Module 6: Data Classification

This week the assignment was to utilize ArcGIS to create two maps with 4 data frames each.  4 data classification with the same data, population over 65 in Miami Dade County, FL. The first map utilized % of population over 65.  The second map utilized a count of those over 65 normalized by square mile.  One of the learning outcomes is to gain awareness of the different distribution of the same data by the different classification methods and different aggregate of the data (% total verses count normalized by square mile).  The 4 data classification methods utilized in this lab are:  Natural Breaks, Standard Deviation, Quantile, and Equal Interval.

Equal Interval: Takes the maximum value and subtracts the minimum value to get the range of the data.  The range is then divided into equal range classes.  The number of classes to be assigned by the map maker.  This option leaves no gaps in the data range and is fairly easy to understand.  However it can force same or similar values being divided into different groups and/or dissimilar values groups together.

Quantile: Takes all the data, ordered numerically, and divides it into classes with equal observations. Each group has the same number of observations.  This option is again fairly easy to understand.  However, this option can leaves gaps in the data range and can force same or similar values into separate categories and/or group dissimilar values into the same category. 

Natural Break: Takes data and runs mathematical algorithms to place similar values together and maximize differences between classes.  This option is much more complicated to explain how mathematical equations determine the class breaks. There option does not allow same values to be put in different classes and should not class values drastically different together.  This classification method is popular among cartographers, and is the default classification method used by ArcMap. 

Standard Deviation:  Takes data and a bell curve approach to classification with equal sections. The majority of data will be in the middle class around the average value, other classes will have fewer and fewer data points as they get farther away from the mean.  This option requires basic understanding of statistics to understand bell curve data and the percent of deviation from the mean to understand the class differentiation.  This method would not allow for gaps in the data range.  This method would not force same or similar values into different classes or dissimilar values into the same class.

Symbolized map for intuitive data acquisition by using graduated color was utilized to symbolize the data.  Lighter color for lower numbers ranging to darker color for higher numbers.   Implemented cartographic design principles by positioning the page title in the largest font at the top of the page and individual data frame titles in smaller font within their frames.  Data information, author and date are all positioned in smallest font at the bottom of the page. 

In my opinion the presentation method best suited to present the distribution is the data that is % over 65 presented in natural breaks classification.  The count per square mile data seemed to wash out the data.  The values were lower resulting in lower values for the map.  The percent over 65 takes into account the areas that are populated and the section of that population that meets the criteria of over 65.  The classification method of Natural Breaks allows for categories to be formulated based on the data.  Forcing the values into equal ranges for equal interval distorts the data on the high end of the values.  Quantile forces a equal count of values into categories and then the rages are set from the data.  This doesn't allow for an natural separation in the data. Standard Deviation assumes a bell curve to the data and this data set is more weighted in the lower range and one outlier on the high end that skews this classification.
 

Module 5: Spatial Statistics

This week I accessed ESRI’s My Virtual Campus Training to complete Exploring Spatial Patterns in Your Data Using ArcGIS.  I downloaded the data provided.  I used the toolbox: spatial statistic tools>measuring geographic distributions>Mean Center, Median Center and Directional Distribution (shown in the map above).  Utilized Geostatistical Analyst Extension.  I added it from customize>extension>geostatistical (checked) and then customize>toolbars>geostatistical (checked).  From the geostatistical analyst drop down box Explore data> Histogram (bar graph) and Normal QQ Plot (data points compared to normative distribution line).  Continuing the exploration from the geostatistical analyst drop down box Explore data>Voronoi Map to determine the variation in data and Expore Data>Semivariogram Cloud to determine if spatial autocorrelation is present.  Lastly Explore Data>Trend Analysis.  Everything beyond the Mean Center, Median Center and Directional Distribution I am not sure what I was doing.  The training indicated that these are all ways to look at and examine data.  

Module 4: Cartographic Design

Several objectives in this week's lab.  I chose to implement visual hierarchy by making the study are as large as possible to fit the page.  They symbols for the schools are large and contrast in color to the study area color.  The title is centered at the top of the page, in large font with a halo to further diminish back ground interference with the text. The legend is also fairly large with the background matching the study area to further emphasize it's importance.  The locator map is at the top part of the page to lend additional information.  The bar scale and north arrow are easily located but not as visually dominant as other elements.  Finally the source data, my name and date are located at the bottom of the page in small font.

More objectives were also incorporated.  Contrast was used in the color of the study area and the color of the symbology (opposite on the color wheel).  Figure ground was employed by the study area being a lighter color than the surroundings with more detail (local roads and neighborhood names) giving the illusion with lighter color and more detail that the study area is closer to the viewer than the surroundings.  Attempts at balance were made by placing the large elements first (the main map in the largest format that the page would allow, the legend and insert to make sure adjustments to the main map were not required, and the title as another large element.  The lesser dominate map elements (scale bar, north arrow, source data, author and date) were placed in the non map space to help balance the page.  Graduated symbols for the schools;  Elementary schools have a smaller symbol than Middle schools which in turn is smaller than the High schools.  Inset map has an extent indicator to show the area of study in relation to the surrounding area of Washington, DC.

I started the map in ArcGIS desktop and then exported it to Adobe Illustrator.  I continued to work in both for most of the project.  There are pros and cons for me already with each.  I feel more control over the layers and information in ArcGIS, but Adobe offers more color and symbol options.  I chose to keep the ArcGIS school symbol for it's simplicity and maybe more recognizable.    I struggled with color choice vacillating often and changing frequently.  I am not totally committed to the yellow cream for the back ground but it seemed to contrast the green color choices of the study area and DC area.

Module 3: Typography

This week I took county data provided in lab from FDGL in Albers Conical Equal Area projection and mapped Marathon Key, Florida.  This required the assistance of Bing Maps as I didn't know Marathon Keys are the middle Keys off the southern tip of Florida.  One of the challenges was to get all of the labels on the map in a clear way, utilizing typographical guidelines from our text.  Water features can be italicized to more resemble water.  Larger structures utilize larger font.  The islands (Keys) are labeled with all caps at 14pt to distinguish them from cities that are at 13pt and the location of interest that are at 12pt.  The type font I chose was Bell MT as it appeared one of the cleanest.  The goal of typology is to utilize the text shape and size to further distinguish items within the map.  Still a bit of a struggle to get all the parts in the right format, fit into the area available and still clearly communicate.  Leader lines should be similar in angle to avoid chaotic looking map (this is more difficult that it sounds) and should not touch points or text while being in close enough proximity to clearly communicate the intent.  Adobe Illustrator (AI) is still new and I find myself overwhelmed at the seemly unlimited amount of options to customize.  There is a limited number of map symbols in AI, so I was able to down load one for the state park, airport and country club.  I chose to further customize my map with 1) a halo on the title adding emphasis as well as tying colors together  2)a border for the data frame to finish and tie colors together 3) added a small light drop shadow to the islands to add depth but limit interference with small water feature labels.

Module 2 Lab: Introduction to Graphic Design in AI

This week I assembled a basic map of Florida in Arc Map (layers included: state counties, state capital, major cities, surface water).  In Arc Map I added a legend, scale bar and north arrow.  Then I converted the basic map to an Adobe Illustrator file.  When I opened the AI file my north arrow was no longer on the document.  TA Austin and a fellow classmate found a web site to explain how to specify the export to AI that would allow the north arrow to come through in AI.  I had already made progress with my map so instead of starting over I just inserted a north arrow.  The lab assignment was to add 3 state elements and document where each was found.  I chose to include the state seal, state flower and the state nickname.  The imagery for the flower and seal I obtained from Wikipedia and the state nickname I designed the imagery.  The lab also ask to run a provided script to change the major city and state capital symbology to that in AI.  The script provided an error message.  I don't know if this had anything to do with the use of the AI file prior to the specification adjustments or if there is an error in the script.  I chose to continue to obtain the results as close to assigned as I could manage.  I chose AI symbology to manually adjust the capital and three cities that I labeled with their names.  I found experimenting with AI to be challenging.  Careful planning to ensure that the map and the scale size were not adjusted separately (until they were grouped together to ensure any resizing would be to both).  I also found the groupings as well as releasing clipping masks (located under the objects tab) to be confusing.  I also found not being able to copy and paste from right clicking and having to go to the main tool bar to the edit tab non-intuitive and cumbersome.  Overall I please with the final product for my first attempt and hopeful the process will become less awkward.

Revision:  Additional Exporting to Illustrator instruction were found at http://pbcgis.com/illustrator/  provided to me by Katherine Sims (classmate) and Austin (TA).  When Exporting to AI clip the option button on the lower left corner of the export dialog to reveal the expo options.  Under general options reduce DPI to 150-200.  Under Format Options tab set the Picture Symbol pull down to Vectorize bitmap and check the box to convert marker symbols to polygons.  This allowed the north arrow to come through in AI as well as the script provided in the lab to change major city symbols to work.  I opened both my first map and my newly exported map and copied and pasted many elements as groups to the new map.  Although it took some time to get the second map together it was not nearly as time consuming as the first one.  I changed some of the format.  I am really not sure which I like better.  But here is the second map.  Very similar but not the same.

Map Critique Week One

This week  Learning Objectives: 1)Upon completion of this exercise students should be able to Understand common map design principles. 2)Identify examples of good and poor map design.  3) Conduct thorough map evaluations, providing an evaluation overview and constructive critique for each example. 

I chose this as an example of a good map:

Here is a little bit about why I choose this as an example of a good map:  This map has lots of map elements: Title, North arrow, Scale bar, Legend, and the author information.  The information is clearly and efficiently communicated.  The title provides the location of the information as well as the information to be communicated (South Carolina, ,game zones).  The color choice is pleasing to the eye, different enough the changes are easily recognizable without being overwhelming.  There is a balance to the map overall.  The information is not crowded together and there are not large empty spaces.  

 And this is an example of a poor map:

Here is a little bit about why I choose this as an example of a poor map:  This map contains no map elements, there is no title, legend, author, north arrow or scale bar.  The streets are sold bold black lines that feel aggressive in nature.    There is no additional information to assist in determining where this is located or why this map was made.  I can not determine the purpose of this map or what information it is trying to convey.

Interestingly, on the spectrum of good to poor maps I could find many more examples of poor maps than I could of good maps.  I speculate that has to do with the source of the maps(internet and provided lab documents).  I hope that there are more individuals out making good maps; informative, clear, concise and aesthetic than this population indicates.  Otherwise my classmates and colleges will have a BIG job making enough good maps to outweigh the bad ones.

Orientation: Story Map

I am Kelley Chastain.  This is my second semester in the MS GIS program at UWF.  I am a divorced mom of two boys, a senior and junior in high school.  I work full time doing financial counseling, insurance explanations and money management for an adult mental hospital.  I started doing this about 4 years ago (wanted to utilize my BA, Psychology).  Before this I worked for 10 years in a local municipality issuing building permits.  I interacted with "the GIS guy" often in that position.  He was often so overwhelmed that I would have to find a work around to achieve the maps that my department would need (often coping, cutting with scissors and taping copies of plat maps together).  I found an interest in maps that I haven't been able to shake, so here I am pursuing that interest into hopefully a new career.  Check out the Story Map that I made on ESRI.

Kelley's Alabama Story

Final Project

I have to say this was the most intimidated that I have been by any assignment so far.  Very little instruction on how to accomplish the assignment was provided.  Question:  Is the Bobwhite-Manatee transmission line placement acceptable and feasible based on the chosen project objectives?  Criteria to be evaluated:  1) Define and quantify Schools (including daycares) within proximity of the transmission line.  2) Quantify homes within proximity of the transmission line.  3) Define and quantify environmentally sensitive lands imposed by the transmission line.  4) Quantify length of the transmission line (cost estimate optional).  Deliverables include:  1)power point presentation.  2) slide commentary for power point presentation. 3)Process Summary.  4) Blog post with links to power point presentation and slide commentary.  (WHAT?!?  I cant do THAT!) 

I spent a few days in what felt like running around in circles.  I explored the data layers that were provided.  Started metadata table, clipped data to study area, and tried to formulate ideas on how to best accomplish the assignment.  Although I did get a better understanding of the data than I had I didn't feel I was making any progress toward the assignment. 

Direction change to formulating the maps needed to accomplish the assignment.  Intersects for environmentally sensitive areas with preferred corridor and 400' buffered line was divided into wetland and conservation. Conservation came together pretty well.  Wetland was too chaotic for me, so I narrowed the classifications to types of wetlands (riverine, lacustrine, palustrine and uplands).  FYI-uplands are the rest of the area that are not one of the 3. So is everything that is not wetland considered an upland?  Not sure. 

Downloaded data for schools and daycares from FGDL.org.  Intersect for these layers with preferred corridor and 400' buffered line.  Another intersect for parcels with preferred corridor and 400' line buffer. 

I had a hard time digitizing homes from the aerial.  Finally I found I had to "create new feature class" rt click geodatabase-new-feature class-name and type. The intersect for the parcel layer with preferred corridor and 400' buffered line was just repetition with differed layers.

The last criteria, length of the line, I did not create a map.  It is the same map as all the others.  I just used the measure tool to drop vertices and continue as close as I could down the center of the preferred corridor.

I will admit I was intimidated and didn't think I knew how to accomplish these things.  I was able to get all the data I needed from the attribute tables for the intersects and the measuring tool for the line measurement.  Not as hard as I thought it would be. 

The next hurdle was realizing that my maps were in portrait layout as that fit the map better, but to use in power point I would probably need them in landscape.  So I changed up the maps.  And then realized when I inserted them to slides it was fine for them to be portrait (learning, learning). 

Write the slide commentary, again have never done it so "oh no, I cant do that".  But I just started writing what I would say and it came together.  Overall - more than a little overwhelming, but how do you climb a mountain?  One step at a time.  I am sure this is not my most professional presentation - but it is a lot better than I thought I could do.

Here is a link to the power point and the slide commentary.
Power Point
Slide Narrative

Week 12 Georeferencing, Editing & ArcScene

This week I was provided shape files of the buildings and roads on UWF campus that had been geographically referenced coordinate data built in to the files (as has been the other files provided so far).  Also proved, two aerial raster files of UWF campus with no referenced coordinate data built in.  "Through a process called georeferencing, also called 'image registration', I tell the raster dataset 'where it belongs'.  This is done by identifying a common point on the target layer and an already referenced control layer and linking these common points."  Since this is a new task, I needed to add the georeferencing toolbar (right click any unoccupied area in the grey area around the toolbars and check georeferencing to add that toolbar).  To make the images line up I add control points (clicking that icon from the georeferencing tool bar), clicking first the point on the unknown (raster-aerial) and then the same point on the known (vector - building or road).  For best results the points should be spread out not clustered together or in strait lines.  Can be tricky accessing points with all of the files in the same window you can either set either the referenced or the unreferenced layer to a transparency (layer properties, display) or a new viewer window (icon in the GeoRef. toolbar) that only displays the raster (this was my choice).  When you aren't satisfied the delete the link with another icon on the GeoRef toolbar.  After 5 points review the links with the 'view links table' icon.  Residual value shows how much each link agrees with how the layer is currently being displayed.  The lower the residual value the more accurately that control point is georeferenced.  Root Mean Square (RMS) is used to indicate the accuracy of the spatial analysis.  The lab pdf indicates the RMS should remain under 15 with 10 control points.  Mine was 5.08232 (so I thought I was an expert!), until the second aerial was added and control points were added.  This photo was intentionally distorted - HOW RUDE!  I was able to get the RMS VERY low with a 3rd Order Polynomial but the map appearance was not matching up well with non control points.  At a 2nd Order Polynomial is the RMS was 14.2713, but the map looked more matched up overall.  "Higher order transformations allow the raster to bend and warp more than lower order transformations."  Update georeferencing (in the georeferencing drop down) to finish.

Next I edited the referenced image by adding a building and a road not in the original building and road files.  Again a new task means adding a new toolbar.  This time click Editor Toolbar icon on the standard toolbar to display.  To begin in Editor Toolbar drop down "start editing".  Select the layer to which the edits will be made from the TOC.  Select "Create Feature" icon confirm the feature template and select the construction tool in the create features window.  Set up additional editing properties like snapping.  Select the strait segment icon and start adding vertex. (End point arc was also suggested along with the strait segment icon, I did not use it).  Add vertex to keep the shape.  Double click vertex to end.   Save edits (this is not saved in the map until this selection) end editing session in the Editing tool drop down.

Next a multiple ring buffer was requested to a point file provided marking an Eagles Nest.  A hyperlink was directed in the attribute table to a photo of this nest (attribute table icon in the editing toolbar).  Add Multiple Ring Buffer tool icon (Customize (main menu-Customize Mode-Commands tab-search Multiple Ring Buffer-click and drag to empty spot on toolbar area).  Multiple Ring Buffer tool window requires input, output, buffer unit, and distances).  This point is not contained in the aerial files provided.  After the buffer was performed a base map was added to show the general location of the nest in relation to the UWF campus buildings.  The protection buffer was set to 50% transparent.  Here is the map!!

Finally this week create a 3D scene in ArcScene.  This was the briefest of introductions.  I opened ArcScene (start-ArcGIS-ArcScene).  Added the same layers; buildings, roads, aerials.  Attempted to more around with navigate and fly icons.  I opened the property of each layer and in base heights tab selected "floating on a custom surface (from UWF_DEM).  I added a layer offset of .1 to the base heights tab of the north aerial to get rid of a black line between the north and south aerials.   In the properties of the building layer in the extrusion tab check extrude features in layer, typed "Height" into the expression box, and in apply extrusion by  selected adding it to each features maximum height.   From the main menu View Scene Properties, general tab, changed vertical exaggeration to 5.  Here is that map!

Week 11: Geodocding, Network Analyst & Model Builder

First on the list this week:  1)geocode Emergency Management Service sites for Lake County, FL (addresses from LakeEMS.org).  Utilized US Census Bureau Tiger line files after downloading and projecting.  Set up and address locator.  Utilized table provided with addresses of the locations for Lake County, FL EMS locations to geocode (tie an address to a place in the map).  Then researched using Bing maps, the source web site LakeEMS.org to locate those unmatched locations and either selected a candidate from the interactive rematch window or manually placed locations.


Next this week 2)Network Analyst: utilize Arc GIS extension Network Analyst to create a simple route map output.  Utilized Network Analyst (customize-extensions-network analyst) to add stops.  In the Route properties window (layer properties) from the network analyst window I specified route to be calculated based on travel time in minutes based on a specific day and time.  Clicked the solve button from the network analyst toolbar.  Looked at the Directions window from the network analyst tool bar.  Saved as mxd.  Here is my map with the EMS locations, Lake County boundary, Streets layer, and the optimal route showing my 3 stops.

Lastly this week 3)explored simple model in Model Builder.  This exercise was completed in ESRI Virtual Campus.  Performed edits to an existing model.  Input=Blue Oval, Rectangle=Tool, Output=Green Oval.  Not sure if the colors and shapes are universal for all models or specific to this one.  This model created buffers, performed an intersect of the buffers and final dissolved borders of the intersect to create polygons.  Here is a screen shot of the model (looks like flow chart!)

Week 10: Spatial Analysis (week 2)

This week the map assignment was to utilize shape files to find potential camp sites that are within 300 Meters of a road, 150 Meters of a Lake, 500 Meters of a River and outside of any conservation area.  First to buffer the road shape file: Arc Tool Box button, Analysis Tools, Proximity, Buffer, 300 Meters and dissolve (overlapping buffer edges are removed).  Next to buffer the water feature class took a few more steps.  Separate buffers of 150 Meters for lakes and 500 Meters for rivers.  Insert field to the attribute table for the water feature class and name the field buffdist (for buffer distance), right click the new column heading and select field calculator and define the buffers for lakes and then "switch selection" button and define for rivers.  Next my first experience with ArcPy (is a Python site package for performing geographic information system (GIS) functions available in ArcGIS).  That's right Python, as in programing language!  I wrote my first script and practiced inputting multiple scripts to run simultaneously.  Next I had to get the water buffer and road buffer together.  The lab assigned a Union Overlay and then to export (TOC-Data-Export Data - Selected Features) to a new feature class.  Then to do another overlay that would result in the same outcome, I chose an Intersect Overlay.  For this exercise, I found no noticeable difference in the outcomes of the Intersect Overlay and the Union Overlay.  Then I needed to exclude any conservation areas from my results of possible camp sites.  I chose to use Erase Overlay, to remove conservation areas from my results.  Next I changed the multi-part layer (only 4 records in the attribute table) to a single-part layer (allowing more specific feature selection).  Back to the Arc Tool Box-Data Management-Features-Multipart to Single part.  Finally adding a field to the single part attribute table for area and utilizing calculate geometry option for the math to be preformed.  It didn't really feel like a lot this week, but writing it all out sure makes it sound like a lot.  Here is my final map!

Week 7 & 8: Data for GIS and Data Quality (Midterm)

How did I get to the middle of the semester already?  How does my instructor think that I can search and find my own data and put it together?  Why does she think she can give me two weeks and expect me to manage my time to accomplish said task with very little instructions?  Where are my training wheels (Lab pdf with step by step instructions)?

This assignments has spanned two weeks.  I was assigned a county, Madison, and told what data I needed to get and to put it together in a maximum of three maps and still display all of the required data.

Most of the data I found by searching FGDL.org meta data explorer.  Here are the layers:

• County Boundary layer from FGDL.org, vector
• Cities and Towns layer from FGDL.org, vector
• Public Land layer from FGDL.org, vector
• Roads from FGDL.org, vector
• Surface Water from FGDL.org, vector
• Two Environmental Layers of our choice
• Wetlands from FGDL.org, vector
• Land Cover from FGDL.org, vector
• DOQQ (digital orthophoto quarter quad) from Labin.org, raster
• DEM (digital elevation model) from NationalMap.gov, raster

All layers from FGDL.org are in Albers projection so I chose to use that projection for the project (less layers I would need to project into Albers to be sure all layers were in the same projection)

All layers had to be clipped to Madison County Florida with Vector Clip Analysis tool for vector data.  The DOQQ did not require clipping as it was smaller than the county.  The DEM required  Raster Clip data Management tool and limiting the extent of the data shown on the map by Clip the Data Frame.  Clip the Data Frame does not clip the data it limits the data features that fall outside of the extent defined (in this case outside of Madison County).

This project had many challenges.  Yes, some of them are probably user error as I am still learning.  Some came in the form of internet issues.  Others I don't know the cause only that when downloading data sometimes it only appears to be downloading as the folder will then turn up empty. 

Here are the maps (and again delayed as the jpeg files have not shown up from exporting the mdx files):

Week 6 Projections cont.

No finished map to post the week, must say that is a little disappointing for me.  I enjoy the satisfaction of a completed map at the end of the lab.  No map does not mean that it was a light week.  This week was most labor intensive so far.  I downloaded aerials (DOQQ), topo quadrangles (DRG), and shapefiles.  This doesn't sound to difficult but getting the files to download to my local drive and then access them through my student drive was challenging.  I finally resorted to saving the zipped file to local drive and upzipped to the student drive.  Either obtained data in State Plane projection or reprojected it to State Plane and then loaded it all in a rough map to be sure it all worked.

1. downloaded aerials from Labins.org through the FTP site in State Plane projection
2. downloaded Toporgaphic DRG files from Labins.org through FTP site in State Plane projection but when I checked the spatial reference it was undefined projection so I defined the projection as State Plane (since I know that is what I just downloaded)
3. downloaded County Boundaries shape file from FGDL.org.  Their data is in Alber's projection so I had to reproject this data to State Plane
4. downloaded Quad Index from FGDL.org.  (Again) Their data is in Alber's projection so I had to reproject this data to State Plane
5. Finally I edited XY Tabular data provided for Eagle Nests for the assignment with Degree, Minute, Second geographic coordinates.  This had to be converted to Decimal Degrees to work in ArcGIS.  I inserted the formula to convert.  Then added XY data in ArcMap with an unknown coordinate system.  Defined the data as WGS1984 per lab instructions and then reprojected to State Plane

Intially when I checked the spatial reference of the XY Tabular data for Eagle Nest my points were not where they should have been.  After checking my formula I realized I had omitted a 0, and as we copied and pasted the formula the same error was throughout.  I corrected my formulas, and found my points were where they should be.  Yeay!  [only two pages left of lab assignment!].  The last two pages were "Do it again" with petroleum tank contamination sites instead of Eagle Nest and pick a different county for the aerials.  WHEW!  I submitted a screen shot showing the mess of all the layers in one map with the same projection.  I have included two of the screen shots.  Not pretty but hopefully it meets all the requirements!