To begin this lab, it was important that I had a good
understanding of query expressions. To do this, I started with simple queries and
progressed towards more complex queries involving attribute and spatial
selections during tutorials and lecture. The goal of this lab was to test my
comprehension of query expressions to extract specific data from a
database. The data I was asked to extract in part one included detailed
components of county data in the U.S. and in part two I needed to select
specific cities and rivers in Wisconsin that met the criteria given in the
statements. At the end of this lab, my results included three U.S. maps
and two Wisconsin map that each met their defined criteria given by each
question.
Methods:
To complete this lab, I began by adding the counties layer
from the U.S. geodatabase to my data frame. To answer question one, key
terms such as 'between', 'also', and 'at least' hinted that I needed to use
Boolean operators 'AND', 'OR', and the greater than or equal to symbol
respectively (Figure 1).
Figure 1. Query for counties with population between 3000 and 4000 people in 2010 and also all counties in 2010 that had a population density of at least 1000 persons per square mile |
In question two, I had to find records for counties again; I
used the 'IN' expression and parenthesis to select multiple states at one
time. Once the states were selected I used the Boolean expression 'AND' to
select counties that met all the criteria. If I would have used the
expression 'OR' results would also include states that only met one of the
criteria mentioned and not both (Figure 2).
Figure 2. Query for counties in Wisconsin, Texas, New York, Minnesota, and California where the male population is greater than female population and also for these states the number of seniors (age 65 and above) is over 6500 |
As I continued to question three, I
was asked to modify the query for question two. I used the Boolean
expression 'OR' because I wanted results for both question two and question
three. I used 'IN' again to select multiple states and the expression
'AND' to include housing units (Figure 3).
Figure 3. Query additionally added to that for question two including all other seniors in Washington, Maryland, Illinois, Nebraska, District of Columbia, and Michigan who reside in counties that have more than 30,000 housing units |
For part two,
I unzipped the file and continued with similar steps and included a spatial
query. To do this I used WI_cities as my target layer, Lakes as my source layer,
and specified within 2 miles.
Figure 4. Query for cities in Wisconsin with population in 2007 between 15,000 and 20,000 people, at least 5 square miles in land area, female population is greater than males, and also the cities are within 2 miles of a lake |
Lastly, I made new map and started my final query for rivers in Wisconsin. I made a field and used the Calculate Geometry to measure the distance for each entry. Then I specifically selected the rivers listed by making the query shown below (Figure 5). Once this was done, I used the Sum feature for the selected features in my MILE_FIELD entry to calculate the total length of the rivers.
Figure 5. Query to use to calculate the total length of these rivers in Wisconsin: Chippewa River, Eau Claire River, Embarrass River, Fisher River, Hunting River, Kinnickinnic River, Maunesha River, Milwaukee River, Moose River, Namekagon River, Pelican River, Platte River, and Potato River |
Results:
I made five maps each displaying the selected features based off the criteria given for each question.
I made five maps each displaying the selected features based off the criteria given for each question.
Figure 6 displays counties with population between 3000 and 4000 people in 2010 as well as counties in 2010 that had a population density of at least 1000 persons per square mile |
Sources:
Price Mastering ArcGIS Database (2016) [downloaded file] SQL Server. URL. geogsql.uwec.edu [February 16, 2017].