skill development

Skill Development Lab 3 – Mapping the use of SNAP benefits in Washington State

In Module 2, you learned how to make choropleth, dot density, and proportional symbol maps.  In this module, we will focus our attention on how we can refine and modify our data to be able to make clearer, and thus stronger, arguments with our maps.  You already know how to make a choropleth map, so what you will learn in this lab is how to classify data in a few different ways and how to normalize data in QGIS.  Since you will be making maps, you’ll also have the opportunity to continue practicing getting the visual hierarchy of your map to align with the intellectual hierarchy.

As we develop the skills you learned about in Module 3 (classification and normalization), we will also be considering a real-world geographic question:

What is the distribution of SNAP benefit use in Washington State?

In particular, we will consider, both what counties require the most resources and where families are using SNAP benefits in relation to the total populations.  As we do, you are asked to incorporate what you are learning about using maps to make arguments into the maps you make as part of this lab.

This lab should take approximately 2 hours to complete. This lab is worth 60 points.

Evidence of skill development will be assessed on:

Submit two choropleth maps made using QGIS.  (Required – 60 points)

Contribute to the lab discussion.

OBJECTIVES

Strengthen your choropleth mapping skills

Effectively classify data using multiple classification strategies

Appropriately normalize data to meaningfully compare data across areal units of various size

Visualize how the same data can be used to display different geographic patterns

Match the visual hierarchies on the map with the intellectual hierarchies of the map’s argument

Skill Development Lab 3 Rubric

or explanation.

GEOG 360
Lab 3: Mapping the use of SNAP
benefits in Washington State
GIS and Mapping
TA: Ben Trumble
Question of the Day:
Office Hours:
Thursday,
3:30-5:30pm, zoom
Tuesday // Thursday
Section AA : 1:30-2:20
Section AB : 2:30-3:30
What is your favorite candy?
Lab 3 Objectives
The geographic question to be explored in lab:
○ What is the distribution of SNAP
benefit use in Washington State?
●
●
●
●
●
Strengthen your choropleth mapping skills
Effectively classify data using multiple classification strategies
Appropriately normalize data to meaningfully compare data
across areal units of various size
Visualize how the same data can be used to display different
geographic patterns
Match the visual hierarchies on the map with the intellectual
hierarchies of the map’s argument
Chowder is watching from his bowl
A bit about SNAP
An economic investment meant to subsidize US farmers. It is
administered by the USDA (United States Department of
Agriculture). AND
An important federal program that helps families access food
●
●
●
●
Often referred colloquially as “food stamps” the Food
Stamp Program was changed to the Supplemental Food
Nutrition Program (SNAP) in 2008.
With origins in the New Deal post-great depression Food
Stamp Program was established formally by Johnson in 1964.
And severely cut by Reagan in the 1980’s.
Families who qualify (based on income, work, resources,
immigration status, etc.) receive monthly amounts placed on an
EBT (electronic benefits transfer) card
Racialized and gendered! This cannot be thought of outside of a
racial capitalist system that marginalizes BIPOC communities.
Reading Rec: The politics of disgust: the public identity
of the welfare queen by Ange-Marie Hancock
Data Classification
Recap
●
Level of Measurement
●
●
●
●
●
●
Nominal data is name
Ordinal data is categorical
Interval data is relative difference or the
interval between data points
Ratio data is the absolute difference
Discrete data is called categorical data
Continuous data is best represented by
ratio and interval values.
Measurement systems—values and what they
represent—ArcGIS Pro | Documentation
Penn State Resource
●
●
●
Quantiles: Each class contains an equal number of
features.
Equal Interval: Divide the range equally. If you specify
three classes for a field whose values range from 0 to
300, three classes with ranges of 0–100, 101–200, and
201–300 are created.
Jenks Natural Breaks: Based on natural groupings
inherent in the data, clustering method designed to
determine the best arrangement of values into
different classes.
Manual: Define your own classes, to manually add
class breaks and to set class ranges that are
appropriate for the data
Data classification methods—ArcGIS Pro | Documentation
Classification – QGIS Lesson
Overview
Lab 3 is worth 60 points.
Two input shapefiles:
●
●
Census Tracts with SNAP attributes in WA
Counties with SNAP attributes in WA
Three key components in practice part:
●
●
●
Examining your data
Normalize your data
Classify your data
Yucca looking regal
A bit more about census data
Census data can be overwhelming because it feels like a
new language- but there is a cheat code!
STATEFP
State FIPS (federal information
processing standards) code
COUNTYFP
TRACTCE
GEOID (or GeoID_1)
NAMELSAD
ALAND
AWATER
Households
HH_SNAP
County FIPS code
Census tract FIPS code
Geographic identifier
Census Tract Name
Land area
Water area
Number of households
Number of households using SNAP
benefits
Number of households under the
poverty line
Number of households with children
Number of households with at least
one individual with a disability
HH_poverty
HH_child
HH_disab
County
Population
County Name
Total population
1. Examine Your Data
Examining the data you have by loading it and open its attribute table.
Most important fields
●
●
●
Households: how many
households are there?
HH_SNAP: how many
households are using
SNAP benefits?
HH_poverty: how many
households are in poverty
based upon census
definition?
A bit more about census
poverty threshold and SNAP
Reflect: Are these amounts livable in
the place you grew up in or live now?
The assumption is those making above these levels are
fine and do not qualify for assistance. I could not live in
Seattle making only 23k a year. ASE’s (academic student
employees) make $2,600-$3,000 /month (gross), half +
of that easily goes to rent. “Rent burdened” is someone
spending more than 30% of income on housing.
WA Snap Income Thresholds
Table downloaded from Census Website
DSHS
2. Normalize Data
What’s normalization and standardization?
Statistics and Machine Learning:
● Normalization: rescaling the values into a range of [0,1].
● Standardization：rescaling the values in such a way that their mean is 0
and standard deviation becomes 1. Also called Min-Max scaling
Learn more at: Normalization vs Standardization
GIS – Geospatial Analysis/Visualization:
● Scale Standardization: Transforming values on different absolute scales into a common
measure based on the statistical distributions. The most common is the z-score, which
measures the number of standard deviations the value deviates from the mean. The
z-score thus measures whether a value is extraordinarily high or low, or if it is “typical.”
● Field Normalization: Transforming a total-count (extensive) variable into a continuous field
(intensive) variable. “Usually describes the division of variables by either area or
population… to regularize the effect that the number of individuals or the size of an area
may have on the raw count values in an area” (Rinner 2013)
○ Learn more at: Normalized Variable
2. Normalize Data
Make attribute table editable (ctrl + I)
Use “Open field calculator” to create a new field – normalize
your data: Ratio_SNAP (copy the formula on the document)
3. Classify Data
Right click on layer → Properties → Symbology Graduated
In most scenarios, you can just classify
using pre-defined mode:
● Equal Count
● Equal Interval
● Natural Breaks
● Standard Deviation
● …
3. Classify Data
Right click on layer → Properties → Symbology Graduated
In some scenarios, you can define
your own classification method. Just
switch to Histogram, add or move the
black division line.
Map Examples
LE
P
M
SA
SA
MP
LE
Deliverable 1: a map shows what percent of households in
each census tract in WA using SNAP benefits
Deliverable 2: a map shows how you think the state
legislature should distribute SNAP benefit eligibility to
the various counties in Washington State
Map 2: Your turn!
How should SNAP eligibility be distributed across WA counties (using the little data you have)?
Possible eligibility standard
Content of the Map
County SNAP eligibility to match census
tract poverty
A choropleth map shows the supposed
SNAP benefit number of each county.
Double the current SNAP use for eligibility
A choropleth map shows the supposed
SNAP benefit number of each county
OR
A choropleth map shows the supposed
SNAP benefit ratio (divided by
households) of each county
Base SNAP eligibility on children in
households
“”
How to aggregate census tract-level data to county-level?
STEP 1: Create centroids for census tract data, it turns polygons into points while
keeping the attributes.
STEP 2: Use “Join attributes by location (summary)” to sum selected attributes of
centroids into county polygons.
Then you can further calculate whatever you like and visualize your data.
Lab 3 Deliverable
Submit two maps made using QGIS:
For your first map: Please create a map showing what percent of households in each census
tract use SNAP benefits. Please use either an equal interval, quantiles, or natural breaks
classification scheme. Remember to put your map into a print layout so that you can add an
informative title and legend. Please also include a text box on your map with 2-3 sentences
explaining the classification scheme you chose and why.
For your second map: Imagine that the state legislature has commissioned you to make a map
that shows how they should distribute SNAP benefit eligibility at the county level in Washington
State. Consider what data best illustrates how eligibility should be distributed and whether it
makes sense to normalize the data. Using a choropleth map, and making sure that you have a
clear title and legend, create a map that has clear, easy-to-understand (i.e. nice, round
numbers) classes. Please include, in a textbox on your map, a one-paragraph explanation of
what the map shows and what that means for how the legislature should allocate funding.
GEOG 360
Lab 3: Mapping the use of SNAP
benefits in Washington State
GIS and Mapping
TA: Olivia Orosco (she/her)
Question of the Day:
What is your favorite candy?
Tuesday // Thursday
Section AA : 9:30 – 10:20
Section AB : 10:30-11:20
Lab 3 Objectives
The geographic question to be explored in lab:
○ What is the distribution of SNAP
benefit use in Washington State?
●
●
●
●
●
Strengthen your choropleth mapping skills
Effectively classify data using multiple classification strategies
Appropriately normalize data to meaningfully compare data
across areal units of various size
Visualize how the same data can be used to display different
geographic patterns
Match the visual hierarchies on the map with the intellectual
hierarchies of the map’s argument
Oscar and his friend Dave
A bit about SNAP
An economic investment meant to subsidize US farmers. It is
administered by the USDA (United States Department of
Agriculture). AND
An important federal program that helps families access food
●
●
●
●
Often referred colloquially as “food stamps” the Food
Stamp Program was changed to the Supplemental Food
Nutrition Program (SNAP) in 2008.
With origins in the New Deal post-great depression Food
Stamp Program was established formally by Johnson in 1964.
And severely cut by Reagan in the 1980’s.
Families who qualify (based on income, work, resources,
immigration status, etc.) receive monthly amounts placed on an
EBT (electronic benefits transfer) card
Racialized and gendered! This cannot be thought of outside of a
racial capitalist system that marginalizes BIPOC communities.
Reading Rec: The politics of disgust: the public identity
of the welfare queen by Ange-Marie Hancock
Data Classification
Recap
●
Level of Measurement
●
●
●
●
●
●
Nominal data is name
Ordinal data is categorical
Interval data is relative difference or the
interval between data points
Ratio data is the absolute difference
Discrete data is called categorical data
Continuous data is best represented by
ratio and interval values.
Measurement systems—values and what they
represent—ArcGIS Pro | Documentation
Penn State Resource
●
●
●
Quantiles: Each class contains an equal number of
features.
Equal Interval: Divide the range equally. If you specify
three classes for a field whose values range from 0 to
300, three classes with ranges of 0–100, 101–200, and
201–300 are created.
Jenks Natural Breaks: Based on natural groupings
inherent in the data, clustering method designed to
determine the best arrangement of values into
different classes.
Manual: Define your own classes, to manually add
class breaks and to set class ranges that are
appropriate for the data
Data classification methods—ArcGIS Pro | Documentation
Classification – QGIS Lesson
Overview
Lab 3 is worth 60 points.
Two input shapefiles:
●
●
Census Tracts with SNAP attributes in WA
Counties with SNAP attributes in WA
Three key components in practice part:
●
●
●
Examining your data
Normalize your data
Classify your data
Oscar feeling autumnal
A bit more about census data
Census data can be overwhelming because it feels like a
new language- but there is a cheat code!
STATEFP
State FIPS (federal information
processing standards) code
COUNTYFP
TRACTCE
GEOID (or GeoID_1)
NAMELSAD
ALAND
AWATER
Households
HH_SNAP
County FIPS code
Census tract FIPS code
Geographic identifier
Census Tract Name
Land area
Water area
Number of households
Number of households using SNAP
benefits
Number of households under the
poverty line
Number of households with children
Number of households with at least
one individual with a disability
HH_poverty
HH_child
HH_disab
County
Population
County Name
Total population
1. Examine Your Data
Examining the data you have by loading it and open its attribute table.
Most important fields
●
●
●
Households: how many
households are there?
HH_SNAP: how many
households are using
SNAP benefits?
HH_poverty: how many
households are in poverty
based upon census
definition?
A bit more about census
poverty threshold and SNAP
Reflect: Are these amounts livable in
the place you grew up in or live now?
The assumption is those making above these levels are
fine and do not qualify for assistance. I could not live in
Seattle making only 23k a year. ASE’s (academic student
employees) make $2,600-$3,000 /month (gross), half +
of that easily goes to rent. “Rent burdened” is someone
spending more than 30% of income on housing.
WA Snap Income Thresholds
Table downloaded from Census Website
DSHS
2. Normalize Data
What’s normalization and standardization?
Statistics and Machine Learning:
● Normalization: rescaling the values into a range of [0,1].
● Standardization：rescaling the values in such a way that their mean is 0
and standard deviation becomes 1. Also called Min-Max scaling
Learn more at: Normalization vs Standardization
GIS – Geospatial Analysis/Visualization:
● Scale Standardization: Transforming values on different absolute scales into a common
measure based on the statistical distributions. The most common is the z-score, which
measures the number of standard deviations the value deviates from the mean. The
z-score thus measures whether a value is extraordinarily high or low, or if it is “typical.”
● Field Normalization: Transforming a total-count (extensive) variable into a continuous field
(intensive) variable. “Usually describes the division of variables by either area or
population… to regularize the effect that the number of individuals or the size of an area
may have on the raw count values in an area” (Rinner 2013)
○ Learn more at: Normalized Variable
2. Normalize Data
Make attribute table editable (ctrl + I)
Use “Open field calculator” to create a new field – normalize
your data: Ratio_SNAP (copy the formula on the document)
3. Classify Data
Right click on layer → Properties → Symbology Graduated
In most scenarios, you can just classify
using pre-defined mode:
● Equal Count
● Equal Interval
● Natural Breaks
● Standard Deviation
● …
3. Classify Data
Right click on layer → Properties → Symbology Graduated
In some scenarios, you can define
your own classification method. Just
switch to Histogram, add or move the
black division line.
Map Examples
LE
P
M
SA
SA
MP
LE
Deliverable 1: a map shows what percent of households in
each census tract in WA using SNAP benefits
Deliverable 2: a map shows how you think the state
legislature should distribute SNAP benefit eligibility to
the various counties in Washington State
Map 2: Your turn!
How should SNAP eligibility be distributed across WA counties (using the little data you have)?
Possible eligibility standard
Content of the Map
County SNAP eligibility to match census
tract poverty
A choropleth map shows the supposed
SNAP benefit number of each county.
Double the current SNAP use for eligibility
A choropleth map shows the supposed
SNAP benefit number of each county
OR
A choropleth map shows the supposed
SNAP benefit ratio (divided by
households) of each county
Base SNAP eligibility on children in
households
“”
How to aggregate census tract-level data to county-level?
STEP 1: Create centroids for census tract data, it turns polygons into points while
keeping the attributes.
STEP 2: Use “Join attributes by location (summary)” to sum selected attributes of
centroids into county polygons.
Then you can further calculate whatever you like and visualize your data.
Lab 3 Deliverable
Submit two maps made using QGIS:
For your first map: Please create a map showing what percent of households in each census
tract use SNAP benefits. Please use either an equal interval, quantiles, or natural breaks
classification scheme. Remember to put your map into a print layout so that you can add an
informative title and legend. Please also include a text box on your map with 2-3 sentences
explaining the classification scheme you chose and why.
For your second map: Imagine that the state legislature has commissioned you to make a map
that shows how they should distribute SNAP benefit eligibility at the county level in Washington
State. Consider what data best illustrates how eligibility should be distributed and whether it
makes sense to normalize the data. Using a choropleth map, and making sure that you have a
clear title and legend, create a map that has clear, easy-to-understand (i.e. nice, round
numbers) classes. Please include, in a textbox on your map, a one-paragraph explanation of
what the map shows and what that means for how the legislature should allocate funding.
GEOG 360
Lab 3: Mapping the use of SNAP
benefits in Washington State
GIS and Mapping
TA: Olivia Orosco (she/her)
Question of the Day:
What is your favorite
animal?
Tuesday // Thursday
Section AA : 9:30 – 10:20
Section AB : 10:30-11:20
GEOG 360: GIS & Mapping
Instructions:
1. Set up the lab.
a. Download the Lab 3 Data folder from Canvas. Remember to unzip it and then
save it to a designated folder for your class GIS work.
b. Open QGIS.
c. Remember to start by saving your file (ideally to the folder you dedicated to your
GIS work for this class).
d. Add the two shapefiles from the Lab 3 Data folder.
2. Start by examining the data
a. Remember that shapefiles typically contain attribute data within them (or can be
joined to tables with attribute data). Right click on the WashingtonCounties
shapefile in the Layers panel and select ‘Open Attribute Data.’
b. This shapefile has a bit more attribute data in it than the data for the last lab
(and more than we need for this lab). Below is a table explaining all of the
column headings present in both shapefiles.
STATEFP
COUNTYFP
TRACTCE
GEOID (or GeoID_1)
NAMELSAD
ALAND
AWATER
Households
HH_SNAP
HH_poverty
HH_child
HH_disab
County
Population
State FIPS code
County FIPS code
Census tract FIPS code
Geographic identifier
Census Tract Name
Land area
Water area
Number of households
Number of households using SNAP
benefits
Number of households under the
poverty line
Number of households with children
Number of households with at least
one individual with a disability
County Name
Total population
GEOG 360: GIS & Mapping
3. While still in our attribute table, let’s learn how to normalize our data.
a. Remember that normalization means that we are dividing one data column by
another in order to standardize it so that it can be compared across enumeration
units of different sizes (in this case counties). You can see that King County has
more people than the other counties in Washington so we would also expect it
to have a higher number of households using SNAP benefits.
b. Now, we have a choice. We can either standardize by population or by
households. Consider which makes the most sense. Since our SNAP data is
observed at the household level, we should be normalizing it by households.
This just means that we will divide the number of households using SNAP
benefits by the total number of households to get the percentage of households
using SNAP benefits.
c. To do this, first click on the pencil in the top left corner of your attribute table.
This is the toggle editing button. Clicking it opens an editing session which
allows you to edit the data itself. You’ll notice that many of the tools that were
previously gray, turn to color when you open an editing session.
d. Now click on the ‘Open field calculator’ button which looks like an abacus.
TIP: Remember that you can hover over a button with your cursor to see its
name.
e. In the Field Calculator pop-up window, you’ll see in the top left that the default
is to “Create a new field.” We want to keep that box checked.
f. Where it says ‘Output field name’ we want to assign our new normalized column
a name.
TIP: QGIS does not do well with spaces, so please do not include any spaces in
your column headings/assigned names.
g. Underneath that, where it says ‘Output field type’ we want to select ‘Decimal
number (real).’ Because we are likely to get answers with decimal points when
we divide one column by another.
h. Now in the Expression box, we need to tell QGIS how to normalize our data. We
want to divide our households using SNAP benefits by the total number of
households (and then multiply it by 100 to get a percent), so you’ll want to enter
the following formula:
(HH_SNAP/Households)*100
GEOG 360: GIS & Mapping
i.
Once you have done that, you should see a value appear underneath the
Expression box where it says ‘Preview:’. If you get a value there, click OK (if not,
double check what you have).
j.
You should see a new column appear in your table with the percentage of
households in each county who are using SNAP benefits.
k. To save your work, click on the pencil (‘Toggle Editing’) button again and click
‘Save’ in the pop up.
TIP: If you changed stuff and it doesn’t look right, then when you click on the
Toggle Editing button, click ‘Don’t Save’ and QGIS will reset your data to what it
was when you opened your editing session.
l.
Once you have saved your edits, close the attribute table and return to your
map.
4. Make a choropleth map
a. You learned how to make a choropleth map in Lab 2. See if you can remember
the steps.
HINT: Right click on layer → Properties → Symbology → Graduated
b. Set the ‘Value’ to the new column you created, adjust the color ramp, click
Classify and then OK.
5. Now, let’s learn how to classify the data using the classification strategies we learned
about.
a. Go back into your Symbology window.
b. Just above the ‘Classify’ button, notice that there is a drop down menu where it
says ‘Mode.’ There you have the option of Equal Count (Quantile), Equal
Interval, Natural Breaks (Jenks), as well as a couple of other less commonly used
classification schemes.
c. Play around with the different classification schemes and examine how they
change your map.
TIP: If you want to see the changes without closing your Symbology window,
simply click ‘Apply’ instead of ‘OK’ and look at the resulting map (you may have
to shift your pop-up window so that it doesn’t cover your map).
GEOG 360: GIS & Mapping
d. Click on the ‘Histogram’ tab at the top of the Classes window to see the graphical
distribution of your data. This can be helpful in thinking about what
classification scheme makes the most sense for your data and it helps you see if
clusters are being slit up by your classes or not.
TIP: If you don’t see a bar chart right away, click on the Load Values button.
6. Let’s learn how to do manual classification.
a. You may have noticed that there isn’t an option for manual classification. The
reason for that is that you have to do it manually.
b. To manually adjust the classes, return to the Classes window (if you were still in
the Histogram window) simply double click on the numbers under “Values” and
next to the color symbols. You’ll get a small pop-up window that lets you edit
the boundaries of each class.
c. You can also change what your legend says in the same way. While we want to
keep the values of our legend matching the values being mapped, we sometimes
want to format what they look like. Try adding percentage signs to your legend
labels.
7. You now have the skills you need to normalize data and to adjust the classification
strategy. Use those skills to make two maps that meet the following criteria:
For your first map: Please create a map showing what percent of households in each
census tract use SNAP benefits. Please use either an equal interval, quantiles, or natural
breaks classification scheme. Remember to put your map into a print layout so that you
can add an informative title and legend. Please also include a text box on your map with
2-3 sentences explaining the classification scheme you chose and why.
For your second map: Imagine that the state legislature has commissioned you to make
a map that shows how they should distribute SNAP benefit funding to the various
counties in Washington State. Consider what data best illustrates how funding should
be distributed and whether it makes sense to normalize the data. Using a choropleth
map, and making sure that you have a clear title and legend, create a map that has
clear, easy-to-understand (i.e. nice, round numbers) classes. Please include, in a textbox
on your map, a one-paragraph explanation of what the map shows and what that means
for how the legislature should allocate funding.
GEOG 360: GIS & Mapping
Skill Development Lab 3 Rubric
Criteria
Map 1
(percentage of
households
using SNAP by
census tract)
Ratings
10 pts.
7 pts.
4 pts.
0 pts.
Map submitted,
shows clear
effort, but does
not show strong
attention to
visual hierarchy.
Map submitted,
but does not
effectively
communicate
with the map
reader.
No map
submitted.
3.5 pts.
2 pts.
0 pts.
Normalization of Data is
data
effectively
normalized.
Data is
ineffectively
normalized
Data is not
normalized.
Map 1
5 pts.
3.5 pts.
Data
normalization
was
unsuccessfully
attempted.
2 pts.
Classification of
data
Data is classified
using a equal
interval,
quantiles, or
natural breaks
classification
scheme.
5 pts.
Data is
classified, but
not using one of
the requested
classification
schemes.
Data
classification
was
unsuccessfully
attempted.
Data is not
classified.
3.5 pts.
2 pts.
0 pts.
Map elements
Map includes an
effective print
layout and
informative title
and legend.
Map includes an
effective print
layout, but title
and legend
could be more
informative.
Map does not
include a title or
legend.
Map 1
5 pts.
3.5 pts.
Map a title and
legend, but the
layout is
ineffective
and/or the
title/legend are
not informative.
2 pts.
Explanation of
classification
scheme (in text
box)
Map includes a
textbox with a
thoughtful
explanation of
the chosen
Map includes a
textbox with an
explanation of
the chosen
classification
Map includes a
textbox with an
explanation of
the chosen
classification
Map does not
include an
explanation of
the classification
scheme.
Overall efficacy
of map/visual
hierarchy
Map 1
Map 1
Map employs
thoughtful visual
hierarchy to
make an
argument about
the distribution
of SNAP use.
5 pts.
0 pts.
0 pts.
GEOG 360: GIS & Mapping
Skill Development Lab 3 Rubric
classification
scheme.
scheme, but
justification for
that choice is
not clear and/or
convincing.
10 pts.
7 pts.
scheme, but
explanation is
seriously flawed
or relies on
incorrect
information.
4 pts.
Map employs
thoughtful visual
hierarchy to
make an
argument about
how SNAP
funding should
be
distributed/how
SNAP use is
distributed.
5 pts.
Map submitted,
shows clear
effort, but does
not show strong
attention to
visual hierarchy.
Map submitted,
but does not
effectively
communicate
with the map
reader.
No map
submitted.
3.5 pts.
2 pts.
0 pts.
Data is
effectively
classified for the
intended
purpose.
5 pts.
Data is
classified, but
could be done
so in an easierto-read way.
3.5 pts.
Data
classification
was
unsuccessfully
attempted.
2 pts.
Data is not
classified.
Map elements
Map includes an
effective print
layout and
informative title
and legend.
Map includes an
effective print
layout, but title
and legend
could be more
informative.
Map does not
include a title or
legend.
Map 2
10 pts.
7 pts.
Map a title and
legend, but the
layout is
ineffective
and/or the
title/legend are
not informative.
4 pts.
Map Argument
(based on map
itself and
explanation in
textbox)
Map effectively
makes an
argument about
how legislators
could/should
Map makes an
argument, but
does not
effectively
respond to
Map attempts to
make an
argument and
includes a
textbox with an
Map does not
include a clear
argument or
explanation.
Map 2
(proposed SNAP
funding
distribution by
county)
Overall efficacy
of map/visual
hierarchy
Map 2
Classification of
data
Map 2
0 pts.
0 pts.
0 pts.
GEOG 360: GIS & Mapping
Skill Development Lab 3 Rubric
allocate SNAP
funding. Map
includes a
textbox with a
thoughtful
explanation of
the argument.
legislator’s
question (lab
prompt). Map
includes a
textbox with an
explanation of
the map
argument, but
argument is not
clear and/or
convincing.
explanation, but
the argument is
seriously flawed.