Introduction to GIS: Lab #2Sophia Robison

18 February 2016

Map 1: Map of NYS counties with a 1999 population greater than 300,000. (10 points)

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Maps 2. Map of the Population of NY State Counties based on 1990’s Data Utilizing Natural Breaks

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Map 3. Map of the Population of NY State Counties based on 1990’s Data Utilizing Quantiles

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Map 4. Map of the Population of NY State Counties based on 1990’s Data Utilizing Standard Deviation

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Map 5. Map of the Population of NY State Counties based on 1990’s Data Utilizing Equal Intervals

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Question 1: Briefly describe and summarize some of the differences between the 4 classifications. Which do you feel represents a better representation of population density? Provide a brief discussion of your reasoning.In assessing the outcome of the four different classification methods (quantiles, standard deviation, equal intervals, and natural breaks), it is evident that the size of the bin for each section/color makes a huge difference in what is being communicated. In using the classifications of equal intervals and standard deviations, for example, the map communicated simply that NYC had a really, really high population density – so high in fact that the rest of the state of New York was all one color. This is not particularly helpful, however, in assessing the greater population density of the state, and so, in this way, population density is represented best by quantiles and natural breaks. Here, it seems that while quantiles communicates more, it also makes some other New York metropolitan areas appear analogous to New York City, which is simply not true. The natural breaks classification did this less so but in turn sacrificed some of its detail with regard to the non-New York City part of the state. Overall, each classification told a different story and so the chosen classification system should reflect the true meaning of the data and communicate the story being told.

Map 6: Normalized Black population by State. Include a sentence or 2 about interesting spatial relationships you may notice. (Note: you only have to depict the continental US for your map!) (10 points)The spatial relationship between geography and black population is apparent in this map, and it reflects the history of slavery in that the black populations are concentrated in the southern United States. The map further demonstrates that black populations are concentrated in more urban states, particularly outside of the southern United States.Map 6. Map of the Black Population of the US by State based on 1990’s Population Data

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Map 7: Multivariate map depicting normalized Black and Hispanic population by state with sentence or 2 about the spatial relationship between the two populations. (10 points)While this map is a bit confusing to use in identifying a correlation, it is apparent that black populations concentrate in the southern US and in other more urban states. Hispanic populations are concentrated in the southwestern US, Florida, and in the presumably New York City and Chicago areas (though the map only shows state level populations).

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Map 7. Map of the Black & Hispanic Populations of the US by State based on 1990’s pop. dataQuestion 2: Is there a spatial relationship between Black and Hispanic populations? Briefly explain.From this map, it is unclear if there is a spatial relationship between Black and Hispanic Populations. Clearly, both populations are concentrated in certain areas for historic reasons (blacks in the south because of slavery, and Hispanics in the southwest because of proximity to Mexico); however, that viewpoint is too simple and oversimplified. Both populations clearly extend beyond these historically significant areas and into urban areas. While I know this correlation to be true, this map does a poor job demonstrating that correlation due to the drastically different visual cues communicated by the two methods of analysis used here.

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Map 8: Population Density map using graduated colors by state (sans Washington DC) (Note: you only have to depict the continental US for your

map!) (10 points)

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Maps 9a & 9b. Hispanic Population by State using Geometric Intervals and

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Proportional SymbolsMAP 9: Create both a graduated symbol and a proportional symbol map of the proportion of 1999 Hispanic population by state (these maps should be presented side by side). How have you chosen to normalize? Justify your choice of classification scheme. Discuss some of the differences between these maps. (10 points)

Geometrical Intervals were used for the graduated symbols map because of the nature of the data as exponential (as can be seen in Figure 1). By using this classification method, the map output shows a proportional number of samples within each category used. The quantiles classification would have had a similar result, but due to the exponential nature of this dataset, as opposed to a normal distribution, it was determined that geometric intervals would be most representative of the data.

The key differences in this map are understandability and amount of variation. The graduated symbols map is actually harder to interpret than the proportional symbols map because with so many dots (48 total) the brain has a hard time differentiating which symbols are the same size and which are different (even though the symbol size was adjusted for a 10-point difference between each different symbol).

MAP 10: Create a multiple attributes map of New York State counties displaying the % of housing units which are renter occupied (normalized to the total number of housing units) and the % population that has never been married (normalized to 1999 population). Justify your choice of classification scheme. (quantiles used for both) Discuss any patterns you may notice. (10 points)

For both attributes in this map, the quantiles classification system was used. Due to the relatively skewed nature of both data sets seen in their respective histograms, a quantile was useful in that it created meaningful divisions among the various bins so as to differentiate despite the skewness caused particularly by the New York City area. One pattern that makes itself apparent is the correlation between renter occupied areas and areas where the population has never been married. Simple inference will also tell you that many of these areas are also cities (though not all), which makes intuitive sense. This also perhaps tells you something about the age of the

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Figure 1. Distribution of US Hispanic Population in 1999Figure 1. Distribution of US Hispanic Population in 1999Figure 1. Distribution of US Hispanic Population in 1999Figure 1. Distribution of US Hispanic Population in 1999Figure 1. Distribution of US Hispanic Population in 1999Figure 1. Distribution of US Hispanic Population in 1999Figure 1. Distribution of US Hispanic Population in 1999Figure 1. Distribution of US Hispanic Population in 1999Figure 1. Distribution of US Hispanic Population in 1999Figure 1. Distribution of US Hispanic Population in 1999Figure 1. Distribution of US Hispanic Population in 1999

populations in different areas, as the never married population is likely to be a younger one (and if it’s not, then you have a different problem!). Overall, this map is interesting in that it provokes a lot of questions and answers very few of them; it would be interesting to overlay this map with one showing urban density and population age, for example.Map 10. Map of NY State Counties: % of Housing Units Renter Occupied and % of Population Never Married (with 1999 Population Data)

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MAP 11: Create a map layout of the continental US by county depicting population density. Use whatever classification scheme and number of classes you wish and make any exclusions you think are appropriate. Justify your decision using the Normal QQ plot as well as the kurtosis and skewness statistics. (10 points)

This map proved quite complicated to make because of the severity of the skewness and kurtosis statistics, which show us that not only is the data highly right skewed (a small number of counties with incredibly dense populations) but it is also very peaked (so one bin contains the majority of the samples), as seen in Figure 2 which shows the histogram for this map. Without excluding outliers, this map would be nearly useless and would simply show that New York City, Chicago, San Francisco and Washington, DC are very dense and that the rest of the country is not. Further, while the map only shows the continental US, the data still includes Alaska and Hawaii. While Hawaii doesn’t pose many problems, Alaska’s incredibly low density population and absolutely massive area provides an outlier skew towards

low density that is really false compared to the rest of the data; Washington, DC and New York City (among other cities) have a similar and reverse effect. For the purposes of this map, it is therefore assumed that these places are dense and they are excluded from the map (highlighted in black). Despite the removal of these outliers, the data is still incredibly skewed, and further analysis made it apparent that a series of massive counties in the southwestern US were skewing the data with their low density. It was decided that these counties would not be excluded, however, due to the fact that they represent a large part of the US, (these areas are selected in the Normal QQ Plot, Figure 3). Once the major outliers were removed from the dataset, it was easier to see the spatial relationship of population density with the geography of the country, as the map exhibits a rather clear line down the middle of the US where the Midwest is. This outcome was achieved by using a geometric interval classification method, which was appropriate due to the still skewed and relatively exponential nature of the data (as seen in the histogram in Figure 2).

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Figure 2. Histogram of US Population DensityFigure 2. Histogram of US Population DensityFigure 2. Histogram of US Population DensityFigure 2. Histogram of US Population DensityFigure 2. Histogram of US Population DensityFigure 2. Histogram of US Population DensityFigure 2. Histogram of US Population DensityFigure 2. Histogram of US Population DensityFigure 2. Histogram of US Population DensityFigure 2. Histogram of US Population DensityFigure 2. Histogram of US Population DensityFigure 2. Histogram of US Population DensityFigure 2. Histogram of US Population Density

Map 11. Map of the Population of the US per Sq. Mi. by County based on 1990’s Population Data

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Bonus map: Create a Multivariate map depicting number of electoral votes (EV_2008) and winning party (party_2008) by state for the continental US. For this you can use the multiple attributes option, as winning party is nominal data. Hint: Electoral votes (interval data) should be displayed using graduate symbols. (5 points possible)

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