CARTOmazing

For our last module this semester, we focus on creating proportional symbols and bivariate maps. We started on creating a cities' population map for India to get familiar with proportional symbols especially when they are overlapping with other features. Good knowledge about  the principle  of hierarchical organization and visual contrast comes into place. For the India map, I focused on the following symbolization considerations: Proportional Size: I used a minimum of 15 with maximum size of 90 so that the lower population areas would still be legible and visible to the viewers Color Symbols: I used an orange color with a 50% transparency and then white outline enough to define its separation from overlapping circles. In addition, I used a lighten feature blend to smoothen the color overlays amongst overlapping symbols. India: I used a gray scale color with a dark gray outline to create a visual contrast to the light gray base map that I added. Overall, this makes an effecti...

This week's module and lab focused on creating an infographic map using  two normalized variablees from the 2018 County Health Rankings dataset. I used the variables excessive drinking and diabetes prevalence as I learned that somehow drinking alcohol in moderation could lower the risk of diabetes but when drinking greater amounts would be a different story. Most doctors don’t forbid their patients in consuming alcohol, like my uncle for example, but should be done in moderation. Some say alcohol can help decrease blood sugar level, but excessive drinking can also cause hypoglycemia which is high risk for people with type 1 diabetes. So, I wanted to use this opportunity to investigate these variables if excessive drinking is correlated to the risk of diabetes. In addition, both variables are normalized as they are calculated percentages of excessive drinkers and diabetics per county population. Scatterplot I used Power BI to create my scatter plot and most of my data visualiza...

 For this week's module we focused on the color choropleth and classification schemes. Understanding this concept lets the author improve on his/her map's impact and map legibility to the map audience. This concept is sometimes overlooked especially when the author wants to convey a message to his/her map audience. The use of different colors sometimes have different meaning especially in other cultures like the use of red and green (HuffPost, 2016). Linear Progression Adjusted Progression ColorBrewer The linear progression color ramp has an equal interval of 20 for R, G and B. Then for the adjusted progression color ramp, I increased R values by 1/3 and decreased G values by 1/3 but remained B at constant value of 20.   Their color differences are quite minimal from dark to light due to a constant value progression amongst R, G and B values. Though I see that linear progression color has more green saturation/contrast that adjusted progression who has a darker contrast making...

This week's topic is about terrain visualization using the Digital Elevation Model (data) in which can be visualized into contour lines, slope, hillshade that can then be used for different analysis like hydrological, flood, landslide and watershed delineation. In this exercise we just focused on contours, proper contour label placement and hillshading in different times of the day.  For Part 1, we were taught how to properly create a variable depth masking on contour labels which was quite cool. I have just used the contour label type and halo whenever I create agricultural maps. Good thing this exercise taught us how to label them clearly so the viewer can easily read the labels. I have always used constant intervals for contours as it defines terrain changes easily (Brewer,   2016). I initially used the same color of blue-yellow-red to show elevation but switched to green-brown-white which can better represent elevation change color in real life. For Part 3, I combined...