Goals and Background:
This lab was designed for students to show their ability to
use the query function in ArcMap. Students had to use Boolean Operators to
create multiple criteria queries to extract data from file databases. Students
also had to create spatial queries from the databases as well as mapping the
results of their queries.
Methods:
To begin the lab, I imported the appropriate shapefiles from
the class folder. I was given the task of developing an attribute query that would
display counties in the United States that had a population between 4,000 and
3,000, and a population density of at 1,000 people per square mile. To do so I
created the following query using the Select by Attributes tool in the attribute
table (POP2010 >3000 AND POP2010 <4000) OR POP10_SQMI >1000. Once I
selected the data, I then created a new data layer that included the newly
selected data and created the map below (Figure A).
The next query I was assigned was to show counites
that had a male population higher than female population and a senior citizen
population of more than 6,500 in Wisconsin, Texas, New
York, Minnesota, and California. The query I developed was (("STATE_NAME"
IN ('California', 'Minnesota', 'New York', 'Texas', 'Wisconsin’)) AND AGE_65_UP
>6500 AND MALES > FEMALES). I again created new data layer and created a
map displaying the information extracted from the query (Figure B).
The next section of the lab involved both multiple criteria
attribute queries as well as spatial queries. This section of the lab
also focused on the state of Wisconsin instead of the entire United States. To
begin, I imported the correct Wisconsin shapefile that contained all the
relevant data to this section of the lab. I had to develop a query that would contain cities
that had a population between 20,000 and 15,000, a land area of at least 5
square miles, a higher female population than male population, and be within 2
miles of a lake. I first created an attribute query that would give me all the
information I needed with the exception of distance from a lake. The query I
created was ("POP2007" >15000 AND "POP2007" <20000)
AND ("AREALAND" >5) AND ("FEMALES" >
"MALES"). I then created a new data layer that contained the
information retrieved from the query. From that data, I then created a spatial
query that would select cities within two miles of a lake and created a map containing
the new layer (Figure C).
The final section of the lab was for me to create a map that
showed the Chippewa, Eau Claire, Embarrass, Fisher, Hunting, Kinnickinnic,
Mauneasha, Milwaukee, Moose, Namekagon, Pelican, Platte and Potato rivers and
calculate their distances. I created the query ("PNAME" IN (‘CHIPPEWA
R', 'EAU CLAIRE R', 'EMBARRASS R', 'FISHER R', 'HUNTING R', 'KINNICKINNIC R',
'MAUNESHA R', 'MILWAUKEE R', 'MOOSE R', 'NAMEKAGON R', 'PELICAN R', 'PLATTE R',
'POTATO R'). Because the river data layer had no information in regards to the
length of each river I created a new field in the attribute table and
calculated the rivers lengths giving me a total length of 759 miles. I displayed the rivers in the map as shown below (Figure
D).
Results
The maps below were created in ArcMap using the queries discussed above.
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| Figure A |
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| Figure B |
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| Figure C |
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| Figure D |
Credits
USA geodatabase data is from the mgisdata that comes with
the Price book. Wisconsin cities, interstates, rivers, roads, and counties shapefiles
are from ESRI, 2011. Wisconsin Lakes were created by Dr. Wilson in 2012.
