http://students.uwf.edu/emv5/IntroGIS/4043FinalProjectPresWeblink.ppsx
http://students.uwf.edu/emv5/IntroGIS/4043FinalProjectPresentText.pdf
The above are links to my final project- an assessment of the ways in which GIS can be used in planning a route through Manatee and Sarasota counties for FPL's new Bobwhite-Manatee Transmission Line, and what effects the line's route may have on local residents and the environment. Included also is an estimate of the line's length, and its potential cost.
GIS is an integral tool for planning construction projects like this. A 25 mile 230kV transmission line running through 2 very densely populated counties in south Florida is a huge project to undertake. The route must be strategically planned, so that it doesn't effect too many local residents, and also shouldn't put too much stress on potentially fragile environments. The potential route also must be accurately measured, so that an estimate of cost can be made.
All of the above objectives can only be completed with the assistance of GIS. Maps can be used as powerful visual aids to convey some concern or idea, and can also be used to perform various analyses essential to the transmission line route's planning and construction.
Monday, April 27, 2015
The Ultimate Thematic Map -or- "Now I actually know something about proper cartography"
At last the semester's end has arrived, and all of the skills and knowledge are put to the test: The Final Project. It looms as a daunting specter, but its creation was nothing to dread, and I rather enjoyed the challenge. The task at hand was the creation of a thematic map depicting two different data sets- mean composite SAT scores for each state and each state's percent student SAT participation.
This is my cartographic opus of the semester. I chose to display the percent participation as a choropleth map with six classes of data, and with the data classed using the natural breaks method, which necessitated some manual adjustment. The natural breaks classes gave the best overall view of the trend in the data; it classed values with the most similarities together, and gave the greatest difference between class members and members of other classes. The highest value class was manually created though, as those three scores were disparate enough from the rest to warrant it. The mean composite scores (addition of the means in Critical Reading, Math and Writing categories for each state added together to form one average score) are depicted as graduated symbols, with classification using the natural breaks method, again using manual adjustments where outliers necessitated.
The map itself is purple for the choropleth and yellow for the graduated symbols, in order to provide the greatest amount of contrast between the two. Gestalt principles of visual perception dictate that the thematic data stands out best when like variables are symbolized with like variables, but the like variables are different in some key respect to allow for comparison of some measure. This means that the student participation percentage for each state is symbolized with the state's shade of purple, so that it is immediately evident that the color of the state means the same measured variable across the map, but the different shades represent different classes. The graduated symbols follow the same principle- they are all yellow circles, but their size varies according to the class of values they represent. This representation for the display allows for easy visual identification of the two categories of information the map is displaying- each state's mean score and percent student participation. The colors purple and yellow are ideal for this display, as they stand out well against one another, and help increase the visual contrast of the two measures.
The interesting thing here is what happens when the above tasks are completed and the picture of the data as a whole emerges. One can easily see that the trend is for mean composite scores to decrease with increase in student participation, and increase as participation decreases. If the mean scores and participation percents are plotted on a graph the trend becomes very evident- which is why I included the graph at the bottom of the map. This obviously doesn't establish a causal relationship, but surely brings up some interesting and important questions.
I remain completely astounded about how little I knew about proper methods of map-making prior to this course. I have been employed to make maps with GIS in a few different professional positions, and am now slightly mortified to think of my outputs therein. The upshot, though, is that I feel as though the work I've done this semester has not only been extremely personally gratifying, but also a boon to my abilities as a GIS professional. I aim to continue my career in GIS, and can absolutely see where I will use the techniques and skills I've learned here on a regular basis.
This is my cartographic opus of the semester. I chose to display the percent participation as a choropleth map with six classes of data, and with the data classed using the natural breaks method, which necessitated some manual adjustment. The natural breaks classes gave the best overall view of the trend in the data; it classed values with the most similarities together, and gave the greatest difference between class members and members of other classes. The highest value class was manually created though, as those three scores were disparate enough from the rest to warrant it. The mean composite scores (addition of the means in Critical Reading, Math and Writing categories for each state added together to form one average score) are depicted as graduated symbols, with classification using the natural breaks method, again using manual adjustments where outliers necessitated.
The map itself is purple for the choropleth and yellow for the graduated symbols, in order to provide the greatest amount of contrast between the two. Gestalt principles of visual perception dictate that the thematic data stands out best when like variables are symbolized with like variables, but the like variables are different in some key respect to allow for comparison of some measure. This means that the student participation percentage for each state is symbolized with the state's shade of purple, so that it is immediately evident that the color of the state means the same measured variable across the map, but the different shades represent different classes. The graduated symbols follow the same principle- they are all yellow circles, but their size varies according to the class of values they represent. This representation for the display allows for easy visual identification of the two categories of information the map is displaying- each state's mean score and percent student participation. The colors purple and yellow are ideal for this display, as they stand out well against one another, and help increase the visual contrast of the two measures.
The interesting thing here is what happens when the above tasks are completed and the picture of the data as a whole emerges. One can easily see that the trend is for mean composite scores to decrease with increase in student participation, and increase as participation decreases. If the mean scores and participation percents are plotted on a graph the trend becomes very evident- which is why I included the graph at the bottom of the map. This obviously doesn't establish a causal relationship, but surely brings up some interesting and important questions.
I remain completely astounded about how little I knew about proper methods of map-making prior to this course. I have been employed to make maps with GIS in a few different professional positions, and am now slightly mortified to think of my outputs therein. The upshot, though, is that I feel as though the work I've done this semester has not only been extremely personally gratifying, but also a boon to my abilities as a GIS professional. I aim to continue my career in GIS, and can absolutely see where I will use the techniques and skills I've learned here on a regular basis.
Saturday, April 11, 2015
Georeferencing and Editing Data
The term "georeferencing" may not mean much to most people, but it's a pretty important concept for those of us working with GIS. Aerial photos, images of the earth's surface taken remotely, can really only be of use in GIS if they are an accurate spatial representation- otherwise they're really just photos. These raster images can be lined up with an accurate map using control points, and linking places on the aerial photo with places on the map, and thereby stretching and warping the raster graphic as necessary to be spatially correct.
The aerial images on the left side, the north and south portions of the UWF campus, were georeferenced to a vector base map of campus buildings and roads. The text in the upper left corner of the northern photo, and the lower left corner of the south, details the root mean square error of the polynomial transformation. This measure can be lowered by manipulating the control points used to align the aerial photo with the accurate map. Control points create a more accurate transformation when they are placed as far from one another, and in as many different portions of the map as possible. The 2 labeled features- the campus gym and Campus Lane- are edits, added to their respective vector dataset feature classes by the addition of a polygon and a line. These differ from a simple polygon and line drawn on the map in that they are spatially referenced, and their shapes and attributes are actually added to the datasets permanently. The eagle nest location on the right has 2 buffer zones added around it, which represent a conservation easement around the point feature.
The aerial images on the left side, the north and south portions of the UWF campus, were georeferenced to a vector base map of campus buildings and roads. The text in the upper left corner of the northern photo, and the lower left corner of the south, details the root mean square error of the polynomial transformation. This measure can be lowered by manipulating the control points used to align the aerial photo with the accurate map. Control points create a more accurate transformation when they are placed as far from one another, and in as many different portions of the map as possible. The 2 labeled features- the campus gym and Campus Lane- are edits, added to their respective vector dataset feature classes by the addition of a polygon and a line. These differ from a simple polygon and line drawn on the map in that they are spatially referenced, and their shapes and attributes are actually added to the datasets permanently. The eagle nest location on the right has 2 buffer zones added around it, which represent a conservation easement around the point feature.
Thursday, April 9, 2015
Google Earth & KML
GIS is a powerful tool, not only for making maps that previously would have been painstakingly time-consuming to create by hand, but also for easy and rapid spatial analyses. This week we explored some of the ways that modern GIS and cartography are changing with new technology and the modern zeitgeist, and delved into creating KML files for use in Google Earth.
This screenshot is a Google Earth image of downtown St. Petersburg, taken from a KML file with a "tour" of various locations in south Florida. The map layer information, visible when the layers are turned on and the image is zoomed out, was created by exporting the info directly from ArcGIS to the KML file format, and loaded into Google Earth. Though the Google platform lacks the ability for visual modification and analysis present in ArcGIS, it is a free download, as opposed to the many hundreds of dollars required for the ESRI product. KML files create an opportunity for sharing GIS information that previously was only available to those with the necessary software, as anyone with a computer and an internet connection can download and use Google Earth free of charge.
This screenshot is a Google Earth image of downtown St. Petersburg, taken from a KML file with a "tour" of various locations in south Florida. The map layer information, visible when the layers are turned on and the image is zoomed out, was created by exporting the info directly from ArcGIS to the KML file format, and loaded into Google Earth. Though the Google platform lacks the ability for visual modification and analysis present in ArcGIS, it is a free download, as opposed to the many hundreds of dollars required for the ESRI product. KML files create an opportunity for sharing GIS information that previously was only available to those with the necessary software, as anyone with a computer and an internet connection can download and use Google Earth free of charge.
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