Second Test Flashcards
storing vector features
a multipart feature may contain several separate pieces
spaghetti models
simple vector data formats; each state is stored as individual polygons; adjacent boundaries are stored twice; it is a robust model because of its simplicity
topological models
test whether features are adjacent, connect, overlap, or intersect; can find data errors such as overlapping states, or a county boundary that falls outside the state; states information on how features
types of data used in ArcGis
shapefiles, coverages, geodatabases
shapefiles
vector feature classes developed for early version of Arcview to ArcGis; stored in a spaghetti data format that has a simple structure; care must be taken when copying or renaming shape files outside of ArcGis
coverages
vector data format developed for ArcInfo and is oldest of data formats. not read by ArcGis Pro; must be converted to a shape file or geodatabase in ArcMap; composed of multiple feature classes
geodatabases
recommend model for stating spatial information for ArcGis;
4 types of geodatabases
personal, file, mobile, and enterprise
personal geodatabase
original format but not recognized by ArcGis Pro stored in a Microsoft access database format; for single users or small workgroups; 2GB limitation on feature classes
file geodatabase
stored as files in a geodatabase folder; for single users or small workgroups; platform independent
mobile geodatabase
newest format available with latest version of ArcGis Pro; use open source database for storage and is optimized for mobile devices
enterprise geodatabases
stored in a commercial relational database management system (DBMS) such as oracle or SQL server; designed for multiple users and large workgroups; supports multiuser editing, versioned editing
queries
used to extract data using an expression based on an attribute field
clip
extracts features within a bounding polygon from another feature class
erase
extracts the features outside of a bounding polygon from another feature class
merging
combines all feature from two or more data sets into a single new feature class; used to combine adjacent feature classes; works when data in attribute table identical; if they don’t match merge rules must be set up to combine the table data in a logical and ideal way
pixels or cells
each pixel contain one numeric value; dimensions of a pixel is the resolution and they are in the units of the stored coordinate; value represents some property of the pixel area
raster resolution
x and y dimensions of each pixel define the resolution of a raster; precision of a raster is limited by the resolution storage requirements increase by the square of resolution
storing raster values
one binary digit is called a bit which corresponds to a single 0 or 1; bits are grouped into sets of 8 called a byte can store a number from 0 to 255
pixel depth
number of bits or bytes used for each pixel
value raster
DEM or land use raster stores a value representing an object or quantity, like elevation or rainfall; used for analysis
picture raster
stores arbitrary color values that have no direct relation to quantity or attribute; used as background pictures or for land use analysis
continuous rasters
store numeric values that can be measured anywhere such as elevation, temp, or precipitation; imagery type of continuous raster that stores brightness
single band picture rasters
binary rasters results in black and white image (black = 0 white =1) grayscale raster creates a grayscale image ranging from black(0) through intermediate gray tones to white (255); colormap may be saved with a single band raster and serves as a look up table to find corresponding RGB color values
multi band raster
RGB values are stored in 3 serpent raster layers and then displayed together as a composited band; 1=red; band 2 = green; band 3 = blue; satellite images this is often reverses
vector data
each individual point has an x-y coordinate value which may be used in the trigonometric equation to convert from one projected coordinate system to another, it is very precise
raster data
is projected new shape results that requires new cells be constructed, often with a change in size as a result, new numerical values have to be stored in each cell according to on elf several rules
projecting rasters
can be converted form cone coordinate system to another by projecting cell centers are converted to the new system, does not preserve original rectilinear spacing of cell grid, so a new cell size must be specified resampling must occur
resampling
during recifitifcation a new cell size is specified for the output grid; cell centers change location and cells may have gaps or overlaps; new cell centers rarely align with old cell centers, and must be resampled to fit the new grid
resampling methods
nearest neighbor resampling; bilinear resampling and cubic convolution
nearest neighbor resampling
grabs the value form old cell that falls at the center of the new cell; preserves the orginianl value and should always be used with categorical data (discrete raster) or when original data values need to be preserved fastest method
bilinear resampling
calculates a new value from the four cells that fall closest to the center of new cell. it uses a distance weighted algorithm based on old cell centers. best used with continuous data such as elevation
cubic convolution resampling
calculates a new value form 16 cells that fall closest to the center of new cells. uses a distance weighted lagrotihm based on the old cell centers. best used with continuous data such as elevation. most time consuming method
digital elevation model (common continuous raster type) DEM
has cells or pixels each of which contains a single elevation. regular spaced array of elevation values
indexed color raster (contains color map)
scanned versions of standard USGS topographic maps; stores a single band of integers that represent specific RGB combinatinons found don USGS maps
raster pyramids
helpful when individual raster cells are too small to display when full image is shown; used to speed display of rasters; create successive lower resolution copies; built once used many times; increases size of file by about 50%
slicing
divide range of values into 356 classes, or bins to create histogram
stretching image improves appearance after..
after slicing, stretching enhances display by removing the less common values at the tails (extreme) of histogram
