Eye-SAH-Ryth-mik Mappin'

This week was isarithmic mapping...or how those contour and weather maps are made. Isarithmic mapping is different from choropeth mapping in that the data is not bound by enumeration units. Isarithmic mapping is good for data that doesn't correspond to to the artificiality of borders, data like weather patterns, other natural phenomena or even human activity that transcends enumeration units (population distribution as opposed to density). Isarithmic mapping works best with continuous smooth data.

First, in order to understand how isarithmic maps work, one needs to know interpolation of the data points. Enumeration units are not used, points are used instead. These points can be "true" (a weather station) or "conceptual" (data from over a larger area placed into one point). This, for me, wasn't that easy to grasp. Our book had tons of little formulas for the variety of interpolation it presented us with. I suppose though, knowing the basics is what's important because as a GIS user, I can better display and explain the information if I know the process that went into creating it. Like a car salesman who knows the basic of automotive assembly.

The objective for the lab was to use isarithmic mapping and display the data through 2 different symbologies. The first was continuous tone which shows colors or shades proportional to the data being displayed. In our case, the 30 years average of precipitation in Washington state.  The colors in a continuous tone map sort of flow into each other and I feel, is easy for any map reader to understand.

The second was hypsometric mapping. It's similar to continuous tone however it's used more to show how the data is impacted by elevation. I feel hypsoemetric mapping is somewhere in between choropeth mapping and the continuous tone symbology in that hypsoemtric almost creates clear breaks in the data. Those breaks are the change in elevation. It's useful when elevation's impact on the data is important. Below is the final map product, a hypsometric map with contours. The contour lines help to illustrate how hypsometric creates breaks based on elevation. Those breaks almost create the appearance of an enumeration units (knowing that there are no enumeration units, but instead points).

I feel the map, with it's symbology, shows use where precipitation is greatest by elevation where as the continuous map shows us a more general trend in precipitation.

This map presents us the 30 year average of rainfall over the state of Washington. It uses hypsometic symbology  and contour lines (see above). The data was created using PRISM. PRISM uses point data underlined with elevation and is related to "climate fingerprint" or the historical weather pattern of an area based on it's physiology (mountains, etc.).  

Data Searchin'

Week 7 and 8 was all about hunting for the data. That meant (literal) hours of downloading from the various websites (FGDL and Labins). Given the fact I'm in the People's (non) Republic of China, the internet is not the fastest especially when I have my VPN running to get to most of the data.

Once I had the data I wanted, it was time to build the map in Arc. The first issue I encountered was the projection. Initially, I added the vector data I wanted and then projected that data into State Plane. I then added the raster data (aerials) and it ended up somewhere in the atlantic despite having the same datum and projection as the frame. This was well, annoying. I did a lot of research and a few GIS blogs mentioned that with projections, UTM and state plane were troublesome in ARC. After some troubleshooting of my own, I decided to see if the order in which I "layered" the map would make a difference...and it did. Hopefully this wasn't something I glossed over in this course. So I put in the raster data first, then the vector and it all lined up. I could project and reproject to my heart's content and all was well.

St. Joseph Bay Invasive Plants:
I decided to go with wetlands in my map, however, Gulf county Florida seems to be nothing but wetlands...I then added a managed (conservation) layer (that I clipped). I clipped the wetlands layer to the managed layer. So now my map only shows wetlands inside the managed areas (surprisingly, not much given the sensitivity). I used this in my St. Jospeh bay map. I then layered the invasive plants layer on top. I finally added an aerial of St. Joseph state park (with the plants on top).  I added a insert to show the location within Gulf county. Everything is projected in State Plane.

 

Invasive Plants in Managed Areas, Gulf County, FL
I overlaid the managed areas with the state park layer and the Fish and Wildlife Commission managed areas layer so in my invasive plant wide Gulf county map, you could see who owns what. The pink is privately managed land. In the southern portion of the FWC area, the invasive plants clump up, so I created an insert to make out it better.    Everything is projected in State Plane.  

DEM Gulf County

This one was pretty straight forward. Also, I didn't reporject. The DEM is in Albers Conical. I realized though, I should've added a blue background Gulf of Mexico and St. Joseph Bay). My original thinking was that the 0 elevation would line up with the Gulf. However, not a great idea. 

 

Europeans and Wine

This week we had to learn the basics of choropleh mapping and proportional symbology. This was building on our work with data classification (which sadly, I'm still wrapping my head around). The last two weeks saw me travelling about frustratingly building maps on my laptop while wedged into airplane seats in turbulent skies.,  Now that I'm settled again, I was really able to put some good work into this map.

To create the map, I starting in ArcGIS. I have it up and running on my pc, though that doesn't mean it's much faster compared to running it through the remote desktop. The first part I focused on was getting the choropleth right. This meant two things: 1) Determing the classification and 2) the color scheme. First, I tried to pick the best classification that best suited the population density. (For this part I picked random wildly different colors for each class so I could see it best). After re-reading the text and some google searches on the classification methods I boiled it down to equal interval and quantile. After classifying and reclassifying, I picked quantile. I felt it displayed population density the best. It's quite easy to determine which countries are most dense and least dense. (I did exclude a large set of outliers, mainly the small regions, statelets and countries. To ensure accuracy I excluded the same areas from the wine consumption data.) I'm assuming this map is meant for a more casual observer given the information so I wanted to be as easily understood as possible. I then selected the green color ramp and tweaked each color somewhat individually through their individual properties. I used colorbrewer but honestly, I'm at a lost at how to use it in arc. I found some information on scripting, but I was running out of time so I decided to use the colorbrewer as a guide as I tweaked the green colors.

Now the wine. For this part, I decided to go with the graduated symbology. I did this for two reasons. First, proportional wasn't displaying nicely on the map and second, the graduated symbols displayed nicely on the legend and was thus easier to understand. For this data, I selected equal interval classification because I felt it displayed better on the map and in the legend when compared to others. Again, I was concerned with how easy it is for the reader to determine the information. I changed the symbology from the default dot to a wine glass. I found this picture on google. In arc, I went to layer properties > symbology > graduated symbols > template > edit symbol. Here by going to type, I can go to "Picture Marker Symbol" it allows me to import the picture (png file) to use as a symbol. I then graduated it from 15 to 50. For the most part, it displayed nicely, though there was overlap in geographically close areas (i.e. the Balkans).  For the Balkans I created an insert map and added the same data from the main map. I then included the basics: legends, north arrow,and  scale.

After that I exported my arc map to AI. In adobe, I added the title and the description. I changed the background to be the pale blue which sat nicely behind the green color ramp. I then had to modify the legend. I was having this problem (which seems somewhat common) in which the symbols wasn't displaying correctly in the legend (or even in the layer bar on the left side in Arc). I had to build the legend from scrap by copy-pasting the wine glass symbol off the map into a rectangle then added the numerical data below each one. I liked the finished product, though I'm not happy where I placed it. I feel with the map, I left too much dead space in the upper left hand corner, but given the layers in AI, it was too cumbersome to re-position the map. I eliminated the glasses that were overlapping or covered too much of the country. For example, I used one symbol for Switzerland and France since they were the same size. I was happy for small countries that don't drink much wine, because it was easy fitting the symbol within the borders. For the insert I removed all the information from the Balkans area on the primary map and ensured that in the insert it was well displayed. I had to use leader lines to clean up the clutter. Over all, I was happy with the final product. I feel AI allowed me more freedom in creating a more polished map (albeit I find it more cumbersome than Arc).

Using choropeth for the population density and a graduated symbology for the wine consumption, we see where wine is consumed the most per capita. The most densely populated countries are the UK, Belgium and the Netherlands. 

Projections Part 2: Half the battle

The thing I’ve learned about doing online classes is that traveling and keeping up with the work are not easy. I’ve spent the last 2 weeks travelling between Asia and North America. Between the 15 hour flights, jet leg and new hotels, there is a general screwing with my schedule. Also, Starbucks is my new favorite place since it offers free Wi-Fi, like everywhere (though one must pay 3 bucks for a coffee which offsets the free-ness of said Wi-Fi). That being said, I will say learning projections has been interesting. Now, a lot of this material was covered in my concurrent cartography class but working with it in Arc was good. Though, I slowly, painfully learned the difference between defining and reprojecting. If I’m not mistaken, it seems vector data is the data to reproject because it has already been defined. The raster data I had to define. Once I figured out how to ensure to project my data cleanly, it was like solving a nice little puzzle. Now that I’m familiar with it, I realize how important this will be for my future work in GIS.

In creating the final map product, I decided to go with Pensacola. I figured working with an urban area would be easier for some irrational reason. I found Pensacola by going to the quad search and searching by city name. I then randomly picked a number for the numbers listed under Pensacola, 5259. Using that, I put it in the map. Initially, I was having problems putting in the county boundaries, the x/y coordinates from the excel spreadsheet and the major roads.  So, I went back in the instructions and carefully went through each step using the data I wanted to use. The first time I went through the pdf was on my flight, with a women placing her feet on my arm rest and the man next me taking up my leg area with his legs (there is no personal space in China… L ). This time, I got it right. The processing projecting raster data (othrophotos, .tiff) is different from vector data. With the raster I used the define projection tool and with the vector data, I reprojected the data using the project tool (data management).  A lot of my trouble shooting in ensuring the projections matched up, was opening and closing ArcGIS. I noticed, when you first open an Arc map there is no projection for the data frame. The projection then comes from the first layer added. If you remove that layer and add a new layer with a different projection, there is a conflict that is created because the data frame is using the projection from the first dataset. When defining projections or reprojecting, I had to make sure the GCS lined up. The way I did this was by double clicking the XY coordinate system in the Spatial Reference Properties, this opens a new dialogue, the Projected Coordinate System Properties. Here, you can change the GCS by selecting change and picking a new one. I didn’t do the transformation when the conflicts were first detected when adding the layers. I did it initially, but I found I had a lot of errors. I used the projection tools. Here, I felt I had more control over the projections. 

My take away for the past 2 weeks….don’t travel mid semester and second, projections make all the difference. If anything, knowing projections is half battle in GIS. 

The map displays the required data in the correct projection for this week. If you look close enough you can the edges of the .tiff file behind the aerials. The hardest part was the excel file. This, I had the hardest time with. You really need to ensure the data is clean and correctly converted in the spreadsheet or it won't project correctly, regardless of how you define/project with the Arc Tools. 

Statistics...ewww but not ewww

This week we worked with STATISTICS statics...actually, despite the use of the word statistics, we worked with a GIS tool that uses statistics to help discern if the given data is displayed or distributed correctly.We used three different scary sounding but easy to use tools (geostatistical analyst) to determine how are data related to each other. For this week, we used an ESRI course. Prior to UWF, I have done a few of the more basic (i.e. FREE) ones and they are dull. Somewhere between the monotone voice of the video and the beyond dry writing, those less than 3 hours of training, seemed like a good day's worth. ESRI makes a great software suite, but they need work in the keeping things lively department.

The primary focus of the lesson (I hope) is determining if the data we have is correct in a statistical sense. i.e. does the data relate to each other correctly. What do I mean correctly? I mean, "normally distributed". Normally distributed means data that when displayed falls along a bell curve. The data shows a logical spatial relationship with each other. In determining the bell curve, we used a histogram which displays a bar graph. If the data is mostly normally distributed, then the bars are displayed with the tallest bar in the center and the remainder creating a pyramid esque look on either side. We then learnt to use a QQ Plot which uses a plot line to show if the data falls along said line. If the data is on or near the plot, then the data is "normally distibuted". In our exercise, we determined that there were a few outliers ( i.e. data that wasn't in line with the majority.) My over all take away was that we used these tools and graphs to figure out if the data we were given is good. Outliers may be the result of bad data entry so to speak or a special phenomenon .

In the exercises, we had to use weather stations in western Europe. Of course, temperature readings in high altitude areas (the Alps) were much colder than other areas. Using the QQ plot and Histogram we found an outlier in Switzerland, Most reported temperatures where somewhere in the winter conditions where as this single station was in the 70s. So there was an obvious problem. The whole exercise was essentially learning to use statistics to figure our if our data is good or not. Bad data used can create issues down the line or at least make a GIS technician's life harder.

The map below shows the distribution of temperature across western Europe. You'll notice an X and a cross, these display the median and mean center of the stations. Then the directional distribution shows the direction in which the stations are statistically distributed. I used a color ramp to help display the temperature range better.

Week 5 Projections GIS 4043

This week we were working with coordinate systems. It's important to know these systems and to learn how to use them and/or change them in ArcGIS. My take away this week is that I need to ensure my data is projected in a similar fashion across the board. Though on a larger scale coordinate systems may not make much of difference if there is a conflict or if the chosen system is an ill fit for the area in question, however, it does begin to matter when working with data in detail.

For the lab, we had to work with the Florida county maps and change the projections. (See below).  When I changed the format to UTM from Albers, I noticed that the UTM map became more compacted. The State Plane N, coordinate seem to distort the counties in the south while keeping the northern counties, particularly in the panhandle, un-distorted (not stretched out or compacted.) We then worked with a raster image of UWF. It had no assigned coordinate system. We had to define the system for it, by going to the properties and clicking the edit button to change the features. State Plane N seem to place the image in the correct location. When I did it in Albers, the image was no where near where UWF is in reality. It was interesting to see how different projections impact the map and the data over all. (Which also means that programs like Google earth, must be constantly changing projection as you zoom in and out.) State Plane N obviously worked the best with the raster image it was projected primarily for northern Florida. 

This was a week in which we dug deeper into ArcGIS and understanding how to prepare data we'll be working with in Arc. Of the lessons thus far, I feel this was probably one of the most important to learn and understand.

Comparison of Coordinate Systems using the State of Florida and selected counties. It shows how the square mileage of a given area can change (quite a lot) as the projection changes. At first it's hard to see the the changes, but looking from a slight distance, you can see the distortions.

Sharing GIS

This week we had to learn how to share maps online via different methods. The principle methods involved ArcGIS online (with an ESRI) account.

For the project, we had to use a personal top 10. I went with the top 10 mountain hikes in Taiwan. I'm a big hiker and I really love the hiking there. I was also impressed at how big mountains are in Taiwan, given that Taiwan is a fairly small island country. Many tower over 10,000 feet, the highest, Yushan mountain, stands at appox. 12,000 plus feet. Most of the mountains I have hiked before except two, which are on the to do list.

For starters we had to create an excel file that would be converted to a txt file. Since I was going with mountain peaks (they don't have street addresses) I went with lat and long except the one mountain hike that does have a street address, the 4 beasts in Taipei. There was a lot of trouble shooting. Initially, I had an excel file with the elevation and lat and long. However, the format when converted to txt wouldn't work on the ArcGIS online map. So I created new tables with one for lat and one for long, I converted those coordinates to decimal degrees and that worked, however, it wouldn't convert the street address, so I created a separate txt file and added it. It became another layer. In ArcDesktop, when making the layers for the package and google earth, I merged those layers using the merge tool.

I created the online map with my dots. I then created the ArcPackage. I used mountain icons in the symbology. 4 of the mountains are all clustered near each other, so no matter the scale, I couldn't make them clear without either making the icons very small or excluding other mountains, so I created an insert that used the extent. I did the extent in reverse and used a leader line to connect the insert.

Then exporting the package was easy. I then created a kml file use the kml conversion tool. After the mountain layer was converted, it was a matter of simply drag and drop.

Here is a link to my ArcGIS online map: http://www.arcgis.com/home/webmap/viewer.html?webmap=cf263bdbd2bd43d7be72521d7588c505

This is me in front of Snow Mountain Taiwan (in the background).