Portfolio

This portfolio presents most of the projects completed during coursework for the Graduate Certificate in GIS for Archaeology at the University of West Florida.  These courses included Cartography, Introduction to GIS, Remote Sensing, GIS for Archaeology, and Special Topics in Archaeology.  It also includes a project begun during the final semester, Spring 2017, when I worked as an intern at the New Bedford Whaling Museum in Massachusetts.  I created an Esri Story Tour based on a logbook of a voyage taken by Horatio Hathaway from New York to China and back in 1850-1851. This project is ongoing.

A link to my portfolio can be found here, and a short video explaining my favorite project can be found here.  The Story Tour, although incomplete, can be viewed here.

Creating this portfolio required me to reflect back on how far I've come and how much I've learned over the past two years.  The maps show steady progress in my GIS skills and abilities, and the variety of topics and purposes for which these maps were made is quite impressive, looking back.  I feel like I have accomplished what I set out to learn and more.  I also realize where my strengths and weaknesses exist as they relate to GIS.  Overall, the process of creating the portfolio has left me feeling proud and hopeful that I can get meaningful, satisfying work in this field in the future.

Final Project

 



In our final project for this course we prepared a map for use in a newspaper article about high school seniors and college entrance scores. Two sets of data had to be presented on one map: test participation rates and average scores.


After preparing a basemap showing the United States and projecting it to an Albers Equal Area projection, since the statistics related to area, the data was tabulated in Excel. Then choices had to be made about how to present set of statistics. 


The data showing the percent of graduates tested by state is reflected by graduated symbols in 5 classes using the Natural Breaks classification method. The graduated symbols easily convey the differences visually, and 5 classes allows for a reasonable amount of variation per class. Average scores were displayed using a sequential color scheme, also with 5 classes using Natural Breaks classification. The graduated colors allow patterns to be easily discerned, and the classification method considers the distribution of data along the number line. Grouping similar data values together was desirable.
Although I am quite pleased with the results, I wish I had time to tweak a few more things.  However, yesterday I turned on my 4-month-old computer and found a blue screen.  It will be a week before it is fixed, or for a new computer to arrive. Here is what I could do with my son's laptop, with its tiny screen and missing "s" key.

This has been a fun, interesting, and challenging course.  I look forward to putting what I have learned to use, and to practicing the skills I have learned so far.  Clearly, we have just scratched the surface of what is possible.  Thank you, teachers and fellow classmates!

KML files and Google Earth

Google Earth is amazing.  Like many people, I've spent quite a lot of time checking out places I've been and places I'd like to go.  This lab taught me that it's also an amazing tool for displaying data I choose to add.

This week we learned how to convert files from ArcMap to Google Earth format (KMZ files).  After converting the Dot Density map of S. Florida from last week's lab, we created a tour of 7 locations within that area, including Tampa Bay (above), as well as Miami, Ft. Lauderdale, Tampa and St. Petersburg.  As we added these stops to the tour, we used Google Earth tools to zoom in and around, choosing an interesting perspective for each.

3D Mapping: Buildings in Boston

After completing the Esri Training Course "3D Visualization Techniques", we were asked to convert 2D features to 3D features using data gathered from LiDAR.

First, the Esri course taught us the vocabulary of 3D - multipatches, Z-values, terrain data sets, TINs, rendering, and extrusion, for example.  After examining the elements of 3D data, we learned how to define base heights, then set about practicing these skills using a map of Crater Lake National Park.  Base height was set for various data types, including an elevation raster, a 3D feature class, a line feature class, a point feature class, and a raster vegetation layer.  Next we learned how to enhance 3D views using Vertical Exaggeration and Illumination.  In the last section we learned how to generate 3D Objects from 2D Objects.  In the exercise, we extruded Buildings and Wells, then added an aerial photo draped over an elevation TIN.  Another step was Extruding Parcel Values so that these differences would be reflected in the height and color of the buildings.

This course provided the skills necessary to complete the final part of the lab, in which a map with a 2D shapefile of buildings in Boston was converted into the 3D map above.  After the two layers were added (the raster base layer and the shapefile buildings layer), the base height was calculated.  Since the raster layer had the elevation information, a new layer was created with random points for each building which had the elevation information added to them. The Mean Z Value was calculated in the SamplePoints layer, then joined to the buildings layer. Once the 2D data had "Z" values, it was possible to create a 3D map by extruding the building features.  For people without access to ArcGIS, these maps can be saved as a .kml file  and can be viewed instead using Google Earth, for example. 

Maps created in 3D are quite attractive when done well.  The example map of Napoleon's Moscow campaign is a fantastic example of how easy and fun it is to view and interpret one of these maps when it is done well.  Being able to fly around, zoom in, explore in a virtual world is exciting.  Still, at this point the ability of most people to access these maps is limited, and there seems to be the potential for bad data and poorly created maps, as there are in 2D.  That said, it seems clear to me that the abilities of a 3D map to convey information clearly, quickly, and accurately, are far above those of 2D maps.

Dot Mapping South Florida's Population

 

 

 

Dot density mapping uses raw total data to display distribution patterns.  After carefully choosing an appropriate size and value, dots are distributed on the map in such a way that they most accurately reflect actual distribution on the ground.  Limitations can be placed on the data so that dots are not displayed in areas where they wouldn't actually be.  For example, population dots in the map above are not placed in lakes, ponds, marshes, etc.  Instead they are confined to areas selected specifically to match the type of data (population) that is the focus of the map - in this case, urban areas are an attribute that relates to population density and distribution, so using a mask, the dots were only included in urban areas. 

 

Dot size and value are also important aspects of making a map of this type.  The goal is to display the data in a way that enhances understanding of the data in an accurate way.  When the dots are too large or too small, patterns can be missed or misinterpreted.

 

This method is especially suited to some types of data.  The text illustrates this with maps of wheat harvests, and the map above with population data.  I have also seen it used successfully in archaeological contexts, and I hope to learn more about this in future courses.

Flow Line Mapping

Lab 9: Map of Immigration to the U.S. using Flow Lines

 

 

This week we were asked to make a map in Corel Draw.  The focus of the lab was Flow Lines.  After choosing one of two base maps already made for us in ArcMAP,  I added curved lines using the Bezier tool, then modified them so that they fit the main criteria - not interfering with the features underneath.  To do this, I made them transparent.  I combined this effect with line colors to match each region.  The exception was Asia, which has a grey line  - yellow seemed too light and too similar to the green of Oceania's line.  Arrows were added to one end of the flow line and resized to fit. 

 

In Excel, a formula was used to find the proportional width of each flow line based on the number of immigrants coming from each region.  This way the flow lines visually represent not only direction of movement, but the number of people moving.

 

The last step was to create a legend and all the other usual map elements, and to experiment with some of the stylistic effects available in Corel.  Options included changing color schemes for the regions and U.S. States, making the flow lines transparent, and using the Drop Shadow, Extrude, and Bevel tools.  I chose to make my lines transparent and to use Drop Shadow on the map title. 

 

I think the most useful part of this lab was learning how to use the Bezier tool to make curved lines.  It isn't easy, in my opinion, and it took a lot of trial and error to get the 6 on my map to come out the way they did, but it will be a good skill to develop as curved lines are so common on maps.  I also struggled at first making the choropleth map legend, and at the end I had learned how to use the eye dropper to match fill color, and had worked with the rotate feature enough to get the hang of it.

 

All in all, it was a productive week and a fun, challenging lab.

Precipitation Maps

The isarithmic precipitation maps we learned how to produce this week are familiar to most people, and it was interesting to see how one is put together after having looked at them so many times on the nightly weather report. 

We worked with raster data from the USDA that was interpolated using the PRISM Interpolation Method devised by people at Oregon State University.  Using the Spatial Analyst Tool we created two maps showing annual precipitation in Washington state.  One map shows the data as a continuous spread of color in a spectrum.  The transition between one tone and another isn't perceptible.  The other map displays the data with hypsometric tinting which creates a stepped appearance to the color spectrum.  Hillshade Effect was added to both maps to display the map in relief. This week we also learned how to make contour lines, using the Spatial Analyst tool "Contour List" where the values for each index contour line were entered, and then had a choice of which map should have contours added to it.

 

 



This was a fun lab, and I can see how useful this technique will be going forward.

Europe: Men, Women, Wine

 

 

This week our task involved using what we learned last week about classification methods, then choosing an appropriate classification for each of three maps of Europe to show population density, males as a percentage of the population, and females as a percentage of the population.  We then added data on wine consumption per capita.

 

This was done in ArcMap.  After creating three data frames and adding the European Population data provided in the lab, I selected the appropriate Field Value, chose "Graduated Colors", and made layers for the assigned map topics.  Each had to be classified, and an appropriate scheme had to be chosen. 

 

Sequential color ramps in red and green classify male and female population percentages into 5 classes.  The contrasting colors work well next to each other, and 5 classes make the variation of color within the ramp easy to interpret. I chose a grey color ramp for the population density, and created 6 classes - the dark tones really stand out, which becomes even more important when the Wine Consumption symbols were added.  Proportional symbols were best suited to showing wine consumption per capita, creating an overall visual pattern that is easy to interpret.

 

Although I didn't have time to experiment with it this time, I look forward to making some customized color ramps sometime thanks to some tips from Lucas.

Lab 6: Four Data Classification Methods

 

 

 

This is an example of how different classification methods produce different results with the same data.  Here we see four possible ways of looking at the percentage of people over 65 in Escambia County, Florida.  While there are similarities among them all, each has its own spin on the information.

 

The Lab required that we create data frames of the same information, presented using the Natural Breaks, Equal Interval, Quantile, and Standard Deviation classification methods, then choose which one best represented the data and explain why.

 

To begin, I added the Escambia County shapefile to the first dataframe, then created 3 more dataframes and dragged the same shapefile into each one.  I renamed them according to each of the 4 classification methods.  Then I went to each layer's properties, chose the Symbology tab, and chose the Field Value PCT_65ABV to get the data about the percentage of population over 65.  Using Graduated Colors, I chose a ramp that would suit the data, ranging from light to dark.   I then selected one of the Classification types we were assigned, making sure there were 5 classes.  When the labels were created I formatted them to 2 decimal places. 

 

Once I had all the required data frames, I switched to Layout view and "owned my map" by including the usual required elements, and using the design concepts we have learned over the past few weeks.  At this point I realized the color ramp I had chosen for Standard Deviation didn't match the data - it went from light to dark, not reflecting the increased values as you move away from the mean.  I went back and chose a different one that has a light color for the mean, and darker colors at either limit.

 

After looking at all 4 classification methods, I decided the Standard Deviation method suited the data best.  The color scheme makes it clear where there is an average percentage of people over 65, and where there are far more and far fewer.  

Spatial Statistics Lab

The lab for Week 5 took us into the world of spatial statistics.  The Esri course we completed introduced topics that focused on looking at spatial relationships within a dataset, identifying patterns, and making choices about which analysis tools to use based on trends in the data.  Exploring the data is the first step.

Using a map of Europe showing weather monitoring stations, with temperature as the data to be analyzed, I looked at spatial relationships using Geostatistical Analyst tools.  I found the mean and median values, noticing if they were located close to one another.  By examining patterns in a histogram, a normal QQ plot, a Voronoi map, and a semivariogram cloud, I found outliers in the data.  However, overall there was a normal distribution of values. The last part of the process before selecting an appropriate analysis technique is to look at spatial trends.

As a result of exploring the data in these ways, the most appropriate choice for analysis seemed to be geostatistical interpolation.  This was a good fit because the data is normally distributed, stationary, autocorrelated, and has no local trends.  If a weather station needed to predict future temperatures, this technique would be useful.

This lesson probably only showed the tip of the iceberg in terms of what ArcGIS is capable of, and it has gotten me thinking about how it could be used for a particular archaeology project I'd like to do in the future.

Week 4 Lab: Typography

      Cartography is definitely an art, and this week we focused on one aspect, typography.  With a base map of a portion of the Florida Keys centered around Marathon, we were asked to locate and label a list of geographic places, then complete the map with the usual required elements.  At the same time, we were asked to incorporate what we had learned about typography from Chapter 11 in the text, and from the lecture and tutorial material provided.
      After giving my map a horizontal orientation to match the lateral spread of the Keys, I went online and found all the required places, then made rough labels for each.  I focused on one category at a time, starting with the Keys, then cities, then the three specialized places (airport, park, and country club), and finally the bodies of water.  With each step, I tried to think of how to use font, size and positioning to make clear labels.  The airport, park and country club each got appropriate standard symbols, while the cities had traditional circle symbols.  I tried to be careful not to overlay labels on top of land.  When necessary, I used short lines to connect labels to their locations on the map.  The most challenging part of labeling was learning to get text to follow a path.  It does look nice when the water label follows the contours of the coastline.
      The last several hours were spent finding an appropriate map for the insert.  I kept on finding .gifs or  bitmaps, and all the .jpgs I found were too small so the pixels were enormous and the map was fuzzy.  Finally I got one, and once settled in the corner, it looked good, balanced against the Legend in the diagonal corner.
      I chose a dark blue background, and made the Keys stand out in a light grey-pinkish color.  Learning how to use fill was not easy at first, but I got the hang of it finally. 
      This week's lab was good practice at using CorelDraw, and at using graphic design elements to produce a basic, useful map.
      Seeing the map here, I guess I needed another border around the page.  This isn't what it looked like when I saw it in Paint.  There is room for improvement, but I'm still proud of my efforts, given my extremely limited previous experience in graphic design.

Cartographic Design Lab: Ward 7 Schools

This week we produced a map showing Elementary, Middle and High Schools in Ward 7 of Washington, D.C.   It included an insert map showing the location of Ward 7 within the city.  The focus of the lab was to use key design elements when producing a map.

I tried using Gestalt's Principles of visual hierarchy, contrast, figure-ground relationship, and balance.  By using larger, bright red symbols, schools become the emphasis. Ward 7 is easy to see because the light color shows up well against both grey and blue.  Streets are less important, so they have a light grey color and thin lines. I balanced the map by placing three similar-sized boxes containing the inset map, legend, and title in a roughly triangular formation.  Less important elements such as the north arrow, scale, data source and author/date are in smaller font with no borders.

It is easy to spend lots of time playing around with design elements - tweaking could be nearly endless if there weren't so many other things to do.  

CorelDraw Lab

Week 2: Using CorelDraw to enhance a map of Florida

This week we exported a map from ArcMap to Corel, then learned how to edit and create elements on the map.  These included essentials such as the title, north arrow, neatline, data source, cartographer, and date created, and also several images imported from the web.  Layout and design requirements included rearranging these items and adjusting colors and styling to create an appealing map.

Module 1: Map Critique

Module 1: Map Critique    

The task for Module 1 involved evaluating two maps, one well-designed and the other not, using criteria learned in the lesson.   The two I chose share a similar base map shape, making it obvious to me that one communicated information clearly while the other was a confusing mess.

The map "North American English Dialects, Based on Pronunciation Patterns" demonstrates a number of bad qualities of map design.  Most notable among these are a lack of clarity, precision and efficiency, and an abundance of Map Crap.  Clearly the author had a large amount of data to convey, but putting it all on one map was a mistake.  There are so many textures, boundary lines, and labels in various sizes and colors, that no patterns emerge.  An excessive amount of text is written next to each label, and there are too many labels on the map itself.  Furthermore, the title is not clearly displayed. It takes a long time to read all the information, so although the map conveys a great number of ideas, it does not do so in an efficient way with the least ink in the shortest space.  It's cluttered and confusing.

The map "Largest Ancestry:2000" is an example of a well-designed map.   The title is large and clear, insets are used to good effect, the main map is uncluttered.  Sources are clearly indicated, scale is easy to find, and extra space to the east of Florida is used effectively with the inclusion of a small amount of informative and useful text.  Text size is appropriate throughout, and the colors chosen for each ancestry group stand out well in contrast to one another.

Hello everyone,

I live in midcoast Maine with my two boys, ages 9 and 12, and my three cats.  It's a frosty, snowy morning here, perfect for sitting down at my desk and getting started on a new educational adventure.  

After teaching high school English and Social Studies for 15 years, I spent a couple of years working at my family's business, Hermit Island Campground, before beginning an MA in Archaeology and Heritage with the University of Leicester.  I completed it in 2011. Their distance learning program was excellent, and much of what I learned about writing, study habits, self-motivation, and working to deadlines will be helpful as I work to complete this online certificate program.  There are several daunting aspects of what lies ahead - the computer/math side of things will be my biggest challenge, I think.  On the positive side, I love using, making and looking at maps, so I have no doubt that I'll enjoy the process and will benefit from it in many ways.  I'm also excited to learn about graphic design.

This GIS certificate program will give me essential new skills, tools and knowledge I need to be a competent archaeologist.  I hope to work in New England where I have always lived, and perhaps also in SE Asia, where I have enjoyed doing research over the past decade.  

I look forward to this course, and to working with this group.   Enjoy the tour - http://bit.ly/1  

Cheers!

Chris