MAPPING GIS DATA

   

Instructions:
Choosing from a given list of metropolitan regions, select one and build a map depicting the percentage of White, Black, Asian, and Hispanic (based on 2000 population) persons at the tract level.

Methods:
Using ArcGIS, I narrowed in on the metropolitan region of Boston. After narrowing down the map size to the metropolitan region of Boston, I copied three additional maps onto the ArcGIS screen. Next, by opening the Insert dropdown menu,

 I built four legends (one for each of the 4 U.S. Census classifications I had been asken to map) by opening tracts in the Layers menu. On the Layer Properties main screen, I selected my four data classifications (one at a time) in the Value drop-down menu. I normalized this data by the 2000 U.S. Census data. I sized these legends proportionally to my maps.

I also placed a north arrow and bar scale on my map from the Insert drop-down menu.

Challenges:
The first challenge for me was locating a way to provide more aesthetically pleasing typed material in ArcGIS. I opened the Customize drop down menu and selected toolbars. From there I clicked on draw and a toolbar with more type options dropped down.

Next, we were asked to make the map easier to follow by providing the same percentage class-breaks for each map. If I had normalized using the predominant White population numbers, the data for less-represented populations would have meant very little. In order to make sure that each population was represented as well as possible, I normalized using the Asian population data as they were the smallest population. This did not detract at all from the predominance of the White population data as can be seen on the map.

In order to change all of the legends, I had to select each one and manually change the range AND label of each one. I tried only changing the labels, but after further investigation, I realized that the ranges were indicating they had been changed without actually changing the mapped data.

The legend titles provided in ArcGIS were very unhelpful but unavoidable. I had to cover them with a box from the draw toolbar I had brought down. Next, I typed my own legend headings over the boxes I had built.

I felt that the percentages were a little ambiguous without some additional information. I did not want to redundantly state these specifications on four maps, so I chose to include additional information in red on the bottom-right of my map.

I did not like the empty space to the right of each of my legends but found them unavoidable. I could not change the width of the legends without changing the height as well. I did not have the room to do so based on the size of my maps.

I wanted to provide a backdrop color but realized when I tried to do this in ArcGIS the backdrop color was visible in the lighter colors of my proportional map. I had to export my map as an Ai file to Adobe Illustrator in order to provide a backdrop color to my map.

Lastly, the term 'race' is used in the U.S. census. In order to represent it properly, I had to include the term against my personal feelings. This term is far too broadly applied and I hope to avoid it as much as possible.

BIVARIATE MAPPING WITH MICROSOFT EXCEL AND ADOBE ILLUSTRATOR CS5

 

 

 

Instructions:
Build a map including two variables of my choice. They must be related to one another. Be sure data sources are reliable. Create the map in ArcMAP and export it to Adobe Illustrator. Complete the map in Adobe Illustrator. Determine approriate symbol sizes. The map must contain 30 geographic units. Include all normal map elements. Include a description of what is being mapped.

Methods:

Because the instructions were very specific, the methods identically follow the instructions.

I had to build a database with proportions of population density from the U.S. census and per capita energy use from the U.S. Department of Energy in order to determine proportions for map symbols. By dividing each data segment by the largest segment, I was able to calculate the proportional size for each of my symbols.

After determining the size of my largest symbol, I had to copy that symbol size another 49 times. By clicking on each symbol, I was able to alter the size of the symbol by entering the percentages I had calculated in my databases.

Using ColorBrewer, I selected a single-hue color scheme because my map colors would be proportions of the same value compared to one another

Challenges:

I had not anticipated as little variation in energy consumption as I found. This meant I had to carefully choos the largest symbol size so as to not overwhelm the northeast with large and illegible symbols. It was also necessary to not make the symbols too large to make the second variable (density per capita) easy to ascertain.

Another challenge was finding data for the same year for as much accuracy and comparability as possible. This took some fairly intense searching.

Because of the small size of northeastern states, it was still necessary to reduce the transparency of the energy consumption symbols to be able to make out the colors behind them.

The population density data contained a few outliers at the top. With fewer classes almost all states fell into the same category in the middle. It was necessary to push the envelope a little and include 7 data classes for population density.

Because energy consumption is measured using a relatively unknown measure, I needed to include the definition of a BTU on this map.

Energy consumption is very nuanced with many contributing variables. As a result, I needed to include some of the other factors contributing to energy consuption. That way, a reader knows not to read TOO much into the findings of the map.

PROPORTIONAL SYMBOL MAPPING WITH MICROSOFT EXCEL AND ADOBE ILLUSTRATOR CS5

 

Instructions:
Create a proportional symbol map of one element. Use ArcMap to create the bases map and export it to Adobe Illustrator to finish it. The map must contain at least 30 geographic units. Include all normal elements included on a map.

Methods:
The main methods of map building were specified in the instructions and followed.

Because I was mapping on the North American continent I chose the Albers Equal Area projection.

I needed to identify a reliable source of data for student to teacher ratios by state. I ran into some conflicting information and had to dig a little further to establish the legitimacy of the data I ultimately chose.

By building a database I was able to calculate proportions based on the largest total student to teacher ratio. From there I determined the size of my largest symbol and copied it once for each state. I opened these copies and reduced the size of the symbol based on the percentage calculated in my database.

Challenges:
Teacher to student ratios are very similar in number. Though there is a large difference between having 19 children and 13 children in a class, it is difficult to portray with a proportional symbol map. I probably should have chosen a different subject to map as a result. If I had the option of usinf graduated symbols, I would have been able to create a map that actually showed something. In this case, it takes a lot of effort.

I really should have decreased the size of my largest symbol even further. Yes, it was less important because there was not other data set being displayed it, but the northeast states are still difficult to make out.

Looking back, I probably should have chosen a lighter background color. I really like the look of the map, but the background is probably too similar.

The definition of a "teacher" in this case is not necessarily obvious to every reader. As such, I needed to include a definition of a teacher as specified in my data set.

CHLOROPLETH MAP OF AMERICAN ANCESTRY IN NORTH CAROLINA

  

 

Instructions:

Using a given file of data, calculate class breaks and build two maps showing absolute number of persons reporting American ancestry (using quintile and equal interval classifications), and two maps showing percentage of all persons reporting American ancestry using the same classifications as  for absolute numbers. Include a short paragraph about the ethics involved in selecting classifications for data.

 

Methods:

All four maps were made in ArcGIS by coying the original data given.

 

Using Color Brewer, I selected two single-hue color schemes for my data.

 

Using the database, I calculated class breaks using the formulas for Equal Interval and Quintile classifications.

 

Challenges:

I found this map to be the most straight-forward and did not encounter as many challenges.

 

I have since learned that is typical to arrange legends from smallest to largest and will incorporate that in future maps.

 

It was very painstaking to locate each quantity based on a reference map of North Carolina counties.

 

I found it necessary to do a count on my final classification when applying the color scheme to it in order to make sure I had not missed any counties from previous classifications.

 

It was also important to make sure that individual counties did not get accidentally moved while applying colors to them.

 

 

 



REFERENCE MAP OF MY HOMETOWN

 

Instructions:
Use ArcMap to obtain an aerial image of my hometown. Arrange the image in ArcMap's layout view and export it to Adobe Illustrator. Use pen, shape, and text tools to trace the desired parts of the aerial image. The map must include an appropriate number of streets, and all applicable elements of my town.
Methods:
The initial methods follow the specified instructions.

Challenges:
I learned very quickly that a large amount of data points are needed after zooming very close in order to replicate the curvatures of rivers and streets.

Making determinations about important features to include in a relatively large town was difficult at first. I had to ask, "what areas would I want a visitor to be able to locate the most?"

I had to adjust the label locations and sizes many times as I realized certains streets would need to be included in my map. The more time I spent on the map, the more streets I felt were necessary to provide a good idea on how to navigate around to the different features I had included.

I finally decided to leave out the islands located on the Chippewa River as they were not integral to the purpose of the map.

I realize now I failed to include a label for Riverview Park.

It was important to consider the direction of labels for roads and water bodies to maintain consistency.

Some amount of cartographic generalization ws required in the congested area around the The Chippewa Valley Technical College, and UWEC.

GPS-BASED MAP

 

Instructions:
Map something including 30 waypoints or a track that exhibits a discernible geographic pattern on a map. Use ArcGIS to obtain a base layer for the map and plot the data onto the map.

Methods:
Using an E-trex, I drove my usual hill-training run at 6:45am to ensure minimal traffic so I could drive slow and collect as many data points as possible. As a result it took me one hour and 5 minutes.

Using the tracking mode, I established the following settings: record method - distance, interval - .01km.

I chose to include the topographic profile layer in ArcGIS because of my focus on elevation change. This will help to indicate where hilly areas are in Eau Claire thus assisting the purpose of my map.

Challenges:
Because I was not able to actually run the course in time to meet the due date, the car travel was painstaking.

I should have set the E-trex for a lower interval in order to include more data points.

The map shops elevation height, but what is more interesting is elevation change. As a result some hilly areas are difficult to make out. There are hills going into Carson Park that most readers will not pick out becuase they are not colored red or orange. Only someone looking for it will notice the change from green to yellow and realize there are hills there.

Because the change in elevation is what really matters, it was necessary to include a large number of data classifications. Even then, some of the colors are muddled. The area to the far left is several hills in a row with increasing elevation change.This is not immediately visible in my map.

PROJECTION AND COORDINATE SYSTEM DIFFERENTIATION

 

Instructions:
Using ArcMap, build maps of South America using specified projections. Export these maps to Adobe Illustrator. Build a Poster with all of the projection systems on it. Include the latitude of origin and standard parallel where applicable. Specify what each projection is best at mapping. Include paragraphs introducing readers to the difficulties in mapping from 3-D to 2-D.

Methods:
Methods follow the instructions above.

Maps were built in ArcMap and exported as Ai files to Adobe Illustrator.

Challenges:
The main difficulty for me was maintaining the same point in ArcMap before transferring over to Adobe Illustrator. Many of my original transfers needed to be redone because I had adjusted the map improperly before transferring.

The color scheme was very important with so many maps lowered over one another. I had to tweak the colors many times. For instance I had to make sure to include more noticeable colors in the most muddled areas. Conversely, I chose lighter colors for the more easy to distinguish areas.I also adjusted the transparencies of each map color to help identify each color in the muddled areas.

Because of my inexperience, establishing a well-ordered legend was difficult. I finally learned to group the different pieces together to make sure they were all even with each other. There was so much change becuase of the SP's and LO's that it was hard to standardize. The many individual pieces made it difficult to move in a time-efficient manner.