Matt's Blog

Lab 8

mpier010 — Wed, 02 Dec 2009 17:15:15 +0000

Here is lab 8. This lab was very simple and very cool.

Lab 8

mpier010 — Wed, 02 Dec 2009 17:14:30 +0000

Here is lab 8. It was very simple but very cool at the same time.

Lab 7

mpier010 — Wed, 02 Dec 2009 17:08:55 +0000

For this lab, I had to create several GIF layout maps for population change by decade from 1950 – 1960 to 2000 – 2008. Here is one of those maps.

Map Critique 5

mpier010 — Mon, 30 Nov 2009 17:33:57 +0000

For this map critique, I chose a map that has to do with obesity rates in the United States. The obesity rates are shown as percentages for all fifty states. This map is a graduated color map with green representing the leanest, or least fat, states and red representing the obese states. The map elements on this map are arranged in a appealing way, but the title is smaller than the legend, so all of my attention quickly went to the legend rather than the title. I think that the color scheme on this map should be more neutral so the percentages can be seen better, but overall this is a good map. I also think that these obesity rates are a reflection of the local quisine and the amount of restaurants in the state. The state that is the least obese and probably the most healthy is Colorado. I think this is the case because colorado is mostly mountainous and cold so people probably do not go out as much. The state that is the most obese is Mississippi. Most of the southern states with the exception of Florida all have high percentages probably because of the way people in the south cook their food. Overall, I think this is a well constructed and very informative map.

Map Critique 4

mpier010 — Mon, 16 Nov 2009 04:46:55 +0000

For this map critique, I chose a map that has information that is a little bit different from my other critiqued maps. This map involves UFO sightings in the United States over a certain period of time. The UFO sightings are shown in the U.S. by county and is determined by the number of reported UFO sightings per 100,000 people. This is a graduated color map with the dark red colors being the highest concentration or “hotspot” of UFO sightings. The overall information on the map is good but the design of the map is not that good. There is no title to indicate what is shown (the only way I found out was the information provided with the map) and there is no information as to where the data was retrieved. The only information shown on the map is the legend, which indicates the number of sightings by color with the lowest number of sightings as yellow and the highest number of sightings as red. The information shown in the map indicates that most of the reported sightings in the United States occurred in the west with a high concentration in New Mexico, Colorado, and Nevada (Area 51 location). Well at least Virginia is UFO-free!

Lab 5

mpier010 — Mon, 26 Oct 2009 16:17:24 +0000

In this lab, I had to create a graduated color map (chloropeth) and a graduated symbol map (proportional symbol). In creating these maps, I just had to manipulate the data within the attribute table of my virginia population shapefile. The main objective of this lab was to create a classification scheme for each of the maps that I eventually created. In the first map, it was pretty simple to create the graduated symbol map, but it was a little difficult to find a color scheme that represented the percent change in the map. I eventually chose one that made the most extreme change a different color from the rest of the values. Since I had negative population changes in some of the counties, I chose a classification scheme that had a break at the zero mark. Then it made it easy to tell on the map which counties lost population.

For the second map, I was a little bit confused at first because I did not know how to represent the population change from 2000 to 2008 at total values rather than percents. What I ended up doing was creating another field in the attribute table of the Virginia shapefile. In the field calculator, I subtracted the 2000 population data from the 2008 population data to get the total numbers of the population that either increased or decreased. In classification, I chose this field in the graduated symbol section of the symbology tab in the properties. On the map, I made the circles to be a certain size so they did not overlap into other counties. Overall, I know how to classify a map now.

Here are my maps:

Map Critique 3

mpier010 — Fri, 23 Oct 2009 12:23:23 +0000

The map I chose to do my critique on this week is a very unique one. This map shows the locations of suicide jumpers on the Golden Gate Bridge based on the locations of the lightpoles that line the bridge. At each location of suicide, there is a vertical bar that extends out to a certain distance based on the number of suicides from that spot. This map caught my eye because I have never seen a suicide map before and I did not realize how many people have actually jumped off this bridge since its construction (1,218 reported suicides since 1937). This map gives a frame of reference to where the most suicides have occured. Although this map portrays good information, the design of the map is not so good. First of all, there is too much information on the map and it is somewhat hard to read. There is no balance of the map elements. The map legend is off center in the middle of the map and contains only one symbol. Also, the title of the map is not in the direct center and it is de-emphasized because of all the wording underneath of it. Also, there is no scale on the map to determine how long the Golden Gate Bridge is. This map somewhat shows direction with the east and west arrows, but in order to get a real sense of direction, I had to turn my head sideways. I think that this my is trying to communicate to the audience that the Golden Gate Bridge is a popular suicide spot. This map indicates that the most suicides occur near the middle of the bridge, which makes since because it is higher than the other parts of the bridge.

Lab 4

mpier010 — Wed, 21 Oct 2009 13:32:39 +0000

Before I begin this post, I must defined some terms related to this lab.

1. dBase file – Also just called DBF, this is simply just a database file format used by ArcGIS for data in tables

2. Select by Attributes – This is a form of spatial selection in which one can pick exactly what they want from a layer and eventually export these attributes to a separate shapefile, which is what occured during this lab.

3. Query – This is related to select by attributes. A Query is a way to find specific information within a layer. Select by attributes is just the process of selection, not finding the information itself.

4. Fields (in a table) – These are columns in attribute tables of single layers that store information on a single attribute.

5. Records (in a table) – These are sets of related data fields containing all of the attribute values of a single feature.

6. Attributes – Non-spatial information about a geographic feature in a GIS, usually contained within tables.

7. Relational database – A data structure in which groups of tables are linked together by shared fields.

8. Join function – Connecting two or more features from different data sets so they can become a single feature.

9. Monitor fire – This can occur when using different functions in GIS that is time consuming, such as the join function used in this lab. Basically, the computer overheats from trying to process a lot of data.

10. Calculate/Field Calculator – This is used to calculate a certain value within a certain field(s) using the query function. For example, in this lab I used the field calculator to find the population change between different sets of years.

I will now explain this lab.

Basically, we used select by attributes to only create a layer for the state we have population values for, which in my case in Virginia. I then had to join the information within the virginia shapefile with the Excel spreadsheet of population values from lab 4. This was difficult and time consuming because ArcGIS is very picky when it comes to combining attributes because of spaces within the file names. I had to change the filenames of the spreadsheet and the file that the spreadsheet was in to single files with no spaces. It was a relief when the data finally showed up in the attribute table of the Virginia shapefile.

Once this was complete, I had to create different fields in the attribute table based on the population changes between the decades (1910 – 2000, and 2008). I then used the field calculator to enter in a function given to me in the lab to find the percent change in population between the years (for example: 1900-1910). This part of the lab was relatively easy compared to the previous part. Overall, this lab was a learning experience in that the data files in ArcGIS and associated with ArcGIS must contain no spaces, or the data will not work in the specific function at hand.

Lab 3

mpier010 — Mon, 05 Oct 2009 16:41:01 +0000

Lab 3

In preparing my text data for the excel spreadsheet, my state of Virginia was very difficult to deal with. Virginia has both cities and counties as separate entities. It was also a pain to fill in all the blank spots in the cities that were no longer cities. In this lab, I leaned how to separate information in a text file to be incorporated into an excel file by columns. The data for the 2000 populations of Virginia by county was in a format that would not translate directly into a neat viewable excel column. I found data of the 2000 population of Virginia by county in the form of an excel file and I directly brought that into the Virginia.xls file that I was working with. The biggest problem that I had was this lab is that I had to manually enter the estimated population data for 2008 in Virginia by county. While entering the population data, the populations kept becoming misaligned because Virginia included cities on the excel spreadsheet that were not on the 2008 estimated population data. When all was said and done, I was reliefed to know that I now had all the correct information for the populations of cities and counties in the state of Virginia from 1900 to 2008. This can be used in a later lab exercise for incorporating this data into ArcGIS

Map Critique 2

mpier010 — Fri, 02 Oct 2009 14:03:05 +0000

The map that I chose to critique this week is a map that shows the distance to the nearest McDonalds restaurants in the United States. The first time that I saw this map, it was visually appealing because the background of the map is black and the locations of the McDonalds restaurants are symbolized as bright yellow and orange. I like this map because it shows just how many McDonald restaurants are in the United States and where most of them are located. This map almost looks like city lights throughout the United States. The design of the map itself is good because it relies on points to form the outline of the United States rather than showing a U.S. boundary. The points are arranged in such a way as to form the border of the United States. As far as good cartography goes, this map is not that good. There is no scale or legend and the title is located in the bottom corner of the map rather than at the center top. This map communicates to the audience that in areas that have high concentrations of McDonalds restaurants, there are big cities and urban areas. This map reflects population density because in areas that have many McDonalds, there has to be enough people in order for all the restaurants to stay in business. This map also communicates that the midwest has a low population density because the McDonalds restaurants are spread apart.

: Distance to the Nearest McDonalds in the United States