Intro to GIS: Week 12 - Geocoding

In this week's lab, we learned the importance of geocoding data. To process this skill, we created a map showing the various EMS stations within Lake County, FL and the optimal route to get to 3 of them. We created our own address locator to help translate the EMS xls datasheet onto the map so we could locate the EMS stations. There were some issue with geocoding the addresses which we used the 'Interactive Rematch' window which allowed us to find candidates that were close to the location of the EMS station or we created our pinpoint by using the 'Pick Address from Map' tool. To create the optimal route to the 3 EMS stations, we used the 'Network Analysis' extension which enable us to find the best possible route to get to these stations.

Intro to GIS: Week 10 - Vector Analysis Part 2

In this week's lab, we learned how to utilize the buffer and overlay tools in ArcMap by narrowing down our search parameters for potential campground sites. We learned how to use ArcPy which we used to create a script to run the buffer analysis tool once or multiple times. We used spatial queries to help us analyze the vector data presence. There are six different overlay operations that can be used to combine or erase data features from analysis. Along with learning about the overlay tool, we leaned about the difference between multipart layers and singlepart layers. 

This lab was a pretty straightforward with its instructions this week. I had some fun playing with the buffer tool when creating the different buffer distances of the roads feature on the map. The six types of overlay operations made things kind of interesting as well. The union operation and the update operation highlighted different areas of the map; however, both of the operations' areas fit together like a jigsaw puzzle. Overall, this week's lab was pretty fun to work on for class.

Cartographic Skills: Module 9

In this week's module, the topic was flow maps which show the movement of any phenomena between different geographic regions. There are three types of flow maps and they are distributive, network, and radial. The type of map that we created for this week's lab assignment was a distributive flow map since we are depicting the movement of immigrants moving from one region of the world to the United States. In this map, it is presenting the number of immigrants from six different regions in the world who migrated to the United States in 2007 which is where the distribute flow map fits the bill for displaying this type of data. The purpose of a distributive flow map is to show the movement of commodities, people, or ideas between geographic regions. The map was created from start to finish in Adobe Illustrator. We also used Excel to help calculate the proportions of the flow lines of each region of the world.

To create the flow lines on the map, I had to use the 'Pen Tool' and while using the 'Pen Tool', I was able to curve the corners of the anchor points instead of having the squared corners which disrupt the flow of the map. I used the ‘Inner Glow’ effects on the flow lines on the map. This effect allowed me to soften the lines which in turn will allow the viewer to not being overwhelmed by bold flow lines that are present on the map. The other effect that I applied to this map was the ‘Drop Shadow’. I used the ‘Drop Shadow’ effect on my flow lines and number of immigrants’ bubbles showing the movement of immigrants from the different regions. The ‘Drop Shadow’ helps me emphasized on those lines and bubbles from the other map features that are present.

Cartographic Skills: Week 8 Module

In this week's lab, we learned about different isarithmic mapping methods, such as continuous tone and hypsometric tinting, contour lines, and PRISM method. To show that we comprehended the topic at hand, we had to create a isarithmic map of Washington's Annual Precipitation. We created the map in ArcMap where we implemented both continuous tone and hypsometric tinting in presenting the precipitation data. The end result was presenting the Washington's Annual Precipitation using hypsometric tinting and contour lines. This method was the best suited for representing this data. 

This map shows a 30 year period of precipitation data in Washington state. To show the symbology of the map properly, the legend had to present the data horizontal instead of vertical. We changed the legend by using the legend's properties window, selecting the 'Item" tab and then selected 'Styles' which allowed me to change the presentation of the legend bar. An important tool that we used was the 'Int (Spatial Analyst Tool)' which changed the raster values from floating to integers. By using this tool, it will make the contour lines more clean cut.

Intro to GIS: Week 7-8 Lab Assignment

In this week's lab, we had to find data to create our own maps of the counties that each of us were assigned for this week. The purposes of this assignment was to test our skills in collecting, managing, and recording the data that we found for our maps which we later used. We also had to figure out which projections would the best in presenting the data that we collected and had to make sure that our maps could be easily interpreted by anyone reading the maps that we created. The county that I was assigned for this week's assignment was Lake County, Florida. Lake County was has a unique shape which caused me some extra work as I was looking for the 'Digital Elevation Model' (DEM) for the county. Lake County has two DEM files which had to be downloaded into ArcMap and clipped to the shape of the county. 

I had a little bit of difficulty with DOQQ raster dataset showing in the Albers projection. I changed the projection to Florida State Plane East in which the raster dataset did appear but was in the wrong location. So I used my thinking cap and thought that Lady Lake city is very close to the Florida State Plane West zone which might affect the location of the dataset. I then changed the projection to Florida State Plane West zone which worked like a charm. Overall, I found this assignment quite an educational experience when dealing with raster data not projecting properly.

Cartographic Skills: Week 7 Module

In this week's module, we learned the importance of cloropleth maps and using appropriate proportional symbols. To make sure that we comprehend the material, we applied what we learned by creating a cloropleth map showing the wine consumption in Europe. We did the majority of the data manipulation in ArcMap, such as, choosing a color scheme for the map, used the SQL Query language to remove outliers in the dataset of our map, and we applied a data classification method to help represent our data. After exporting our map from ArcMap to AI, the finishing touches were added. The touches that I added were labeling the countries in Europe, adjusting the 'Wine Consumption' legend, and adding a solid color background to the map. 

The data classification scheme that I chose was quantiles due to there being more color variation with the data being presented. We are also observing the population of wine consumers in each country so quantiles method is more suited for this type of map as well.The thematic color scheme I chose started with a light tan color graduating to a dark brown. I chose this color scheme as to not overwhelm the map reader with all of the symbol elements we will be adding to the map thus allowing the map reader to easily look at the data on the map. Along with the color schemes, I used the graduated method for my symbols to help present wine consumption in each European country. The reason for me choosing this method and not the proportional method is the proportional method seems to overwhelm the map with huge symbols and one could not make out what country it resided on. With the graduated method, I’m able to select the size range of my symbols and it also allows me select how many classes I want to utilize to showcase my data.

Cartographic Skills: Week 6 Data Classification

In this week's lab, we learned about the four different data classification methods which in turn had different ways of presenting the data that was given. To make sure that we understand the material that we read, we had to used ArcMap to present the four data classification methods using the Miami-Dade Census dataset. We placed each classification method in its own data frames which helped us compare and contrast the different methods being used in this lab. By visually examining the dataset through each classification, we were able to determine which method would be most effective with presenting the dataset that was given. The map that we first created from this dataset was based on  the percent above 65 data presentation which we then later used to created another map utilizing the population count normalized by area data presentation. This allowed us see what presentation method was more accurate in representing the data. 

The map that I have posted above is the Miami-Dade Census of people who are over the age of 65 utilizing the percent above 65 data presentation method to present this data. This map could be use for Miami-Dade County Commissioners to examine the population of the senior citizens and by selecting the right data presentation will make the map more effective. The data presentation that seems to depict the distribution of senior citizens accurately would the percent above 65. This presentation allows the commissioners to have a general idea what areas more heavily inhabited by senior citizens within Miami-Dade and it gives a much greater visual impact of the population of senior citizens. The commissioners might be able to implement more informed planning to come up with projects to provide better resources to assist senior citizens within the heavier populated areas. The population count normalized by area presentation isn’t the most effective in presenting the data since it is not as visually impacting as the percent above 65 presentation. The population count normalized by area  type of presentation seems to show a reduce presence of the senior citizen community in Miami-Dade which might lead the commissioners to not know the extent of the need by not having the true percentages and this all caused by not using the most apt presentation. The map shows the four data frames with different classification methods in each one. These data classifications are called equal interval, standard deviation, quantile and natural breaks.

• Equal Interval: This classification makes the data have equal ranges within the classes that are created. To create these equal ranges, the total data ranges are divided by the number of classes which create the range values. With this classification, the data can be easily interpreted by anyone viewing the map. The down side is there being no limit for what data is being distributed along the number line within these classes.
• Quantile: In this classification, the data distribution is divided by an equal number of observations. The data values are then put into classes which are arranged in numeric order. With this method, the equal number of observation and the percentage of each class are the same which helps condense the need to farther discuss the mapped data. The down side is that this method has the same issue as equal interval; there being no limit for what data is being distributed along the number line within the created classes.
• Standard Deviation: In this classification, the area beneath the graphed curve is separated out into equal sections. Most of the observations will be placed into one class surrounding the average value; meanwhile the other classes will have less and less data markers as they moved farther out from the mean. This method of classification is very useful because the mean can be used as a dividing point which allows the viewer to see the contrast of values that are above or below the mean value. The down side of this method is that it can only work within the data that is normally distributed.
• Natural Break: This classification uses algorithms which are trying to make all the values within a class equivalent to one another as much as possible; however, it also creates the opposite as well by creating the equal difference in the class values.. In other words, the variation of in-class is reduced and the variation of the inter-class is expanded upon. In this method, the data breaks are visually selected in a logical process. The down side is that this type of classification is subjective which makes the data presentation vary between different mapmakers and their take on the data.