Final Project Computer Cartography

This was the most coordination of principles yet.  The assignment was to either take information provided to map academic test scores or to create your own project.  I chose to create my own project and answer my brother n law's question from March "which state Kansas or Missouri has more wheat".  It was challenging to find data on my own (usually provided or hints to locations of data).  The state and county shape files were downloaded from the US Census web site.  The was later projected to Albers Continental US to maintain area in the projection.  The wheat data was obtained from US Department of Agriculture Statistical Service.  I first limited the state shapefiles in the attribute table to the states I was mapping.  The intersected the limited layers with the county file.  I created a choropleth map from the harvest data from 2007 and a dot density layer for the planting for the same year.  I changed the dot to a diamond shape as it resembled the top of wheat.  I did an insert map to show the location of Kansas and Missouri in relation to the continental US.  The choropleth for the Missouri side was limited to 5 classifications where the Kansas layer matched Missouri's 5 and added 3 to show the significant abundance of wheat in Kansas.  A simple table was inserted to show the value of wheat from 2012 to 2017.  I elected to reproduce the wheat data in a separate spreadsheet as the downloaded data seemed to contain some links that caused issues (limited data showing back up later) in ArcMap.  The Kansas data contained planting data for all counties that provided harvest data.  However Missouri data had more counties that had harvest data than counties that provided planting data.  The missing planting data seemed to be conglomerated under an "other" category.  The "other" planting was calculated out to the missing counties based on a percentage of those counties harvest data.  The harvest data was standardized by area before mapping.

Just to get to this stage of the project took much longer than I anticipated.  Data had to be down loaded multiple times due to to technical glitches (layers took a long time to down load, would finally be available in the file explorer on my computer but would not show up in the file explorer in ArcGIS through argoaps for a day (apparently more for the water files I tried to download), and then if the download was corrupt it would not load the file in ArcMap.  I would get into mapping and run across another issue and have to back up to find a solution (choropleth symbolization when applied would make the counties in the state shatter all over the work space, I had to back up and project the layers to stop the problem, or the wheat data that I had limited by deleting what I did not need would all of a sudden have all the rows I had deleted back in the attribute table - finally my solution was to reproduce the data in a new spreadsheet).  Each step taking more work and more research to figure out what I forgot or what I had done incorrectly.

Once I transfered from ArcMap to Adobe Illustrator to add citations and finalize layout I realized it would have been good to have a surface water layer, maybe main streets and possibly a background for the main data frame that had the state outlines for them to be labeled for additional orientation.  I again backed up to try to find these layers to add.  I still have not been successful getting any of the water layers that I have downloaded to load in ArcMap.  I discontinued looking for the roads layer as I felt that that layer would block some of the data and not be all that helpful.  The state background layer I already had but to get it to transfer at the same scale as what I already had in Adobe Illustrator proved too time consuming .  Had I started with all of these layers already in ArcMap before moving to Adobe Illustrator I would have had more luck.

Over all I am pleased with all the information that I was able to obtain and coordinate together, while learning lots of lessons about timing and data correlation and transition from one program to another.

Module 12: Future of Cartography

This week in lab we took our south Florida population density dot map and converted the map to a KMZ file using Map to KMZ tool and converted the population density layer to a KMZ file using the Layer to KMZ tool.  We then took the KMZ files to Google Earth, added landmarks to specified cities and then a tour of those landmarks.  Not a lot of "work" this time for lab, used the "work" from module 10, but the results were very cool. 

Neo-Cartographer is map making that is conducted outside of professionally trained cartographers.  One method of Neo-Cartography is Volunteered Geographic Information (VGI).  VGI projects can range from public participants gathering and posting data to a website that contains a opensource map to participants reviewing visual data and providing observations.  For me, the greatest consideration of VGI data is the intention and audience of the data.  If the data is to be used by the public for information purposes (posting locations of bicycle routes and the condition of those routes for other bicyclist) then the accuracy of the data would be of low concern, other than those using the site.  If the data is to used for research purposes to address funding for conservation (number of a species is declining in an area and should be funded for improvements) the data would, in my mind, need to be obtained in a responsible, measurable, accurate scientific process.  These: accuracy, responsibility and measurability, are some of the concerns with VGI.  Bioblitz is a term that is being utilized for public events being held around the world to invite volunteers out to a defined area to gather information.  Typically the event is to gather data about species in the area.  Although the data could be used locally for a preliminary evaluation of the health of the defined area it seems to usually be used to create community awareness and interest in conservation.  Geo-collaboration is also utilizing opensource maps as well as other electronic collaboration methods for multiple users, both professional and novice, to work together on projects.  Geo-targeting is the process of delivering content to users based on their geographic location (adds for swimwear goes to Florida and Hawaii where winter parkers go to Alaska).  Geo-targeting is being used often by marketers. 

Cloud functions in three base service models – Software-as-a-Service (SaaS); Platform-as-a-Service (PaaS); and Infrastructure-as-a-Service (IaaS) (https://www.gislounge.com/learn-about-gis-in-the-cloud/).  Cloud Computing and Cloud based GIS utilizes the cloud to house large amounts of data to allow for easier access, easier distribution, capture data in near real time, less IT management.  Cons to the Cloud GIS include web access is required, security is a concern, lack control with external hosting, volume of data increase time, and data formatting may be effected by the cloud. ( http://geoawesomeness.com/gis-cloud/).



 

Module 11: 3D Visualization and Mapping

The top frame shows the building extruding from the landscape as well as the blue points of the wells that were set to an offset of 3 to show their location above ground.  As the images progress through the perspective change in the end showing the wells extruding underground.

This week 3D.  Lecture information included a video conference presentation by Nathan Shepard, member of the 3D team with ESRI, and the presenter of the 3D Cartography Techniques: an Introduction, 3D Urban Mapping from Pretty Pictures to 3D GIS a white paper by ESRI and finally ESRI training module 3D Visualization Techniques Using ArcGIS.  

The 3D data types: photorealistic scenes, cartographic scenes and augmented reality scenes seem to be more of a continuum from photorealistic (looks like photo) through a hybrid area classified as augmented reality (both real looking elements and non-real informative cartography) to cartographic (informative but no realism). Extent of 3D maps can be planetary (global) or local (fish tank).  The primary elements of 3D include surface, texture, features and marginalia and effects.  There are challenges to 3D mapping, it can be disorienting and hard to navigate, map content can be hidden and continuous and progressive scale.  The advantages of 3D: can show vertical info, intuitive symbology, human style navigation and superhero style. (Shepard, N., 2015, ESRI)  

The ESRI training provided information as well as exercises to reinforce concepts.  Elements discussed in the training: elevation terrain model, raster, triangulated irregular network (TIN), terrain dataset, terrains built from feature class, feature class data, multipatch features, and shape files. 3D GIS requires a surface, texture- Z values (height information either contained within or obtained from another layer) and features (with Z or interpolable data for Z).  The exercises 1) base height for raster and feature data 2) roles for 3D data 3) vertical exaggeration 4) illumination and background color 5) extruding building & wells, extruding buildings for value and extruding for multipatach.  

Work was performed in ArcScene with Analyst Extension.  Data was exported as KMZ file and opened in ArcGlobe.  

This week has been the most difficult and frustrating to date.  Utilizing the navigation in the 3D is interesting.  I often flew to outerspace and had to figure out how I got there and how to get back.  I don't feel I have an understanding of the terminology or concepts for this week.  More exposure and practice will be required for me to feel I have any grasp on this new and different world of 3D. 

Module 10: Dot Mapping

This week was Dot Mapping.  Dot maps indicate locations of an occurrence (in this case conceptual points), where each dot represents a set amount of that occurrence.  Consideration should be taken with enumeration unit (small unit best), dot value (trial & error, nomograph or software) and dot size.  The dots can be placed:  1)uniformly in the enumeration unit (not preferred: impression of continuity across the area), 2)geographically weighted, those of higher values are weighted to be closer to other higher values or 3)geographically based, utilizing ancillary information both limiting attributes and related attributes to place dots based on area attributes.  Data criteria for Dot Density is conceptual data (raw counts), discrete, used to compare or portray variations or patterns. 

There are advantages of Dot Density maps.  Dot Density maps are an easy concept to understand.  They are effective at portraying variations.  Ideally the dots could be counted to recover data.  These maps can be adapted to include other phenomena: urban areas, slope, bodies of water.  Dot Density can be used in correlation with other types of maps. 

There are also disadvantages of Dot Density mapping.  The map can be hard to estimate density.  The map reader could misinterpret a dot as a single occurrence.  Computer dot placement can misrepresent patterns or lack of patterns.  Possible additions to counteract some disadvantages could include a clear legend to avoid the dot as a single occurrence and utilizing ancillary data to avoid random placement.

This week's lab was to represent the 2000 population density of South Florida in a dot map.  Provided in the lab materials were shape files of South Florida, Surface water and urban land use as well as table with census population data.  

The tabular data was joined to the south florida shape file (with both shape file and tabular data (utilizing add data button) displayed in ArcGIS>right click south florida layer>join and relates>join>based on county name, joined based on area).  Dot Density symbology was accessed through the south florida layer properties under the symbology tab (quantities, dot density. population was selected and added as symbol).  Dot size and dot value (1-5) were adjusted until max preview started to coalescence.  For my specific map I utilized a dot size of 4.5 and a dot value of 10,000.  I adjusted the color (several times) to find a color that would stand out and clearly be the highest in the visual hierarchy.  I finally, chose to utilized a bright full purple.  The counties included in the south florida shape file were adjusted and examined with different colors and outlines and without. "Maintain Density by" feature was turned off so the dot value or dot size would not change when zoom in and out.  Properties window within symbology tab was opened and the dots were set to fixed placement, this prevented movement when ArcGIS redrew as changes were made.  Finally, masking was also specified in the properties window.  Initially the mask was set to exclude the surface water layer.  Although this did keep the population points from being located in a body of water, the arrangement of the dots density still had clear differentiation at county boundaries.  A second mask option was applied that placed dots within urban land.  This option presented the dots clustered in and around urban land, more as it would be in the natural world.  The rest of the map was adding essential map elements (title, legend, north arrow, credits, projection,author, date).  Masking the dots really bogs down ArcGIS, so the mask was turned off while final elements were added and style choices made.  Lab instructions requested geographic reference by labeling some major cities in the area.  I choose cities that would be large enough to be recognized.  I added a short integer field to the Major Cities (obtained shape file from previous lab) attribute table named display.  I started editing to input for that field.  I utilized "1" for those cities I wanted displayed and "0" for those I did not want to display.  I stopped editing and saved my edits.  In the symbology tab, category unique feature, value field set to my new field "display", turned off "all other values", "add all value" button, chose symbol for "1" and highlighted the "0" option and clicked remove button, ok.  Due to the placement of the city names amongst the dots I added text for the labels.  I left Tampa's label covered up by dots.  I could not find a place where it would work.  I differentiated the surface water types and added a legend for that layer.  The typical legend by ArcGIS, created for the population layer, only provided the most basic information, one dot = value.  Three visual anchors showing low, medium and high densities. I drew these features in ArcGIS.  I drew a box and copied and pasted it twice to ensure all three where the same size.  I aligned them with the align function.  I added a dot (rectangle drop down box and chose marker), adjust the dot to the same size and color as I utilized in the symbology, and copied and pasted the desired amount.  I symbolized low density with 3 dots (30,000 people), medium density with 20 dots (200,000 people) and high density with 50 dots (500,000 people).  The layer organization I decided to leave the layer with the dot density hollow - without fill and without outline.  I set a second florida shape file as the lowest layer with a bright green for the background.  The surface water layer and major city symbols were in the middle and finally the dot density layer with the urban mask turned back on is the top layer.

Module 9: Flow Mapping

This week the focus was Flow Mapping.  The flow map created in lab this week is a radial flow map, all the spokes (geographic regions) feeding into a centralized hub (U.S.)  There are two other  types of flow maps Parks considered:  a network map shows connectivity of multiple locations (airline route map) and finally distributive map shows flow of  data between geographic regions.  There are two main subcategories of distributive flow maps:  one shows the entire world and attempts to depict actual routes of flow and second depicts flow within a land mass and precision is not as important as general direction and magnitude.  There are also two types of flow maps the text explains that Parks did not consider:  Continuous flow maps, which depict movement of continuous phenomenon such as wind or ocean currents and Telecommunication flow maps, that Parks considered network maps.  The data for this map is quantitative, however, flow maps can be used for qualitative data as well.  The flow lines utilized stylized placement to show spatial interactions as opposed to a specific route from geographic regions to the U.S.  Borden Dent provided essential design decisions in creating flow maps:  flow lines should be depicted as the highest in visual / graphic importance, if flow lines cross smaller flow lines should appear on top of larger, arrow heads are important if flow directions is important, land and water contrast are essential, projection is important, keep it simple, legends clear.  I utilized these guidelines in this map.  The flow lines are the most stylized element in the map keeping it at the top of the visual hierarchy, my flow lines do not cross, arrow heads are included and directional, definite land water contrast, Winkel Tripel projection (compromise projection, relatively minimized distortion of shape, area, distance, and direction, although none of these is really preserved), design is simple, and only the choropleth legend was included as the flow lines are labeled with the actual numeric representation.  The proportional line widths of the flow lines were calculated in excel per the lab instructions.  I chose to indicate that the map was not to scale as opposed to trying to show two scales for seperate elements of the map.  I changed the color of the continental U.S. to white to infer the enlargement of the choropleth in the middle.  I left Alaska in it's location and changed the color to correspond with the data for the choropleth map.  I did enlarge and include Hawaii separately.  I decided to not follow the same orientation for Alaska and Hawaii because Hawaii had to be enlarged to be seen, but Alaska took up too much space if enlarged at the same rate.

Module 8: Isarithmic Mapping

This week the focus is on Isarithmic Maps.  An isarithmic map depict smooth, continuous phenomena.  Isometric maps utilize true point data, data that is measured at a point location.  Isopleth maps utilize conceptual point data, data collected over an area or volume and symbolized as a point (usually the centroid of the area).  This type of map is second most widely used thematic map, behind the choropleth map of last week.  

This week, raster data was provided from USDA Geospatial Gateway.  Data was published by US Dept of Agriculture, Natural Resources Conservation Service, National Geospatial Management Center 09-2012.  Data was originally created by The PRISM Group at Oregon State University. 

I implemented continuous tone by accessing the symbology in the layer properties, selected "precipitation" color ramp. The legend required more work.  Choosing a horizontal style, creating an alternate legend with inverted colors, converting the alternate legend to a graphic, ungrouping and moving the labels.

I utilized the spatial analyst extension (after I remembered to active the toolbar). I used the Int Spatial Analyst Tool to convert the raster values from floating (fractional numbers that have decimal places) to integers, to allow crisp contours.

I implemented hypsometric symbology in the layer properties by opting classified with 10 classes with manual break values (per lab instructions).  Adjusting the manual breaks to whole numbers.  I again chose "precipitation" color ramp, and utilized hillshade relief.

I added contours to the hypsometric tint by utilizing the analyst toolbar this time utilizing the contour list tool.  I included required map elements, followed cartographic design principles.  I added description of the data to the map (after I remember the draw toolbar and the text in shape).  

More from the lecture portion this week:

The fundamental problem in isarithmic map is that data must be interpolated to cover the unknown values between control points, or data collection points.  Methods of interpolation the text discussed for true point data were triangulation, Inverse distance, and Kriging,  Basic (very basic) explanations of each:

• Triangulation: connects neighboring control points to form triangles, utilizes Delaunay triangles similar to Thiessen polygons forms triangles with all points contained in the triangle are closer to that triangles control point than any other. Once triangles are formed contour lines are created by interpolating along the edges of triangles. Finally the contour lines are smoothed.
• Inverse Distance (gridding): layes a grid on top of the control points, estimates values at each grid node, contour points are weighted as inverse function of the distance from grid points.  Consideration is distance only between grid point and control points.  Strategies including search radius for grid points for minimum number of points to fall within radius, and maximum number of whole, quadrants or octants of radius without data are set.  Interpolated contour lines are placed and finally smoothed.
• Kriging (ordinary kriging): used for data without trend or drift.  Similar to inverse distance uses weighted average to compute a value at a grid point.  Unlike inverse distance, consideration is not only the distance from control points to grid points, but also the distances between the control points themselves. More complex method of interpolation, it can produce more accurate map (optimal interpolation). ONLY if one has property specified the semivariograms and associated semivariogram models.  This model also provides a measure of the error associated with estimate, standard error of the estimate, can be established with a confidence interval. 

Symbolization of Isarithmic maps:

• contour lines - lines that mark amounts of a phenomena, can be difficult to visualize
• hypsometric tints - light and dark shades (grey or color) are added between the contour lines to enhance visualization
• continuous-tone - unclassed so color or gray fades and darkens across the entire area to depict different values, can be difficult to get specific data as there are no clear boundaries
• fishnet- gives the effect of a fishnet being draped over the results, can cause blocked information of lower values from higher values.

Module 7: Choropleth Mapping

This weeks assignment we produced a choropleth map showing overall population densities in European countries using 2013 European census data.  Then tied in wine consumption, data from Wine Institute 2012 liter per capita, as a graduated symbol overlay.  In the choice of graduated or proportional, I chose graduated as proportional has too many options for my brain.  Proportional scales the symbol utilized to the data.  Each symbol is proportional to other data, leading to a range of the scale symbol instead of a set number of classes.  I chose a ramp color scheme for the choropleth map to depict the population density, using from forest green to almost white.  I stuck with a green scale to symbolize a ground type color.  The darkest color represents the highest data range.  I chose a more blue green as opposed to a yellow green to help contrast the red for the wine symbol.  The population density legend has no spaces between the individual categories to indicate a continuous range of data.  I chose to utilize natural breaks for the classification method as it seemed to allow for a more natural division as opposed to Equal Interval that forces data into equal divisions or Quantile that forces an equal amount of values into each category.  I eliminated four outliers of small countries with high population density that would have skewed any classification method.  The scale of the map to fit the page would not have clearly shown these four small countries, Monaco, Gibraltar, Malta, and Jersey and with very low wine consumption served more of an element of clutter than information.  I utilized Data Exclusion with a SQL Query to manipulate the data.  I almost utilized a 7 class division as it seemed to allow for even more natural breaks to be represented, however I chose a 5 class division to keep the color distinctions at a maximum.  I labeled the countries in ArcMap and corrected some of the names into english by adding a field to the attribute table with the labels I wanted to utilize.  I further adjusted location of the labels in AI and added a small halo to allow for clearer reading.  The wine symbol started in ArcMap as a half circle in a red wine color.  I added an outline to that symbol as well as added a stem to form a wine glass in Adobe Illustrator.  I chose to keep the stem of the wine glass consistent across the classes to add weight to the bowl of the glass.  The detail of the stem to the wine glass may be too small when the map is considered in the small thumb print for Blogger, but I felt it was adequate to communicate the information.  I added required map elements: legend, scale bar, north arrow, data frame and for this map a small blurb about what the data offers in way of information. 

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