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Ulead gif animator 20081/22/2024 ![]() The Natural Breaks and Quantile are giving a better distribution and representation of the data, whereas Equal Interval is breaking the data up into insignificant break points. If I had to choose out of these three which one is best, I would probably go with the Quantile or Natural Breaks, and not go with the Equal Interval. These maps are more detailed because they are using 6 classes instead of 5. The last 3 maps posted are of the population change from 2000 to 2008 classified by Natural Breaks, Quantile, and Equal Interval using 6 classes. I think looking at the histograms helped me more than the maps. After making the maps, I looked at histograms for each map to get a feel for the data distribution. The first 11 maps I made to help me decide my classification are posted below – – this is not the final way I classified my maps –just a guide to get me there. we made maps of the data with using the natural breaks method and looking at histograms of the various maps. To help us decided how we will classify the data. Our challenge is to reduce that skewness to a minimum. To choose one way to work well with all the maps is going to be skewed no matter how well you try to cover the distribution. This can be very challenging to choose how to classify the data because the data is so spread out over periods of time, the distribution varies greatly. Because we are looking at multiple maps over an extended period of time, it is critical to classify all of the data the same way to maintain consistency. I really liked how the Proportional Symbol Maps turned out because you can get a real idea of how BIG of a population is actually in San Francisco.įor this Lab our main goal was to decide how we will classify our data. ![]() I ended up changing the dot from 1 dot equals 20,000 down to 10,000. The area is so small with a large population that it starts to become just a big blog but if Increase the dot size too much, it shows very very very sparse population elsewhere. The Difficulty I ran into when making the population maps was in the dot density map in the San Francisco Region. Next I had to make dot density and proportional symbol maps for the population. Right: Graduated Symbol Map for Population Change from 1900 to 1910 Left: Choropleth Map for Population Change from 1900 to 1910 After I figured my break values, I applied the classification to my choropleth and graduated symbol maps. This way of classifying also has a good representation of the legend thoughout the years – meaning that in every map there is an representation of each color from each class with the exception of one map. This was the best way to classify the maps becuase the majority of my histograms falls around the range of -20 to 40 range. For my classification I choose to do my breaks as the following: -100 – 0, 1 -10, 11-20, 21- 40, 41- 230. We had to choose a classification for consistency. The lab consisted of over 40 maps to be made. Because the last was long, we got extra time to complete it.
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