Module 6

 

This week I learned about Isarithmic maps, continuous symbology, and hypsometic tint. The map about depicts the the precipitation data for the 30 year period was derived by using point precipitation data. This data was then used in the PRISM analytical model to make gridded estimates of precipitation and temperature throughout months and years of the 30 year period.

I used hypsometic tinting to symbolized the data. Hypsometric tinting uses light and dark shades to add a 3D effect to the map. This uses different colors and shades between contours. I implemented this into our map by using our Annual Precipitation layer and running it through the “Int” spatial processing tool to enhance the shading from the original continuous tone. Then I added the hillshade effect for more depth. 

I enjoyed this weeks lab and learning the many different ways to depict data of a raster image.

Module 5

In this lab we created a map showing population density and wine consumption across Europe.

 

I created a chloropleth map to show the population density across Europe. I used the natural break scheme to represent this data, with 6 classes. I believe the natural break was best for this data class because it excludes outliers, and doesn't give misleading information of the more sparse areas. I chose a graduated color scheme of purples. This color reminds me of wine because it is purple and using shades of one color is easier for the color blind to distinguish and leads to less confusion.

Then i used graduated symbols to show the wine consumptions in each country. I chose the graduated method for this data. I think this gives a better representation of the wine consumptions increase throughout Europe.

I struggled with this map because using all these functions really slowed down arcpro to the point where I had to take many breaks while waiting for it to load.

Module 4: Data Classification

Figure 1. Data Classification of Seniors using percentages

Figure 2. Data Classification of Seniors using counts 

In this module I learned how to use different data classification method to show how populations of seniors are distributed uses percentages and counts.

The data classifications I used were the following: 

1. Equal Interval-this uses the highest and lowest amount of 65 years old's and breaks that up into 5 categories. This showed the left census tract having more 65 year old's than any other method. Equal intervals do not take into account outliers so this can inaccurately display data

2. Quantile-this method breaks the total number of classes into equal categories. This helps eliminate outliers skewing the data. This and the natural break class ended up having very similar looking maps. 

3. Standard Deviation- Standard deviation method is using the standard deviation to add or subtract from the mean of the data. Showing how far data deviates from the mean can cause outliers to stand out more. Using standard deviation is best with “normal” datasets. this map has the least variation than any other map, having the largest amount of the map be in the <-1.5 Std. Dev. category. 

4. Natural Break-Natural breaks uses the natural grouping over data within the dataset. This method used the Jenks optimal method. This method does consider the outliers when creating the classifications. This map ended up looking very similar to the quantile method. 

I believe the population count normalized by area using natural breaks gives the best idea of where most of the seniors are distributed in Miami Dade County. This map shows where the seniors actually are, and not showing misleading data from percentages living in sparse areas. If one area has a small population of people, but most of those people are seniors, the percentage map would show this area as an area of interest, even though across the map, only a very small number of seniors actually live there. The natural break method gives the most accurate representation of the population. This map takes the outliers into consideration.