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gis final Flashcards

1
Q

vector data model

A

uses discrete objects to represent spatial features on the earth’s surface

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2
Q

how vector data are prepared

A
  1. classify spatial features into points/lines/etc over an empty space using x/y coordinates to show location
  2. structures properties and spatial relationships of objects in a logical framework
  3. codes and stores vector data into digital data files
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3
Q

buffering

A

creating an output polygon layer containing a zone of specified width around an input point, line, or polygon feature

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4
Q

constant width buffers

A

require users to input a single value for which all features are buffered

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5
Q

variable width buffers

A

call on a premade buffer field within the attribute table to determine the buffer width for each specific feature in the dataset

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6
Q

multiple ring buffers

A

a series of concentric buffer zones are created around the originating feature at user-specified distances

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7
Q

donut buffer

A

excludes input polygon area

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8
Q

setback buffer

A

buffer only the area inside the polygon boundary

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9
Q

dissolve operation

A

combines adjacent polygon features in a single feature dataset based on a single predetermined attribute

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10
Q

topology

A

arrangement of how point, line, and polygon features share geometry (studies features that stay in place when the map is bent and stretched)

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11
Q

geographic base file/dual independent map coding

A

straight line segments represent streets etc, each segment ends when it changes direction or intersects another line, nodes identified with codes

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12
Q

TIGER

A

early application of topology in geospatial technology from the US Census Bureau

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13
Q

importance of topology

A

ensures data quality and integrity
enhance GIS analysis
topological relationships between spatial features allow GIS users to perform spatial data query

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14
Q

connectivity

A

arcs connect to each other at nodes

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15
Q

area definition

A

area is defined by a series of connected arcs

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16
Q

contiguity

A

in coverage, topological identification of adjacent polygons by recording the left and right polygon for each arc
OR a numeric description of boundary connectedness

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17
Q

geodatabase

A

collection of thousands of objects, properties, and methods that provide the foundation for ArcGIS

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18
Q

raster data model

A

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

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19
Q

cell value (elements of raster data model)

A

each cell caries a value which represents the characteristic of a spatial phenomenon at the location denoted by its row and column

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20
Q

cell size (elements of raster data model)

A

refers to the size of the area represented by a single cell. determines the spatial resolution of a raster

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21
Q

cell depth (elements of raster data model)

A

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

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22
Q

raster bands (elements of raster data model)

A

may have single or multiple

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23
Q

spatial reference (elements of raster data model)

A

allows raster data to align spatially with other data sets in GIS

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24
Q

satellite imagery

A

can be passive or active, for both systems, the spatial resolution refers to the pixel size

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25
Q

active remote-sensing systems

A

send a beam of energy at a surface and analyze the energy reflected back (LIDAR)

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26
Q

passive remote-sensing systems

A

record wavelengths of energy radiated from a surface (infrared radiation from the earth)

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27
Q

landsat

A

passive satellite system that has produced most widely used imagery worldwide

28
Q

digital elevation models (DEMs)

A

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

29
Q

raster reclassification

A

aggregating each pixel value in a few discrete classes

30
Q

mathematical raster overlay

A

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

31
Q

boolean connectors

A

AND, OR, and XOR can be used to combine the information of two overlying input raster datasets into a single output raster

32
Q

relational operators

A

use relational operators (<,=,>, etc) to evaluate conditions of the input raster datasets

33
Q

single raster local operations

A

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)

34
Q

multiple rasters local operations

A

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)

35
Q

neighborhood operations

A

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)

36
Q

zonal operations

A

works with groups of cells of same values or like features that may be contiguous or noncontiguous

37
Q

aggregate

A

condensing raster into a lower-resolution summary output (condensing a 4x4 grid into a 2x2 grid of the cell averages)

38
Q

global operations

A

statistical values for the raster as a whole

39
Q

tiff file

A

not very high compression, keeps a higher resolution of data than a jpeg

40
Q

raster data compression

A

eliminates redundancy, tries to find a code with less data volume

41
Q

cell-by-cell raster encoding

A

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)

42
Q

run-length raster encoding

A

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)

43
Q

quad-tree raster encoding

A

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

44
Q

data compression

A

reduction of data volume related to how raster data are encoded. can cause losses or not

45
Q

raster to vector data converstion

A

vectorization

46
Q

vector to raster data conversion

A

rasterization

47
Q

spatial interpolation

A

process of using points with known values to estimate values at other points, creates surface data from sample points

48
Q

control points

A

points with known values, provide data necessary for the development of an interpolator for spatial interpolation

49
Q

interpolation

A

potentially complex statistical technique that estimates the value of all unknown points between the known points

50
Q

spline

A

interpolation method forces a smoothed curve through the set of known input points to estimate the unknown intervening values

51
Q

Inverse Distance Weighting (IDW)

A

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

52
Q

trend surface

A

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

53
Q

kriging

A

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

54
Q

universal kriging

A

most often used when the surface is estimated from irregularly distributed samples where trends exist

55
Q

punctate kriging

A

elementary form that assumes that the data exhibit stationarity and are collected at equally spaced point locations

56
Q

terrain mapping and analysis

A

includes quantitative measures of the land surface such as slope, aspect, and surface curvature

57
Q

triangulated irregular network (TIN)

A

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

58
Q

input data to a TIN

A

DEM
surveyed elevation points
GPS
LiDAR
line data such as contour lines and breaklines (subdivides triangles into smaller triangles)

59
Q

terrain mapping techniques

A

contouring, vertical profiling, hill shading, hypsometric tinting, and perspective (3-D) view

60
Q

slope

A

change in elevation with a change in horizontal position

61
Q

aspect

A

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)

62
Q

viewshed

A

collection of areas visible from that point. does not include locations blocked from view by terrain

63
Q

shaded relief map

A

(hillshade map) depiction of the brightness of terrain reflections given a terrain surface and sun location

64
Q

computing algorithms for slope and aspect using TIN

A

the x, y, and z values of points that make up a TIN are used to compute slope and aspect for each triangle

65
Q

surface curvature

A

rate of change of slope. can determine if the surface at a cell location is upwardly convex or concave

66
Q

raster vs TIN for terrain analysis

A

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

college (10 decks)

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