Description
Skill Development Lab 6 – Racial Inequalities in Healthcare: Mapping Breast Cancer Mortality Rates by Race
You are now skilled at making thematic maps. You have experience making choropleth, dot density, and proportional symbol maps. In Module 6, you learned about data management and how to bring new data, both primary and secondary, into a GIS. In this lab, you will practice finding secondary data, bringing it into a GIS, joining data tables with shapefiles, and then mapping that new data.
As we develop the skills you learned about in Module 6, we will also be considering a real-world geographic question:
How are breast cancer mortality rates
distributed geographically and by race?
In this lab, you will be given two shapefiles, and two tables of breast cancer mortality rates by race, and instructions for how to access other demographic data. The lab will walk you through bringing that information together so that you can map it. As you do, you’ll have the opportunity to use your maps to make an argument about racial disparities in breast cancer mortality rates.
This lab should take approximately 3 hours to complete. This lab is worth 70 points.
Evidence of skill development will be assessed on:
Submit answers to the lab questions and two maps made using QGIS. (Required – 70 points)
Join data in QGIS
Collect and prepare data for use in QGIS
Assess data quality
Map and analyze patterns of racial inequality in healthcar
Skill Development Lab 7 – Analyzing Electoral Politics in King County
Thus far in the quarter, you have primarily been making maps to showcase patterns in data. In this module, we turn our attention to the analytical capabilities of a GIS. In Module 7, you learned about vector analysis tools. In this lab, you will practice using those tools while considering the placement of ballot dropboxes in Seattle and King County. Specifically, we will be exploring the question:
How well distributed are the King County ballot dropboxes?
In this lab, you will be given multiple shapefiles and two tables with spatial, electoral and demographic data relating to King County voters. The lab will walk you through the various analytical tools discussed in Module 7 as we analyze the data.
This lab should take approximately 3 hours to complete. This lab is worth 70 points.
Evidence of skill development will be assessed on:
Submit answers to the lab questions and two maps made using QGIS. (Required – 70 points)
OBJECTIVES
Join data in QGIS
Conduct both spatial and attribute queries in QGIS
Use overlays to answer a geographic question
Create buffers in QGIS and use them to answer a geographic question
Identify the appropriate analytical tool for answering a geographic question
Map and analyze electoral politics in King County
Unformatted Attachment Preview
GEOG 360: GIS & Mapping
Instructions:
1. Set up the lab.
a. Download the Lab 6 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 6 Data folder.
e. Add the two tables from the Lab 6 Data folder. To do this, simply add them as
you would a shapefile. They will appear in your list of layers, but they won’t
appear on your map as they only contain attribute data (not spatial data).
2. Start by examining the data
a. Take a few minutes to examine the attribute data associated with the shapefiles
(right click on layer → Open Attribute Table). Pay particular attention to any
columns that might make a good unique identifier for joining the data in your
table to the data in your shapefile.
For reference, the data associated with the shapefiles is explained below:
STATE_NAME
State Name
SUB_REGION
Region
STUSPS
Two letter state postal abbreviation
STATEFP
State FIPS code
COUNTYFP
County FIPS code
AFFGEOID
Long Form Geographic Identifier
(International Country Code + United
States FIPS Code)
FIPS (or FIPS1, or FIPS2)
FIPS code (State and county)
NAME (or NAMELSAD)
County name
ALAND
Land area
AWATER
Water area
Population
Total population
b. Now examine the attribute table in your tables. To do this, right click on the
table you want to look at and select “Open Attribute Table” (just as you would
for a shapefile).
GEOG 360: GIS & Mapping
The tables contain age-adjusted breast cancer mortality rates in females by state
or county. For reference, the data associated with the tables is explained below:
Table Header Significance
Values for
the United
States
County
Full Name of the County
State
Full Name of the State
FIPS
FIPS Code (unique identifier)
RateAll
Breast cancer mortality rate amongst women 20.1
of all races (including Hispanic ethnicity); rate
measured per 100,000 people
Count
Average annual count of breast cancer
41737
mortality amongst women of all races
(including Hispanic ethnicity)
RateWhite
Breast cancer mortality rate amongst White
19.6
women (including Hispanic ethnicity); rate
measured per 100,000 people
RateWHis
Breast cancer mortality rate among White
14.6
women of Hispanic ethnicity; rate measured
per 100,000 people
RateWNonHis Breast cancer mortality rate among White
20.1
women not of Hispanic ethnicity; rate
measured per 100,000 people
RateBlack
Breast cancer mortality rate among Black
27.3
women (including Hispanic ethnicity); rate
measured per 100,000 people
RateAIAN
Breast cancer mortality rate among American 11.6
Indian / Alaskan Native women (including
Hispanic ethnicity); rate measured per
100,000 people
RateAPI
Breast cancer mortality rate among Asian and 11.7
Pacific Islander women (including Hispanic
ethnicity); rate measured per 100,000 people
RateHispanic Breast cancer mortality rate among Hispanic
13.8
women of all races, rate measured per
100,000 people
*Full metadata can be found at:
https://statecancerprofiles.cancer.gov/deathrates/index.php?stateFIPS=00&are
atype=county&cancer=055&race=00&sex=2&age=001&year=0&type=death&sor
tVariableName=rate&sortOrder=default#results
c. This lab includes a series of questions scattered throughout the lab (the first of
which are below). Take a few minutes to find the answers to the questions
GEOG 360: GIS & Mapping
below. Make note of your answers as you’ll submit them, along with your maps,
in the Lab 6 Submission Portal.
Question 1:
What is the breast cancer mortality rate among Black women in King County, Washington
(FIPS: 53033)?
a. 10.8
b. 12.3
c. 18
d. 19.1
e. 20.2
Question 2:
What is the breast cancer mortality rate among White women (regardless of Hispanic
ethnicity) in King County, Washington (FIPS: 53033)?
a. 10.8
b. 12.3
c. 18
d. 19.1
e. 20.2
Question 3:
What is the breast cancer mortality rate among Asian and Pacific Islander women (regardless
of Hispanic ethnicity) in King County, Washington (FIPS: 53033)?
a. 10.8
b. 12.3
c. 18
d. 19.1
e. 20.2
Question 4:
In what coordinate reference system are the state and county shapefiles stored?
a. EPSG: 3857 – WGS 84 / Pseudo-Mercator
b. ESRI: 102009 – North American Lambert Conformal Conic
c. EPSG: 4326 – WGS 84
d. EPSG: 32148 – NAD83 / Washington North
GEOG 360: GIS & Mapping
3. Let’s take a minute to consider what we have.
a. First, consider what we should use as a unique identifier to join our county table
to our county shapefile. Hopefully you came to the conclusion that we should
use the FIPS codes to join our data. In the table, this column is labeled as ‘FIPS’.
In the shapefile, we have three categories that align with the FIPS code: ‘FIPS’,
‘FIPS1’, and ‘FIPS2’. Why do we have three categories? Well notice that some
are right-aligned and some are left-aligned. This gives us a clue. What we have
here are the same data stored in different ways. ‘FIPS1’ is stored as numeric
data (integers). ‘FIPS’ and ‘FIPS2’ are stored as text (string). You can see this if
you hover over the column heading in the attribute table. The difference
between ‘FIPS’ and ‘FIPS2’ is that one includes the leading zero and the other
doesn’t (ex. one lists Autauga County, Alabama as 01001 and the other as 1001).
If we go back to our table, we can see that the ‘FIPS’ column there is stored as
text (string) and does not include leading zeros, so it will match up perfectly with
the column ‘FIPS’ from the shapefile.
Question 5:
Why would we rather use FIPS codes instead of county names to join our table and shapefile?
a. FIPS codes are shorter, so it takes the computer less time to process the join based on
FIPS codes as compared with county names.
b. Using a numeric code as a unique identifier is less likely to introduce errors in the join
as text is more likely to include things like extra spaces or typos that can cause the
join to not function properly.
c. QGIS can only perform joins based on numbers, not based on text.
Question 6:
What is the cardinality relationship between the data in the county table and the data in the
county shapefile?
a. One-to-one
b. One-to-many
c. Many-to-one
d. Many-to-many
4. Now let’s join our county table and shapefile.
GEOG 360: GIS & Mapping
a. Remember that when we talk about cardinality we frame relationships as
SOURCE TO DESTINATION. As such, we are always joining TABLES TO
SHAPEFILES since in order to be able to map our data, our desired destination is
the shapefile. Since we are joining the table to the shapefile, we initiate the join
from the shapefile. Right click on the counties shapefile, go to Properties, then
select the Joins tab.
b. To add a new join, click on the green plus button in the lower left-hand corner of
the window. Select the counties table as the ‘Join layer’. For the join field select
the table column header that corresponds with our unique identifier (in our case
‘FIPS’). For the target field, select the column header from the shapefile that
corresponds with our unique identifier (in our case, this is also ‘FIPS’). Click OK
and you’ll see the join appear in your list of joins. Click OK again, to close the
pop-up window.
5. Let’s examine our data again.
a. Open the attribute table of the counties shapefile again and notice how the data
from the table has now been added to the shapefile.
b. Before we can map it, we need to put it into a useable format. Unfortunately,
QGIS isn’t great at recognizing the difference between text and numbers, so will
often bring all data in a table in as text. That is likely what you are seeing here.
We can confirm this by hovering our cursor over the column heading. If it says
‘STRING’ you are looking at something that QGIS recognizes as text and will have
to change it to numeric data before we can map it. So, let’s do that.
c. To convert our STRING data into numeric data, we will actually create a new
column that matches our old column, but that stores the data as numbers. To
do this, click on the field calculator button.
d. We will start by mapping the breast cancer mortality rate among Black women,
so let’s convert that column. Where it says ‘Output field name’ give your new
column a name. Make sure that the output field type is set to Decimal number
(since we have a decimal in our rate data). Then, in the expression box, put in
the name of the existing column (unless you’ve changed it, this should be
“CountiesTable_RateBlack”). Click OK and examine your new column.
e. Don’t be alarmed that there are some NULL values, those are simply counties we
don’t have data for. For those counties, there are few enough women who have
died of breast cancer that exact numbers are not being released.
6. Let’s see what the data looks like.
GEOG 360: GIS & Mapping
a. Because we have so many counties without data, let’s map what we have so that
we can see what is going on. Use the symbology tab and the new column you
created to map what we’ve got.
b. Notice that this really isn’t that much data, so let’s use our state layer
underneath with the same data, to fill in some of those gaps.
c. Use what you have learned to join the state table to the states shapefile layer.
Note that the states shapefile layer only includes the state FIPS code in numeric
form (‘STATEFP’), while the table includes it as text. You’ll have to use the field
calculator to add a text FIPS column to the state shapefile in order to be able to
join the data.
d. Once you have the state data joined, and have converted the mortality rate
among black women to numeric data, use the symbology tab to map that,
putting that layer underneath your county layer. Ideally, you’ll want to use the
same colors and class intervals for both maps.
e. What you will see is that most states have data, but a few of the least populated
states are still missing data. Consider adding an additional states shapefile
underneath either as a no data layer or using the value for the US as a whole
(from the table in these instructions).
Question 7:
Map 1: Create a map that includes two map views (do this exactly as you would an inset
map), each mapping the breast cancer mortality rates amongst women of different races (ex.
one map showing the mortality rate amongst white women and the other amongst black
women). Please use choropleth mapping, a consistent color ramp and classification bins in
both maps, and an informative title and legend. Use your map to make an argument about
the racial disparities between mortality rates in women of different races. It is up to you
whether you include the entire US or some portion of it. Upload your map.
7. Now, let’s learn how to find your own data and get it into a format that we can bring
into QGIS.
a. Go to data.census.gov/cedsci/.
b. Click Advanced Search.
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c. Under ‘Filters’ on the left let’s start by adding a geography filter. Under
geography, click county, then check the box for “All counties in the United States
and Puerto Rico”.
d. Then, under topics, choose ‘Race and Ethnicity’, and then check the box for ‘Race
and Ethnicity.’ This will add ‘Race and Ethnicity’ to your list of filters.
e. Then click search at the bottom right of your screen.
f. You should see a list of tables appear. Feel free to poke around and see what is
there. What we want is “ACS Demographic and Housing Estimates”, which is
Table DP05. ACS stands for the American Community Survey which is a survey
that is collected annually by the Census Bureau.
g. It should say it is too big to display. Click download table and select the 5-Year
Estimates table for 2019 and download it as a CSV file. Note that when you click
download it will open a pop-up telling you that it is preparing your files. It may
take a minute or two, but you should then get the option to ‘Download Now.’
8. Let’s clean up the data we downloaded.
a. The census data will download as a zipped file. Go ahead and unzip it and open
the folder.
b. You should see three files – two .csv files and one .txt file. The first file, the one
that includes ‘-Data’ in the title, is the one that includes our data. The other two
files, both have different types of metadata. This is the standard format for
publicly available Census Bureau data.
c. Go ahead and open that first file (ACSDP5Y2019.DP05-Data). It is easiest to do
this in a spreadsheet program like Microsoft Excel or Google Sheets.
d. Notice that we have the long form geographic identifier (international country
code and then US FIPS code) in the first column and then the county name in the
second column. After that, we get into data itself, so those are the only two
options to use as a unique identifier to join the table.
e. Next notice that the first row has an encoded header for each piece of data in
the top row. The second row then has a description of the data column. For
columns that start with the word ‘Estimate’, we are looking at estimated data
values. For columns that start with the word ‘Margin’ are telling you the margin
of error of the column before.
f. You may be noticing that data tables like this (from the Census Bureau) have a
lot of data columns! The columns we will be using are:
GEOG 360: GIS & Mapping
Header 1 (Code)
DP05_0064E
DP05_0065E
DP05_0066E
DP05_0067E
DP05_0068E
DP05_0071E
Header 2 (Text)
Estimate!!Race alone or in
combination with one or more
other races!!Total
population!!White
Estimate!!Race alone or in
combination with one or more
other races!!Total
population!!Black or African
American
Estimate!!Race alone or in
combination with one or more
other races!!Total
population!!American Indian
and Alaska Native
Estimate!!Race alone or in
combination with one or more
other races!!Total
population!!Asian
Estimate!!Race alone or in
combination with one or more
other races!!Total
population!!Native Hawaiian
and Other Pacific Islander
Estimate!!HISPANIC OR LATINO
AND RACE!!Total
population!!Hispanic or Latino
(of any race)
Plain Text Meaning
Total White population
(including Hispanic and
those of two or more
races)
Total Black population
(including Hispanic and
those of two or more
races)
Total American Indian
and Alaska Native
population (including
Hispanic and those of
two or more races)
Total Asian population
(including Hispanic and
those of two or more
races)
Total Native Hawaiian
and Pacific Islander
population (including
Hispanic and those of
two or more races)
Total Hispanic
population (regardless
of race)
g. Let’s erase the columns we won’t need to get the data to a more manageable
level. Keeping the columns listed above and our first two columns (GeoID and
County names) go ahead and erase everything else.
h. Next, we need to clean this up a little further in order to use it in QGIS. First, we
want to end up with only one column heading and we want to remove any
spaces from those headings. There are features in QGIS that do now work when
a column heading has a space in it. So, we can use the first line as our header if
we want, of you can give your columns more intuitive names (ex. White, Black,
etc.) so long as you know what is what and you don’t have any spaces in the
headings. When you have done that, delete the other header row so you only
have one header row.
GEOG 360: GIS & Mapping
i.
Next, we need to save this file with a name that also doesn’t have any spaces.
This is because when we join it, the table title will become part of the column
name and if there are spaces, that will cause problems for us. So, save your
table with a title that does not include spaces. Put your table in your class GIS
folder alongside your Lab 6 data.
NOTE: Please keep your table in .csv format when saving.
9. Now we are ready to bring our table into QGIS.
a. Add your table just as you did the other tables we used in this lab.
b. Join your table to the counties shapefile using the long form GEOID column.
c. Take a screen shot of your joined table (see Question 4 below).
Question 8:
Upload a screenshot of your joined table (table from the Census Bureau, joined to the
counties shapefile).
Question 9:
What can we tell about the quality of the Census data we collected?
a. This data is from the Census Bureau, which is a reputable organization, so likely this
data is of high quality.
b. This data includes significant metadata, so likely this data is of high quality.
c. This data includes margins of error for each estimate, so likely this data is of high
quality.
d. All of the answer options are correct.
Question 10:
What is the estimated American Indian and Alaska Native population of King County,
Washington (53033)?
a. 0
b. 7,122
c. 12,538
d. 43,846
GEOG 360: GIS & Mapping
Question 11:
What is the estimated Hispanic population of Franklin County, Alabama (FIPS: 01059)?
a. 79
b. 2,404
c. 5,400
d. 29,703
Question 12:
Map 2: Create a map that makes an argument using some element of the Census data from
your joined table and that somehow relates to breast cancer mortality rates. (Think about
what having population data and rate data allows you to do.) Your map can use any thematic
mapping style (choropleth, dot density, proportional symbol, etc.) so long as you can make a
strong visual and intellectual argument with it.
GEOG 360: GIS & Mapping
Instructions:
1. Set up the lab.
a. Download the Lab 7 Data folder from Canvas. Remember to unzip it and save it
to a designated folder for your class GIS work.
b. Open QGIS and save your file.
c. Add the six shapefiles and one table from the Lab 7 Data folder.
2. Start by examining the data
a. Look at the attribute data we have and consider how we might use it to better
understand the placement of ballot dropboxes. Also consider what in our table
we might be able to use as a unique identifier and what it matches up to.
For reference, the data that we will be using in this lab is explained below:
Column
Shapefile / Table
Explanation
GEOID
CensusTracts
Census Tract FIPS Code
GeoID
Demographics (table)
Census Tract FIPS Code
TotalPop
Demographics (table)
Total Population
MalePopulation
Demographics (table)
Total Male Population
FemalePop
Demographics (table)
Total Female Population
Age65andOver
Demographics (table)
Total Population aged
65+
WhiteOnly
Demographics (table)
Population that
identifies as White and
no other race
BlackOnly
Demographics (table)
Population that
identifies as Black or
African American and
no other race
AIANOnly
Demographics (table)
Population that
identifies as American
Indian or Alaska Native
and no other race
AsianOnly
Demographics (table)
Population that
identifies as Asian and
no other race
AsianIndian
Demographics (table)
Population that
identifies as Indian
GEOG 360: GIS & Mapping
AsianChinese
Demographics (table)
AsianFilipino
Demographics (table)
AsianJapanese
Demographics (table)
AsianKorean
Demographics (table)
AsianVietnamese
Demographics (table)
NHPIOnly
Demographics (table)
OtherRaceOnly
Demographics (table)
TwoOrMoreRaces
Demographics (table)
HispanicPop
Demographics (table)
EligibleVote
Demographics (table)
(subset of AsianOnly
category)
Population that
identifies as Chinese
(subset of AsianOnly
category)
Population that
identifies as Filipino
(subset of AsianOnly
category)
Population that
identifies as Japanese
(subset of AsianOnly
category)
Population that
identifies as Korean
(subset of AsianOnly
category)
Population that
identifies as Vietnamese
(subset of AsianOnly
category)
Population that
identifies as Native
Hawaiian or Pacific
Islander and no other
race
Population that
identifies as some other
race (but only one race)
Population that
identifies as being two
or more races
Population that
identifies as of Hispanic
or latinx ethnicity
(regardless of race)
Population that is 18
years of age and a US
citizen (estimate of
eligible voters)
GEOG 360: GIS & Mapping
PercentofEligible2019 Demographics (table)
Percent of eligible
population that is
registered to vote
SUM_VOTERS
VotingDistricts
Registered voters
SLDLST
WashingtonStateLegislature State Legislature District
C_DISTRICT
SeattleCityCouncil
Seattle City Council
District
kccdst
KingCountyCouncil
King County Council
District
All data from the US Census Bureau, the City of Seattle, or the King County Board
of Elections. All data from 2019.
b. Like Lab 6, this lab includes a series of questions scattered throughout the lab
(the first of which are below). Take a few minutes to find the answers to the
questions below. Make note of your answers as you’ll submit them, along with
your map, in the Lab 7 Submission Portal.
Question 1:
What is the name of the eastern-most ballot dropbox in King County?
HINT: Use the identify tool, to see the data associated with any feature.
a. Issaquah City Hall Ballot Drop Box
b. Vashon Library Ballot Drop Box
c. North Bend Library Ballot Drop Box
d. Garfield Community Center Ballot Drop Box
e. Snoqualmie Library Ballot Drop Box
Question 2:
What shapefile can we join our tables to given the data they contain and the unique
identifiers available?
a. VotingDistricts
b. CensusTracts
c. SeattleCityCouncil
d. KingCountyCouncil
e. WashingtonStateLegislature
3. Using your answer above, join the table to the appropriate shapefile. Take a screenshot
of your successfully joined tables.
GEOG 360: GIS & Mapping
Question 3:
Upload a screenshot of your joined table (Demographics table, joined to the census tracts
shapefile).
Question 4:
How many people in Census Tract 1 (GeoID: 53033000100) identify as of two or more races?
a. 0
b. 260
c. 575
d. 756
Question 5:
What percent of eligible people in Census Tract 14 (GeoID: 53033001400) are registered to
vote?
a. 0.71 (71%)
b. 0.83 (83%)
c. 0.87 (87%)
d. 0.91 (91%)
4. First, let’s learn how to query data.
a. We will start with an
attribute query. Let’s select
all of the census tracts
where less than 50% (.50) of
people eligible to vote are
registered. To do this click
on the ‘Select Features Using an Expression’ button (see image above).
NOTE: If you don’t see the selection/query tools in your toolbar, right click on any
blank area in the toolbar part of your screen and check the box next to ‘Selection
Toolbar.’
b. You should see a ‘Select by Expression’ pop-up window. In the Expression
window, we will write an expression to select or query all of the census tracts
where less than 50 percent of eligible voters are registered. We can use the
following expression:
Demographics_PercentofEligible2019 < 0.5
GEOG 360: GIS & Mapping
When you have entered the above expression, click Select Features.
NOTE: When you start typing the name of the column, QGIS will start giving you
a list of options. A bug prevents you from simply clicking on the option you want,
but you can use the up and down arrow keys on your keyboard to navigate to the
one you are interested in and then click enter to select it.
c. You should see 12 features selected. They will light up in yellow on the map and
simultaneously in blue in the attribute table.
NOTE: You can clear the
selection using the
‘Deselect Features from All
Layers’ button (see image
to the right).
d. We can also use an
attribute query to find a specific record. Say, we want to examine the census
tract that is home to the UW’s Seattle campus. The UW campus is in Census
Tract 53.02 (GEOID: 53033005302). Use the ‘Select Features Using an
Expression’ button with the expression below to select the census tract that
houses the UW.
GEOID = 53033005302
Scroll through the attribute table to find the selected record and use it to answer
the questions below. Alternatively, you can use the identify tool to click on the
selected features on the map and use the results to answer the questions below.
Question 6:
Are there more males or more females living in Census Tract 53.02?
a. There are more males.
b. There are more females.
c. There are the same number of males and females.
Question 7:
How many people aged 65 or older live in Census Tract 53.02?
a. 0
b. 105
c. 378
d. 1467
GEOG 360: GIS & Mapping
5. Now let’s try a spatial query.
a. Let’s examine which ballot drop boxes are within the City of Seattle. Our
SeattleCityCouncil layer is of city council districts so matches up perfectly with
the boundaries of the city, so we can use that.
b. Click the ‘Select by Location’ button to bring up a pop-up window. There we are
going to set it to select features from ‘Dropboxes’ where the features ‘are
within’ ‘SeattleCityCouncil.’ While we won’t be adjusting using this option now,
note that there is a box we can select to only compare our first layer (the one
being selected) with features that have been selected in the second layer (the
one we are comparing the features with). We also have the option to either
create a new selection, add to the current selection, select from the current
selection, or remove from the current selection. For now, let’s keep this set to
“creating new selection.” When your pop-up window looks like the image
below, click Run.
c. Examine the resulting selection.
GEOG 360: GIS & Mapping
Question 8:
How many ballot dropboxes are within the City of Seattle?
HINT: You can count them up on the map or you can open the attribute table where at the top
it will tell you the total number of features and the number that are selected.
a. 0
b. 15
c. 23
d. 31
e. 36
6. Using what you have learned about queries, answer the following questions:
Question 9:
How many ballot dropboxes are in King County Council District 3?
a. 8
b. 10
c. 12
d. 14
Question 10:
How many census tracts have more than 1000 people who identify as Chinese?
a. 16
b. 26
c. 36
d. 46
Question 11:
How many ballot dropboxes are in census tracts that have more than 1000 people who
identify as Chinese?
a. 0
b. 3
c. 6
d. 9
GEOG 360: GIS & Mapping
7. Now, let’s make some buffers.
a. We will start by making a buffer showing us the area that is within one mile of a
ballot dropbox site. To do this, click on Vector in the topmost menu bar. Select
‘Geoprocessing Tools’ and then ‘Buffer.’ This will open a pop-up window.
b. For the ‘Input layer’ we want to select whatever it is we want to draw a buffer
around. In our case, this is the Dropboxes layer.
c. Next, we want to set the distance to 1 mile.
d. Next, we want to check the box to dissolve the results (this just means that in
areas where dropboxes are within a mile of one another, instead of producing
unique overlapping circles around our points, those circles will be merged
together).
e. Finally, we want to click on the … button at the end of the line that says [Create
temporary layer] and tell QGIS where to save the resulting buffer.
f. When your pop-up menu looks like the one below, click ‘Run.’
GEOG 360: GIS & Mapping
g. When it is done, click close to return to your map.
8. Use what you have learned to create a map showing the parts of King County that are 1,
2, 3, 4, and 5 miles from the nearest ballot dropbox.
Question 12:
MAP 1: Make a map that uses concentric buffers to show the parts of King County that are
within 1, 2, 3, 4, and 5 miles from the nearest ballot dropbox.
9. Finally, let’s explore our overlay tools.
a. Remember that the most commonly used overlays are union, intersect, clip and
erase/difference. You may have noticed that these tools are next to the Buffer
tool in the Vector → Geoprocessing Tools menu.
b. Let’s start with an intersect. Say we want to know how many voting districts
have a ballot dropbox within one mile of (at least somewhere within) the district.
We can use an intersect and then look at how many features there are in the
resulting layer.
c. Go to Vector → Geoprocessing Tools → Intersection.
d. Choose the one mile buffer layer you created and the VotingDistricts layer as the
input and overlay layers (order doesn’t matter).
e. Tell QGIS where to save the output file.
f. Everything else can stay the same, but note that we have the option to only
include some of the fields (columns) from the attribute table of each layer. Also
note that we can choose only the selected features in either layer if we have
features selected from a query. While we don’t want to do this now, this is a
useful way to tie together different analytical operations.
g. Click Run.
h. When the intersect is complete, click close and examine the results.
i.
If you open the attribute table, we can easily see the total number of features at
the top of the table. That is how many districts have at least somewhere within
the district that is within a mile of a ballot drop box.
GEOG 360: GIS & Mapping
Question 13:
How many voting districts are within a mile of a ballot dropbox?
e. 392
f. 763
g. 1251
h. 1712
10. We can also use the intersect tool to help us focus in on the city of Seattle.
a. Use the intersect tool to intersect the voting districts and Seattle City Council
districts. This will return a layer that only includes voting districts in the City of
Seattle and that includes the council district as a column in the attribute table.
b. Take a screenshot of the new intersected layer of your map.
Question 14:
Upload a screenshot of the new intersected layer of your map (Voting Districts intersected
with Seattle City Council Districts).
11. Now let’s consider whether minority status affects the likelihood of someone living
within walking distance of a ballot dropbox. Let’s say that walking distance is one mile.
a. To do this, we first need to estimate how many registered voters in each census
tract are minorities. This is a little more complicated than it initially looks as the
data we have about race is all people of that race, not just those registered to
vote. So, we will need to make some calculations.
b. First, let’s figure out what percentage of all people in the census tract are
minorities. While we can do this by adding up the minority categories, it is easier
to just look at what percentage of the population is White and subtract that from
100% (or 1). Use the Field Calculator (with field type decimal) and the following
formula to determine what percentage of people in that census tract are
minorities:
1 - (Demographics_WhiteOnly/Demographics_TotalPop)
c. Now, we need to figure out what percentage of the population is eligible to vote.
Use the Field Calculator (with field type decimal) and the following formula to
determine what percentage of people are eligible to vote:
GEOG 360: GIS & Mapping
Demographics_EligibleVote/Demographics_TotalPop
d. Now, we can tie our different percentages together. Again, in Field Calculator
(this time with with field type integer) and the following formula we can
estimate how many minority registered voters there are in each census tract.
Demographics_TotalPop * PercentMinority * PercentEligible *
Demographics_PercentofEligible2019
In the above formula PercentMinority is the percent of the population that is a
racial minority (whatever column you calculated in step 11b above). And
PercentEligible is the percent of the population that is eligible to vote (whatever
column you calculated in step 11c above).
e. Now that we have a column of the estimated number of minority voters, we can
use that to create a dot density layer of minority voters. You’ve done this before
for the sake of visualizing data. Here, we are doing it to analyze data.
f. Go to Vector → Research Tools → Random Points in Polygons…
g. Set the Input polygon layer as Census Tracts.
h. Click on the button at the right side of the ‘Number of points for each feature
line’ and select Assistant (see image below).
GEOG 360: GIS & Mapping
i.
In the assistant menu, select your Minority Voters category and use the refresh
button to find the value range. For the output, make sure that your output
range encompasses that range (i.e. change the output values to match the input
values above.
j.
Now use the blue arrow at the top of the assistant menu to go back to the
original pop-up window (Random Points in Polygons). Where it says [Create
temporary layer] tell QGIS where to save your file and then click ‘Run.’
k. This may take a couple minutes. Click the Close button when the operation
finishes.
l.
There will be more points that we can reasonably see and that is totally fine.
Here, the point isn’t to demonstrate anything visually, but to perform an
analysis.
GEOG 360: GIS & Mapping
m. Now, let’s do that analysis—we want to know what percentage of the points are
within one mile of the ballot drop boxes. Use the Selection tool to select all
points within the 1-mile buffer.
n. Use that selection to answer the following question
Question 15:
Approximately how many minority voters live within one mile of a ballot dropbox?
NOTE: These are approximate values because as the points are random, there will be some
variation in your results.
a. ~90,000 people (or about 23%)
b. ~120,000 people (or about 30%)
c. ~155,000 people (or about 39%)
d. ~19
