users to input a single value for which all features are buffered
variable width buffers
call on a premade buffer field within the attribute table to determine the buffer width for each specific feature in the dataset
multiple ring buffers
a series of concentric buffer zones are created around the originating feature at user-specified distances
donut buffer
excludes input polygon area
setback buffer
buffer only the area inside the polygon boundary
dissolve operation
combines adjacent polygon features in a single feature dataset based on a single predetermined attribute
topology
arrangement of how point, line, and polygon features share geometry (studies features that stay in place when the map is bent and stretched)
geographic base file/dual independent map coding
straight line segments represent streets etc, each segment ends when it changes direction or intersects another line, nodes identified with codes
TIGER
early application of topology in geospatial technology from the US Census Bureau
importance of topology
ensures data quality and integrity
enhance GIS analysis
topological relationships between spatial features allow GIS users to perform spatial data query
connectivity
arcs connect to each other at nodes
area definition
area is defined by a series of connected arcs
contiguity
in coverage, topological identification of adjacent polygons by recording the left and right polygon for each arc
OR a numeric description of boundary connectedness
geodatabase
collection of thousands of objects, properties, and methods that provide the foundation for ArcGIS
raster data model
uses a regular grid to cover space, represents a continuous surface, represents points by single cells, lines by sequences of neighboring cells, and areas by collections of contiguous cells
cell value (elements of raster data model)
each cell caries a value which represents the characteristic of a spatial phenomenon at the location denoted by its row and column
cell size (elements of raster data model)
refers to the size of the area represented by a single cell. determines the spatial resolution of a raster
cell depth (elements of raster data model)
number of bits for storing cell values. a bit is the smallest data unit in a computer and has a single binary value of either 0 or 1
raster bands (elements of raster data model)
may have single or multiple
spatial reference (elements of raster data model)
allows raster data to align spatially with other data sets in GIS
satellite imagery
can be passive or active, for both systems, the spatial resolution refers to the pixel size
active remote-sensing systems
send a beam of energy at a surface and analyze the energy reflected back (LIDAR)
passive remote-sensing systems
record wavelengths of energy radiated from a surface (infrared radiation from the earth)
landsat
passive satellite system that has produced most widely used imagery worldwide
digital elevation models (DEMs)
represents a regular array of elevation points. data sources include topographic maps, aerial photos, satellite images, LiDAR, etc
refers to bare-earth elevations, ignoring buildings or vegetation
raster reclassification
aggregating each pixel value in a few discrete classes
mathematical raster overlay
numbers within the aligned cells of the input grids can undergo any user-specified mathematical transformation
an output raster is produced that contains a new value for each cell
boolean connectors
AND, OR, and XOR can be used to combine the information of two overlying input raster datasets into a single output raster
relational operators
use relational operators (<,=,>, etc) to evaluate conditions of the input raster datasets
single raster local operations
compute each cell value in output raster as a mathematical function of the cell value in the input raster (ex. multiply every value in input by 10 to produce the output)
multiple rasters local operations
measure based on cell values or their frequencies in the input rasters can also be derived and stored on the output raster of a local operation with multiple rasters (Ex. output grid of numbers produced by taking the mean of three input grids)
neighborhood operations
involves a focal cell and a set of its surrounding cells, which are chosen for their distance and/or directional relationship to the focal cell (ex. square, circle, annulus, or wedge around a central cell)
zonal operations
works with groups of cells of same values or like features that may be contiguous or noncontiguous
aggregate
condensing raster into a lower-resolution summary output (condensing a 4x4 grid into a 2x2 grid of the cell averages)
global operations
statistical values for the raster as a whole
tiff file
not very high compression, keeps a higher resolution of data than a jpeg
raster data compression
eliminates redundancy, tries to find a code with less data volume
cell-by-cell raster encoding
minimally intensive methods that encodes a raster by creating records for each cell value by row and column (thought of as a large spreadsheet where each cell represents a pixel in the raster image)
run-length raster encoding
encodes cell values in runs of similarly valued pixels and can result in a highly compressed image file (helpful when large groups of neighboring pixels have similar values)
quad-tree raster encoding
divides a raster into a hierarchy of quadrants that are subdivided based on similarly valued pixels. division of the raster stops when a quadrants is made entirely from cells of the same value
data compression
reduction of data volume related to how raster data are encoded. can cause losses or not
raster to vector data converstion
vectorization
vector to raster data conversion
rasterization
spatial interpolation
process of using points with known values to estimate values at other points, creates surface data from sample points
control points
points with known values, provide data necessary for the development of an interpolator for spatial interpolation
interpolation
potentially complex statistical technique that estimates the value of all unknown points between the known points
spline
interpolation method forces a smoothed curve through the set of known input points to estimate the unknown intervening values
Inverse Distance Weighting (IDW)
estimates the values of unknown locations using the distance to proximal, known values. the weight placed on the value of each proximal value is in inverse proportion to its spatial distance from the target locale
trend surface
most complex interpolation method, fits a multivariate statistical regression model to the known points, assigning a value to each unknown location based on that model
kriging
geostatistical method for spatial interpolation. assesses the quality of prediction with estimated prediction errors. most appropriate when you know there is a spatially correlated distance or directional bias in the data
universal kriging
most often used when the surface is estimated from irregularly distributed samples where trends exist
punctate kriging
elementary form that assumes that the data exhibit stationarity and are collected at equally spaced point locations
terrain mapping and analysis
includes quantitative measures of the land surface such as slope, aspect, and surface curvature
triangulated irregular network (TIN)
approximates the land surface with a series of non-overlapping triangles. DEMs are usually the primary data source. can also be converted into a DEM
input data to a TIN
DEM
surveyed elevation points
GPS
LiDAR
line data such as contour lines and breaklines (subdivides triangles into smaller triangles)
terrain mapping techniques
contouring, vertical profiling, hill shading, hypsometric tinting, and perspective (3-D) view
slope
change in elevation with a change in horizontal position
aspect
the aspect at a point is the steepest downhill direction. typically reported as an azimuth angle, with zero in the direction of grid north, and the azimuth angle increasing in a clockwise direction (recorded in degrees or N, NE, SE, etc)
viewshed
collection of areas visible from that point. does not include locations blocked from view by terrain
shaded relief map
(hillshade map) depiction of the brightness of terrain reflections given a terrain surface and sun location
computing algorithms for slope and aspect using TIN
the x, y, and z values of points that make up a TIN are used to compute slope and aspect for each triangle
surface curvature
rate of change of slope. can determine if the surface at a cell location is upwardly convex or concave
raster vs TIN for terrain analysis
differ in data flexibility and computational efficiency
main advantage of TIN is the flexibility with input data sources
main advantage of raster is computational efficiency
