3D GIS Lecture 3 (Geoproccesing Tools)

Clip

Output contains areas within both datasets, and only attributes from the clipped layer

act like a cookie cutter - extract features from a larger dataset using a polygon boundary (ex: can trim contour lines for just one neighbourhood)

Dissolve

new boundaries and aggregated data’

ex: all US states then dissolved into regional areas (pacific, west south central etc..)

Append and Merge

new feature classes from separate feature classes of the same type

Append

adding features from multiple input data sets into one existing dataset

The input feature and the target feature must have a matching schema (be the same type - point, line, or polygon)

it modifies the target dataset directly, no new file is created

ex: append the wilkinsburg burrough to the city of pitsburgh streets

Merge

Takes two or more features of the same type (e.g., points, lines, or polygons) and combines them into a new feature class that combines the inputs.

Intersect and Union

new feature classes from separate feature classes of different types

Intersect

outputs contains areas within both datasets, and all attributes

inputs can have different geometry types

Union

output contains all polygons from input datasets, and all attributes

contains all the polygons from the inputs, whether or not they overlap

Erase

delete areas that correspond with another layer

Proximity Analysis

point, line, and polygon proximity buffers

often used to delineate zone around features or to show areas of influence

sometimes used to clip features

can be used to select features in another feature class

ex: buffer around lights for a campus safety study

Multivariate cluster analysis

data clustering: finds clusters of data points that are close to each other but distant from points of other clusters.

Exploratory method, no right or wrong clusters

Ex) housing units and young renters '

partitions a dataset with n observations and p variables into K clusters

n = 402 number of observations (census tract polygons)

p = 3 variables for clustering (vacant housing units, renter occupied housing units, population ages 25-29)

k = 3 clusters

Zonal statistics and zonal histrogram

Zonal histogram tool calculates the frequency distribution of values within those zones

zonal statistics tool calculates summary statistics (like mean, median, max etc.) for the values within each zone