- Ames, IA grew significantly between 1991-2011 in both population and area. The population increased by 23.7% while the area classified as urban grew by 116%. - The main areas of expansion were northward, southwestward near the university, and eastward near the interstate and industrial areas. However, floodplains limited southern development. - While growth correlated with increased population and university enrollment, other factors like industrial development, transportation infrastructure, and flood risks also influenced expansion patterns. The study provided insight into how Ames has changed and could inform future planning.

1. Joel Heilman
Urban Development in Ames, IA 1991-2011
Ames is a fast growing city in central Iowa. According to the United States Census
Bureau, the population grew from 47, 198 in 1990 to 58,965 in 2010, which is close to the time
period the project studies. This is a change of 23.7%. The state of Iowa grew by only 9.5% in the
same amount of time.
The city has not only grown in population but also in land area. This project sets out to
study this change in land use. I grew up in Ames during most of the study time and I have
observed a lot of changes. I wanted to see what these changes looked like as a whole, and see if
what I observed was corresponding to what actually happened.
There were three main objectives for the project. The first objective was to create a
change detection map for the Ames area. From that, the goal was to see how much Ames has
grown in the twenty years from 1991 to 2011. Also, I used the map to determine what areas of
Ames had grown the most.
From this map and the conclusions, the city of Ames can determine how to use its
resources. They can create policies to help smooth growth and maintain urban sustainability. The
school system could use it to determine bus routes, and find a good spot for a potential new
school. Iowa State University can also use the results for expansion and to determine bus routes
for CyRide, the university bus system.
The location of the project was the city of Ames, located at 42.0° N, 93.6° W with an
elevation of about 940ft. Ames is located on the Des Moines Lobe, the area of Iowa that

2. experienced the last glacier during the last ice age. This area of central Iowa is typically flatter
than the rest of the state and has very good farmland.
Ames is on the western side of Story County, located just 30 miles north of Des Moines.
As of 2010, the city was home to over 58,000 people. From which about half are enrolled in
Iowa State University. In fall 2013, the city reached record enrollment at 33,241 students. The
University is also the largest employer in town. Ames and Iowa State University have become
almost synonymous.
The first step of the project was to collect data to use. I found images on the Glovis
website. I found the right location and set the cloud cover to zero percent. Clouds can make a
project impossible. The ground cannot even be seen if clouds are in the way. Even a few are not
good, especially while performing a classification. Clouds, and their shadows, are usually
classified as something else, so having no clouds in the area is necessary. Despite the setting,
both my images had a few clouds, but fortunately none were in my study area, so I could still use
the images. I found images from August 1991, and July 2011.
Figure 1: Ames, IA

3. Once I downloaded the images and loaded them onto Erdas Imagine 2013, I had to
georeference one to the other. The images did not have the same reference. I could not tell which
one was correct, or if both had the wrong reference. But as long as the images matched, I could
make a change detection map from them. So, I georeferenced the 1991 image to the 2011 image.
The next step was to create subset images from the two images. The subset feature in
Erdas Imagine allows the user to crop the image to a certain area. This way I could look at just
Ames. This made the rest of the process much easier. I drew a rectangle around Ames so I could
look at all the change around and within the city. I saved the Aoi file so I could use the same area
for both images.
The next step was to classify the two subset images. Since I was very familiar with the
area, I decided to use supervised classification. However, the colors of the images for different
classes were very similar to each other, and I had to redo the classification several times to get it
Figure 2: Methods for Project

4. remotely accurate. I used about 20 training sites for each image. The grass and farmland
appeared to be the same color, so I joined the two into one class. This was fine since my main
concern was the urban class and its change. I also noticed the water was more apparent on the
2011 image. The rivers were wider, and the lakes were bigger. This actually made sense because
2011 was a wet year and Ames experienced some minor flooding. 1991 was a lot drier in
comparison.
Urban 10 Water 11 Cropland & Grass 12 Forest 13
Urban 1 10 11 12 13
Water 2 20 22 24 26
Cropland & Grass 3 30 33 36 39
Forest 4 40 44 48 52
Figure 4: Table showingpixelmultiplication. The 1991 image is onleftand the 2011 image
ison top.
Figure 3: The recodedclassifiedimages. 1991 ison the leftand2011 is on the right.Redis
urban,blue iswater,greenisforest,andbrownis croplandandgrass.

5. I then recoded both images so that there were only four classes, instead of 20. I then
recoded the 2011 image again to get different values so I could multiply the image values to get
the proper classes. Figure 4 shows how this multiplication works.
I used the two image function to multiply the image values together. Figure 4 shows the
resulting values. These values allowed me to determine which areas had changed and precisely
how they had changed.
Figure 5: The final urbanchange detectionmap.

6. I then recoded the image to show only five classes: urban (no change), water, cropland
and grass, forest, and urban growth. Many of the changed classes were small and mostly due to
inaccurate classification. Most of the true change was urban, so I was able to fit each class into
the five categories to get the resulting map as shown in figure 5.
I also performed an accuracy assessment of the two classified images. I could tell already
that the images were not classified perfectly. Much of the farmland in both images had spots of
forest and urban intermixed which I knew was incorrect. The 1991 image didn’t detect the rivers
as water nor did it classify all the freeways as urban.
I chose to do the accuracy assessment for each image with 50 stratified random points. I
compared the points to the original image and used my knowledge of the area to determine the
accuracy of the individual points. The results were a lot better than I expected. The 1991 image
had an overall accuracy of 84% and the 2011 image had an overall accuracy of 86%.
From the change detection map I was able to determine how much Ames had changed in
20 years. If the change corresponded to population, the result should be about a 23% increase in
population. Instead, the urban class had grown by 116%. According to the map, the size of Ames
had more than doubled.
The map also shows which areas grew the most. The most notable change was the
expansion of the city northward. The residential areas have grown here more than anywhere in
town, this matched my hypothesis. This expansion is directly related to the population growth,
but may or may not relate to the university’s growth.
Another notable area of expansion was the southwest part of town, along with other areas
of growth south of town. This development involves many types of new buildings. It involves

7. new housing and new apartments—some, not all, is for students. It also involves new stores and
restaurants, including Perfect Games—a hangout for both students and the community. Some of
it is also the expansion of the university, including a new athletic facility for the basketball teams
and research facilities. Much of the southern expansion is related to the university; in fact many
of the “holes” that are classified as cropland and grass is actually Iowa State athletic fields and
research fields for the college of agriculture and life sciences.
The third area of expansion was east of town. The area is home to many of the industrial
businesses in town. The area is mostly commercial buildings and factories. There’s also a
university research facility in the northern part of the region. A small Des Moines Area
Community College (DMACC) campus is also present, though there doesn’t seem to be any
significant growth. The area is also right next to the interstate which runs north to south just east
of town, so there are a good number of hotels and restaurants present.
The growth in the eastern part of town may or may not be related to the growth of the
university. The hotels accommodate for people visiting town, perhaps to visit the university.
Much of the industrial growth seems to be associated with the town, and not specifically the
university. However, the growth of Ames is usually related to the growth of Iowa State
University.
The expansion of Ames seems to make sense; it expands outward, especially near the
university and near the interstate. But there are other factors as well. For example, a flood map
can tell us a lot about why certain areas do not see urban growth.
Figure 6 shows the flood zones in Ames. These areas do not experience growth because
Iowa sometimes experiences flooding in the summer. Nobody wants to build on these lands

8. because the risks are too high. The map shows areas near Squaw Creek and the South Skunk
River only. A smaller creek, College Creek, also runs through the forested area northwest of
campus. These rivers and their flood zones account for the gaps in urban development. This is
the main reason why a lot of housing development has been pushed north—to avoid the
floodplains.
Figure 6: FloodZonesinAmes

9. The floodplains are not totally unused. Most of them are turned into parks and golf
courses. There are plenty of parks along the South Skunk River for example. There appear
classified as either cropland and grass or forest.
The biggest surprise of the project was the amount of growth; 116% growth seemed
absurd. The accuracy assessment was surprising; I did not think the classifications were as
accurate as they were. I decided to get a more accurate accuracy assessment would take many
more points.
However, through visual interpretation I could find some of the leading causes for the
problem. Both interstate 35 (runs north to south, east of town) and U.S. highway 30 (runs east to
west, south of town) are not classified correctly. The entirety of both highways is not classified
as urban, and most that is urban, is classified as urban growth. Both highways existed in 1991, so
they should both appear as no change. I also observed spots of urban growth outside of town.
Most of these areas were not urban at all.
Another problem was inaccurate georeferencing. Although the images had almost the
same reference, they did not line up exactly. I used about 15 points in my georeferencing, but
apparently this was not enough to get accurate results.
Although there was some error, Ames did grow more than I expected, and most likely
more than the 23% estimated. Part of this may be due to the housing boom in the early 2000s.
Another factor may be the increase in the size of houses. Many of the new homes in north Ames
are large houses with bigger plots. Therefore, the density of people is becoming smaller, which
means the land area of the city is growing faster than the population.

10. There are several things I would like to do for the future. One is to get better
classifications. This may mean doing a classification again with more training sites, but also I
think different raw images could give me different results. I also would need to be sure the
georeferencing is completely accurate.
I would also like to overlay the flood zone map with the classified change map. This
would give me a better understanding of how flood zones affect urban development. I could also
look at how different water heights affect urban development differently. The flood zone map I
used was also limited because it didn’t show College Creek or the areas south of U.S. highway
30.
Another interesting idea would be to try different years. Perhaps I could observe the
change half way during my study time by using an image from 2001. I could also use older
images from the 1980s or 1970s. It would be interesting to see the change all in one map
showing how much growth occurred each decade.
Despite difficulties, I was able to get some decent results. I produced a change map of
Ames between 1991 and 2011, and was able to determine which areas grew the most. Although
it is hard to say how much Ames did grow, it certainly grew more than I expected.

11. Works Cited
"Ames (city) QuickFacts from the US Census Bureau." United States Census Bureau, 27 June
2013. Web. 7 Dec. 2013.
"City of Ames." City of Ames, 2013. Web. 8 Dec. 2013.
"IFIS – Iowa Flood Information System." Iowa Flood Center, Web. 16 Dec. 2013.
"Iowa State University." Iowa State University of Science and Technology, 2013. Web. 7 Dec.
2013.
"USGS Global Visualization Viewer." USGS, Web. 2 Dec. 2013.