Key takeaways from a GIS job search

 A job search can be daunting, time-consuming, and frustrating. There are words to add to that short list that are more-or-less synonyms of each other. Creating and editing a proper resume is part of this process as well. However, if you have completed a resume that fits into a GIS mold that covers an assortment of acquired skills then a job search can be a little less overwhelming. Having a good resume is ideal for confidence in the job search. 

It is important to note that a job search is not all bad because it can open up possibilities for a career you may have never thought about. Sure you have an idea of what you want to do and where you want to work, but searches can be frustrating when you limit yourself to what you are applying to. Don't sell yourself short on your learned skills and the ability to quickly pick up others on the job. ESRI has many free online courses and YouTube is a great resource for learning tips or revisiting anything you know you learned but may have forgotten some details of.

It is important to be organized in the search process, have a good resume, know what you want to do, be open-minded to options, and don't dismiss your abilities. Something I love about GIS is the potential for working remotely, many employers have this as an option. This allows for more options no matter where you live. There are many websites available to search GIS positions so plan to spend some time when the search begins. Open positions may be the same from one to another but not always so take notes if necessary to keep up with applications, dates, and employers that have been applied to. 

Keep in mind the job search is inevitable. It is a process that is often repeated over time as you progress in your career. It may or may not become easier but the process should be similar, the task of searching hopefully easier. 

GIS User Group

Group name - Florida chapter of the Urban and Regional Information Systems Association (FLURISA) - FLURISA 

FLURISA is a regional chapter of URISA and is a non-profit for GIS professionals. There are three regions of Florida this organization covers; north, central, and south, offering several options to potential members. They range from professional to student and young professionals, while also extending memberships to different types of organizations, such as educational institutions or government agencies. The main page is 

I have chosen the Florida chapter of the Urban and Regional Information Systems Association (URISA). Although I looked at other organizations to join, URISA was the best fit. It favors GIS more than other groups and offers a Florida chapter, other organizations may offer something comparatively but being specific to Florida convinced me. The student membership rate was also an attractive incentive. I was not very familiar with FLURISA before but after a conversation with our instructor and current president of the Florida chapter, I was motivated to become a member.

The annual membership options are professional at $195, young professionals at $125, students at $20, and retired, and unemployed at $75. The student requirements include minimum coursework at the time of enrollment. Each of these has pro-rated amounts according to first-year join dates which vary based on the year quarter you join. The Florida Chapter of URISA has annual dues of $25, although why it is different or how different it is from other state chapters I am unsure of.  To join, go through the main page URISA, click on membership, select the type of membership, and then click the hyperlink titled “join online today”. 

The benefits of a professional membership extend wide for FLURISA. Some highlights are education and training event discounts, access to an electronic library of journal articles, networking with fellow GIS Professionals, and a nice bonus for many, contribution points toward a GISP certification. Note -  “A GISP® is a designation awarded to a certified geographic information systems (GIS) professional that provides him/her a professional distinction in the GIS profession.” (https://www.gisci.org/) 

Lab 6: Proportional Symbol and Bivariate Choropleth Mapping

 

When an attribute table has negative values they must be converted to positive before mapping. First you must separate the values to be mapped, in this case, jobs lost and gained. Make a selection of the values needed to create another feature, then use simple math of multiplying a negative by -1 to keep the same values. Then you have two different features to be mapped, positive and negative but it is easy to work. Proportioned dots stacked on each other with different sizes are effective. (The purple background in this map is the result of fatigue and is terrible.)

I used the ColorBrewer website to pick a 3-color classification. Once selected then the information was displayed for me. I chose three 3 color classifications with similarities in tones, which allowed to keep hues that complemented each other. In ArcGIS I opened each color of the symbology to ensure I had the matching numbers when applying the color scheme to each value.

H 321 S 86 V 77	H 277 S 48 V 65	H332 S 87 V 86
H 345 S 36 V 98	H 210 S 27 V 85	H 302 S 26 V 78
H 6 S 12 V 99	H 204 S 8 V 95	H 266 S 5 V 26

Bivariate choropleth mapping is great because of its efficiency in providing information. Having two variables that are related drives home the point better because of the link. In the lab, obesity and physical inactivity are two very good examples of complementary variables. Typically, one does not go without the other, although that is not always the case. In creating this type of map, it can also help bring an understanding that there may be an issue of concern is being addressed and shows a trend in specific areas. For obesity and physical inactivity, it may seem common knowledge they go together but creating a legible, balanced, and good visually contrasted map really can be a powerful tool of information. 

Lab 5: Analytical Data

 This week’s lab was especially tough, I found. It was tough because of the time it took to work through the lab. The to also work through the challenges that naturally come with GIS and data. The objective was to practice the use of different visualizations techniques to present data as well as designing communication material combining maps and graphics. My end result is not pretty but was completed.

I had a challenging time with the layout and working with the design for scatter plot and bar chart. The data was downloaded from the Census Bureau in an Excel csv format. It was the 2018 County Health Rankings National Data, which is quite interesting. We picked two variables to work with and had to create an infographic with maps displaying the relationship between the two. Finalizing the layout is where I ran into some issues when creating one with so much information in it. Putting so much into one layout to convey the message in the data results, such as adding a bar chart with multiple inputs, and pie charts are a couple of them. This was the toughest lab I have had yet and especially to work within the time constraints.

Lab 4: Color Concepts & Choropleth Mapping

The linear and adjusted progression color ramps are similar in their intervals and have a six-class sequential color scheme. In the linear progression, the intervals are evenly placed and calculated by subtracting the class low end from its high end and then dividing the result by 5 to get the intervals. In doing this the results make each separation between the classes 25. The linear progression is easy and simple using the formula. For the adjusted progression, the interval is random and has no specific formula to determine the intervals. I used the intervals from the lab example which followed the rough guideline of 1/3 higher than the largest interval and 1/3 lower than the smallest interval. The ColorBrewer site was easy, with a few clicks I got a blue-to-green multi-color scheme complete with RGB values. Afterward, the color ramp was created in Arc Pro using the RGB values found in the color ramp. The great thing about this site is its ease of use but it also is an “at a glance” way of understanding color ramps.

Linear Progression

Adjusted Progression

ColorBrewer composed

Once I had these the field calculated I went into primary symbology and chose Graduated Colors and Natural Breaks with 7 classes. The 7 classes were defaults, but I changed the percentages to make sense by removing the extra decimal places. For the ranges, the negative changes in population had a greater number at the end than the positive. I felt it was best to leave the numbers as they were because they were not large numbers and made sense of what was produced in the results. I chose 7 classes because there should be a middle label/color for 0 since the data has negative and positive percentages.

For the legend, I removed the word legend and the layer name. I left the label (layer name) in the legend and renamed it so the data can be interpreted more easily. I added the legend at the bottom of the map dragging it wide enough for it to be displayed across the bottom of the page. I had trouble removing the label (layer name), which is why it is on the map. If I could have removed it, then the legend would have looked more balanced with the symbology filling the bottom of the page.

Lab 3: Terrain Visualization

 

This week’s lab introduced us or had us revisit, contours, hillshade, and land cover. For the land cover map, I included an elevation layer that I used a raster function Hillshade on. In the Hillshade function, I created two new layers, first the traditional followed by a multi-directional hillshade type. The multi-directional, in my opinion, did not work as well visually as the traditional. I left the traditional hillshade type in the map using the default settings of 315 for the azimuth and 45 for the altitude. 

The next layer was the land cover to determine the symbology for. This type of land cover was set in Yellowstone at altitudes of a few thousand feet with ground cover having trees and without trees. The land cover layer is a raster layer with 15 different features in the attribute table. When setting the symbology I grouped similar attributes, for example, lodgepole pine with lodgepole pine. This resulted in three groups making it easier to classify, which makes it easier to understand. The land cover layer has a transparency set at 25%, it was hard to determine what was best at first and it does take time to find out the best percentage. This was optimal because of the elevation colors. The elevation has no transparency, I chose the ESRI elevation #2. It does not have the distinctive greens and browns but still properly conveys elevation. Hypsometric tinting shows relative elevation but in this map, it was intended to be more supportive of the land cover layer. A method recommended by Cynthia Brewer is curvature to “deepen the valleys and highlight the ridges.” I tried it out but I will need to work on it more, it did not make very much sense when looking at it compared to the Hillshade function. 

Lab 2: Coordinate Systems

I chose the state of Oregon, mainly because I lived there once upon a time and loved it. It is also where I received my B.S. in Geography at Portland State University. Oregon has added new coordinate systems for the whole state that are broken down into different locations, the Oregon Coordinate Reference System (OCRS). These total 39 and are in addition to the other state coordinate systems, NAD 1983 Oregon Statewide which number 10. Choosing one ideal coordinate system was not necessarily hard but somewhat timely in going through several of them. There is not much difference between them, except the obvious, which is knowing that OCRS Ontario NAD 1983 (on Idaho border) will not work for Oregon Coast NAD 1983. The OCRS will be less appropriate because I am mapping the whole state and not focusing on one specific area.

I chose the NAD 1983 2011 Oregon Statewide Lambert (meters) projection. The other statewide projections I tried did not seem to affect the outcome of the reprojection at all. I chose meters instead of international feet because the USA Contiguous Albers Equal Area Conic projection used for the US states was in meters, keeping things consistent. However, for the scale bar I left the units as miles, converting between is easy enough to do.

 

Lab 1: Map Design & Typography

 

This week’s lab was a revisit of map design principles. These are Visual Contrast, Legibility, Figure-Ground Organization, Hierarchical Organization, and Balance. These are principles but also essential elements of creating a good, quality map. The maps and data were provided to us, the only thing needed to be done was to follow the principles to create quality maps. The map I uploaded is littered with labels but, in my opinion, is still legible. The contrast in the colors allows for balance and invites the reader to take a closer look at the map to gather more information.  I used a halo effect for the rivers and the capital city of Mexico City. This aids in the visual contrast as well.