GIS Day 2024 Event at FDOT District 7

Following months of planning, of which I contributed starting on the second day of my internship, GIS Day is finally here! Beyond brainstorming ideas in which to better spread the word at FDOT District 7 of the event, I was tasked with creating one or two GIS Day maps for display on the wall of the auditorium.

As the semester progressed, I took inspiration from Special Topics assignments and learned skills from Computer Cartography and GIS Applications for several mapping concepts to share on GIS Day. My idea was to show a few examples of the capabilities of GIS, both from an analytical standpoint, and also in the different ways data can be visualized.

After reading several classmates discussion board posts on presentations they made for GIS Day, I decided to follow their lead and create a presentation of my own. My goal was to provide an overview of maps in GIS, then cover each of the five maps I created with a mix of technical information such as the geoprocessing that went into it or the type of map (choropleth, graduated symbol), principles of design, and inspiration for the map subjects.

Our efforts paid off, and the D7 GIS Department's three hour event this morning was a great success! We had around 30 attendees, many of which stayed for all presentations, and received several positive comments on the event. My presentation went over well and I thoroughly enjoyed sharing some of the GIS knowledge gained from my time with the University of West Florida.

2024 GIS Day at FDOT District 7

My GIS Day 2024 presentation and the maps I created for the event follow:

D7 GIS Day Map Overview

There are two general categories of maps, Reference maps and Thematic maps. We are all familiar with Reference Maps, such as a road map or a political map. On display in the auditorium here are examples of Thematic Maps, which are maps that focus on a specific theme, such as climate, population, or in our case, transportation. This leads me into our first GIS Day map…

Hurricane Tracks Map

Florida Hurricanes quantifying direct impacts from 1851 to 2024

When we were planning our GIS Day event, one of the map concepts discussed was a Florida map of hurricane tracks impacting the state over the last 20 years. Sounds simple enough, but as the map was in production, Hurricane Milton formed, and one fact mentioned by media outlets was that Tampa had not been hit directly by a major hurricane since 1921.

This ultimately factored into me deciding to expand upon the hurricane tracks map concept to quantify the number of hurricanes that have directly passed, the center that is, over each county in the state.

I opted to cover two sets of temporal data. A choropleth map shows the number of hurricanes per county in the last 50 years. It uses dark colors for higher values, conveying that higher values have a heavier visual weight. The graduated symbols map, which quantifies the number of hurricanes per county since 1851, the first Florida hurricane in the dataset, correlate size of the symbol with quantity, i.e. larger means more.

As for how the map was created, the geoprocessing for the choropleth and graduated symbols maps were based upon the number of hurricane polylines crossing any part of the county polygons. These calculations are automatic in GIS and no manual comparisons are needed.

D7 Interstates History Map

FDOT District 7 Interstate opening dates color coded by decade

This thematic map aggregates sections of the District 7 Interstate system by the decade in which they opened to traffic. This also shows how the use of graphics can enhance the presentation of a map.

I also factored into the design the Gestalt Principles of Perceptual Organization, which in cartography includes Visual Hierarchy, where important features are emphasized, and less relevant ones deemphasized. The Figure-Ground relationship accentuates certain objects over others by making these appear closer to the map user. Visual Balance is where the size, weight and orientation of map elements are adjusted to achieve balance in the center of the map. Contrast and Color are other principles used in good map design.

D7 Lighting Raster Map

Raster quantifying light poles in FDOT District 7

I created this map to show how raster data can be used by GIS. The concept took the point feature class for all light poles within District 7 and overlayed them with a fishnet grid in ArcGIS Pro. This is also referred to as grid-based thematic mapping. I aggregated the light poles by 1 square mile grid cells and obtained a density unit via geoprocessing. I then symbolized the raster set where lighter colors convey more light fixtures. The end result is a map clearly showing where we maintain the most lighting.

D7 Storm Surge Map

Areas in FDOT District 7 inundated for storm surge by Saffir-Simpson category

Storm surge data is another form of raster data. These are generally calculated by the use of a Digital Elevation Model or DEM. One useful aspect of ArcGIS Pro is the ability to use geoprocessing to convert a raster into a polygon feature class, such as was done here with this NOAA storm surge dataset.

This expands the options for the GIS analyst. Among others, geoprocessing options include least cost path analysis, buffer analysis, and data interpolation, where unknown values between known data points such as rainfall rates, can be estimated.

3D Traffic Count Map

3-Dimensional representation of traffic counts (AADT) on the FDOT D7 state road system

When you think of 3D mapping, you probably think of modeling buildings or terrain, but there are several other uses. One such concept of 3D mapping is to visualize 2D data in a different, and perhaps more thought-provoking way.

That was the idea behind this 3D traffic count map of District 7. ArcGIS uses the Extrusion method to add a 3D element to our 2D feature class. Extrusion bases the height of data on a Z-unit, where the unit can be based upon real-world units, such as the height of a building, or upon ranges of data, such as with the traffic counts here.

ArcGIS Pro renders data three dimensionally differently for points, polylines and polygons. Points will appear as columns. Polylines will appear as a wall, as it does here, and Polygons appear as solid objects, which is probably easiest to imagine when applied to a building footprint.

One thing revealed with this 3D traffic count map was that a stretch of traffic count data for Interstate 4 was missing. So, the 3D map produced an unintended benefit, revealing a section of missing data that we could correct.

So, as you can see, GIS allows you to show geospatial data in a more meaningful way. And these maps are only the tip of the iceberg when it comes to the types of deliverables that can be produced.

3D Mapping - TINs and DEMs

Moving on from Spatial Data Quality in GIS Special Topics, the next Lab focuses on surfaces with a comparison of the Digital Elevation Model (DEM) and Triangular Irregular Network (TIN). A surface in GIS is a geographic phenomena represented as continuous data. Continuous spatial data references geographic objects characterized by very gradual boundaries such as temperature or elevation.

The most common way to represent elevation data is with contour lines. Contour lines are 2-dimensional features with attributes containing the value of the surface at a given location. They can be derived by the TIN vector model or the DEM raster model.

TINs are used exclusively to represent a 3-dimensional surface. A series of linked irregular triangles comprised from elevation points (nodes) in 3D (X,Y,Z) coordinates (Manandhar, 2005) occurring at any given location represent the 3D surface. The topological relationship of the network of triangles creates a continuous surface. The normal vector of each triangle is used to assign the properties of Slope and Aspect.

 

DEMs are the simplest way to represent a topographic surface. A DEM is a regular raster that uses a regular rectangular grid method (Manandhar, 2005) with cell values representing elevation or spot height. The cell size of a DEM determines the resolution. Therefore a DEM with a high number of smaller sized cells provides more accuracy than a DEM with less larger sized cells. Data becomes more implicit with larger cell sizes.

One part of this week's lab utilizes a DEM to develop a 3-dimensional Ski Run Suitability Map. Initially the supplied DEM was converted to a TIN for the 3D component for the Local Scene. The suitability parameters included Elevation where areas exceeding 2,500 meters are most favorable, Slope where angles between 30 and 45 degrees rank highest, and Aspect where south and west facing slopes are most preferred.

Following reclassification, respective rasters were generated from the DEM using geoprocessing tools in ArcGIS Pro. These in turn were input into the Weighted Overlay tool where the suitability rate for aspect is 25%, elevation is 40% and slope is 35%.

The output Ski Run Suitability Map. Lighting enhancements include shadowing and adjustment of the sun angle. The Vertical Exaggeration is 2.50.

The next part of the lab further explores TINs with adjustments to symbology between elevation, slope and aspect. The deliverable included the generation of contours and selecting appropriate colors.

Cividis color TIN with 50 meter contours and 250 meter index contours.

The last section of the lab provides a point feature class that will represent the mass points for a TIN. Geoprocessing of these points were input along with a study area soft clip polygon boundary in the Create TIN tool. The resulting TIN was modified symbolically to show contours set at an interval of 100 meters.

The same mass points feature class was input into the Spline tool to create a DEM. Contours were subsequently generated from the DEM with additional geoprocessing. The two contour feature classes were then compared.

While not necessarily more accurate, the DEM based contours have smoother curvature resulting from the implicit data values from each grid cell (Manandhar, 2005). Appearing more jagged in areas with less slope, the TIN based contours are derived from every node, where 3D coordinates are more explicit. There are less Faces (triangles) in flatter areas.

References:

Manandhar, N. (2005). Comparison of TIN and Grid Method of Contour Generation from Spot Height. Nepalese Journal on Geoinformatics, 4, 1-8.

https://www.nepjol.info/index.php/NJG/article/view/51271/38351

Isarithmic Mapping - Washington State Precipitation

The semester is accelerating and we move into the 6th lab covering Isarithmic Mapping! Following choropleth mapping, this thematic map type is the second most widely used in cartography. Isarithmic maps consider geographic phenomenon to be continuous and smooth, with measurements in the area of interest presumed to change gradually between data point locations instead of abruptly. There are two primary types of isarithmic mapping.

Often associated with meteorology, isometric maps depict smooth, continuous phenomenon, such as temperatures, rainfall, barometric pressure and wind velocity derived from data occurring at true points where values are actually measured at that location. The most common form of isometric maps are contour maps, which are lines marking equal value across a geographical area.

Collectively, contours used in isometric maps can be referred to as isolines. Iso in Latin means equal or the same. Variations of isoline terminology include isobars for lines of equal barometric pressure, isotherms for lines of equal temperature and isodrosotherms for lines of equal dew point.

Isopleth maps are comprised from data that occurs over geographic areas using conceptual points, where values are presumed to be at point locations. Isopleth maps show variations in quantity of features as a surface. The volume can be represented using contour lines or by filled contours with color shading representing quantitative values. Data for isopleth maps must be standardized to account for the area in which the data was collected.

Various interpolation methods on raster data sets are implored in the creation of isopleth maps. These methods generate data values over a given area using samples measured at control points. An algorithm in turn processes the data to predict the values of unknown points on an isopleth map. Values between the control points are predicted under the premise that spatially distributed objects are spatially correlated. Also referenced as the Concept of Spatial Auto Correlation, this basis of interpolation assumes that values of locations close together tend to share similar characteristics than those located farther apart.

The focus of lab this week is the creation of an isopleth map showing the average annual precipitation for a 30 year period across the state of Washington. The provided dataset was derived using PRISM, an inverse distance weight (IDW) interpolation method developed by the University of Oregon.

The Parameter-elevation Regressions on Independent Slopes Model (PRISM) stresses elevation as the most important aspect in a localized region for the distribution of climate variables such as rainfall, temperature and dew point. The model calculates a climate-elevation relationship for each cell of a raster data set based upon data from nearby weather stations. The regression function used with the IDW method weights station data points to incorporate a wide range of physiographic variables that have a direct correlation with precipitation amounts and other climatological aspects.

Two types of isarthmic maps were created in Lab 6. The first was a continuous tone map, where geographic surfaces represent the values that exist across an entire area. Data collected at sample points, by mapping the density of points or the values they represent, factor into the interpolation that generates the continuous surface. This method portrays a more fluid appearance where data values in a raster set gradually transition from cell to cell.

The second was Hypsometric Tint, which reminds of me of the Futurama character the Hypnotoad, that classifies data into bands. These bands represent a method of coloring different values to enhance changes, such as in elevation with a Digital Elevation Model (DEM).

Using contours, hypsometric tint separates raster data into bands with uniform data values. These bands can represent a single value, or a range of values with lower and upper limits. An advantage of hypsometric tint is that changes in data are more clearly visualized over the smooth transitions of a continuous tone map. A drawback is that local variation of data values is lost with the generalization between contours.

Reprojecting the Washington precipitation data into State Plane coordinates, I ran through the lab again to create a second map showing Washington in a more aesthetically pleasing projection. This both gave me more practice with creating continuous tone and hypsometric tint maps, but also some of the difficulties with projecting data, as the hillside shading values changed from using world statistics to local statistics.

PRISM

PRISM was initially developed in 1991. Enhancements over time garnered the interest of the USDA Natural Resources Conservation Service (NRCS), which sought improvements for updated digital precipitation maps. With funding support, PRISM precipitation maps were generated for the Pacific Northwest and Intermountain West region of the U.S., where topographic features made mapping precipitation complex.

State Climatologists evaluated the maps produced by PRISM, offering their own suggestions for improvements. Following two years of trial and error, they concurred that PRISM produced maps equaling or exceeding previous ones produced by hand. The result is that the NRCS utilized PRISM to map averages for temperature and precipitation nationwide for the period from 1961 to 1990.

Sources:

Daly, C., & Bryant, K. (n.d.). The PRISM Climate and Weather System – An Introduction. University of Oregon. Retrieved April 20, 2024, from https://www.prism.oregonstate.edu/documents/PRISM_history_jun2013.pdf

The Hypnotoad may or may not approve of hypsometric tint!

via GIPHY

Hybrid Mapping - Choropleth and Graduated Symbols

Module 5 for Computer Cartography advances our understanding and usage of choropleth maps while introducing us to proportional and graduated symbol map types.

A choropleth map can be described as a statistical thematic map showing differences in quantitative area data (enumeration units) using color shading or patterns. Choropleth maps are not to be used to map totals, such as ones based on unequal sized areas or unequal sized populations. Instead data should be normalized using ratios, percentages or another comparison measure.

Proportional symbol maps show quantitative differences between mapped features. This is the appropriate map type designed for totals. The map type shows differences on an interval or ratio scale of measurement for numerical data. Symbols are scaled based upon the actual data value (magnitude) occurring at point locations instead of a classification or grouping.

Graduated symbol maps also show quantitative differences in data, but with features grouped into classes of similar values. Differences between features use an interval or ratio scale of measurement. The data classifications use a scheme that reflects the data distribution similar to a choropleth map. Previously discussed data classification methods, such as Equal Interval and Quantile, can be applied to generate classes.

Our lab for Module 5 was the creation of a map dually showing the population density of people per square kilometer and wine consumption at the rate of liter per capita for countries in Europe. A dual choropleth map will display population densities for the continent while a graduated or proportional symbol map will quantify wine consumption rates for each country.

The lab exercise tasks included the creation of both a proportional symbol map and a graduated symbol map of Europe. The ultimate map type used to portray the country data is partly based upon the anticipated ease of a map user to visually interpret the maps.

Generating a proportional map in ArcGIS Pro is a more rigid process with less user options. The scale classifications are preset to five breaks partitioning data into ranges of 20%. However, the feature class labels are not clearly understood, as the range array is 1, 2.5, 5, 7.5 and 10. The minimum size of the symbol proportionally determines the maximum value.

The raw and mostly unstylized output of the Proportional Symbol Map, with arbitrary values showing the rank of counties in wine consumption from lowest to highest, while the sizes convey the actual wine consumption rate of liters per capita:

A graduated symbol map for this assignment provided more flexibility with various methods of classification, more easily understood class separations and automatically generated labels, the ability to adjust classes using Manual Breaks, and absolute control over setting symbol sizes. The final output:

An added aspect of this lab was the introduction of picture symbols, which can be used in place of the default ArcGIS symbol set. Picture symbols allow for more personalized customization to a map, as long as they appropriately distinguish between differences of data magnitude.

Using a blue color palette from the Color Brewer web site, used the Natural Breaks data classification method to generate the choropleth map of European countries by population. The graduated symbol element of the map uses picture symbols that I created in Adobe Illustrator based off the Winery sign specifications used on Florida roads.

The winery icons incorporate a color scheme to aid in visually distinguishing the differences in data magnitude. The highest wine consumption rate equates to the largest symbol size where all grapes in the graphic are colored magenta. The next tier down in order reduces the symbol size by 15% and the proportion of graphics colored magenta versus those shaded green.

A series of three insets were created to better show detail on some of the smaller countries or groups of countries. These required some data exclusion so as not to conflict with data on the main map frame. Prior to creating the insets, I used the Polygon to Point geoprocessing tool to generate a separate point feature class for the graduated symbols. This provided me with the flexibility to relocate the placement of symbols in addition to the option of moving annotated text for the final layout.

The inset creation utilized a definition query with the SQL expression "not including values(s)", where wine consumption data for countries not to be displayed were omitted from the respective inset dataset. The annotation layer for the main map frame was also replicated for each inset to reduce conflict and speed up labeling time.

Chose Garamond font to give a more elegant look to the final map, since the wine is often equated with fine dining or culture. Additionally the blue color palette was specifically selected so as not to contrast with the color of the winery symbols.

Cartographic Design - Gestalt Principles of Perceptual Organization

Module 3 for Computer Cartography builds on Module 2, where we started developing a routine for good map design with guidelines for labeling, annotation and layout text. Building upon that knowledge base, we focus on cartographic design, the method with which maps are conceived and created. The Gestalt Principles of perceptual organization factor into the design process for the Module 3 lab assignment.

There are several key concepts integral to the cartographic design process. Good design should meet the needs of map users and develop maps that are easy to interpret. Maps should be accurate and present data without distortion. Data should be legible and aesthetically pleasing, using either communicative or thought provoking symbols, color, layout and typographic appearance.

The design process focuses on how the data will be reproduced or disseminated. This initial factor helps determine the color scheme, map scale and the file format considered for potential printing methods. Next to strategize is how to classify the data and what symbolization to use. Ranking map elements, emphasizing what is most important and reducing the visual impact of the more irrelevant information contribute to the intellectual hierarchy of the map. The design process is repeated until the map is completed.

Cartography, Designing a good map

The second module for Computer Cartography expounds upon some of the lessons learned from the first module. These include a refresher of the essential map elements (map title, scale bar, north arrow [orientation], data source information, etc.) from Introduction to GIS (GIS 4043), and general typography principles in cartography ranging from type placement, variation dependent upon features and appropriate type size.

The concept of map clutter from module 1 was again stressed, and the underlining lesson I gained from module 2 is to keep things focused and not add unnecessary details or features. This can be hard for a cartographer, as we often have a tendency to want to use available white space and are picky about what to omit. More on that later.

Supplemental reading for the module provided quite a lot of insight when it comes to map layout and design. The textbook Cartography reaffirmed a lot of what I had learned working for map companies when it came to cartographic design. Specifically text placement, hierarchy of importance and the use of halos and masks for text resonated with me.

Delving further into the textbook, there were several principles that I had not considered so concretely before. When attempting to show the difference in labeling for features ordinally (differences between value or rank), a general guideline is that the optimal difference in height (type size) of the associated features is approximately 25%. Furthermore, avoiding a type size difference of 15% of less should be avoided.

Cartography also references that keeping the same font type for all essential map elements is ideal. It also reiterated from lecture that you should not use the word "Map" in the map title. It furthermore states that a legend should not be titled with the word "Legend" or "Key", as this conveys the obvious. Throughout the maps I have produced for class, I never included "Legend" as the legend title, so I've been on the right track.

The "Type Colour" section in Cartography included a map principle I had not considered before. While text in a legend usually is decorated with black type, an option to introduce color in the type can be useful in providing a connection with the feature itself.

The map to be produced for this week's lab assignment is pretty basic, showing the state of Florida with select majors cities and major rivers. The objective was to place three kinds of text: labels, Annotation and Layout text. Labeling and Layout text were commonly used in previous classes. Annotation however was introduced.

Annotation is a layer where labels are converted to graphic features. They display separately from the features in which they are associated, and can be edited, stylized and repositioned independently of the label class that generated them.

I am not stranger to working with Annotation layers, having previously both output maps for print and web sites using the feature. However, it has been quite some time since I regularly worked with Annotation layers, so my skillset needed a refresher.

Following numerous revisions as I continued to read the textbook, the finalized map:

But all that white space! As a cartographer there were times where I was tempted to add a point for Orlando. I also sought to instill a transportation theme, and had actually colored coded the counties by Florida Department of Transportation (FDOT) districts by adding a column to the counties attribute table. There were other map additions that I nearly started, but then rereading the lab instructions and focusing on the British Cartographic Society's Design Group principle "Concept before compilation," where "Think about what the map needs to contain, how it should look, and who is going to read it," I thought better of it. Furthermore we were to make three customizations to the map, not make additions!

Cartography, the Good and the Bad

Advancing to our second week of Computer Cartography, the first module requires us to think about how we look at and interpret a map. Our task was to select for critical analysis and evaluation, both a map that we consider well designed, and another that is poorly designed.

What a task that was, as there have been several over the years that fit both contexts. Trying to recall any that stood out proved to be challenging, because as the saying goes "out of sight, out of mind." Fortunately I have a growing repository of map documents that I use for researching page creation and updates for AARoads. Sifting through the various folders, I found two that fit the criteria.

The well designed map selected is the most recent Overview Map of the ongoing PA Turnpike/I-95 Interchange Project in Bucks County, Pennsylvania. Within lecture, we were introduced to the map design principles of the British Cartographic Society's Design Group. One that stood out for me is "Simplicity from Sacrifice" where great design tends toward simplicity or more simply "less is more."

The PA Turnpike/I-95 project map colorizes only the affected roads within the project area. Having a full color map of the entire area is not necessary in this context, so reducing the detail and keeping the design focused solely on the subject is appropriate for this type of map. The map audience can clearly view the project and the simple color scheme conveys what is currently under construction, and what to expect in the future.

Many of the maps I have been tasked with creating or updating were all-inclusive. Street atlases for Mapsource, Wall Maps for Universal Map Group, products that included an array of points of interest, every public road, detailed hydrology features, etc. Designing a map with less correlated to producing an incomplete product or omitting features out of laziness. This philosophy was engrained into my cartographic style and I did not question it until this module...

The second principle of the British Cartographic Society's Design Group discussed in lecture is "Hierarchy with Harmony." The concept is to emphasize what is important on a map, to reduce the less important and remove the unimportant. The PA Turnpike/I-95 project map conveys only the necessary information, with a substantial amount of street level detail reduced in prominence. Not all roads were deemphasized to the same level, as intersecting highways to the project area were made to stand out somewhat against the rest of the area.

So less is more works out well in this context. Viewers do not need to see unaffected roads and areas with the same level of detail or colors as the map's primary focus. Yet keeping some of the detail in the background still conveys the population density of the area, showing that the project will have impacts to the nearby communities.

This Salt Lake City Community Councils and Neighborhoods Map immediately stood out as a poorly designed map candidate. It nearly looks like raw, unstylized GIS data, yet some effort was placed in the layout and output to consider it fit for use by the public.

The first map design principle of the British Cartographic Society's Design Group is "Concept before compilation." This stresses that it is important to understand the concept of your map entirely. What does the map need to contain, how should it look, who is the intended audience and what will they want or get from the map?

I originally downloaded this map of Salt Lake City to learn what were the neighborhoods in the city and what were their general boundaries. The map conveys this, but not in an efficient or appealing manor. The amount of black linework from the street rights of way overwhelms the map, making it hard to parse neighborhoods from community council districts. The background results in just noise, and without any emphasis on major streets or legible street names, another map has to be consulted to formally locate a neighborhood within the street grid.

The thick neighborhood polygons dominate the feel of this map. Lost within their bounds is the small red italicized text referencing the neighborhood names. The way it is presented, the neighborhoods and community councils appear synonymous with one another, but that is not apparent until analyzing an area of the map with less detail. Clearly this map does not adhere to the Hierachy with Harmony map design principle.

It is arguable what may be more important in this Salt Lake City map, neighborhood boundaries or community council areas? Without any descriptive text somewhere on the map telling the audience what the community councils are, or what is their purpose, their significance is unclear. Is the label size appropriate for those councils? This map conveys that they are important, yet the boundaries of the neighborhoods hold just as much weight in their line thickness. So the hierarchy is not readily known for the end map user, another poor design aspect of this map.

Another topic stressed in this week's module are map elements (title, legend north arrow, scale bar, etc.) and more specifically the placement of them. Utilizing areas of white space for elements is one thing, but also leaving room for them in the map layout is another. All the while balancing map elements with the overall composition of the map is important. An aesthetically pleasing layout goes a long way.

Computer Cartography, the next step on the GIS Road to Fruition

When I saw that Computer Cartography was my next class in the UWF GIS Certificate Program, I got pretty excited. Cartography has been an interest of mine going well back to childhood. When my siblings got a sketch pad to draw art, mine instead was used to draw maps. This passion stayed with me into high school and then college, where I would often doodle a small map in the margin of my notes. When I took  AutoCAD at Delaware Technical & Community College, one of my first drawings was a fictional road map. You get the idea!

I first learned of Geographic Information Systems (GIS) in 1997, and with some encouragement, enrolled in the University of Delaware's GIS class (GEOG 372) when I transferred to UD from DelTech that Fall semester. The following Spring semester, I took Advanced Geographic Information Systems (GEOG 472), where we used both ArcMap and ArcInfo in lab. I posted two examples of our GIS work from back then on a previous blog post. My professor Tracy DeLiberty encouraged me to further pursue GIS, but my career aspirations at the time were meteorology, and I eventually transferred to the University of South Alabama (USA) to pursue that and did not work with GIS again until 2006.

I landed my first job in mapping at Mapsource, Inc. out of St. Petersburg, Florida, where I eventually was promoted to Assistant Chief Cartographer. The boss/owner was Gene Ingle, an old school cartographer who originally worked in the newspaper industry. I learned quite a lot from Gene, and one of the things he instilled in me were standards in cartography.

My coworker's station at Mapsource

Gene had an ironclad set of rules for the creation of our maps, which were all based in AutoCAD. Having this rule set took out some of the guesswork on how or where to place map elements, text orientation along features, acceptable abbreviations, what font type to use, appropriate text sizes, etc. To this day, I still incorporate many of these principles.

Sadly, I learned of Gene Ingle's passing in 2018. He was 76 when I worked for him.

My career in cartography continued beyond Mapsource with a job as a map researcher for Universal Map Group out of DeLand, Florida. There I was tasked with updating existing map products with the use of GIS (ArcMap). I created Geodatabases with a variety of map updates from changes or additions to streets, city limits, points of interests and parks among other map elements.

My station at Universal Map

First learned of the University of West Florida's GIS Certificate program back when I started working for Geographic Information Systems Cartography & Publishing Services (GISCAP) as a mapping specialist. Last year when I was considering my future career goals, I had an epiphany and recalled the UWF GIS program.

Having been removed from regularly working with GIS for several years, I sought to both renew my skillset and also broaden it. As I've seen through my wife's GIS work with the Florida Department of Transportation (FDOT), there is a lot more to GIS than just the creation of maps. Thus far the Introduction to GIS class and Remote Sensing and Photo Interpretation class have shown me just that.

Our first assignment for Computer Cartography was to create a StoryMap. I have some familiarity with the concept of StoryMaps thanks to my wife, but prior to this past week, I had never attempted to create one myself.

Creating the StoryMap is very similar to compiling a blog post with a content management system (CMS) such as Wordpress. There are widgets, style options, several ways to embed media, and of course multiple map tour options. For my first StoryMap, I opted to create a short photo tour of the Interstate Highway System using photos I shot and posted on AARoads.com as the media. It can be viewed at https://storymaps.arcgis.com/stories/863cdd9091cc4d2d830d5ff4b7d520bb

June 28, 2024 Update!

Just started my 5th class in the UWF GIS Certificate program, GIS Applications. Computer Cartography gave me a new perspective on the art and presentation of map design. GIS Programming, which I just completed this week, introduced me to Python. Python was surprisingly easier to interpret than I anticipated, and part of me has contemplated installing Django on AARoads to use Python on the site...

For my second StoryMap, I decided to share some of my love of weather. Severe weather and hurricanes have always interested me and I have been amazed and even overwhelmed by seeing what mother nature is capable of. Shared of my storm experiences at https://storymaps.arcgis.com/stories/7940f15d8f72458cade49d59926c9968

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Going forward, I am hoping to land a position as a GIS Analyst, ideally in something transportation related. Working with others and being part of a team are things I've missed with running a business solo for so long. I've received a lot of encouragement from folks I know at FDOT, and perhaps I can join them on a professional level in the future.

Celebrating my 50th State at Tok, Alaska!

Besides taking GIS classes, I enjoy traveling, working out at the gym, riding coasters at theme parks, playing video games and binging Anime with my wife.

Bobwhite Manatee Transmission Line Analysis - Final Project

The final project for GIS4043/Intro to GIS conducts analysis on the Bobwhite Manatee Transmission Project in Southwest Florida. Part of the Florida Power & Light (FPL) infrastructure, the 24.5 mile long transmission corridor was developed to serve growing areas of eastern Manatee and Sarasota Counties, including Lakewood Ranch. Additionally the new line offers redundancy during hurricanes, something tested since it was completed with Hurricane Irma in 2017 and Hurricane Ian in 2022.

GIS analysis was used in part to determine the optimal location for the proposed transmission corridor. The design of the route took considerations for reducing impacts on sensitive or protected conservation land, avoiding schools and daycares, and providing a buffer from existing homes. Community input factored heavily with the corridor ultimately selected. FPL also worked with Schroeder-Manatee Ranch (SMR), the developer of the Lakewood Ranch community, to select a route that preserves the natural beauty of the area.

The study area was 273 square miles wide, mostly spread across central Manatee County along with a portion of northern Sarasota County. The project was announced by FPL in June 2006. With input from a community advisory panel, open house events and surveys mailed to area residents, FPL developed formal plans, which were unveiled in October 2006.

The Bobwhite Manatee Transmission Line project was eventually certified by the Florida Department of Environmental Protection. It subsequently cleared the Transmission Line Siting Act and was approved by the Florida Cabinet and Governor on October 28, 2008. Construction was anticipated to begin in 2010. It ultimately did in 2013, following additional compromises made between FPL, SMR, area homeowners and Taylor & Fulton, an area agricultural group.

Our project looks at four criteria analyzed by GIS for the selection of a preferred corridor for the transmission line. The first objective considered the number of homes and overall properties within proximity of the corridor.

Using the buffer geoprocessing tool, a 400-foot buffer was created around the preferred corridor of the planned transmission line. A feature class locating all homes within the corridor and associated buffer was next created with heads-up digitizing using 2006 aerial photography. With all visible homes added to GIS, running the "select by attribute" geoprocessing tool on created fields that indicated if a home was either within the corridor or within the 400-foot buffer, provided the totals. A map output of the homes and parcels intersecting the corridor:

The transmission line that was eventually built comes no closer to 600 feet from an existing home. No doubt GIS aided in achieving this buffer.

The second objective of GIS with the Bobwhite Manatee Transmission Line project was a simple one. Are their any schools or daycare centers within the preferred corridor, or the associated 400-foot buffer? Some work outside GIS was required to analyze this, as point feature classes for schools and daycares did not exist.

Researching area schools with the Department of Education website, and other websites for daycares falling within zip codes that crossed the preferred corridor, lists were compiled in Excel. These were in turn geocoded into GIS, using more recent street centerline files to complete address matching for automating the location process.

With school layers compiled, the select by attributes geoprocessing tool determined that no schools or daycare centers were within the preferred corridor or buffer:

That fact that FPL avoided all schools and daycares certainly reduced community opposition to the overall project.

Moving on with the analysis, environmental impacts to both conservation areas and wetlands was considered. National Wetlands Inventory (NWI) and Florida Managed Lands data were provided. The question to be answered is how many acres of each land type was within the preferred corridor?

For wetlands, the NWI feature class was clipped within the preferred corridor polygon. The result were records for uplands and two wetland types, with the Shape Area field providing the areas in square meters. After converting the values into acres, calculating the total acres of uplands and wetlands was easily achieved using the Summary Statistics geoprocessing tool.

Conducting spatial analysis on the conservation areas, a different approach was taken using the Select by Location geoprocessing tool with the Intersect relationship. This extracted all polygons in the conservation land feature class that were within the preferred corridor into a new feature class. The resulting data revealed that relatively small portions of a conservation easement, watershed and state park were in the preferred corridor:

The final objective analyzed by GIS was to estimate the total length of the then-future transmission line, and to use that figure in an equation to estimate construction costs. This was a straightforward process using the Polygon to Centerline geoprocessing tool.

However one data discrepancy occurred with the creation of the centerline feature class. The centerline split into separate branches within the triangular shaped wedge at the south end of the preferred corridor. I considered these to be outliers when it came to the determining the overall length of the transmission line.

One option was to take an average of the length between the two and consider adding that to the main centerline vector. Another option was to omit them entirely, as the project included constructing the Bobwhite Substation within that wedge shaped area.

GIS analysis determined an estimated total length of 24.76 miles. An East County Observer news article on the Bobwhite Manatee Transmission Line in 2013 referenced the line being built at the time as 24.5 miles in length. So this was a pretty good result from GIS.

Living in Bradenton from June 2013 to April 2015, I drove by this project several times without knowing much about it. While doing photography for AARoads, I captured work in progress along State Road 64. Looking back at the photos, what was built was a 230kV single circuit transmission line on a steel tubular pole. Using the equation provided with the GIS project documents, that resulted in a rate of $1.1 million per mile. The $27.236 million I calculated was well above the $20 million cost reported in the East County Observer article.

In conclusion, it appears that FPL designed the Bobwhite Manatee Transmission Line with a priority in the feedback from the community. The route was designed to follow existing right of way for several major highways. Using that space instead of a new corridor, the impacts to protected lands was minimized. Beside one home that was eventually demolished to make way for the Bobwhite Substation, it appears as if most existing homes were avoided by the power line.

Wrapping things up, beyond the deliverables posted above, we were tasked with creating a Power Point Slide Show presentation and accompanying transcript. Both are uploaded to my Google Drive:

• Bobwhite-Manatee Transmission Line Project Power Point slide show
  
  
• Bobwhite-Manatee Transmission Line Project Power Point transcript

Map Projection Variation with Florida County data

This week's GIS lab delved into map projections, the manipulation of attribute data and the creation of a feature class out of selected data. We were tasked with taking a polygon shapefile of Florida's County Boundaries and reprojecting it from the original Albers Conical Equal Area (Albers) coordinate system to both the State Plane Coordinate System (State Plane Florida North) and the Universal Transverse Mercator (UTM 16 North) system.

Following the completion of that task, we were asked to compare the three maps to note any differences. The Albers and State Plane map projections were very similar, while the UTM projection was noticeably tilted more. Further analysis involved extracting the area in square miles in tables for four select counties across the state. When comparing them, Escambia was nearly identical in area on all three. Alachua was only four square miles larger on UTM 16 N as compared to the other two. More substantial differences were noted in Miami-Dade and Polk Counties, where the square mile numbers for UTM 16 N remained the highest.

Final map showing Florida with a selected Counties layer in Albers, FL State Plane N and UTM 16 N

Our process summary included a section to report my results and conclusions. While I knew of the concept of projections from previous cartography work, I really did not know much about the origins of their geographic coordinate systems. So I devoted several hours of research, composing a word document on them for future reference. Followed this by practicing reprojection more and creating overview maps showing each of the three projections nationally with graticules.

With an understanding of their methodology, the differences in the three Florida County map projections become more clear. The original shape file uses the Albers coordinate system, which it turns out is optimal to use for east to west geographical areas located in the mid latitudes. More specifically, the coordinate system uses a two standard parallels to minimize distortion in the region between them. This results in spatial accuracy along the parallels and minimum distortion between them. However scale along the lines of longitude does not match the scale along the lines of latitude, resulting in the final projection not being conformal. Albers is best suited for projecting the entire state of Florida.

There are 60 UTM zones per hemisphere, each spanning 6° longitude with a central meridian located around 180 kilometers between the edges. Best suited for north-south regions, the scale error does not exceed 0.1% per zone. Error and distortion increases for regions that span more than one UTM zone. A UTM zone is not designed for areas spanning more than 20 degrees of latitude.

Within this lab, Florida was projected using UTM 16 N, with a central meridian crossing the Florida Panhandle. The entire Florida Peninsula is within UTM 17N. Therefore while the scale error is still minimal, county areas are larger than with Albers or State Plane.

The State Plane Coordinate System divides the 50 U.S. states and associated U.S. territories to over 120 numbered zones. An assigned code for each zone defines the projection parameters using one of three conformal projections. The system is designed with a maximum scale distortion of one part in 10,000 per zone, with a central meridian or standard parallels maintaining this level of accuracy.

The Florida County boundaries layer projected in the lab uses Florida State Plane North, one of three zones assigned to the Sunshine State. This zone uses Lambert Conformal Conic projection, which is best for middle latitudes, as long as the range does not exceed 35°. Similar to Albers, Lambert portrays shape more accurately than area. Latitude spacing increases beyond the standard parallels, with minimal distortion near them. Scale is correct along the standard parallels, but reduced between them and increased beyond them.

The Florida Peninsula generally falls within Florida State Plane West or East. Alachua County represents the eastern extent of the North zone, so the area is accurate. Polk and Miami-Dade are located in the other zones, and therefore are scaled slightly higher due to being beyond the standard parallels for Florida State Plane North.

Florida State Plane East and West both use  Transverse Mercator projection, as they have long north to south axes.

Cartography Overview - University of West Florida Map

 Following several days of anxiousness following the formation and movement of Hurricane Idalia, I finally was able to resume work on the second Lab assignment for GIS4043. This week's focus is on cartographic basics and design, something I have familiarity with having worked for three map companies. However my experience goes back to ArcMap, and the ArcGIS Pro system definitely requires some time investment to acclimate.

The general purpose of the map is to show the location of the University of West Florida (UWF) Main Campus located north of Pensacola in Escambia County. We were tasked with creating two maps, a small general overview map showing Escambia County's location relative to the whole state of Florida, and a larger map showing Escambia County in more detail with the placement of the campus location.

An early step was to add a folder connection via Arc Catalog. Previously in ArcMap, Arc Catalog was somewhat separate. Now with ArcGIS Pro, catalog is more seamlessly worked into the main functions of the software. A nice improvement.

Using Catalog, the metadata is easily viewed. Collapsible ticks next to section headers aid in better parsing the data for locating desired information. The only drawback is that if you exit from a particular metadata for one file, upon return all closed ticks are again open.

Upon reviewing metadata for the six shape files downloaded for the Lab, discrepancies with how detailed the metadata with each respective file becomes apparent. Some metadata is extremely detailed while other metadata is more limited. Reviewing the metadata for this project, data sources included U.S. Census Bureau Tiger files, the University of Florida GeoPlan Center and FGDL among others.

Moving forward with the Lab, creating and editing the map layouts in ArcGIS Pro was next. The basic elements of this map include Florida County lines, the Interstate Highway system, major rivers and populated cities, towns and census designated places. The idea is to present a simple map conveying where the UWF Main Campus is located without being overly busy with unneeded information.

This Lab involved Clipping Layers, something that I had prior experience with at my cartography job. The clipping aspect was previously located within Arc Catalog on ArcMap. Having it more readily accessible with ArcGIS is convenient. For this map, Escambia County was clipped from the overall state file showing all 67 of Florida's Counties. The Interstates shape file was also clipped to only display I-10 and I-110 within Escambia County, as were the rivers within the Major Rivers shapefile. Lastly the cities shapefile was clipped to only show Pensacola and the census designated place of Ferry Pass, the more specific location of UWF.

Queries were also introduced in the Lab, first with a simple equal statement to select Escambia County from the Florida Counties shapefile. A subsequent query was created to select Pensacola and Ferry Pass from the Cities shapefile. I have previous experience using queries for labeling feature classes, and some knowledge of query structure from managing the mySQL database for AARoads.

Editing the symbology followed and color suggestions were provided. I used green for Escambia County, with a darker shade on the inset. The rest of the counties were set to gray, to minimize their attention. That background could be left white (blank) or a Basemap could be selected from the Layer group. I opted for the World Light Gray Canvas on the UWF Campus Location map. However with selecting that base, a "Reference" entry appeared at the top of the Drawing Order. Unchecking that preventing it from superseding the map.

Labeling included placing a Title, creating a Legend and adding map elements such as a North Arrow, Scale Bar and Source information. There were some differences in the verbiage used to describe actions in the Lab PDF document versus completing those processes in the current version of ArcGIS Pro. This was noted on the Lab discussion board, and previously caused some minor issues with Lab 1.

The final aspect of the map was placing the PNG of the UWF logo. Where to place it and at what size were considerations made as to not have the graphic complete strongly with other map elements. The resulting map in PNG format: