Mapping Census Year 2000

The point of this assignment was to use our skills in ArcGIS in order to map the US Census in the year 2000. When looking at data that was essentially collected by counting, there are many ways to organize it and reflect it. Looking at the first map above, we can see that this map shows the number of people in the year 2000 by counties in the US. In order to map this data and make this first map as well the following two maps, it was essential to join our census data to the counties data. Specifically to this map, it is clear that the darker purple areas represent the most populated counties while the lighter blue areas represent the least populated counties. Using a gradient color ramp in the legend is very visually effective because anyone can simply glance at the map and notice the counties or regions in the US where most people reside. For example, one can look at Alaska and Hawaii and notice that almost no county in Alaska contains more people than any county in Hawaii. Furthermore, the intervals used in the legend are also effective because it neatly categorizes the data and allows us to only use a few colors in the color ramp. That way, no two colors are alike and it is easier to make distinctions.

This next map above reflects the difference in number of people from the census 2000 and the previous census of 1990 by county. Joining our census data to the counties data was also necessary for this map and while this map looks a bit more complicated (mainly because of the title), the data is easy to explain. The difference in the number of people between 1990 and the year 2000 is simply calculated by subtracting the number of people in each county in the year 1990 from the number of people in the corresponding county in the year 2000. Population gain is exhibited if the difference is positive while a negative difference reflects a loss. Once again, the color ramp is very efficient in this map because it makes it easy to see which counties grew in population in that 10-year time period. Clearly, the dark green shows a larger population increase while the bright pink shows a larger population loss. While the color ramp looks a bit odd because it is not necessarily a gradient, it is still very useful because clearly the green shows population increase while the pink shows population loss. Hence, it is very easy to tell at first glance which counties and/or regions had greater population increase as well as which had greater population loss. Taking Hawaii and Alaska again, one can easily see that some counties in Alaska exhibit population loss and most of the counties  that did gain in population did not gain as much any county in Hawaii. Moreover, the intervals used in the legend are once again useful at effectively separating the data.

This third map above shows the percent change from 1990 to 2000 in the total population per county. This map is a bit different from the previous two because the data is in terms of percentages as opposed to whole numbers. The data in this map is also simple to explain. The percent change in the total population per county is calculated by subtracting the total population in each county in the year 2000 by the total population in the corresponding county in the year 1990 and then dividing the difference by the total population per county in the year 2000. Of course, we then multiple each result by 100 to get our percent change. One may initially think that this map is very similar looking from the previous map when printing in black and white. However, this map does not just reflect population gain and loss per county. This map shows us the relationship between the population gain or loss per county and the total population per county. In a way, it tells us which of the two pieces of data dominates the ratio. Once again the color ramp is very useful because it shows which counties exhibited a large percent increase in population as well as which counties exhibited a large percent decrease in population. Looking at Hawaii and Alaska once more, we notice that the counties that have a net loss in population in Alaska also have a percent decrease in population. Meanwhile, in Hawaii some counties that exhibited the same or similar population gain have entirely different population percent increases. The ratio interval used in the legend is useful in this case because it neatly categorizes the percent changes.

This final map shows the population density for every county in the US according to the 2000 census. This map did not only require us to join our census data to the counties data but also to use the field calculator in order to compute the different densities. However, the calculations, while automatically done when inputting the formula, are quite simple. The densities are calculated by dividing the total population per county in the year 2000 by the total area (in square miles) of the corresponding county. Therefore, this map shows us how heavily populated counties are with respect to their areas. The gradient color ramp used for the legend in this map is reminiscent of the legend for the first map we observed. Since there are no negative values, it is not necessary to have contrasting colors in the legend. Furthermore, the gradient color ramp is effective because it makes it easy to notice which counties have a large population compared to their areas and which counties have more "openness and space." Taking our example of Alaska and Hawaii one last time, we can see that Hawaii is mostly made up of counties that are dense while Alaska is mostly made up of counties that are less dense. A map of population density such as this one can be useful for emergencies because it tells us that the darker navy blue areas are more difficult to evacuate due to how heavily populated the area is.

Overall, it is interesting to see how data such as counting the number of people in the United States can be reflected in maps when joined with other useful information. While the four maps above tell us different things about population, not one is more useful than any other. In fact, one map may be more useful for emergency purposes while another may be more useful for studies. However, this map series as a whole is very interesting especially because the same data source was used for every map. I think that ArcGIS was very easy to use for this assignment.  The tutorial was quite easy to follow and there were minimal complications along the way. This time, I felt that every step I did made sense, but that could be because I am getting more used to ArcGIS. All in all, ArcGIS served as a very useful tool to neatly and effectively create all four maps.

Elevation Models

3D Model

The extent of the area we worked with is 39.8291666661 decimal degrees from the top, 39.3838888883 decimal degrees from the bottom, -105.788888889 decimal degrees from the left, and -104.969444445 decimal degrees from the right. The coordinate system used is the GCS North American 1983 and the datum is the North American Datum of 1983. The specific area selected is somewhere in middle of Colorado state. Specifically, the area spans from the Arapaho National Forest, to the city of Westminster, to the city of Castle Rock, and to the city Jefferson. Therefore, it covers all the cities and regional parks in between.

ArcMap: Map Projections

Map Projections: The Good and the Bad

Working with ArcMap to explore different map projections made me understand the purpose and importance of map projections as well as the potential dangers. In this assignment, we used our basic knowledge of ArcMap to estimate the distance between Washington DC to Kabul using any six map projections (2 conformal, 2 equal area, and two equidistant). There are several stark differences between the projections I used. The most important one specific to this assignment is the different distances between Washington DC and Kabul depending on the projection used. It is clear that the distances vary from about 5069 miles using the Mercator projection to approximately 8763 miles using the Behrmann Equal Area Cylindrical projection. This discrepancy between projections is a big disadvantage when trying to measure the distance between two points. This can potentially be dangerous when planning travels or even secret missions between two places. 

Furthermore, another downside to map projections is that earth distortions are inevitable, meaning that there is not a single projection that retains all properties such as shape, area, direction, and distance. Hence, this can be very time-consuming when using projections being that different map projections must be used for different types of measurements needed.  This is especially important when looking at a small-scale map. The distortions can also be a negative because not everyone is familiar with all the different types of map projections. In fact, the Mercator projection is probably the one most people have seen while the Lambert Conformal Conic, the Two Point Equidistant, and the Equidistant Conic may look very foreign to many people.

Aside from the many downfalls, map projections are also very important. For instance, map projections are a necessary part of maps because the earth is a three-dimensional object that must be somehow displayed onto a two-dimensional surface. While distortions are always present, sometimes they are not important. For example, map projections are not important when looking at large scale maps. Furthermore, map projections have been categorized in such a way that it is convenient and easy to choose one in map making. When trying to preserve distance it is convenient to choose an equidistant projection. For preserving areas, it is smart to choose an equal area projection and for preserving angles locally, it is good to choose a conformal projection. Even within these useful categories, though, different projections provide more accuracy in other areas. For this particular assignment, it is probably more accurate to choose to use the equidistant projections because we are trying to measure the distance from Washington DC to Kabul. In fact, when looking at the Two Point Equidistant and Equidistant Conic, the discrepancy of the measured distance (6649 miles versus 6972 miles) is not as significant as when looking at the conformal projections or equal area projections.  

However there are even more advantages to map projections. As previously mentioned, without map projections, it is impossible to transform the three-dimensional earth into a two-dimensional map. Hence if we did not use projections, we would have to refer to a globe model to get measurements. However, there are many downsides to the globe that are in fact advantages of map projections. For instance, three-dimensional objects such as the globe are difficult to store and carry around. Also, even if we have a very large globe, it will always have a very small scale and hence it is difficult to see any detail. Furthermore, globes are very costly to make and difficult to update. These are all things that map projections make convenient to do. Specifically, map projections allow us to easily but carefully make maps that conveniently fit in pockets. Also, projections allow us to make very large scale maps that can show a small town with great detail. Lastly, maps are clearly cost-efficient and easy to update. 

 Referring back to ArcMap, it is a very useful tool to see the different types of projections. It was actually very easy to explore different projections and accurately portray the data. On another note, map projections themselves are a very powerful tool in society. They can easily manipulate people's minds. Using the example of the Mercator projection, Europe  and North America appear to be much larger than Africa. This projection, which is one that most of us grew up with, thus somehow gives more importance and power to Europe and North America to the rest of the world. This is an advantage of map projections as well as a peril. It is important to see that map projections can be used to make statements. 

Understanding GIS: ArcMap

This Geography 7 class was the first time I had ever heard about GIS and even when the professor explained what it was used for, I could not visualize its usefulness. For this lab, we worked with ArcMap to create a proposed airport expansion. While following the steps for the tutorial, I realized that in order to be able to use ArcMap, one would have to have a relatively extensive knowledge of computer skills and  technical language. Users must definitely be able to properly and accurately read the tutorial. I was very careful reading each of the steps, but I still encountered some problems such as taking as long as ten minutes to find a certain button on the program just to be able to edit an attributes table. I think it would definitely be helpful for users to have some sort of experience with other programs like Photoshop. While working with ArcMap, I saw similarities between the two programs, some of which are working with several layers or zooming into the page. However, the features in ArcMap were helpful in making our series of maps easier to read. For instance, changing the symbols, colors, and font size is key to making a successful map because the audience needs to be able to understand the information.

An advantage of ArcMap is its ability to display the data selected in a short amount of time. One no longer has to collect data and manually figure out how to implement it into a map. ArcMap makes it easy to represent the already collected data onto a map with clicking a few buttons. Certainly, users must first know how to extract the data they want to use and know the right steps to place it under the correct data frame. Something else that amazed me was how quickly ArcMap was able to calculate the population density of the county we were working with. We only needed to enter one equation and the calculations were done in a few seconds. This is probably something that would manually take hours. Also, mistakes--something I often came across--can be easily fixed using ArcMap. Even something as simple as renaming a layer and having it immediately reflected in the title or legend could take several drafts if done manually. Hence, ArcMap becomes very useful in its speed and simplicity.

The potentials of ArcMap and GIS go beyond anything I can imagine. Since ArcMap is well-connected to the digital world, this program serves us not only to print our maps. We can also export the work done on ArcMap and digitally share it and distribute it to anyone almost instantaneously. Furthermore, ArcMap deals with geographic information that is very important to us human beings. It helps us answer questions about our location, our relation to others, and the impact of our decisions. The raw data itself also serves to build the credibility of the maps created using this software, which has tremendous importance because while a large amount of people can afford and use ArcGIS, it does not take away from the level of professionalism used to acquire the data. Additionally, I do not see the price tag on ArcGIS as a downfall necessarily because its relatively steep price ensures that only people with a purpose will buy and use the program.

We, however, cannot forget that ArcGIS is not some magical program that contains every piece of information we seek. Rather, we must see that the data used is collected through the dedicated fieldwork of geographers. Hence a lack of people interested in the field can lead to its downfall. While this can sound a bit contradicting, the steep price of ArcGIS can potentially lead to its downfall as well because not everyone is able to purchase and explore the program. The inability to explore the program hence leaves no room for people to discover and investigate the field. Furthermore, a more obvious disadvantage is that the program is difficult and confusing to use. With the amount of jargon and  previous computer knowledge needed to successfully use ArcMap, it gives people less of an incentive to use it. However, with future generations becoming more technologically savvy than the previous one, this might not be a problem.