Study Guide for Quiz 3 (Lectures 11-14) PDF

Study Guides for Quiz 3 (Lectures 11-14) NOTE: Some knowledge points require the previous pages as important contexts to understand them. Please do NOT only read the marked pages. Lecture 11: What is geodatabase and its common two types? (p11) - A collection of geographic datasets of v...

Study Guides for Quiz 3 (Lectures 11-14) NOTE: Some knowledge points require the previous pages as important contexts to understand them. Please do NOT only read the marked pages. Lecture 11: What is geodatabase and its common two types? (p11) - A collection of geographic datasets of various types held in a common file system folder - Personal geodatabase and enterprise geodatabase What are relationships? Examples (p13-15) - Between spatial objects (vectos/rasters), nonspatial objects (rows in a table), or spatial and nonspatial objects - Simple = between nonspatials only or between spatials only - Complex = between nonspatial and spatial What are the four cardinality types and their examples? (p25-26) - One to one = one feature attribute record to one non spatial record - Ex: county boundary and county name - Many to one = many feature attribute record to one non spatial record - Ex: islands of hawaii and state name - One to many = one feature attribute record to many non spatial records - Ex: county boundary and census data - Many to many = many feature attribute records to many non spatial records - Ex: islands of hawaii and census data Lecture 12 What is target and source table (p4)  - Target table = receives the additional information - source /join table = provides the additional info What is the rule of joining? (p5) - Each record in target table must match one and only one record in the join table - Goal is to have an exact solution/output after joining tables How to differentiate one-to-many and many-to-one joins? Do they satisfy the rule of join? (p5-6) - One to many does not satisfy the rule to join - Target table has larger spatial units than the join table - Many to one does satisfy the join rule - Target table has smaller spatial units than the join table What are join and relate? (p8) - Join = when data in the tables has one to one or many to one (join table is one) - Relate = when data in the tables has one to many or many to many relationship (join table has many - doesn’t satisfy rule of join) What are foreign key and primary key? (p9) - Foreign key = an item in a target table that may be used to unambiguously link to rows in another table - Primary key = used in join table to link to a foreign key of another target table Lecture 14 What are the common types of raster data and their examples? Can discrete raster data have attribute table? (p9-10) - Continuous raster data (elevation, temperature, etc) - Discrete or categorical data (can have attribute table) (zoning, land use, soil types) What is spatial resolution, cell size, and scale? What are their relationships (p12- 13) - Spatial resolution = level of detail represented by a raster (dependent on cell size) - Spatial resolution is the opposite of scale - The higher the resolution, the smaller the size, greater detail What is resampling? Why do we need to resample? What are the common resample techniques? (p14-16) - Resampling = changes the cell size but the extent of the raster dataset will remain the same - Required wgen projecting rasters or when analyzing 2 rasters with different resolutions - Coarser resolution = degrades precision and accuracy - Finer resolution = does NOT improve accuracy bc you are simply using more cells to store the same info - nearest : fastest; used for discrete data; doesn’t change the values of the cells - majority : new value - the most popular value within the filter window; discrete data; smoother than nearest - bilinear : new value - weighted distance avg of the 4 nearest input cell centers; continuous data - cubic : slowest; new value of a cell based on fitting a smooth curve through the 16 nearest input cell centers; continuous data What is map algebra? What are some common operations of map algebra? (p18- 19) - Map algebra = combination of raster data layers - Mathematical functions (+,-,/, etc) - Reclassification (assgns output values dependent on specific sets of input values) - Conditional statements (CON, ISNULL) What are different types of operation in raster analysis? Examples (p23-24) - Local = used only the data in a single cell to calculate output (reclassification) - Neighborhood (zonal, focal) = use data from a set of cells (focal, zonal) - Global = use all data from the raster data layer (distance analysis and cost surface analysis) What is reclassification? What are the procedures and common approaches of reclassification (p26-28) - Often applied to reduce the number of classes displayed in a land use raster - By unique value or categories How to operate a conditional function? Example (p33-34) - To select info from input layer based on a condition, results in true or false outcome - Output = CON (test, out if true, out if false) How to operate Boolean overlay? Example (p37) What are the drawbacks of Boolean overlay (p39) - Determine suitable areas for lodgepole pine - Areas where precipitation is greater than 60 cm - Areas where elevation is above 1500m  - AND is applied where both conditions are met - Drawback = conditions may be more gradational rather than a true/false condition, assumes all input layers are of equal importance How to operate weighted overlay? Example (p40-43) - Ranks conditions for a more realistic approach - Weighted overlay can be illustrated by the problem of siting a landfill based on these conditions - Lower slopes are better, to minimize site bulldozing - Low soil infiltration is important to prevent leajage of contaminants into groundwater - Closer is exisiting roads is better, to minimize the cost of a building an access road - Further from streams is beter to minimize contamination of steams by runoff from the landfill - We have k = 4 conditions and will rank them on a scale of 1 to 3 (N=3) How to operate masking and use it to clip? (p44-45) Masking: Uses a binary raster or vector layer to define areas of interest. Cells inside the mask are retained; others are set to "NoData." Clipping: Extracts a specific area from a raster using the shape of a mask (e.g., a polygon boundary). How to Use: 1. Select a mask layer (e.g., a vector boundary like a city outline). 2. Apply the mask to your raster using a GIS tool. 3. The result keeps only the raster data within the mask's boundaries.