Google Earth KML

This week was pretty straight forward and simple compared to previous lessons. ( A well needed breather before out final project, perhaps?)

We had to take a mxd from a previous lesson (Module 10 Dot Density map) and convert it into a KML file that allows the information to be displayed in Google Earth (or another similar program).

The only issue I had was going back to an old problem with the dot map, having the dots display onling in urbanized areas. ArcGIS kept crashing everytime I attempted to display it. This time, I got it to work. Either because I reduced the amount of information on the map (no background color, etc.) or because I played with the size of the dots and the number of people each dot represented. Though it did take a good 5 minutes to draw. Once I had the needed elements on my layout displayed, I saved then converted the file via the Map to KML tool in Arc.Once a KML file is created, it's matter of double clicking and displays nicely in Google Earth.

After that, we had to create a tour of the major cities in South Florida. This was also very easy. Clicking the camera button on the tool bar and then clicking the record button. Whatever we do while it's recording, is recorded. Though I had to do it a few times until I had the tour I liked. There was also the trick of displaying and not displaying features, like the pins for the cities or the dots. The trick was find the right moment to uncheck them as the map transitioned to a new area. It's cool how Google Earth does the flying bit. Almost makes any tour look like a professional piece of work. Once in the respective cities, I tried to do a little flying around using the w-a-s-d layout (PC games do matter...).

The other aspect of this lesson was discussing VGI. Google Earth is a tool that seems to be made for anyone to create a basic map. You can add photos, points of interest,etc and share them with a community. If you know how to create KML files, you can go a step further and really create something that can be easily shared with others. I like the open source aspect of it. This is where ESRI fails and companies like Google, Open Street maps or programs like Q-Gis do well. Giving people the ability to map what they want. It's obvious ESRI is attempting to compete with their ArcGIS online and their ArcGlobe, obviously though ESRI is more geared towards organizations that need this tools. However, I'll venture to say that as mapping technologies become more streamlined and easier to access, more and more organizations might opt for open source over paid subscriptions.

Google Map Image with Dot Density layer and related information (legend, etc.)

Geo-Referencing

This week's lab focused on geo- referencing, where by we take un-referenced data (i.e. no geographical projection). We had to take raster data create a reference for it. The data consisted of aerial photos of the UWF campus near Pensacola, Florida. For our purposes we referenced in a basic but effective way. And as beginners in GIS, basic is where we start.

The process works by adding data that is already referenced. In this case, polygons and polylines of roads and buildings on the campus. We then added the raster data. Of course the raster doesn't line up. So we enabled the georeference tool bar. (Everything in ArcGIS comes down to the toolbar). In the tool bar we can first get the raster data to appear in the same space as the buildings/roads layer by selecting Fit to Layer. Then we enable the points. Using the buildings/roads layer, we can connect various points to points on the raster data. I can see the corner of a building, and connect it to it's corresponding polygon. You need to start with the unreferenced data and link it to the referenced data. You need to have at least 5 points.We can then check something called the RIMS error, this will tell us how close those two points are. The error should be under 15. I was able to get the error around 4 on the first raster while still making sure it looked accurate. Having it look accurate is as important as getting a good error value. So one needs to find the right balance. This balance is helped along by using polynomial transformation. There are three levels and the transformation helps to warp the raster data to the points. It can help create a more accurate look but if you use higher levels (as I discovered) it can stretch the raster out and distort it in various ways. The northern half of UWF used a level 1 transformation where as the southern hald used a level 2. Level three really distorted the image. After creating and deleting links I finally got something I felt good with.

We then had to create a buffer zone around an Eagle's nest using the multibuffer tool. This was the easiest part. Input, ouput boom...buffer zone! multi because we created two in one, 330 ft and 600 ft. There was no base map for where the eagles nest is. So I had to add a basemap..for some reason, every time I exported it as a jpeg, it distorted and created a pixalted green mess. I couldn't figure that out. We also used the editing tool to digitize shapes on the map. In our case, the gym and a road. The road was a polyline and the gym, a polygon. I had done this in the past using one of the free ESRI courses which has you digitize a lake. The tool is a little tricky. With the gym, I first tried making a square for each part of the building, but it was hard to line them up and merge them. I went on YouTube, find a viedo and realized there was a way to do it in one sweep...make a lot of little points that match the outline. Though, it didn't come out perfectly, I felt like it worked great.

The next part was the part that you can impress people with the most...no one cares that can you do an awesome job analyzing the best place to set this up or geo code, no the thing that gets the lay people interested are 3D maps! We put our now referenced raste data along with a DEM feature class in Arc Scene. Using the DEM plus the base heights of the buildings we were able to create a 3D map of UWF. This is actually a fun process that I really enjoyed in my Carto class. And your friends often say...ooooh...aaahh....even though in reality the process do it is quite simple despite the number of steps.

Overall this lap helped us to better understand some of the basic mechanics of ArcGIS and how georeferences can be created when none are found. As I near the end of the course, I feel more confident in using ArcGIS...I'm also happy I saved all of those pdfs on my pc so I can look them up when in doubt!

3D Models

This week we had to learn and understand how to create 3D models in arcgis. This was actually quite fun and I felt like I was learning the details and methods behind programs like Google Earth.

In the lab we mostly followed the ESRI training directions and then followed along on the pdf. We learned how to create 3d models using tools such as extrusion. The ESRI training sections are really good at taking you step by step of the various tools in Arc that you can use. At the end of the day, I learned how to take any scene (in which I can create a Z value based on elevation or height) and create a 3d representation.

If I had a choice, I'd stick to 2d maps. Though 3d maps can display information in interesting ways, I feel that they don't have much practical value. Ultimately ArcGIS is about Data anaylsis, and though there are times in which a proper visualization can  improve this process, those times are limited. Personally, I feel 3d models best work for 2 situations: 1) Seeing something that you can't normally see and 2) keeping people interested.

As for number 1, that can apply to maps that have subterrian information, such as the first map we looked at. There I could see that types of rock and the well depths. Also it can apply to visualizing non spatial information in a spatial way such as the property prices for the parcels map.

3d maps definitely are interesting to look at and will keep people's attention. That being said, it's hard to think of situations in which 3d maps provide better information than a 2d map. And unless you're concerned with the actual physical shape of geographic information, it almost seems unnecessary. But ultimately, making 3d maps are fun and look really cool.

Geocoding

This week we spent time learning what network analysis is and how to perform this in ArcGIS.  So essentially I was able to learn what the mechanism behind google maps directions are (though I'm sure it's more complicated than what we dealt with...because of computer languages etc.)

First we had to gather the data starting with TIGER lines (I love the American..er sorry...USG's love of creating acronyms for everything) from the US Census Bureau's website. Well, this was was actually a pain in the ass for me. I live in China and there's this thing they do, where they censor the whole internet including a lot of USG websites. This is not a cool thing (partly why I'm moving out of this repressive country after only a few months of living here). My VPN, for some reason, couldn't connect to the website. After much frustration, I switched to my other VPN which worked (never doubt the simplest solutions to any problem in life).

Once I had the data needed, I followed the lovely pdf instructions and created a Lake_Roads file for Lake County. I then proceeded to add a smattering of data files (new and old alike) to the Lake County database. Many of the files I added, such as the water, county boundaries, etc were from past modules. I then clipped, exported, re-projected, etc my way through them until I had a set of data that I could work with to make the best possible map. I ended up creating a new water features layer by cutting out water features under 1.5 sq km (I never realized how much water Florida has...it can really clutter a map). I wanted to include the water inside lake county, because the many lakes in Lake County had a real impact on the network of roads and I felt it best to ensure my audience is aware of the "lakeyness" of Lake County.

After my data was arranged into feature classes I created the address locator using the EMS file. The matching of unmatched addresses was a little interesting. I matched using the map for all of them, as each time I added said address to google maps they were no where near the ones on the candidate list (those that had candidates anyway). After I matched the unmatched with the assistance of google maps, I then created the Geocoded results.

I picked the three stations you see displayed in the insert and performed the route network analysis creating the nice blue line. I kept the line in both data frames. For the EMS stations, I selected the cross and had them labeled according to their station number. Using the station numbers I could list all of the addresses in the box on the lower right hand corner of the map. I decided to have the stops look the same as the rest, only I kept the color blue. You'll notice I didn't add the stops to the legend. Here I'm assuming the audience can figure out that those are stops from the numbered order of each stop (1...2...3) and from the title of the insert map "Best Route from Station 141 to 241 to 231".  I was trying to decrease clutter, aiming for minimalism. I attempted to make the EMS stations as preeminent as possible by sort of washing the county out with the color scheme while still trying to make sure the street network was visible.

Hope you like it! 

A map displaying EMS stations in Lake County, Fl.
The insert shows the best possible route for 3 of those stations.
The addresses of the stations are displayed based on the station number,
which are labeled on the map it's self.

Dot Pop!

This weeks class focused on creating a "dot density" map. Essentially, it's a map that has a lot of little dots that represents certain data (in our case, one dot representing many, one-to-many). We had to create a map showing population density in South Florida. Each dot represents 5,000 people. The interesting part about dot mapping with one-to-many dots that they are randomly distributed and do not correspond to geographic points. Though I tried so hard to get the dots to only appear within urban areas, ArcGIS being the ever expensive glitchy  program it is, crashed repeatedly. So I had to make do with excluding the dots from the water layer.

I created this map entirely in ArcGIS, never using AI once (I know, I know, I can't believe it either. It's such a good looking map). The first step in creating any map is figuring out the base layers and the colors to use. I went with natural colors. A blue background (the sea) and a light green for the south Florida area. You'll notice a grayed out section of Florida to the north. This is the county layers from previous modules. I decided to include this layer to create continuity instead of making south Florida look like it had been hacked off and is floating in a nebulous space.It think it's quite clear that the green area is the area we're concerned with. For the water layer, I used a layer from a previous module as opposed to the one given to us. I did this because, the water layer you see is more refined and shows major water sources. The layer given to us for Module 10 was all water and instead of taking hours to clean up that layer, I took the easy route and just clipped this layer! ( I think it was from my Intro to GIS class). For the colors, a dark blue for the lakes and a lighter blue for the streams. As for the wetlands,I went with the ESRI wetland but removed the background color and changed the foreground color to blue. By removing the background color, it allows the wetland feaure to blend into the green of the county layer map. For the urban areas, I went with the color you see. I think it's easy to see on the map while not conflicting with the colors. Essentially, it's not jarring to the eye. I then included an insert map using a US State boundary shape file I had on my computer. Next was the cities. I went with the following cities because I felt they gave the best reference points on either coast. Originally, I was using the city picture to represent the cities on the map, however, when the dots were added, the cities became covered in red dots and it was hard to see not aesthetically pleasing. Instead, I went with the circle dot and turned it blue. It's easier to see when the dots were added. The dots, I decided to go with red. Red is easy to see against blue and green. Also, the red made the dots the most prominent feature.

For the dots, I tried to have them mask by the urban layer. However, every time I attempted to mask it to be only in the urban area, Arc crashed. I tried changing the ordering of the layers. I tried deleting everything and relayer it and masking it different times in different ways. But every time I selected the urban layer and try to mask the dots to only appear there, it crashed. So I had to settle with excluding the water layer. This then inspired me to write a summary about the map (something we've done on other maps in the class and in the intro to GIS course). I wanted to make it clear that the dots are not geographic points. (Not one to one dots). Originally, I went with dots that represented 15,000 people but I felt it wasn't accurate. Instead, I went with each dot representing 5,000 people.

Dot Density map of overall population in South Florida. As the disclaimer explains above, the dots are not geographic points. The map was created entirely in ArcGIS. 

Bufferin'

This weeks class objective was ti use buffer tools to perform a vector analysis. The task was to determine the best possible location for camp sites based on certain parameters. We were given feature classes of roads, rivers, lakes and conservation areas within Desoto National Forest.

First, before I dove into the pdf guide, I downloaded some shape files from the Mississippi Geospatial Clearing house. (I knew where Desoto was located). The files were parks & reserves, state boundaries and county boundaries. I wanted to see where exactly this data was located as well as provide a base map to make it more aesthetically pleasing and detailed as opposed to having lines and polygons floating in an ambiguous space.  Using these layers, I figured out that the given feature classes were inside Desoto National Forest that was inside of Perry County, Mississippi (So, not the whole forest).

I then added the base layers and created an insert using the state boundary maps. I put our feature classes on top and then followed the pdf verbatim. The buffering was quite straight forward, if you knew what tool works best. We were tasked with joining the buffers of the rivers with the roads. I found the intersect tool worked best because it only took one step as opposed to several with the union. The next part was to find the buffers that intersected at within the buffers of both the water features (150m for lakes, 500 m for rivers) and the roads buffer (300m). Once I found those, I then had to erase buffers that were inside conservation areas. For this, I used the erase tool. Once I used the tools to determine the best places for camp sites, the next task was making it look good.

For that, I map the forest a light green. I left the conservation areas with a dark green. Though the examples given don't have the conservation areas displayed, I felt it best to display it so people could better understand how the best places to camp came to be. (If you knew the area within a certain distance of roads and water is where the best camp spots are  you would be curious why some of those spots were missing from the map even knowing that it excluded conservation areas.)
I then added the insert map, the title, legend, neatline and north area. I tried to keep the map simple in terms of placement and color to keep it aesthetically pleasing. When it comes to design, I'm a minimalist.

Map of possible camp sites in the Perry County portion of Desoto Natinal Forsest. The salmon color areas are the possible sites. An explnation of the map is in the lower portion between the legend and insert. 

Flowy Lines of Migrants

This week's lesson in cartography was in flow lines. Essentially, they're nice long, flowing lines with an arrow on one end. (In simplistic terms.) We were tasked with creating a flow line map that displays immigration by continent to the US. During this process I realized something, AI is not meant for maps. Thank god the file we were given already had the layers organized. Exporting an arc map to AI, creates such chaos on the layer front. I digress.

In making this map, I first considered how the lines would work. I decided to have all the lines run into each other. I did this to reduce clutter. I do regret, however, making each line a different color. Looking at it below, I feel it didn't quite come out the way I wanted. It's hard to see the Oceania line, for example. I then changed the color of each continent, except for the US. For the US, I changed the color to a red so it could stand out. Europe, sadly, looks washed out. (oh the pain of last minute work).  For the insert, I didn't go with an at scale. I blew up Hawaii and Alaska, though I kept their directionality. I made the legend in ArcGIS and then copied it over to AI.

I made a legend using the arrows and matching the arrows to the line width I figured out in the lab exercise (5pt being the maximum). I then put the respective number of immigrants next to each one.

I added drop shadows where I though would best show the lines and other information. Also, I like to keep things simple. I felt the large white border with the space to show my legend without a neatline, worked well. I felt my color choice on the map is also simple. The colors aren't too bright. I find the colors to be demur.

I like the central concept of flow lines, however, I'm I feel most stories can be told in better ways than with flow lines. They're thematic and can look compelling but also can clutter the map quite easily.

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).

Thinkin' Design: Module 4 Lab GIS 3015

This week's objectives are to use Gestalt's principles in creating a map. We had to use visual hierarchy to help display information. Using VH well creates a balanced map that shows the 

end user the most important information while maintaining the needed geographical references.  

In my map, the first thing to do was to decide the colors I was going to use. I had to make sure the colors provided contrast without impeding balance. Sort of like using the right amount of seasoning

in a meal. Rosemary adds a nice flavor, but if overdone, it can ruin the meal. Again, I went with green. I don't know why but I find myself using this color a lot.  I feel I was able to create a good contrast using green. As you'll notice,  the DC area to the west and south of Ward 7 is lighter than Ward 7. If you look at it, your eyes start pushing towards Ward 7. Those DC areas provide reference without overwhelming the map. Now that your eyes are in Ward 7 (you may also notice that the white  background outside of the DC areas pushes your eyes toward Ward 7) you'll immediately notice the red icons. They jump out and you can quickly discriminate between  the different levels based on their shape. The river, I made a dark blue. Initially, the river was a lighter blue but the bridges blurred into the river and that blur was distracting. The colors were ultimately created in AI. 

Creating the map, I began in ArcGIS. I created the basics there: the symbology, the insert, the roads, etc. With the roads, I added state hwy 295. It's the only highway that cuts through Ward 7 (as far as I could tell). Adding the other highways and interstates didn't make much sense to me because they were outside ward 7 and my scale is around 1:35,000. When I turned those layers on, they would be in the left hand corner or towards the lower left hand side. The insert was easy to make. All you need to do is create a new data frame and place the layers inside that. You can even duplicate the layers (right click, copy). The extent was created by going to the properties and extent tab. I went with a red box. With the legend, I inserted it but that shows all the active layers. So I right clicked, properties and then moved all the items out except the schools. I listed the schools by level by going to the layer, right clicking and going to the symbology tab. In there I created categories based on levels (which were already in the data). I assume ES, MS and SHS were all abbreviations for the various school levels. I edited the labels, removed the heading. I then searched for a good icon. At first, I went with the little red school house icon that was used in the pdf. I decided that I didn't like because, even if you changed the sizes, it was still hard to discriminate. Instead, I went with the icons you see below. They're similar but different. I feel those icons make it easier to know what kind of school you're looking at without going back to the legend to keep checking. After all of that, I exported the mxd to AI. I changed the colors to my "better" greens and added the title. I kept the title inside the map area and created a color pattern (white with black outline) and a drop shadow that would help make it stand out and easy to read. I added a drop shadow t the ward 7 area that I think helps, but it is inconsistent. I added the border by creating a  hollow fill rectangle with a thick gray border. I'm really starting to enjoy using AI (my swearing was considerably less when manipulating layers and figuring out other things). 

The whole time, I was thinking in minimalist terms for everything including the insert map. My take away from the text was keep it simple ( albeit based on the needed information and audience)  

Side notes:

In ArcGIS I used the selection tool to select elements within Ward 7 and then exported that data as a new shape file. I then removed the previous shape file that I didn't need anymore. I did this with the schools and I even did it with the river and parks. I really enjoy using that selection feature. It really helps. 

I'm not too worried that I cut off bits of ward 7 (this is also shown with the extent box in the insert). It helped me make the area  a little bigger without taking any relevant information out. Our design starts with looking at the geography of an area.  It dictates the layout and placement of everything. (well duh right).

I'd like to take a litte time here to discuss alternate GIS programs. I was having connection issues (I need to use a VPN) and so I exported the data to Q-GIS which is already on my PC and played around with it. I found Q Gis to be faster than Arc and the maps look nice as you create them. Ultimately, I abandoned it (mostly because there's a lot to learn in Q-GIS) but this open source suite (along with others) may be worth the time to learn. It's free and seems quite powerful, at least for map making. I'm unsure of it's GIS analysis capabilities. 

Marathon Key; Typography

This week was typography or the process of, well, labeling according to cartographic principles. For this week we had to create a map of Marathon Key to exhibit our understanding of good cartographic typography as we understood it from both the lecture and chapter 11 in our text.  

The tricky part with Marathon Key is that it is not easy to label the required areas via the guidelines. So the use of leader lines was heavily employed. As for the various keys, I placed the text alongside the coast lines. I thought this would make it more apparent and easy to read. Except for Crawl Key, it's such a small feature that I used a leader line. 

I used two font styles. For the cities and keys, I used Casteller and for the title, water, and various places on the key, I used century new gothic. Both fonts are easy to read. For labeling the water features (except for Florida Bay) I went with a white font. This was to help differentiate the feature from the keys and I also enjoyed the aesthetic look of how the text seems to sink into the drop shadow. I used drop shadows to make the features easier on the eyes. Without the shadows, the key itself just bled into the blue. 

I went with a simple color scheme of blue and green. This is more intuitive for most people especially when dealing with an island. It makes it more apparent.  

Also, the island was rotated to be horizontal with the north compass also rotated to reflect the directionality. I did this for two reasons. First it made it easier to work with. I was able to label the features batter and include the basic elements (insert, legend, scale, compass) in a more linear fashion. Because I did this, I went with a north arrow that included east, west and south to make it easier to understand that the map is not true north from bottom up. I went with a simplistic compass. Second, I feel it's easier for the map reader to understand. Admittedly, I may of created too much dead space between the title and the island. 

I attempted to adhere to the principles of topography as outlined in the text and discussed in the lecture. However, I did have a hard time lining up the leader lines with the points. Also, given the lack of land I tried my best to label features such as the keys in the best way possible. I realize there is room for improvement. 

The process was simple. First, I created the map in ArcGIS. I found GIS data made by FDOT so I could include US route 1. The highway shield was created in arc as were the points for the towns. I created the insert by using Florida counties shape file and finding a good scale for it. I used the extent feature in the properties to create the small red box. The labels, legend and tile were all created in AI. 

I created the border by making a new layer that had blue (the background) with a thick grey edge.  I really developed a better sense of the layering and used that to my advantage in making the map. I was mindful of the hierarchy so to speak. 

And that's it in a nutshell. Hope you enjoy. 

"Cartography in GIS"

This week we learned the basic concepts behind cartography. I was actually surprised that most GIS users don't have much formal cartographic training. I feel like that's important to know when making maps or just working with maps in general. Though this week was mainly an introduction to cartography, I'm sure I'll grasp it (in my concurrent cartography course). This week I also worked more with ArcGIS 10.3. This is a program with a lot of abilities and ways to display or work with data. This week showed how to label and display data while keeping to good cartographic principles. It's quite easy to make a cluttered unappealing map in Arc. The take away this week, is that a good map shows data that is easy to read and understand.

The map I choose for this post is the population map.  I choose it because I feel like it came out the best of the three I made.

This project was to create maps in ArcGIS using the basic principles of cartography. These principles include the more obvious, such as including legends, scale, north arrow and  authorship. It also includes making a map that is at once informative while being aesthetically pleasing. I feel with the population map, I made an aesthetically good map that conveys the information in an easy to read manner.

The map above shows the population of Mexico by state. The color ramp chosen uses a gradient of greens. The darker, the more populated a state.  The greens stand out and draw the eye, while  information (other counties, etc)  providing context blend into the background. 

The State of Florida Map

The objectives were to learn the basics of AI functions, exporting maps from Arc to AI, using the essential map elements as per the UWF GIS Essential Map Elements, demonstrate competency in AI, and using scripting to improve functionality.

I'm going to be honest, I suck at graphic design. Mainly because I'm unfamiliar with the software, but as time goes on, I'll develop the skills needed. AI is a huge suite with lots and lots of capabilities and surprisingly, some limitations. Figuring out how to make a border wasn't easy. I ended up using youtube videos. What I did in the end was use a rectangle with the inside white and the outside a green edge. I find that given the complexity of the program, it is not intuitive so it's something that needs to be learned.

I started with ArcGIS and obtained the needed data. I modified the legend by changing the layer titles. I also used ArcGIS to help create my background color. In AI I put the map on 80% transparent so the background color could emerge a bit. The scale and North arrow were also created in Arc GIS.

Once the map was exported to AI, I set it to transparent as stated above. I then added the graphics using wikipedia which provides public domain and creative commons photographs and graphics.

To make it a little unique, I used palm trees as the symbol for the cities. The palm trees were freely available (no copy right) off of icons etc. Now, figuring out the find and replace script was a slightly frustrating experience. However, I finally figured it out and magically my cities turned to palm trees.The trick was, besides un-grouping the cities, selecting the palm tree first, then the graphic I wanted to replace, finally running the script. I kept the state capital as is from Arc: a big red star.

I modified the legend in AI to match with the font I was using, "Intergraph Architectural". I thought it was a fun font and it's easy to read making the information very clear. The key tools was the direct select function, because it allowed me to select certain objects without disturbing others. Also, learnig how the layers work was important. The other key tool for me, was the hand tool. It allowed me to move the image as a whole. Initially, I was moving the image with the cursor and screwing everything up. Learning the colors and how the work was also important. I can see that becoming an issue in the future. There is a gradient on the the border colors. It's light, but it adds a little je ne se pas.

The three features I went with was the state nick name, the seal and the state animal.

My Own Map Week 2 GIS

This week we had to make our own map. Admittedly, I ended up doing the bulk of the work at the last minute. It was finals time in China and I was sitting my desk grading a huge stack of English lit finals. That being said, I really enjoyed this. I feel like I got a better understanding of how ArcGIS works. In the past I had done those ESRI lessons, but this week I got into more of the minutiae and I only look forward to more.

See my map below. I seem to have forgotten the borders! :( So now it's a nebulous map that floats in space.

I now feel I can better use the tools given, including the clipping. It's a mighty powerful tool I never knew existed. Learning how to inlay a smaller map was quite useful, though you can't see the work unless you're in layout view.

The most important for this week was learning how to source the metadata and the other related information (ctrl+f came in handy) inside the arc program. I realize the importance of this as it can have serious implications in future work.

I know in the future our labs won't have such a step by step feel, though I feel for now that was needed, as ArcGIS seems to be a large program where hand holding (initially) helps a lot.

It took me a solid 3 hours to go through the lab and to build the map. However, despite my thinking that I was being careful, I feel like the final product could've came out better. I had a hard time aligning all the elements. Is there a way to lock the elements as a total, so I can align them together instead each individual part? Was that already covered and I didn't pay attention? Questions for the discussion board.