Spatial Analysis –
vector data analysis
Topic 2
Starting 2/1/2007
Recap: three data models



Vector
Raster
Geodatabase (object-oriented data model)
• Vector data and table
• feature class is the basis
• Raster data
The real world is complex. Either discrete (object) or continuous
(field) can not efficiently represent some of real world situations. We
need combined model: dual, hybrid, or object-oriented approach
Spatial Analysis tools in
ArcToolBox
Shapefile &
Feature
class/table
Coverage
Raster
Details
Shapefile and feature
class/table
Coverage
Raster
Spatial Analyst extension
1. Extract

To create a new subset from the input
(shapefile, features and attributes in a
feature class or table) based on spatial
intersection or an attribute query.
• Clip
• Select
• Split
• Table select only
Clip

ff
Select
Split
2. Overlay

Joining two existing sets of features into
a single set of features to identify spatial
relationships between the input features.
• Erase
• Identify
• Intersect
• Symmetrical difference
• Union
• Updata
3. Proximity

Identify features that are closest to one
another, calculate the distances around
them, and calculate distances between
them.
• Buffer
• Multiple ring buffer
• Near
• Point distance
Not dissolved
Dissolved
Example
Some specials for coverage
How to form Thiessen polygons


Also known as 'Voronoi networks' and
'Delaunay triangulations', Thiessen
polygons were independently discovered in
several fields of study, including climatology
and geography. They are named after a
climatologist who used them to perform a
transformation from point climate stations to
watersheds.
Thiessen polygons can be used to describe
the area of influence of a point in a set of
points. If you take a set of points and
connect each point to its nearest neighbor,
you have what's called a triangulated
irregular network (TIN). If you bisect each
connecting line segment perpendicularly
and create closed polygons with the
perpendicular bisectors, the result will be a
set of Thiessen polygons. The area
contained in each polygon is closer to the
point on which the polygon is based than to
any other point in the dataset.