Monday, February 20, 2017

Cartographic Skills: Spatial Statictics

This weeks lab we learned how to do Spatial Statics within ArcMap. Spatial Statics is useful in determining how to analyze your data for certain outcomes for using and displaying data. We created a map of weather monitoring stations for Western Europe and applied spatial statistical tools to see how the data is distributed.

We used a training module from ESRI's online classrooms. We downloaded  data from the module and applied a method of spatial analysis as seen in my map above. We used an mxd file, contained within the training module data, which had an ESRI topo base map saved and centered mainly on Western Europe. There was also a layer for weather monitoring stations, containing a record of temperature and other weather data for a each station.
Next we needed to visually understand how our data is spatial distributed. The first step in spatial analysis is find the middle or the center of the data. There are two methods we used in locating the center, both of which can differ locations depending on the spatial quality of the data. The first method is the Mean Center which represents the average x and y coordinates of all values. The second method is the Median Center which takes the middle value of the data when it's ordered by rank. ArcMap has separate tools that do find the median and mean center and we located the tool by simply searching for it. The tools are very similar and we kept defaults the same to calculated the mean and median center. The red and green dots in the center of the map above is my median a mean center.
Now that we have the center located, we calculated the directional distribution of the data in order see in which direction the data is spatially located. We did this by using a tool found in the Spatial Statistics toolbox, then expanded the Measuring Geographic Distributions tool set and the tool name is called the Directional Distribution (Standard Deviational Ellipse) tool. Using the Temperatures layer, this tool creates an Ellipse. This tells us the general orientation of our data. So in the map above you can most of our data is concentrated in an east/west direction.
Having done these first steps in visually understanding how our data is spatially located you can start to understand where features are arranged within the map. Now you  can start to run other spatial analysis tools such as a Histogram, QQPlot or a Voronoi Map. But the fist step in understanding spatial analysis is to visually look at how the data is arranged and then start to ask questions or run analysis on the data. 


Sunday, February 12, 2017

Cartographic Skills: Cartographic Design

In this weeks class we created a map for Ward 7 Public Schools in Washington DC. For this map we followed Gestalts Principles of Design. Gestalts Principles refer to visual perception of how elements are arranged within an image and uses a visual hierarchy in order to gain better prospective of certain elements.

We started with open data from the City of Washington DC using Arcmap. We layered city boundary, city streets (including all the major roads), neighborhoods, Ward 7 boundary and lastly Ward 7 public schools. I centered the Ward 7 boundary as close to the netline as I could get. Doing this gave the map balance as it centered Ward 7, making it the main object or area of reference.

After adding all the necessary map layers it was time to figure out how to create my visual hierarchy. I had a rough looking map at the beginning, with all elements of similar contrast. So I started by adjusting the Ward 7 boundary to a darker gray. I then adjusted the rest of the city boundary to a lighter gray contrast. In my mind I was using the principle of "Figure-Ground" in creating a backdrop for future higher hierarchy objects to be placed upon.. My rough draft idea was to create basic colors for the ground features and then later apply the schools on top using bright color icons to make them stand out against the simple dark background of the map.

I then applied the same theory of basic contrast to the road layers. I clipped the roads that were completely within Ward 7 in order to separate them from roads outside Ward 7. Doing this allowed me to create more contrast for the roads within Ward 7 and then create lighter contrast for the roads outside of Ward 7.

I did not stop there with the road layers. Having all the roads on the map would be too messy. I created thicker lines for the major roads and applied a darker contrast to show these roads as the major thoroughfares. I used a lighter contrast for the minor city streets, which are low items for visual hierarchy. In other words the minor city streets do not need to be in great detail because they are not the main theme. Having the streets adjusted to a soft color does not overwhelm the eyes and also gives the map good balance.

I had created a good rough draft of my map in Arcmap so I then exported to Adobe Illustrator for adding further touches to the map. I added labels for the neighborhoods that had a school within its boundaries. The instruction was to create labels of one neighborhood from each cluster but I felt that adding neighborhoods with a school nearby, made more sense for my map. I used a red color for the neighborhood labels but set the opacity down to keep them from looking like a main theme object or of lower hierarchy than these schools. I wanted to make the labels appear as if they are a more in the background.

For my inset map I trimmed one side of the border to match the angle of the city boundary. I took advantage of the unique angle the city boundary creates. This gave the inset map proper size and good balance in flow from the main map space into the inset.

This weeks lab had a lot of trial and error. The steps above sounds easy but there was a lot of adjusting back and forth in trying different contrast, colors and location of map objects. This week I had time in my busy schedule to create this map early in the week so I ended up coming back to it throughout the week. I have found that if I sleep on it and come back the next day, I start to see  things I didn't see before. So I'm trying to get a good rough draft done ahead of the deadline, in order to look again and edit throughout the week.

Sunday, February 5, 2017

Cartographic Skills: Design & Visualization

This weeks class we dug even deeper into the use of Adobe Illustrator for map design. We designed an informational map referencing map objects in the Florida Keys. 

We used a Florida county data set from the U.S. Census Bureau and zoomed to the Florida Keys, near the city of Marathon. We added a scale bar and inset map for referencing the location of the Keys in relation mainland Florida. Then we exported to Adobe Illustrator. Once in Adobe Illustrator, we mostly started with a blank canvas, adding all the major map elements within AI..There was a lot of trial and error this week as we used many of the AI tools from last but also new ones.

We added map elements for hydro features, areal features (islands) and point features. We followed guidelines from our text book for properly adding labels to our features. We used mapping guidelines/standard practices as highlighted from our text. These mapping standards greatly helps portraying map objects. Simply using your own "off the street" method of adding labels and icons would be a mess without guidelines. Examples of these guidelines is the proper use of type (ie; font) for labeling features. Did you know you can only use italic for two types of features on maps? Italic type should only be used for Hydrographic and data source features. I didn't know this before this weeks assignment and I love to use italic type. Having mapping guidelines and standards helps viewers understand features better as all maps should follow a standard of uniformity  As in the italic example, when you see an italic label on a map, you can assume it's related to a water feature.

We added points of interest for cities, an airport, a golf club and a state park. I chose to make these features stand out more by using Halo’s around their labels. This allowed me to place the label directly over the areal feature. The detail in the land features are not of high detail and that detail is of minor importance for this map.  Therefore, you can place labels over the land feature for clarity of the label even though you hide away the land feature. I also used matching colors for labeling features to match the color of their symbol. This way you can clearly understand which label goes to which symbol by color.

I also chose to do a drop shadow under most of my map objects. This gives objects more clarity and in a way it helps separate features from each other when they overlap. The labels almost appear to be hovering over map objects.

Using drop shadows gave the map a look as if the sun is casting a shadow from the west. So I chose a bright fading gradient color for the background outside of the neat line. Using this bright color appears as if the sun is the background, casting the drop shadows. Using a bright color background also gave the map some well needed flare as I used basic colors within the map space.

This was a very challenging project this week with a lot of trial and error. Control+Z (undo) came in very handy. However I feel comfortable now with the major features but it seems we've only scratched the surface with AI. I look forward in developing more AI skills in the weeks to come.