GIS Portfolio

For my final project for my internship course, I have created a GIS portfolio on a website showcasing maps and projects I have completed since beginning the GIS Master's Certificate program here at the University of Florida. It was fun to go through all of my work from the beginning. I could definitely see where I made improvements and noticed I have a certain map style.
My favorite type of maps to make are bivariate choropleth maps. I like the way they convey so much data in a very succinct manner.


https://janerowen84.wixsite.com/sarahjanebuchanan

https://drive.google.com/file/d/1XaTB47amCzDTo-3ZM4njTpuUw2U3fwea/view?usp=sharing

Spectral Classification

For this week's lab, we learned how to complete an unsupervised and supervised classification on an aerial image in ERDAS Imagine. First, we practiced classifying an unsupervised image of UWF campus by accurately classifying different spatial and spectral resolutions. To finalize this, we reclassified and recoded images from the spectral signatures created in an AOI layer. 

After learning the steps needed to create a supervised classification map, I used the inquire tool to enter in the coordinates for all the features to begin creating spectral signatures (except for the roads and water-I created them without the inquire tool). When I completed creating the spectral signatures, I evaluated them using the Histogram and mean plots. The bands with the greatest distance from each other determined the color band that was used. For this map, I used R4, 5G, 3B. The output distance file did show some bright pixels that let me know the classification was not correct. I had to go back to my spectral signatures and adjust the Fallow, Water, and Road area. I could never get the Road and Urban area perfect but I finally got an image that made sense.

Spatial Enhancements & Multispectral Bands

For this week's lab, we learned how to apply spatial enhancements, perform a multispectral analysis, and create band indices.

The spike on the histogram between pixels 12 and 18 is very tall so that means the feature is very large. I viewed in multiple color combinations and greyscale and noted features I was confident with identifying such as water, mountains, town. When I moved the breakpoint on the histogram between 12 and 18 pixels, the rivers and lakes remained dark so I was able to conclude that the feature was water. For the bands, I used the TM False Natural Color 5,4,3 Landstat 4 TM-6 Bands because this combination band makes water delineation stand out.

I viewed this image in greyscale and different multispectral bands and it was white in Layers 1-4 and dark in Layers 5-6. When I viewed the Histogram, there was a small spike on the right which means it is very bright.  I was able to identify the feature as snow. I used the True Color combination 3,2,1 with the Landstat 4 TM-6 bands because the white snow stood out in natural color. 

Since I knew this feature was going to be water as well, I just had to deduce what type of water. Layers 1-3 became brighter in certain areas and somewhat brighter in certain areas in Layer 4. I was able to quickly identify the feature as being shallow water because the shorelines were brighter and then what appeared to be a landlocked water basin was very bright as well.  I chose the TM False Natural Color IR 4,3,2 combination with Landstat 4 TM-6 bands.

GIS Event Day

For my GIS Day Event, I decided to do something personal and informal. This is partly due to the fact that I have a ton on my plate right now and then partly because I am a sensitive creature. I decided to invite my family to be guests for my event and shared a story map I made earlier this year about my experiences in 2018. It was an assignment where I created something really personal for myself and had not yet shown anyone (that I know personally). I wanted to share with my family that one of the reasons why I love GIS so much is how versatile it is for everything. It geographically and spatially relates ANY kind of data in a manner that drives industries. But it can also be used as a creative expression! I went to my mother’s house and showed her. I had hoped for a larger audience but Jim was hunting and my sister ended up not being able to make it. That’s ok because my Mom made me talk about GIS LOL. She works as a freight broker and said that she is glad I understand how to use GIS because it is beneficial to her. Like, when she has an oversized load and needs me to look up low clearance maps from TxDOT on arcgis, so she can route her driver properly. We had an emotional moment after she viewed my story map. She appreciated how it created a timeline of my life. Here is the link for my story map: https://arcg.is/1bXSrT

ERDAS Imagine

 For Module 4, we were introduced to ERDAS Imagine and how to use basic tools and view data. This lab really jump started our work with remote sensing. We learned about EMR, electromagnetic radiation, and how to calculate its wavelength and frequency based on Maxwell's Wave Theory. The size of the wavelength is related to the amount of energy and the frequency of that energy. The amount of energy is inversely related to the wavelength of the light: the shorter the wavelength, the greater the energy of each photon of the light. Maxwell's Wave Theory, which is the speed of light = (wavelength in meters)*(frequency in Hz), and Planck's Relation, energy of photon/quantam in Joules = (Planck's constant- 6.626*10^-34 J*s)*(frequency in Hz), calculate and derive variables that explain EMR. This is important to understand when studying remote sensing because it helps you identify features in the photography. In ERDAS, we learned how to view raster layers and understand the data being displayed. Raster layers can be raw/multiple layer continuous data, single layer panchromatic continuous data, or categorical/single layer thematic data. Continuous data is quantitative data that has related, continuous data. Thematic data is qualitative data that is categorical. We compared AVHRR images to TM (thematic mapping) images to determine that the former has a coarser spatial resolution than the latter. On the TM image, many features can be identified, so we changed the band combinations for multispectral data to enhance vegetation differentiation, snow, water delineation, and bare ground/urban areas. We also learned that to switch between the TM False Natural Color and TM False Color IR, the former makes the bare ground more distinguishable. These are important tools to use when identifying features. For the map delivarable this week, we used ERDAS Imagine to create a subset image to use in ArcPro to make a map.

Land Use/Land Cover Classification and Ground Truthing

For Module 2, we utilized the identifying features we learned last week, such as shape/size, color, shadow, pattern, and association, to help locate features in a natural color aerial image and create a land cover/land use map in Pascagoula, MS. Land cover is the biophysical description of the Earth's surface. Land use is how humans interact with the landscape and therefore change it. I used Level I and Level II classification scheme to create codes for different features I found. There are 9 Level I classes and I used 6 of them: Water, Urban, Agricultural, Wetland, Barren Land, and Forest Land. The trickiest part of this lab was getting comfortable creating polygons and editing the vertices in a way to keep overlap at a minimal.For the ground truthing calculation, I created 30 random sample points by distributing them as evenly as possible across the map. Then, I utilized Google Maps street view to check the accuracy of each point to polygon. In the shapefile attribute table, I used Yes or No to confirm the accuracy. 

Visual Interpretation

For exercise 1, I had to learn how to identify features using tone and texture. I began with “tone” which is how light or dark something is, and created a feature class for each classification. This is done by highlighting the .gdb file in the ArcCatalog pane, right-clicking, and selecting new feature class with polygon geometry. I added the Tone feature class to the map by dragging it over. Then, I added each feature name opening the attribute table for Tone, and adding a field for each feature. Lastly, I would create the polygon for each feature by highlighting the feature in the attribute table, selecting edit>create feature>polygon. I repeated this process for Texture For labeling purposes, I chose the polygon lines to be a bright blue color (Tone) and a bright red color (Texture) with a 2pt width because I needed it to stand out on an otherwise colorless background. The 5 classifications I used for Tone were Very Light, Light, Medium, Dark, and Very Dark. The 5 classifications I used for Texture were Very Fine, Fine, Mottled, Coarse, and Very Coarse. Some examples of features I chose for tone and texture were an airport for very light, calm water for very fine, and a neighborhood for coarse. Some features had varying tones/textures such as vegetation.

 For exercise 2, I had to visually identify features using the criteria of shape/size, shadow, pattern, and association. I used the same steps as in exercise 1 to create feature classes for each criteria except this time I used point geometry. Shape/size is the most intuitive and easiest identifying feature to begin with. Without having to zoom in or study the aerial photo long, I was able to identify a pier. I was pretty sure I located a two swimming pools but after zooming in slightly, I was 100% sure. I did have to zoom in very close and pan around the photo to see other features more clearly. This allowed me to easily identify a vehicle. I remained zoomed in when I chose the three features for shadow. I chose trees, a motel sign, and a water tower because they had clear shadows being cast. I almost chose buildings because so many had shadows but decided to use the examples that would be very difficult to identify without that one criteria (buildings have multiple criteria). Pattern is almost as intuitive as shape/size for identifying features, in my opinion. It was easy to locate the waves in the body of water, the multiple transmission lines, and the lined spaces for the parking lot. For association, I chose the motel and the neighborhood clubhouse. Each building contained a swimming pool which helped signal the association. For labeling purposes, I employed a few techniques for the map to stand out, as I was dealing with another colorless background. I used 5pt circles with different colors for each criteria. For the labels, I used the Point of Interest text symbol with “white halo”. However, I changed the color of the halo to match the symbol color, used with the underlying black text. I felt this aesthetic was pleasing to the eye. 

For Exercise 3, I explored color interpretation by comparing a true color image and a color infrared image. The mixed pine forest area southwest of the area initially appeared green to me but it was entirely red in the false color image thus its true color was near infrared. The marsh was easy to discern as its true color was green and false color was blue. There were some areas that I interpreted as rivers/water retention areas that were white in true color and blue in false color. I concluded that these could be possible construction or industrial areas that do not contain runoff all of the time. The buildings appeared to be white in both true color and false color because they reflect light. We did not have to make a map of this exercise but it was good practice to see how having a thorough understanding of your area and how true/false color can help you understand features in an aerial photograph.

Final Project

Cartography mapped us through a journey of 12 modules that led to this point. For the final project module, I created a bivariate map, where I displayed 2 thematic datasets over one geographic area. The topic of the map was to show the SAT mean composite scores in 2014 in each state coupled with the participation rate of each state. The importance of creating such maps is that it shows the empirical association between two variables. Presenting a map of the SAT scores in each state can be deceptive in how the data is perceived. Other variables can influence why one state has a higher performance than another state. The map that I produced will highlight how the participation rate of persons taking the SAT exam in each state relates to that state’s mean composite score.

To juxtapose the United States’ SAT 2014 mean composite scores next to each state’s participation rate, I chose to display the thematic datasets as a choropleth map for representation of the SAT mean composite scores and employed graduated symbols to signify the participation rate in each state. For the choropleth map depicting the  SAT mean composite scores, I chose a manual 5 class data classification with a graduated color scheme of blue. For the graduated symbols, I selected them over proportional symbols because they are classified data whereas proportional symbols are unclassified. Since the participation rate represented a percentage, I wanted to be able to break it down into classes that represented the data as evenly distributed as possible. Instead of employing 5 classes at Natural Breaks, I manually created 6 classes: 0%-10%, 11%-30%, 31%-50%, 51%-70%, 71-90%, and 91-100%. The intervals for 5 classes was much too large. For the map layout, I chose a landscape orientation because it is best suited for a map of the Unites States. I created 3 inset maps; an inset map each for Alaska and Hawaii so I could zoom on the mainland at an appropriate size without leaving Alaska and Hawaii off. For the third inset map, I zoomed in on an area of the east coast where the states are geographically small but they had the highest participation rate. I wanted the map reader to be able to see that while their test scores were lower than the national average, they also had the highest participation rate. I used Adobe Illustrator to isolate the graduated symbols and move them around each state to create the ideal visual hierarchy and balance. I chose a map projection of North America Albers Equal Area Conic because equal area projections are the ideal choice for thematic maps (Esri).

Every year, students across the country prepare for months for SAT Exams in the hopes of getting accepted into as prestigious a university as possible. If this map had only included the Mean SAT composite scores per state, people would have a skewed perception of what that data truly means. By employing a second dataset that showed the participation rate, the audience can get a better understanding about what those scores mean. The map does a good job depicting that while a cluster of states in the Midwest have the highest scores, they also have the lowest participation rate.

Google Earth

For Module 12, we created a kmz file from our Module 10 Dot Density map for Google Earth and created a tour of Miami, Ft Lauderdale, St Petersburg, and Tampa, FL. Google Earth is an interactive 3D representation of Earth based on satellite imagery. Data can be uploaded into Google Earth and saved as a kml file that is accessible by any ESRI, KML, and Google Earth client. It is a simple way for users who lack GIS knowledge to access geographic data. To create the kmz file, I converted the appropriate features, Surface Waters and Florida Counties, to KML. To prevent ArcPro from crashing, I had to add an attribute field POP10000 on the dot density layer and calculate the field by using the Python expression: !Sheet0_Population!/10000. Once the new field was calculated, I used the Create Random Points tool for the dot density feature class. Then I ran the Layer to KML. I adjusted the legends for the features and dot density in ArcPro and used the snipping tool to save each image and added the image overlay into Google Earth. For the tour, I created placemarks for each location. I created the tour by recording each placemark and zooming in and around the area for an in depth view of the 3D buildings.

3D Mapping

For Module 11, I completed numerous Esri exercises about 3D mapping and converted a 2D map to a 3D scene. 3D view consists of four main elements: surfaces, textures, features, and marginalia and effects. This will include ground surface, aerial imagery/cartographic maps, relative to ground features/know their own absolute z's, and reference aids/atmospheric effects. They can be represented as photorealistic (real world) or cartographic (representative). 3D maps are powerful tools for a user because they are immersive and eye-catching.  Below is the 2D map that was converted to a 3D scene of Downtown Boston. There are two applications of the 3D building layer. The buildings polygons are extruded by height value to create 3-dimensional building shapes. This enables the viewer to visually focus on a specific area and to view the buildings from different perspectives as well as compare building heights. A map user could also focus on a single building and examine the shadow effects and sun exposure and its surrounding landscape. 

Dot Mapping

This week's Module 10 lab we learned about the dot mapping method. Dot maps are a type of thematic map used to help visualize distribution and densities of a large number of discrete numerical points. Dot maps rely on visual scatter to show spatial patterns and are commonly used for population maps.They display a good visual representation of variations and can also be used for statistical analysis. Dot maps have good spatial representation. Dot maps are easy for map readers to interpret. Dot maps visually match phenomena that changes smoothly over a space. There are a few disadvantages to using dot maps. The clustering can make it impossible to plot and interpret dot maps. Large numbers of dots are difficult to count and calculate actual figures. The size of the dot has to be carefully selected to display data clearly. The areas with no data give a false sense of emptiness. 
For our assignment this week, I mapped the population density of South Florida. I chose a size 3pt for the the dot symbol and the color red. I opted to no color for the county borders and the bodies of water for the main portion of the map because it made the map look cleaner. For the urban areas, I chose a mauve color and used a 50% transparency. I created an inset map for the state of Florida that included the county border outline for reference. I created two legends for more clarity for the map reader. One of the legends depicted the dot symbols and the proportional count. I created this by using the rectangle tool to make one square and copied it to make two more. I then used the circle tool and added the dot symbols. 

Flowline Mapping

For the Module 9 lab, we employed a radial flow map to depict the migration of immigrants from around the world to the United States. The map has a spoke like pattern in nodal form with all of the flow lines ending at the same destination, the United States. For my map, I opted to go with the Flowline Basemap A with the choropleth map located in the inset map. I chose to leave the continents in place and made all of the flow lines the same red color. I used the weight point that I calculated in excel to show the proportion of the migration count from each country. I set the opacity to 75% and added a drop shadow to all of the flow lines. I chose a brown to light tan graduated color scheme for the choropleth inset map. These colors were noted in the legend in the manual breaks method with the lightest colors correlating to the lowest immigration percentage. The legend also contained the immigration totals per country in descending order to match the color legend. I changed the color scheme for the countries and chose slightly vibrant colors since it was a world map and my map was not heavily stylized.

Isarithmic Mapping

Module 8 introduced the class to Isarithmic mapping. Isarithmic maps depict smooth, continuous phenomena across an area using varying symbology methods. The phenomena are measured at control points and interpolated using the appropriate method. We mapped the state of Washington depicting the annual average precipitation from 1981-2010. The precipitation data was derived by measuring at control points and interpolated using PRISM (Parameter-elevation Relationships on Independent Slopes Model). This interpolation method accounts for major physiological factors (location, elevation, coastal proximity, topographic orientation, vertical atmospheric layer, topographic position, and orthographic effectiveness of the terrain) influencing climate patterns. As precipitation generally increases with elevation, PRISM integrates elevation into the surface by utilizing a digital elevation model (DEM). I created two maps with one using continuous tone symbology and the other using hypsometric tinting. The first map is a continuous tone map showing the annual average rainfall in Washington state from 1981-2010 using smooth stretch symbology. The second map implements hypsometric tinting and utilized classified symbology with the data manually divided into 10 different classes. Relief was incorporated into both maps by employing the hillshade effect but the hypsometric tint map also shows contour lines. The hypsometric tint map was ultimately used for the end product because it is ideal for geographically smaller areas such as a state where the continuous tone map would have been more appropriate if the map was of the United States.

Choropleth Mapping

This week's lab we produced a choropleth map denoting the population density of European countries and their wine consumption. A choropleth map is a thematic map in which enumeration units are shaded by intensity proportional to the data values associated with those units. The data needed to be normalized by using population density versus raw population count because the latter can be deceptive when land polygons are not the same size. The eye naturally follows areas of the same color giving larger polygons undue ranking and minimizes the significance of smaller polygons. For this map, I used the Europe Albers Equal Area Conic because equal area projections are ideal for choropleth maps especially when choosing to map population density. It shows the map is displayed in true proportions to its size on Earth. For the data classification, I chose the quartile method because it included all of the classes and contained a clear breakdown of the data displayed. Some of the other classifications did not include the darkest color class. I chose a graduated color ramp of brown light to dark that is color blind friendly. Since choropleth maps display alot of data by using color, I wanted my map to be easily read by every potential user. For the wine consumption, I chose the graduated symbols because the proportional symbols overlapped my entire map. I did not normalize this data because graduated/proportional symbology represents  numerical data associated with point locations, not area. 

Data Classification

This week's lab we learned about the 4 common data classification methods: Equal Interval, Quantile, Standard Deviation, and Natural Breaks. We compiled two maps using the Miami Dade County 2010 Census tract data to display each classification method. The first map showed the population percent of senior citizens aged 65 and above. The second map showed the senior citizen population normalized by area. Equal Interval is a classification method where data is represented by classes that contain an equal amount of data values. The range of the data is divided by the amount of classes you want to have. This method is the easiest for the reader to interpret and it is also the easiest to prepare. However, there can be an unequal amount of distribution within the classes that can cause entire classes to be unrepresented with fill color on the map or for one class to dominate the map. Quantile is a classification method where data is sorted into a certain amount of categories with each category containing the same number of values. The total number of observations is divided by the total number of classes. While you will never have empty classes, you have to manually adjust your break values to compensate for tied classes. Similar features can be placed in adjacent classes or features with grossly different values can be placed in the same class. This distortion can be decreased by adding classes. Standard Deviation is a classification method where the standard deviation is added/subtracted from the mean of the data. The data needs to be normally distributed to give your classes clear dividing points. The audience target should be considered as this statistical representation might not be easily understood. Natural Break is a classification method where the natural groups in the dataset are considered. This minimized the differences between data values in the same class. It does consider outliers and places them in their own categories but clusters are placed in one or two classes. It can be difficult to compare two or more maps with the natural break classification because each map range is data specific.

The first map, under the symbology tab, I selected the graduated color (green hue, light to dark) with the field PCT_65ABV for all of the data classification methods except for the Standard Deviation. For the Standard Deviation method, I used the dark brown-tan to light blue-navy blue. The map contained all map essentials with four data frames that contained a legend in each. I used the same layout for the second map except I normalized the area in square miles under the field AGE_65_UP. The population count normalized by area more accurately depicts the distribution of senior citizens of Miami Dade County. The percent above 65 data presentation can be misleading in that a large tract can have a high percentage of senior citizens residing there but contain a low population count. Since the percent above 65 presentation does not factor in area, a small tract can be densely populated while a large tract can be sparsely populated. When the data is normalized by area, the reader can focus on the areas that are densely populated.

Land Partitioning and Cartographic Design Principles

              This week's lab is land partitioning and cartographic design principles. We learned the four Gestalt's principles to follow for map organization. The steps to follow are visual hierarchy, contrast, figure ground, and visual balance. Following these processes should help achieve a well-designed map that is concise, informative, accurate, creative, and user friendly. This week we created a map of the Ward 7 public schools in the Washington DC area. I implemented visual hierarchy in my map by labeling the roads with larger point lines for larger roads and the point size decreased as the roads got smaller. I implemented a white halo around all of the neighborhoods that were labeled. For the hydrographic feature, I used curved text for the label to follow the natural curvature of the river. I also used double spacing between each letter, 12 pt, and blue font. The title of the map has a 42 pt font and the subtitle has a 36 pt font. They both contain the Arial Bold font as I wanted it to stand out on the page.
               The symbology I chose for the road features achieved adequate contrast in my map. I chose a 0.25 pt, light grey color for the Ward 7 streets that really helped make Ward 7 stand out in comparison with the rest of the map. For the US Highways and Major Streets in all of the Washington DC area on the map, including Ward 7, I chose the ArcGIS 2D standard symbols. I felt using these symbols really added some depth and character to my map and kept it from being too bland. The school symbols were sized smallest to largest to correlate chronologically with elementary schools, middle schools, and high schools. I also colored them lightest to darkest as well. I established figure ground relationship in my map by using a lighter color scheme for Ward 7, the focal area of our map. I chose a darker tan/burnt orange color for the entire Washington DC area. It complemented the light cream color I chose for Ward 7.
                This map was a little tricky to achieve optimal balance due to the "pac-man" shape of the Washington DC area being displayed. I chose to put the inset map on the bottom right portion of the map where there was the most space. I chose to put the legend in the northern portion of the map. This was because I was only required to include 3 symbols in my legend. I put the date and author name under the inset map. I felt if I included it on top, like I did the Data Source, it would look cluttered and take attention away from the more focal areas. The map frame and inset frame were sized appropriately to claim the majority of the space on the map. I really put for the effort to ensure that everything was centered and aligned correctly.

Typography

This week we completed Module 3: Topography where we produced a map of Marathon, Florida and its neighboring islands. The main focus of the assignment was how to label a map in accordance with general typographic guidelines. Font type, size, orientation, and placement of text are important to consider when labeling the map. For point features, symbols should be placed on the left and defined to the right. Leader lines should be very thin, with no arrowhead, and point to the center of the symbol without touching it. While this assignment had the main focus of typography, producing a quality map with all the essential map elements was an important scope of the project as well.  First, I made the basemap in ArcPro and shared it to ArcGIS Online. I exported the map from there to Adobe Illustrator where I created a mapboard. I created layers for a legend, north arrow, neatline, and map title. I also created a layer and sublayers for all the Keys, one for the cities, the state park, country club, and airport. This helped me stay in order when I edited. This week we added an Inset Map to our map. I struggled for a while to get this step completed correctly. The step where I was supposed to make a clipping mask was not yielding what it was supposed to. The first <path> object was not showing up as the border of the topography. It was just one county, therefore I was clipping one county for the Inset Map when I ran through the steps. Finally, I just started from scratch and paid close attention to each step and I got it right. I attribute this to getting familiar with the Adobe Illustrator interface. Part of the assignment called for some map customizations. I applied a drop shadow to the topography since I used a beige color and it helped it stand out a little more on the map. For the hydrographic features, I used a blue font and italicized them all. For the Florida Bay label, I used a wave transformation on the text. I also transformed the harbor texts 30 degrees to fit them inside their respective harbors. When I labeled the keys, I used leader lines to help organize the labeling. While I did not get too crazy with the map, this exercise helped me play around with different styles on the labeling. I also got some experience using more tools, such as the line tool. Adobe Illustrator is proving to be a handy map making tool!

Intro to Graphic Design with Adobe Illustrator

Just as the title says, this week was an introduction to graphic design with Adobe Illustrator. While I am used to producing maps in ArcMap and find it easier and quicker to use, I can definitely see the value in AI for a cartographer. This week I chose the portrait layout for this assignment because it is my preference to highlight the shape of Florida which is more elongated than it is wide. I changed the font of the title (FLORIDA) to Algerian and set it to bold at 60pt. I felt like the font that I chose was bright, artistic, and added some flavor to an otherwise simple map. I set the state nickname as a subtitle, chose a different font MV Boli, 24pt, and 80% opacity. The state capital, Tallahassee, was written at 14pt with the standard ArcGIS capital symbol, while the two major cities, Jacksonville and Tampa, were labeled at 12pt with the ArcGIS city symbol. I chose to eliminate the Swamps and Marshes layer from the map because they took up a large portion in two areas. I felt leaving them in added extra attention which was not pertinent for a basic state map. Streams and lakes were the hydrographic features displayed. Since streams were thinner and harder to discern on the map, I chose a darker blue to help make them stand out more, while I went with a light blue for the lakes. I did a basic map frame by using the rectangle tool and sizing it to my aesthetic. I displayed the state bird with a title (mockingbirds are common but I would say less known by sight than a Bald Eagle) and the state seal. Honestly, I spent most of my time getting familiar with Adobe Illustrator since it is my first time using the software. Photoshop has always been overwhelming for me but I have never taken a structured course using it. The AI interface is complex for a new user but after a few do-overs and learning what layers to click and how to isolate when I am working on them really helped me feel more comfortable. There was a good portion of time spent using the selection and rectangle tool. I would like to learn how to make fancier borders for data frames, titles, and legends, but right now I feel good that I could produce minor elevations to a basic map.

Map Critique

The first lab assignment for Cartography is Module 1: Map Critique. We were instructed to locate one well designed map and one poorly designed map and evaluate each using the map design principles. The well designed map I chose was a geographical map of Mora County, New Mexico. The map was beautifully structured with a good layout and contained all of the important map elements. There was an inset map, two scale bars (one for elevation and one for distance), all of the roadways in the county, and relief was shown for the mountainous terrain. It also contained a well structured legend and data source. The layout and aesthetic design of this map is one I aspire to obtain in my own map making skills. The map that I chose for the poorly designed map really paled in comparison to the map of Mora County, New Mexico. It was a map of the United State's capital populations in 1999. The map author chose to use different sized circles to show the different populations. This pushed some of the city labels off of the map and the big circles even took up space in multiple states. The author should have used a choropleth map to show the different population. I would have chosen different shades of brown to correspond with the population, with the lighter colors going for the less populated and the darkest for the most populated..There were many map elements missing from the map such as a north arrow, scale bar, border, data source, and author name. The map is also not effectively labeled due to some of the large circles crowding space on the map of the United States. Indianapolis is off of the map and located up near the map title. Santa Fe has double spacing where it should be single spaced. The legend only contains numbers to the corresponding circles with no units. The map layout is poorly designed with too much blank space and a legend that is too near the actual map space. The map title should be capitalized. This map is bland and awkward to look at.





We also downloaded Adobe Illustrator and practiced opening a simple map we produced and shared via ArGIS online. I am looking forward to future lab assignments to learn this new ArcGIS version. I currently feel comfortable with the ArcMap 10.4 version, as I use that at work. The new version seems easy enough to follow. The assignment did not take me very long and in fact, I had time to go through it twice.

All About Sarah Owen

Greetings! This is the second course I am taking in pursuit of my Master's Certificate in GIS from the University of West Florida. I took last semester off because I moved to Houston,TX, bought my first home, switched jobs twice, and got married. I am so excited to get back on track though because GIS is FUN! I completed my BS in Geology from the University of Houston in December 2015. After graduation, I moved near Austin, TX and worked on a Google Maps project. This experience really cemented the realization that I love mapping. Thus, I made the decision to go back to school to get certified to gain knowledge and make myself more marketable for employment. In 2018, I landed my first GIS job working with ArcMap for Centerpoint Energy. Unfortunately, I came on when the project was wrapping up, so I was only able to work there for 8 months. In October, I got hired on another project, this time for Plains All American. I am working as a GIS Data Integration Specialist. My job consists of locating pipelines through ArcMap, extracting pertinent data, and entering it into PODS. My goal is to get a GIS Analyst position where I can produce maps and perform data analysis. I am confident that when I complete the program at UWF, I will be ready for this type of position.

I decided to base my Story Map on the past year of my life. It was an incredible year for me in so many ways. Personal and professional growth abound, 2018 will always be in my memories. If you would like to take the journey, please click on the link below!

https://arcg.is/1bXSrT