Dot Density Mapping:Population in Southern Florida

Time is moving too fast. Spring Break was great, but where did it go? This week, we are working on Dot Mapping. We use conceptual data (or raw-count data) for dot mapping. The first dot map was created by French Cartographer Montizon, depicting the population of France in 1830. Our task this week was to create a dot map depicting the population of Southern Florida.  Specific objectives are:

- Join spatial and tabular data
- Utilize dot density symbology
- Select suitable dot size and unit value
- Utilize mask function to manipulate dot placement
- Compile map in accordance with typographic guidelines, cartographic design principles, and the Gestalt Principles
- Provide an overview of the dot density mapping method
- Describe advantages and disadvantages of the dot density mapping method.

All the above was completed using only ArcMap (no CorelDraw-- hurrah)!  The tools to Join and Relate a table or excel sheet (Right-click on the layer and select Join and Relates>Join) and to create a dot map (open the Layer Properties, go to Symbology tab, select Quantities, and the Dot Density) are readily available in ArcMap. Once I had the basics of a dot map, I had to ensure that the dot size, value and placement were appropriate for the data. ArcMap, via the ESRI software, places dots randomly so I used the "Properties button" from the "Symbology tab" on "Layer Properties" and I checked the radial buttons for "Fixed Placement" and "Place dots only in these areas" to limit dot placement to "Urban areas."  Do you think this sounds confusing? I did too until I ran through the execution a few times. The only frustrating part of this lab was waiting for the layers to draw. I found that if I turned off everything I wasn't working with at the moment, it went much faster. However, when I was close to the end and finalizing my map, I wanted to view all the layers to ensure I had everything complete and accurate. This is where I executed a change and left the room to grab another cup of coffee, or something to eat, or a walk around the house, or...you get my message.  I learned great patience in the creation of my Dot Map.  Hope you like it!

  

Vector Analysis: How to Find a Good Campsite

Another fantastic week! Can't believe I am so close to finishing GIS4043. Not that I am happy to be done, but I am happy I made it this far.  This week, we continued our examination of Spatial Analysis of Vector and Raster Data.  The specific objectives were:

- Define and use two of the most common modeling tools in ArcGIS: buffer and overlay
- Use the Dissolve tool to merge overlapping borders of buffer zones
- Create a script in ArcPy to run the buffer tool
- Analyze vector data using spatial queries
- Create a simple buffer around vector features
- Create a variable distance buffer around polygon features
- Identify the 6 overlay operations available and recall when to use each
- Use the overlay modeling tool to combine or exclude multiple features
- Distinguish between multipart and singlepart layers and convert between the two
- Quantify and explain the difference between results derived from buffer and overlay operations

Wow! That seems like a lot to do and believe me, it was. The main objective was to become familiar with Analysis Tools and ArcPython. The ArcToolBox is full of great items to use, though some are difficult or tedious to find when you've never used them before. As was my case. I learned to use the Search Icon often. This helped me tremendously and probably saved me countless hours of searching for the right tool. Kind of how I am in the garage.  Anyway, I used the Buffer tool to create zones (buffers) around Lakes, Rivers and Roads to ultimately find a suitable campsite somewhere in The DeSoto National Forest.  The criteria for the campsite was:

- Within 300 meters of a Road
- Within 150 meters of a Lake
- Within 500 meters of a River and
- Outside the Conservation areas

Additionally, I learned how to use the Multipart to Singlepart tool-- this one was definitely hard to find and I couldn't have done that without the Search Icon.

As for the ArcPython, it's a good thing I am familiar with Python from my website development study of Drupal. The most challenging part for me was to ensure I had the code syntax correct. Sure enough, I forgot the "close parenthesis" on one of my scripts and it just blew up in my face.  But, I did catch my omission and the code ran smoothly afterward.

Below is my final map.

Flow Line Mapping: Fighting with Corel Draw

This week's torment, I mean assignment:

Complete a map of Immigration to the United States.

The specific objectives included:
- Assess design issues for flow line mapping
- Calculate proportional line widths using excel
- Construct flow line map using proper design techniques
- Apply style and visual effects in CorelDRAW

The new skill I learned: Flow Lines
The headache I had: Corel Draw

To be fair, there is certainly a case to be made for using Corel Draw instead of ArcMap. For instance, when it comes to making a Flow Map, Corel Draw is a better choice, so I am told. Also, if you want your Flow Lines to have proportional thickness, then Corel is a better choice.  I did learn a great deal this week using Corel Draw, however, I have to believe there is a tool in ArcMap that will allow us to make Flow Maps.  In fact, there were a few blogs that discussed this very fact:

http://blogs.esri.com/esri/arcgis/2007/06/29/making-maps-that-show-flow-from-place-to-place/

http://blogs.esri.com/esri/apl/2012/09/12/generating-distributive-flow-maps-with-arcgis/

http://blogs.esri.com/esri/arcgis/2011/09/06/creating-radial-flow-maps-with-arcgis/

I reviewed all the above and even downloaded the Flow Line Tool, but did not use it for this Lab (honest).

The computation to make proportional Flow Lines was straight forward enough, though on the quiz, the correct answer did not appear to be one of my choices...I couldn't have done the math wrong (maybe)? The formula goes like this:

Width of line symbol = (maximum line width) x (SQRT value / SQRT maximum value)

Where SQRT is, Square Root. This was the easy part; computing what width each Flow Line from the various Regions (Africa, Asia, Europe, North America, Oceania, South America and Unknown) would be.  The challenge for me was using the Bezier Tool on Corel Draw.  I cannot tell you how much fun I had trying to get those curves looking like curves. Nope, I really can't say. Nonetheless, I persevered and had extra practice when my entire map was locked-up and then disappeared from Corel Draw--did that happen to you too?.  Did I mention Corel Draw is not my favorite GIS Tool?  

In the end, I completed a map with multiple legends, including a choropleth legend; added a bevel and transparency to my Flow Lines; and I used Posterize on my map, because I could.  I am happy to say, I now know what Flow Lines are, and how to make them using Corel Draw.

GIS3015: Isarithmic Mapping

The last two weeks have been quite challenging and more fun than anyone should be allowed to have. I am so happy to finish week 8! This week, I learned all about Isarithmic Mapping.  The learning objectives were:

- Understand the PRISM Interpolation Method
- Work with continuous raster data
- Implement continuous tone symbology
- Utilize the legend wizard and properties to make map appropriate legends
- Utilize the Spatial Analyst Extension
- Implement hypsometric symbology
- Employ hillshade relief
- Use the Int Tool to convert floating raster values to integers
- Manually classify data
- Create contours using the Contour List Tool
- Use the Spatial Analyst Toolbar to create graphic contours
- Compile maps using craftsmanship to create unique and polished Isarithmic maps.
- Summarize and present lab objectives and outcomes

After the Choropleth map (last week's assignment), the isarithmic map is probably the most widely used thematic mapping method dating back to the 18th century. Isarithmic maps depict smooth, continuous phenomena, such as rainfall, barometric pressure, and topography or elevation. The most common form is the contour map. Contours are lines that connect locations of equal value. They can also be referred to as isolines (iso means equal or the same in Latin). Additional terminology is used in reference to contour lines depending on what is being measured: Isopleths depicting general meteorological features, isobars depicting barometric pressure, and isotherms representing points of equal temperature. All of these are forms of isarithmic maps.

The task was to make two maps (depicted below) with data for the state of Washington. The data is prepared using PRISM. That stands for: Parameter-elevation Relationships on Independent Slopes Model. I elected to use contours on my hypsomatric map. Hypsometric tinting differentiates contour values with stepped color shading. The Continual Tones map makes a pleasant presentation but my favorite is the Hypsometric Tints with Contours map.  Hope you enjoy both these maps!

GIS4043: Journey to Find Data

This was a mammoth task in hunting for Data! I have survived week 7 and week 8 as this was an extended Lab for the Mid-Term interrogation.

Our task was to find data for an assigned county in Florida (mine is Charlotte county) and create a map (up to three maps) that depict five vector layers, two environmental layers, and two raster layers. This seems like a lot a data to me. The specific learning objectives are:

• Review and record metadata for data coming from multiple sources
• Select GIS data that meets the needs of a defined project (scale, attributes, geographic extent, time    sensitive, software being used or required format)
• Download data from online sources
• Practice data management of GIS data coming from multiple sources
• Detect and correct data errors for use in a GIS
• Intelligently select a geographic projection to be used in a defined project
• Detect and correct geographic projection issues
• Reproject data from Albers to UTM
• Utilize select by location and clip tools in ArcGIS to isolate a study area
• View selected records and create a new map layer from selected feature
• Create easy to interpret maps presenting multiple downloaded data layers using ArcGIS

I acquired most of my data from FGDL and one file from LABINS. The files I selected were:

o   cities_feb04 (cities & towns)

o   countyshore_areas_jul11 (County boundary)

o   fdot_localnames_jan15 (major roads)

o   fnaiip_jun10 (Environmental 1: Invasive Plants)

o   gc_parks_aug14 (Public Land: Parks)

o   nhd24waterbody_dec12 (Surface Water)

o   rarimpwaters_2013 (Environmental 2: Strategic Habitat)

o   ned08 (Raster1: DEM)

o   q2417nw.jp2  (Raster2: DOQQ) Acquired from LABINS

The above data search took a very long time-- good thing we had two weeks to conclude this task. Sorting through FGDL to find data such as Parks, Roads, Surface Water, etc. was a challenge by itself. Initially, I placed all the Data Frames on one map; however, as I viewed the requirement to not exceed 3 maps, I was concerned that 3 data frames plus an inset map would actually be 4 maps. I learned a great lesson here: Data Frames are not maps-- by themselves. I was still somewhat confused because we call an Inset Map a Map-- presumably the Inset Map is in reference to another, perhaps more detailed Map? That sounds like two maps to me. But alas, these are data frames that make up "the map." So, I divided my data frames into two maps: 

Map 1:  I sized the county clip to 1: 400,000.  This was much better than the original size I had to use when all the data frames were on one sheet (one map). Surface water was a problem as there was so much of it. I used a lighter blue. Transparency and turned off the OUTLINE. This presented a much better image.

MAP 2: For the second map, DEM and DOQQ, I was not satisfied that the aerial photo was so small against the county DEM; I needed to use an inset (map). In order to better see the detail in the aerial, I used a 1: 100000 scale and used the county clip behind it with a very light green to help highlight and pop-out features. By enlarging the DEM to a scale of 1:400000, like the first map, you can better see and appreciate the elevation change over the county.  I also added a text box explaining a little about the Orthoimage; how it is presented and what it shows. Finally, I changed the DOQQ Legend to say Vegetation- Soils – Water, instead of, Red- Green- Blue.