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Chapter 10

Joins and Overlay

Mastering ArcGIS Pro�Second Edition

Maribeth H. Price

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Intro to GIS

Lesson 10: Joins

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What are joins and overlay?

Source: South Dakota Geological Survey

One primary use of G I S is to combine layers of information

Slope and rock competence maps could be combined to study landslide hazards.

Both join and overlay combine the data tables from two sources but differ in treating overlapping features

Joined features remain whole.
Overlay features are split to create new features, as in the lowest layer shown here.

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Reviewing a table join

Source: Esri

Table joins allow two tables to be used as a single table
Records are linked using a common field, or key, like STATE_FIPS
The target table receives the additional information
The join table provides the additional information

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Spatial joins

In a spatial join, the records are matched using a spatial relationship instead of a key field

The relationships are the same variations of the spatial operators (intersection, proximity, and containment) used for spatial queries.

The target feature class is copied to a new data set that contains the combined tables

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Spatial join using intersect

Source: City of Austin

Parcels is the target table, so a new parcels feature class is created
The zoning record that intersects each parcel is attached to the parcel in the output table
The new table contains the zoning information for every parcel

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Spatial join using proximity

Source: Esri

Cities is the target layer, so a new feature class of cities is created (a)

Each city in the output table contains information from the closest airport. In the map (b), cities with the same color are closest to the same airport.
A field is added to record the distance. The map (c) shows the distance to the closest airport for each city.

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Spatial join using containment

Source: City of Austin

Septics is the target, so a new septics feature class is created
Each output septic system is assigned the record of the geology unit that it falls within

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Comparison of operators

Source: City of Austin

In this example, the intersect and within operators give nearly the same result

They would only differ if a septic landed on a boundary

The within operator would yield a null output field.
The intersect operator would have two matches.

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Cardinality and spatial joins

Source: Esri

As for table joins, the cardinality between the feature tables is an important consideration when setting up the join
You should anticipate the expected cardinality prior to beginning the join
As for table joins, a one-to-many cardinality must be dealt with because the tables cannot be joined directly

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Managing cardinality

Spatial joins come in two types to offer options for handling a one-to-many cardinality

Create multiple copies of the target feature until there are enough for each matching record in the join table.
Summarize the multiple matching records in the join table using a merge rule, creating a single summarized record that can be joined to the target feature.

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Join types

The multiple copies option is called a one to many join type, because a target feature may have many output copies
The merge rule option is called a one to one join type because each target feature has one output copy
Do not confuse the join type with the cardinality between the tables. It is common to do a one to one join on tables with a one-to-many cardinality

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Cardinality of parcels and zones

Source: City of Austin

In the parcels and zones example, parcels usually lie within a single zone, so the expected cardinality is many to one

A parcel in two zones would be an exception or a logical consistency error

A one to one join works well and no merge rules are needed because only one zoning record is expected to match each parcel

If an exception or error yields two matches, the target parcel will have <Null> zoning fields, alerting you to the error.

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Cardinality of cities and airports

Source: Esri

In the cities and airports example, only one airport can be closest to a city
A one-to-one cardinality is expected
Identical distances would be a rare exception
A one to one join works well and no merge rules are needed because only one airport record is expected to match each city
If a distance exception yields two matches, the target city will have <Null> airport fields, alerting you to the exception

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Cardinality of septics and geology

Source: City of Austin

In the septics and geology example, a many-to-one cardinality is expected
A septic that lies exactly on a geology boundary would be a rare exception
A one to one join works well and no merge rules are needed because only one geology record is expected to match each septic
If a distance exception yields two matches, the target septic will have <Null> airport fields, alerting you to the exception

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Cardinality of highways and soils

Source: USGS, Esri

You wish to determine the soil characteristics for each highway to help plan maintenance schedules (some soils degrade roads more quickly)

Each highway is expected to cross multiple soil units, so a one-to-many cardinality is expected

A one to many join type would create multiple copies of each highway to match every soil unit it crosses, which is not very helpful

A one to one join type with merge rules works well for this problem

Each field from the soils table can be assigned a merge rule to summarize the information from the records that match each highway.

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Merge rules for a one to one join

Source: USGS, Esri

The Join_Count field indicates how many soil units matched each highway

Numeric fields can have statistical merge rules like Mean or Sum

The CLAY and SLOPE fields were assigned a Mean rule, so that each highway gets the average of the matching records.

Text or categorical fields cannot use statistical rules

The HYDGRP field defaulted to a First rule and received the value of the first match (usually not very helpful).
A new HYDJOIN field was created to store a list of the values from each soil unit crossed (the Concatenate rule).

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Classic overlay 1

Source: South Dakota Geological Survey

Overlay of slope and rock competence yields a feature class with fields from both, useful for assessing landslide hazards
The original slope and competence polygons are split and combined to create new ones
Each new polygon receives a unique combination of fields

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Classic overlay 2

Source: South Dakota Geological Survey

The pink polygon in the lowest map is a unique combination of two parent polygons from slopeclass and competence
Information from both parents is copied to the child polygon from the output table
By splitting features, overlay avoids the one-to-many relationships that plague spatial joins

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Types of overlay

Intersect only keeps the areas that overlap

Only the polygon regions within the circles remain in the output.

Union keeps all areas from both inputs, whether they overlap or not

All areas from both inputs are retained in the output.

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Union example 1

Source: USGS, Black Hills National Forest

In union, the usual goal is to combine all the data and the extent of the data sets is typically the same

The combined table can be queried (pink) for combinations of values that are susceptible to geologic hazards

Sink hazards in areas of limestone and low slope.
Rockfall hazards in areas of high slope and high competence.
Landslide hazards in areas of low competence and high slope.

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Union example 2

Source: USGS, South Dakota Geological Survey, Black Hills National Forest

In intersect, the usual goal is to find overlapping areas where certain conditions hold (suitability analysis)
Queries are performed first to find the suitable conditions (elevation range, limestone, and dense conifers)
The selected features are then intersected to find the areas of snail habitat

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Intersection and geometry

Source: USGS, City of Austin

Union can only be performed with two polygon inputs

Intersection can be applied to all geometry types

a) A line-in-polygon intersection can assign geologic unit information to streams

b) A point-in-polygon intersection can assign school district information to houses for sale

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Intersection output geometries

Different output geometries may be chosen depending on the input

The output geometry cannot be higher than the highest input geometry type

a) Intersecting two polygon layers can yield polygons, lines, or points

b) Intersecting two line layers can produce lines or points

c) Intersecting lines and polygons can yield lines or points

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Comparing extraction and overlay

Although the spatial output of intersect and clip look similar, they are not the same

In extraction functions

Only the outside polygon boundary is used.
Features may be split but are not combined.
The output attribute table has no new fields.

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Data quality in spatial analysis

Source: Esri

Data quality can significantly impact the results of a spatial analysis

Tract boundaries should coincide with county boundaries.
These two data sets are not logically consistent and do not match.
Intersecting them produces tiny polygons, or slivers, along the boundary.
Slivers are artifacts, not real combinations of values.

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Using an XY tolerance

Source: Esri

An X Y tolerance can be applied during overlay to reduce slivers
Vertices separated by less than the X Y tolerance will be collapsed into a single vertex
The X Y tolerance shown here will fix these slivers except for the largest pink one
However, all close vertices get collapsed, so using a tolerance will degrade the accuracy and precision of the data set overall

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Areas and lengths

Source: South Dakota Geological Survey

Source: Esri

Overlay often changes the shape, area, perimeter, and length of features
The geodatabase fields Shape_Length and Shape_Area are automatically updated
User-defined geometry fields, such as an ACRES field, must be updated manually using the Calculate Geometry Attributes tool

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Setting up a spatial join

Stepping through this series of questions can help you determine how to set up a join

What should the final output layer or table look like?
Which is the target layer?
Which spatial operator is appropriate?
Which fields do I want in the output table?
Could a target feature match multiple join features? If yes, how should it be handled?

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Setting up a join 1

Source: Esri

A congressional staffer must provide a list of representatives from districts with at least 10 deaths from earthquakes
He has a feature class of districts and a feature class of earthquakes with deaths and damage information

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Setting up a join 2

The desired output table is a list of districts with the representative names and the total number of deaths
Therefore, districts (cd114) is the target layer and earthquakes (quakehis) is the join layer
The records should be matched if the quake touches the district, so intersect is the appropriate spatial operator
The required output fields include the district ID, the party, the representative’s name, and the sum of the earthquake deaths
One district can match many quakes, but we want summed values rather than duplicate districts, so a one to one join with a sum merge rule on the deaths field is needed

Source: Esri

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The join result

Source: Esri

The output table shows the District I D, the state, the representative’s name, the party, and the sums of the deaths and damages
Districts with no earthquakes have null values for deaths and damages

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Accessibility Content: Text Alternatives for Images

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What are joins and overlay? - Text Alternative

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The three maps of South Dakota show different information. All three maps lay horizontally one after the other. The first map is labeled 'Slope class' consisting of blue gradients. The second map is labeled competence consisting of yellow gradients. The third map is an overlay of competence and 'Slope class' which appears to be a collage of the first two maps.

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Reviewing a table join - Text Alternative

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The first table titled US states is the target table which consists of four columns and seven rows. The data given in the table are as follows: Column 1, OBJ: 1, 2, 3, 4, 5, 6. Column 2, Shape: Polygon, Polygon, Polygon, Polygon, Polygon, and Polygon. Column 3, NAME: Hawaii, Washington, Montana, Maine, North Dakota, South Dakota. Column 4, STATE underscore FIPS: 15, 53, 30, 23, 38, and 46. The second table titled Pop data is the join table which consists of four columns and seven rows. The data given in the table are as follows: Column 1, STATE underscore FIPS: 13, 15, 16, 17, 18, and 19. Column 2, POP 2000: 8186453, 1211537. The other data in the column is not visible as a box reading ‘Join tables on common field’ is present. Column 3, POP2010: 10014045, 1309580, 1581697, 13089726, 6479832, 3057995. Column 4, W..: 532, 29, 117, 912, 532, 274. The right end of this column is not visible as the table has reached its end. Upon joining, the third table titled the 'US States' is created. The table consists of seven columns and seven rows. The data given in the table are as follows: Column 1, OBJ: 1, 2, 3, 4, 5, 6. Column 2, Shape: Polygon, Polygon, Polygon, Polygon, Polygon, and Polygon. Column 3, NAME: Hawaii, Washington, Montana, Maine, North Dakota, South Dakota. Column 4, STATE underscore FIPS: 15, 53, 30, 23, 38, and 46. Column 5, POP 2000: 1211537, 5894121, 902195, 1274923, 642200, 754844. Column 6, POP… (not visible,: 13… (not visible), 6756150, 983932, 1338645, 662194, 827263. The two invisible cells are hidden as a box reading ‘Joined table’ is present on them. Column 7, header is not visible: 294102, 4821823, 817229, 1236014, 593181, and 669404.

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Spatial joins - Text Alternative

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The first illustration of the target feature class shows a pink cylinder labeled cities and a pink blank sheet. The second illustration of the join feature class shows a green cylinder labeled airports and a green blank sheet. The third illustration of the output feature class shows a pink cylinder labeled cities2 and the empty pink and green sheets joined to form a single structure.

Intersection: intersect, proximity: within a distance geodesic and containment: contains. Intersection: Intersect 3 D, proximity: within a distance, and containment: completely contains. Intersection: Are identical to, Proximity: within a distance 3 D; and Containment: contains Clementini. Intersection: Boundary touches, proximity: closest geodesic; and containment: within. Intersection: Share a line segment with; proximity: closest; and containment: completely within. Intersection: crossed by the outline of having their center in; proximity: closest; and containment: within Clementini.

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Spatial join using intersect - Text Alternative

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The illustration shows an aerial view of what appears like a map. The map consists of several zones represented in different colors. The first table titled Parcels consists of five rows and five columns. The data given in the table are as follows: Column 1, BLOC: A, B, 8, C. Column 2, LOT: 2, 2, 22, 2. Column 3, ADDRESS: 614, 205, 402, and 205. Column 4, STREET underscore NAM: BISSONNET, CROSLIN, BLACKSON, and O DELL. Column 5, PARCEL underscore ID: 0234111302, 0232120702, 0231140418, and 0232120902. The second table titled Zoning consists of five rows and three columns. The data given in the table are as follows: Column 1, Zone: Residential L, Residential L, Residential H, Residential L. Column 2, Setback: 10, 10, 5, 10. Column 3, Tax Rate: 0.5, 0.5, 0.6, 0.5. The first and second tables are joined together to form the third table which has a single structure.

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Spatial join using proximity - Text Alternative

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Part a consists of three tables. The first table has two columns and six rows. The data given in the table are as follows: Column 1, Name: Adair Village, Adams, Adrian, Albany, and Aloha. Column 2, POP2007: 594, 309, 144, 44957, 48048. The second table has two columns and six rows. The data given in the table are as follows: Column 1, Name: Astoria Regional, Eastern Oregon Regional, Klamath Falls International, Mahlon Sweet Field, Mcnary field. Column 2, Type: Regional, Regional, International, Blank, Blank. The first and second tables join together to form the third table with five columns and six rows. The data given in the table are as follows: Column 1, Name: Adair Village, Adams, Adrian, Albany, and Aloha. Column 2, POP2007: 594, 309, 144, 44957, 48048. Column 3, airports underscore NAME: Mcnary Field, Eastern Oregon Regional, Eastern Oregon Regional, Mcnary Field, Portland Intl. Column 4, TYPE: Blank, Regional, Regional, Blank, International. Column 5, Distance: 31539, 23161, 258469, 31420, 24056. Part b shows a map of an area with seven plane symbols at various locations and tiny plots scattered all over the map. Part c shows the same map of Part b with seven plane symbols at various locations. The tiny plots are relatively larger in this map and are scattered all over it.

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Spatial join using containment - Text Alternative

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The gradient map consists of regions represented by different colors. The areas of light brown color are marked as ‘Qt.’ Three plots, 800, 836, and 839 are located on the map. The first table titled ‘Septics’ consists of four columns and four rows. The data given in the table are as follows: Column 1, ID with a star symbol: 800, 836, and 839. Column 2, cntycode: 453, 453, 453. Column 3, basin: 14, 14, 14. Column 4, gma: 8, 8, 8. The second table titled ‘Geology’ consists of two columns and four rows. The data given in the table are as follows: Column 1, LABEL: Kpg, Knb, Qt. Column 2, NAME: Pecan Gap Chalk, Navarro Group, Terrace deposits. The first and second tables join to form the third table consisting of a single structure. The data given in the table are as follows: Column 1, ID: 800, 836, and 839. Column 2, cntycode: 453, 453, 453. Column 3, basin: 14, 14, 14. Column 4, gma: 8, 8, 8. Column 5, LABEL: Qt, Knb, and Knb. Column 6: NAME: Terrace deposits, Navarro Group, Navarro Group.

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Comparison of operators - Text Alternative

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The gradient map consists of regions represented by different colors. The areas of light brown color are marked as ‘Qt.’ Three plots, 800, 836, and 839 are located on the map. The first table titled ‘Septics’ consists of four columns and four rows. The data given in the table are as follows: Column 1, ID with a star symbol: 800, 836, and 839. Column 2, cntycode: 453, 453, 453. Column 3, basin: 14, 14, 14. Column 4, gma: 8, 8, 8. The second table titled ‘Geology’ consists of two columns and four rows. The data given in the table are as follows: Column 1, LABEL: Kpg, Knb, Qt. Column 2, NAME: Pecan Gap Chalk, Navarro Group, Terrace deposits. The first and second tables join to form the third table consisting of a single structure. The data given in the table are as follows: Column 1, ID: 800, 836, and 839. Column 2, cntycode: 453, 453, 453. Column 3, basin: 14, 14, 14. Column 4, gma: 8, 8, 8. Column 5, LABEL: Qt, Knb, and Knb. Column 6: NAME: Terrace deposits, Navarro Group, Navarro Group.

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Cardinality and spatial joins - Text Alternative

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The target table is titled ‘States’ and consists of three columns and eight rows. The data given in the table are as follows: Column 1, Shape: Polygon, Polygon, Polygon, Polygon, Polygon, Polygon, and Polygon. Column 2, STATE underscore NAME: Hawaii, Washington, Montana, Maine, North Dakota, and South Dakota. Column 3, STATE underscore FIPS: 15, 53, 30, 23, 38, and 46. The join table is titled ‘Counties’ and consists of three columns and eight rows. The data given in the table are as follows: Column 1, Shape: Polygon, Polygon, Polygon, Polygon, Polygon, Polygon, and Polygon. Column 2, NAME: Lake of the Wood, Ferry, Stevens, Okanogan, Pend Oreille, Boundary, Lincoln. Column 3, STATE underscore NAME: Minnesota, Washington, Washington, Washington, Washington, Idaho, and Montana. Column 4, FIPS: 27077, 53019, 53065, 53047, 53051, 16021, 30053. The rows 3 to 6 from column 1 to column 3 of the join table are outlined and directed towards the second row of the target table.

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Managing cardinality - Text Alternative

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The first illustration represents Input features. Three round structures representing Join features lead to a single round structure representing the target feature. The second illustration represents Multiple copies join type. Three round structures representing Join features lead to three round structures representing target features individually. The third illustration represents the Merge rule join type. Three round structures representing Join features merge to form a single round structure representing a join feature. The single join feature leads to a single round structure representing the target feature.

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Join types - Text Alternative

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The first illustration represents Input features. Three round structures representing Join features lead to a single round structure representing the target feature. The second illustration represents Multiple copies join type. Three round structures representing Join features lead to three round structures representing target features individually. The third illustration represents the Merge rule join type. Three round structures representing Join features merge to form a single round structure representing a join feature. The single join feature leads to a single round structure representing the target feature.

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Cardinality of parcels and zones - Text Alternative

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One end of the illustration is labeled Parcels while the other end is labeled Zoning. The illustration shows an aerial view of what appears like a map. The map consists of several zones represented in different colors such as pink, orange, yellow, and blue.

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Cardinality of cities and airports - Text Alternative

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The illustration shows a map of an area with seven plane symbols present at various locations. Tiny plots are scattered all over the area on the map. The plane symbols represent airports while the plots represent cities.

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Cardinality of septics and geology - Text Alternative

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The gradient map consists of regions represented by different colors. The areas of light brown color are marked as ‘Qt.’ The area in a relatively darker brown is marked as ‘Knb.’ Three plots, 800, 836, and 839 are located on the map.

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Cardinality of highways and soils - Text Alternative

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The gradient map consists of regions represented by different colors. Several pathways are located on the map along with plots such as 730, 84, 207, 74, 206, and 207.

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Merge rules for a one to one join - Text Alternative

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The gradient map consists of regions represented by different colors. Several pathways are located on the map along with plots such as 730, 84, 207, 74, 206, and 207. The table consists of eight columns and six rows. The data given in the table are as follows: Column 1, Join underscore Count: 3, 3, 3, 3, 3, 2. Column 2, TARGET underscore FID: 638, 665, 676, 681, and 640. Column 3, HWY: 206, 74, 207, 74, 206. Column 4, MUID: OR148, OR153, OR153, OR160, OR160. Column 5, CLAY: 23.3; 9.6; 9.6; 15.8; 17.9. Column 6, SLOPE: 23.3; 9.1; 9.1; 5.8; 8.2. Column 7, HYDGRP: D, B, B, B, B. Column 8, HYDJOIN: D, C, C; B, B, B; B, B, B; B, B, C; B, C. Column 8 is outlined.

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Classic overlay 1 - Text Alternative

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The three maps of South Dakota show different information. All three maps lay horizontally one after the other. The first map is labeled 'Slope class' consisting of blue gradients. The second map is labeled competence consisting of yellow gradients. The third map is an overlay of competence and 'Slope class' which appears to be a collage of the first two maps. The menu box titled ‘geohazard – White River Gr…’ shows the following data: OBJECTID: 2788, FID underscore SD geology: 104, Symbol: Tw, Infiltration: Low, Competence: Low, Unit Name: White River Group, Rock Type: Siltstone, FID underscore Slope class: 1937, and Slope class: 1.

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Classic overlay 2 - Text Alternative

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The three maps of South Dakota show different information. All three maps lay horizontally one after the other. The first map is labeled 'Slope class' consisting of blue gradients. The second map is labeled competence consisting of yellow gradients. The third map is an overlay of competence and 'Slope class' which appears to be a collage of the first two maps. The menu box titled ‘geohazard – White River Gr…’ shows the following data: OBJECTID: 2788, FID underscore SD geology: 104, Symbol: Tw, Infiltration: Low, Competence: Low, Unit Name: White River Group, Rock Type: Siltstone, FID underscore Slope class: 1937, and Slope class: 1.

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Types of overlay - Text Alternative

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Attributes joined intersect, input 1 a rectangle with two diagonal lines, input 2 a concentric circle, and output a concentric circle with two diagonal lines. Attributes joined the union, input 1 a rectangle with two diagonal lines, input 2 a concentric circle, and output a concentric circle inside the rectangle with two diagonal lines.

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Union example 1 - Text Alternative

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The menu box titled ‘geohazard – White River Gr…’ shows the following data: OBJECTID: 2788, FID underscore SD geology: 104, Symbol: Tw, Infiltration: Low, Competence: Low, Unit Name: White River Group, Rock Type: Siltstone, FID underscore Slope class: 1937, and Slope class: 1. The second illustration shows the map of the Black Hills National Forest. The background of the area is orange while the surface consists of several streaks of red. A few locations are marked in tiny pink polygons. The third illustration shows a map of the Black Hills National Forest. The areas are represented in three different colors, yellow, orange, and red. The map legend states the following: Yellow: Sink, Orange: Rockfall, Red: Landslide.

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Union example 2 - Text Alternative

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The first map shows areas represented in brown, yellow, and green. The map is labeled Elevation range. The second map shows areas represented in violet on a white background. The map is labeled Limestone. The third map shows several minute areas represented in green on a white background. The map is labeled Dense conifer. The fourth map shows areas represented in light yellow and violet with several minute areas in dark pink. All three areas in different colors are located on a white background. The map is labeled Snail habitat. After the third map is a right arrow that points towards the fourth map. The fourth map appears to be a collage or overlay of firth three maps.

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Intersection and geometry - Text Alternative

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The first map consists of several regions represented in gradients of blue and green. On the surface of the areas, several streaks in dark blue are located. The second map consists of several regions represented in solid colors. Several tiny, rhombus-shaped plots are present in various locations of the region.

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Intersection output geometries - Text Alternative

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Part a shows two shaded circles intersecting one another. This produces the shaded-intersecting part devoid of the circles, non-shaded intersecting part, and the two end plots of the intersecting part. Part b shows two streaks of blue and orange intersecting at one segment. This produces the streak of the intersecting segment and the plots of the streak on either end. Part c shows a shaded circle within several black streaks. This produces plots around the circumference of the circle and one section of the streaks.

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Comparing extraction and overlay - Text Alternative

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Attributes joined intersect, input 1 a rectangle with two diagonal lines, input 2 a concentric circle, and output a concentric circle with two diagonal lines. Attributes joined the union, input 1 a rectangle with two diagonal lines, input 2 a concentric circle, and output a concentric circle inside the rectangle with two diagonal lines. Attributes not joined, clip input 1 a rectangle with two diagonal lines, input 2 a concentric circle, and output a circle with two diagonal lines. Attributes not joined, erase input 1 a rectangle with two diagonal lines, input 2 a concentric circle, and output a circle inside the rectangle with four lines on the corners to the circle.

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Data quality in spatial analysis - Text Alternative

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The illustration of the map shows an unnamed location. The map consists of several areas represented in different colors. A pink region towards the left is labeled as Tract. A red pathway on the tract region is labeled as slivers. The tract region is separated from other areas by a county boundary. One of the areas consists of a circular mark which is labeled s XY tolerance.

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Using an XY tolerance - Text Alternative

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The illustration of the map shows an unnamed location. The map consists of several areas represented in different colors. A pink region towards the left is labeled as Tract. A red pathway on the tract region is labeled as slivers. The tract region is separated from other areas by a county boundary. One of the areas consists of a circular mark which is labeled s XY tolerance.

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Areas and lengths - Text Alternative

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The three maps of South Dakota show different information. All three maps lay horizontally one after the other. The first map is labeled 'Slope class' consisting of blue gradients. The second map is labeled competence consisting of yellow gradients. The third map is an overlay of competence and 'Slope class' which appears to be a collage of the first two maps. The dialogue box is titled ‘Calculate Geometry Attributes.’ Under the title, two major options Parameters and Environments are given. The option ‘Parameters’ is selected. Under ‘Parameters,’ options such as Input features: streets, Geometry Property, Length Unit: Miles (United States), and Coordinate system: NAD underscore 1983 underscore State Plane underscore Texas underscore Central underscore along with the globe icon are given. Under the option ‘Geometry Property,’ two options such as ‘Target field’ with a dropdown icon and ‘Property’ are given in the same row. Below the Target field, two drop-boxes, MILES and blank are given. Below Property, two drop-boxes, Length, and blank are given.

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Setting up a join 1 - Text Alternative

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Cd114. District 0101, state AL, name Bradley Byne, and Party republican. District 0102, state AL, name Martha Roby, and Party republican. District 0103, state AL, the name Mike Rogers and Party republican. Quakehis. State MO, deaths 7, damage 0, MAG 7.88, MMI, 12, and LOC – incomplete. State, SN, deaths 51, damage 0, MAG 7.36, MMI 12, and LOC – incomplete. State, AK, deaths 0, damage 0, MAG 8.15, MMI 11, and LOC incomplete.

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Setting up a join 2 - Text Alternative

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Parameters: Target features Cd114. Join features, quakehis. Output feature class, quake impacts. Join Operation, join one-to-one. Checkbox labeled keep all target features checked. Field map of join features. Output fields. Last name, party, semi, shape length, shape area, state, depth, deaths, damage, MAG, MMI, match option intersect, search radius, and meters. Source properties, merge rule sum, quakehis deaths, add a new source.

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The join result - Text Alternative

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The table consists of eight columns and nine rows. The data given in the table are as follows: Column 1, Join underscore Count: 0, 0, 0, 1, 4, 1, and 29. Column 2, TARGET underscore FID: 1, 2, 3, 4, 5, 6, 7, 8. Column 3, DISTRICT: 0101, 0102, 0103, 0104, 0105, 0106, 0107, and 0200. Column 4, STATE: AL, AL, AL, AL, AL, AL, AL, AK. Column 5, NAME: Bradly Byrne, Martha Roby, Mike Rogers, Robert B. Aderholt, Mo Brooks, Gary J. Palmer, Terri A. Sewell, Don Young. Column 6, Party: Republican, Republican, Republican, Republican, Republican, Republican, Democrat, Republican. Column 7, DEATHS: <NULL>, <NULL>, <NULL>, 0, 0, 0, 0, 125. Column 8, DAMAGE: <NULL>, <NULL>, <NULL>, 0, 0, 0, 0, 31100000.

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