Sunday, April 2, 2017
This lab was the second half of student's work on Vector Analysis. There are countless impressive operations that are available for use in GIS analysis. Two of those operations this lab focused on were buffer and overlay. Students were introduced to ArcPy, as well. ArcPy is yet another important skill to possess in GIS expertise. Buffers are a simple concept: an area of certain proximity surrounding a specific map featrue. The map below shows buffers as "camp sites" and the specifications for said buffers are listed below, which was the first few exercises students performed. Next, we created a script in ArcPy to complete multiple buffers at once- what a convenient tool! I am looking forward to utilizing ArcPy more in the future. The final steps of our map-making this week consisted of overlay analysis to combine information from two separate topics.We used the union tool to combine the water and roads buffers previously created, I chose to use the erase tool to exclude campsites from conservation areas, but I now think that symmetrical difference tool might have worked better. Visually, I do not like my map. I struggled to chose colors that worked well together and looked "outdoorsy," since this is a map of the DeSoto National Forest in Mississippi. Although I am not a fan of my own color choice, I tried to add a little bit of extra with the shadow effects.
This week's lab was Dot Density mapping. Dot mapping is widely used when raw data has been collected and the map is intended for illustrating the lack of uniformity in the data/phenomena. Each dot represents a certain amount and placed where the phenomena is likely to occur or has previously occurred. The class mapped 2000 census data for populations throughout south Florida. In addition to simply placing dots on the map (1 dot symbolizes 14,000 people) we used a mask feature to show patterns more effectively. The mask feature allowed us to specify that the dots were only placed where there is urban land. Many of the dots were initially on water and places that it's highly unlikely there was truly anyone living there! To give a reference of location we were instructed to chose 3 major cities and map those, as well. I think that in addition to reference, the end user could use these major cities and make inferences based on dot density and the relationship between population and major cities.
The masking feature takes up an enormous amount of processing and we were warned to be patient. I noticed in the class discussion board that a lot of people were having trouble with ArcMap crashing, so I decided to build my map on a UWF campus computer instead of using the server at home. This helped a lot and I only had ArcMap crash once white it was masking and drawing all the data for surface water. I chose the blue background color because it shows the edges of Florida really well. The varying greens for the basemap and surface water are especially aesthetically pleasing.
Monday, March 20, 2017
This lab combined Modules 7 and 8 and served as a midterm assignment for students. The objective for this lab was to utilize the majority of skills learned the past 7 weeks. This included the selection, download and management our own data for a given project. The given project was an assigned county in Florida for each student and presenting the data in an effective map. I was assigned Okaloosa county and chose to map the following two (environmental) vector data layers, as required: land cover and invasive species. The two required raster datasets were form of aerial photography: DOQQ and a DEM. As you can see, second map with the DEM did not export correctly, despite my efforts to troubleshoot the issues. My quadrangle that I mapped is the northwest corner of an area in the city of Crestview, Okaloosa County. We were given 2 weeks to complete this lab, unlike most labs, which are given 1 week of time to complete. Finding the data and getting it to download correctly was the biggest hurdle for me. After trying on my personal PC for days, I finally used a university computer as a last-ditch effort and everything finally began to work (still not sure why!) This lab really helped me see what the strengths and weaknesses are so far in GIS. For example, I found clipping the data and aligning all the projections quite easy to do. Although getting the projections aligned was easy, I still struggle to really get a firm grasp on all the differences and best uses/practices for the different projection options out there.
Monday, March 13, 2017
The exercises from Module 7 taught students how to create choropleth maps and use proportional symbols. For those readers who are not familiar with choropleth mapping, it is a thematic map where the data is simply shaded. The shading is representative of the data via the intensity of the shading. This lab tied in previous labs about projection and data classification because choropleth map's clarity is totally based on the choice of data classification. After students classified data, they were instructed to chose an appropriate color scheme and map the European population data we were given. We then proceeded to add proportional symbols to show how much wine per capita is consumed in the European countries that were just mapped. We were given the choice to use picture symbols instead of simple circles for the proportional symbols, but I thought they looked a little silly. I chose the blue color ramp because I think the contrast between the colors makes it easiest to understand the population data best, I chose purple for the proportional symbols because it represents wine.
Tuesday, February 28, 2017
Lab 6- Data Classification allowed students to gain a better understanding of the four most common methods for data classification: natural breaks, equal interval, quantile and standard deviation. Each method has it's best uses and worst uses, pros and cons- just like every other method for doing things in map-making. Below you will find a very crude explanation of aforementioned methods:
a. Equal Interval: Is the most straight forward, easy to compute method. It presents data in equal amounts based on the amount of classes you want. There are not many obvious drawbacks to this method and it can be computed by hand.
b. Quantile: Equally divides the total number of values into the number of classes one desires. Each class also has equal values like equal interval.
c. Standard Deviation: Requires repeatedly adding/subtracting the standard deviation from the mean of the data. This method requires normally distributed data and a 6 class system.
d. Natural Break: Consider natural breaks within a dataset. The positive is that it considers the outliers but can break up important clusters.
I think that equal-interval or quantile will be best-suited for an audience looking to target the senior citizen population. I could be completely wrong, but if I was a senior citizen looking to retire, I would prefer to retire in a quiet neighborhood, even possibly with more seniors. With natural breaks and standard-deviation, the clusters tend to be broken up and that would hinder my goals of living in an age-appropriate community. I would likely chose quantile distribution of percent above 65 data if I were presenting this data to the Miammi Dade County Commissioners. I think that if one is presenting data, you want it to be easy and simple to understand from the start. The quantile method maintains equal classes that are proportionate to the observations, as well as rank-ordered. Standard deviation and natural breaks would be hard to present to a group of people/end-users if they are not familiar with exactly how these methods work. It would be tedious and confusing. An important aspect of map making is considering the end-user and the possibility it will be used in a collective decision making environment.