Download CHAPTER6_SpatialJoins

Chapter 6
Spatial Joins
Outline
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Spatial joins
Cardinality
Types of spatial joins
Feature geometry and distance joins
Coordinate systems and distance joins
Spatial Join
• Function that combines the attributes of features
in two layers based on distance (one feature
closest to another) or containment (one feature
inside another).
Destination feature class
cities
Output
feature
class
Source feature class
airports
cities2
Cardinality
• Relationship between records in two
tables :
– Simple joins
• One-to-one or many-to-one cardinality
– Summarized joins
• One-to-many or many-to-many
A distance spatial join appends
records based on which source
feature (airport) is closest to the
destination feature (city.)
Destination feature class
Unique values map by airport
Source feature class
Graduated symbol map by distance
What is the cardinality?
An inside join appends the record
of the source feature (geology) to
the destination feature (well) that
falls inside.
Destination feature class
Source feature class
What is the cardinality?
What if you switch the destination to airports
and the source to cities? Each airport is serving
many cities and the cardinality is now one to
many. The Rule of Joining is violated and no join
is possible.
If you could instead use Summarize to
group the cities according to the airport
they served, and summed the population,
you would get a table like this. Now every
airport has one record.
You can do this with a summarized join.
Simple Spatial Join
•One-to-one or
many-to-one.
•No ambiguity in
assigning fields.
Summarized join
Destination table is
counties.
Source table is
schools.
User can optionally
choose a statistic to
calculate, for
example, to sum the
total number of
students in each
county.
Output table gives
total schools located
in each county.
Count field is always
generated
automatically.
Types of spatial joins
Simple
Summarized
Inside
Schools  Counties
Counties  Schools
Which county is each school in?
How many schools in each of the
counties?
Hotels  Attractions
Hotels  Attractions
Distance
Which attraction is closest to each
hotel? How far is it?
How many attractions are closest
to each hotel?
Feature geometry and distance joins
Distance: Points to Polygons
Join each county to the
hospital that is nearest it.
Each county features gets
name of closest hospital and
the distance.
Note on polygon distances
In measuring distances for polygons, the
centroid of the polygon is used. For
each of the counties, the centroid of the
county is closest to the hospital.
If the county contains a hospital, the
distance is zero.
Pennington County has
three hospitals all with a
distance of zero, so one is
randomly chosen to
match.
Distance: Points to Lines
Evaluate impact of septic systems on
various streams based on distance.
Streams is the destination. Each point
represents one or more septic
systems. What is the number of septic
points closest to each stream segment
and then summarize the totals.
What assumption is made here? How
valid is it?
Several possible combinations
Geometry
Type
Points to
Points
Lines to Points
Polygons to
Points
Join Type
Example
Simple distance
Find the hospital closest to each town.
Summarized
distance
Find all the towns closer to one hospital
than to any other hospital.
Simple distance
Find the water main closest to the
proposed building site.
Summarized
inside
Find the total voltage of all electric lines
meeting at a substation.
Simple inside
Find the soil type that underlies each gas
station.
Simple distance
Find the lake that is closest to each
campground.
Points to Points
Destination: hospitals
• Simple distance
– Find the source
feature that is the
closest to the
destination feature.
– Find the hospital
closest to each town.
• Summarized distance
– Summarize the
attributes of all the
source features that
are closer to the
destination feature
than to any other.
– Find all the towns
closer to one hospital
than to any other
hospital.
Polygons to Points
Destination: hospitals
• Simple distance
– Find the county that is
closest to each
hospital and give the
hospital the county
attributes.
• Simple inside
– Find the county that
each hospital is inside
and give the hospital
that county’s
attributes.
Polygons to Lines
• Simple distance
– Find the park that is
closest to each road
and give the road the
park attributes.
• Summarized inside
– Give the interstate the
total population of all
the counties that it
crosses.
Beware and think…
Some choices don’t make sense for
particular layers.
Finding the closest county
to each river has no
meaning here.
Finding the county a river
is inside does have
meaning…BUT
…some rivers cross county
lines.
Destination layer: Rivers
These issues to be
addressed in the next
chapter.
Polygons to Polygons
• Simple inside
– Give each urban area
the attributes of the
county that it falls
inside.
• Summarized inside
– Give each county the
summarized attributes
of the urban areas that
fall inside it.
In this case you need to switch the
destination layer for the join to
make sense.
Polygons to Polygons
• Simple inside
– Find the park that
each lake is inside and
give the lake the
attributes of the park.
• Summarized inside
– Give the park the total
area of all the lakes
that fall inside it.
In this case you need to switch the
destination layer for the join to
make sense.
Also notice that some lakes do not
fall cleanly inside one park or
another. Not all joins are capable of
giving valid results.
Coordinate systems and distance joins
Source coordinate system
Look again at the join of
hospitals to counties.
What happens if the
source layers are in a GCS?
Distance join units
The source data
were in a GCS
(Geographical
Coordinate System)
with units of
decimal degrees.
• Distances are given in stored units.
• Decimal degrees cannot be easily converted to
miles or km because the conversion factor
varies with latitude.
• Better to use a projected coordinate system.
Distance joins and the CS
A
A
B
C
Distance join with GCS source
B
C
Distance join with UTM source
Use source data with a projection that conserves distance!
• A GCS or distorted projection may yield incorrect
results.
Beware
The data frame coordinate
system may be different
from the source data
coordinate system.
SD State Plane
Projected on the fly
GCS
Setting the data frame CS is
not enough to fix the
problem.
You must project the source
data using the Project tool
(Chapter 11) and do the join
again.
References
• Price, M. (2013). Mastering ArcGIS (6th
ed.). McGraw-Hill
• Price, M. (2013). Mastering ArcGIS (6th
ed.). McGraw-Hill. Mastering ArcGIS, 6/e
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