Creating Quick and Dirty Web Maps to Visualize Your Data – Part 2

Welcome to part two of our two-part series on creating web maps! If you haven’t read part one yet, you can find it here. If you have read part one, we’re going to pick up right where we left off.

Now that we’ve imported our CSV into a web map, we can begin to play around with how the data is represented. You should be brought to the “Change Style” screen after importing your data, which presents you with a drop-down menu and three drawing styles to choose from:

Map Viewer Change Style Screen

Map Viewer Change Style Screen

Hover over each drawing style for more information, and click each one to see how they visualize your data. Don’t worry if you mess up — you can always return to this screen later. We’re going to use “Types (Unique symbols)” for this exercise because it gives us more options to fiddle with, but feel free to dive into the options for each of the other two drawing styles if you like how they represent your data. Click “select” under “Types (Unique symbols)” to apply the style, then select a few different attributes in the “Choose an attribute to show” dropdown menu to see how they each visualize your data. I’m choosing “Country” as my attribute to show simply because it gives us an even distribution of colors, but for your research data you will want to select this attribute carefully. Next, click “Options” on our drawing style and you can play with the color, shape, name, transparency, and visible range for all of your symbols. Click the three-color bar (pictured below) to change visual settings for all of your symbols at once. When you’re happy with the way your symbols look, click OK and then DONE.

Now is also good time to select your basemap, so click “Basemap” on the toolbar and select one of the options provided — I’m using “Light Gray Canvas” in my examples here.

Change all symbols icon

Click the three-color bar to change visual settings for all of your symbols at once

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Now that our data is visualized the way we want, we can do a lot of interesting things depending on what we want to communicate. As an example, let’s pretend that our IP addresses represent online access points for a survey we conducted on incarceration spending in the United States. We can add some visual insight to our data by inserting a layer from the web using “Add → Search for layers” and overlaying a relevant layer. I searched for “inmate spending” and found a tile layer created by someone at the Esri team that shows the ratio of education spending to incarceration spending per state in the US:

"Search for Layers" screen

The “Search for Layers” screen

 

 

 

 

 

 

 

 

 

 

 

 

 

You might notice in the screenshot above that there are a lot of similar search results; I’m picking the “EducationVersusIncarceration” tile layer (circled) because it loads faster than the feature layer. If you want to learn why this happens, check out Esri’s documentation on hosted feature layers.

We can add this layer to our map by clicking “Add” then “Done Adding Layers,” and voilà, our data is enriched! There are many public layers created by Esri and the ArcGIS Online community that you can search through, and even more GIS data hosted elsewhere on the web. You can use the Scholarly Commons geospatial data page if you want to search for public geographic information to supplement your research.

Now that we’re done visualizing our data, it’s time to export it for presentation. There are a few different ways that we can do this: by sharing/embedding a link, printing to a pdf/image file, or creating a presentation. If we want to create a public link so people can access our map online, click “Share” in the toolbar to generate a link (note: you have to check the “Everyone (public)” box for this link to work). If we want to download our map as a pdf or image, click “Print” and then select whether or not we want to include a legend, and we’ll be brought to a printer-friendly page showing the current extent of our map. Creating an ArcGIS Online Presentation is a third option that allows you to create something akin to a PowerPoint, but I won’t get into the details here. Go to Esri’s Creating Presentations help page for more information.

Click to enlarge the GIFs below and see how to export your map as a link and as an image/pdf:

Share web map via public link

Note: you can also embed your map in a webpage by selecting “Embed In Website” in the Share menu.

 

Saving the map as an image/pdf using the "Print" button in the toolbar. Note: if you save your map as an image using "save image as..." you will only save the map, NOT the legend.

Save your map as an image/pdf. NOTE: if you save your map as an image using “save image as…” you can only save the map, NOT the legend.

While there are a lot more tools that we can play with using our free ArcGIS Online accounts – clustering, pop-ups, bookmarks, labels, drawing styles, distance measuring – and even more tools with an organizational account – 25 different built-in analyses, directions, Living Atlas Layers – this is all that we have time for right now. Keep an eye out for future Commons Knowledge blog posts on GIS, and visit our GIS page for even more resources!

Creating Quick and Dirty Web Maps to Visualize Your Data – Part 1

Do you have a dataset that you want visualized on a map, but don’t have the time or resources to learn GIS or consult with a GIS Specialist? Don’t worry, because ArcGIS Online allows anybody to create simple web maps for free! In part one of this series you’ll learn how to prepare and import your data into a Web Map, and in part two you’ll learn how to geographically visualize that data in a few different ways. Let’s get started!

The Data

First things first, we need data to work with. Before we can start fiddling around with ArcGIS Online and web maps, we need to ensure that our data can be visualized on a map in the first place. Of course, the best candidates for geographic visualization are datasets that include location data (latitude/longitude, geographic coordinates, addresses, etc.), but in reality, most projects don’t record this information. In order to provide an example of how a dataset that doesn’t include location information can still be mapped, we’re going to work with this sample dataset that I downloaded from FigShare. It contains 1,000 rows of IP addresses, names, and emails. If you already have a dataset that contains location information, you can skip this section and go straight to “The Web Map.”

In order to turn this data into something that’s mappable, we need to read the IP addresses and output their corresponding location information. IP addresses only provide basic city-level information, but that’s not a concern for the sample map that we’ll be creating here. There are loads of free online tools that interpret latitude/longitude data from a list of IP addresses, so you can use any tool that you like – I’m using one called Bulk IP Location Lookup because it allows me to run 500 lines at a time, and I like the descriptiveness of the information it returns. I only converted 600 of the IP addresses in my dataset because the tool is pretty sluggish, and then I used the “Export to CSV” function to create a new spreadsheet. If you’re performing this exercise along with me, you’ll notice that the exported spreadsheet is missing quite a bit of information. I’m assuming that these are either fake IP addresses from our sample dataset, or the bulk lookup tool isn’t working 100% properly. Either way, we now have more than enough data to play around with in a web map.

IP Address Lookup Screencap

Bulk IP Location Lookup Tool

The Web Map

Now that our data contains location information, we’re ready to import it into a web map. In order to do this, we first need to create a free ArcGIS Online account. After you’ve done that, log in and head over to your “Content” page and click “Create → Map” to build a blank web map. You are now brought to the Map Viewer, which is where you’ll be doing most of your work. The Map Viewer is a deceptively powerful tool that lets you perform many of the common functions that you would perform on ArcGIS for Desktop. Despite its name, the Map Viewer does much more than let you view maps.

Map Viewer (No Data)

The Map Viewer

Let’s begin by importing our CSV into the Web Map: select “Add → Add Layer From File.” The pop-up lets you know that you can upload Shapefile, CSV, TXT, or GPX files, and includes some useful information about each format. Note the 1,000 item limit on CSV and TXT files – if you’re trying to upload research data that contains more than 1,000 items, you’ll want to create a Tile Layer instead. After you’ve located your CSV file, click “Import Layer” and you should see the map populate. If you get a “Warning: This file contains invalid characters…” pop-up, that’s due to the missing rows in our sample dataset – these rows are automatically excluded. Now is a good time to note that your location data can come in a variety of formats, not just latitude and longitude data. For a full list of supported formats, read Esri’s help article on CSV, TXT, and GPX files. If you have a spreadsheet that contains any of the location information formats listed in that article, you can place your data on a map!

That’s it for part one! In part two we’re going to visualize our data in a few different ways and export our map for presentation.

CITL Workshops and Statistical Consulting Fall 2017

CITL is back at it again with the statistics, survey, and data consulting services! They have a busy fall 2017, with a full schedule of workshops on the way, as well as their daily consulting hours in the Scholarly Commons.

Their workshops are as follows:

  • 9/19: R I: Getting Started with R
  • 10/17: R I: Getting Started with R
  • 9/26: R II: Inferential Statistics
  • 10/24: R II: Inferential Statistics
  • 10/3: SAS I: Getting Started with SAS
  • 10/10: SAS II: Inferential Statistics with SAS
  • 10/4: STATA I: Getting Started with Stata
  • 9/20: SPSS I: Getting Started with SPSS
  • 9/27: SPSS II: Inferential Statistics with SPSS
  • 10/11: ATLAS.ti I: Qualitative Data analysis
  • 10/12: ATLAS.ti II: Data Exploration and Analysis

Workshops are free, but participants must register beforehand. For more information about each workshop, and to register, head to the CITL Workshop Details and Resources page.

And remember that CITL is at the Scholarly Commons Monday – Friday, 10 AM – 4 PM.You can always request a consultation, or walk-in.

DIY Data Science

Data science is a special blend of statistics and programming with a focus on making complex statistical analyses more understandable and usable to users, typically through visualization. In 2012, the Harvard Business Review published the article, “Data Scientist: The Sexiest Job of the 21st Century” (Davenport, 2012), showing society’s perception of data science. While some of the excitement of 2012 has died down, data science continues on, with data scientists earning a median base salary over $100,000 (Noyes, 2016).

Here at the Scholarly Commons, we believe that having a better understanding of statistics means you are less likely to get fooled when they are deployed improperly, and will help you have a better understanding of the inner workings of data visualization and digital humanities software applications and techniques. We might not be able to make you a data scientist (though certainly please let us know if inspired by this post and you enroll in formal coursework) but we can share some resources to let you try before you buy and incorporate methods from this growing field in your own research.

As we have discussed again and again on this blog, whether you want to improve your coding, statistics, or data visualization skills, our collection has some great reads to get you started.

In particular, take a look at:

The Human Face of Big Data created by Rick Smolan and Jennifer Erwitt

  • This is a great coffee table book of data visualizations and a great flip through if you are here in the space. You will learn a little bit more about the world around you and will be inspired with creative ways to communicate your ideas in your next project.

Data Points: Visualization That Means Something by Nathan Yau

  • Nathan Yau is best known for being the man behind Flowing Data, an extensive blog of data visualizations that also offers tutorials on how to create visualizations. In this book he explains the basics of statistics and visualization.

Storytelling with Data by Cole Nussbaumer Knaflic

LibGuides to Get You Started:

And more!

There are also a lot of resources on the web to help you:

The Open Source Data Science Masters

  • This is not an accredited masters program but rather a curated collection of suggested free and low-cost print and online resources for learning the various skills needed to become a data scientist. This list was created and is maintained by Clare Corthell of Luminant Data Science Consulting
  • This list does suggest many MOOCS from universities across the country, some even available for free

Dataquest

  • This is a project-based data science course created by Vik Paruchuri, a former Foreign Service Officer turned data scientist
  • It mostly consists of a beginner Python tutorial, though it is only one of many that are out there
  • Twenty-two quests and portfolio projects are available for free, though the two premium versions offer unlimited quests, more feedback, a Slack community, and opportunities for one-on-one tutoring

David Venturi’s Data Science Masters

  • A DIY data science course, which includes a resource list, and, perhaps most importantly, includes links to reviews of data science online courses with up to date information. If you are interested in taking an online course or participating in a MOOC this is a great place to get started

Mitch Crowe Learn Data Science the Hard Way

  • Another curated list of data science learning resources, this time based on Zed Shaw’s Learn Code the Hard Way series. This list comes from Mitch Crowe, a Canadian data science

So, is data science still sexy? Let us know what you think and what resources you have used to learn data science skills in the comments!

Works Cited:

Davenport, T. H., & Patil, D. J. (2012, October 1). Data Scientist: The Sexiest Job of the 21st Century. Retrieved June 1, 2017, from https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century
Noyes, K. (2016, January 21). Why “data scientist” is this year’s hottest job. Retrieved June 1, 2017, from http://www.pcworld.com/article/3025502/why-data-scientist-is-this-years-hottest-job.html

Adventures at the Spring 2017 Library Hackathon

This year I participated in an event called HackCulture: A Hackathon for the Humanities, which was organized by the University Library. This interdisciplinary hackathon brought together participants and judges from a variety of fields.

This event is different than your average campus hackathon. For one, it’s about expanding humanities knowledge. In this event, teams of undergraduate and graduate students — typically affiliated with the iSchool in some way — spend a few weeks working on data-driven projects related to humanities research topics. This year, in celebration of the sesquicentennial of the University of Illinois at Urbana-Champaign, we looked at data about a variety of facets of university life provided by the University Archives.

This was a good experience. We got firsthand experience working with data; though my teammates and I struggled with OpenRefine and so we ended up coding data by hand. I now way too much about the majors that are available at UIUC and how many majors have only come into existence in the last thirty years. It is always cool to see how much has changed and how much has stayed the same.

The other big challenge we had was not everyone on the team had experience with design, and trying to convince folks not to fall into certain traps was tricky.

For an idea of how our group functioned, I outlined how we were feeling during the various checkpoints across the process.

Opening:

We had grand plans and great dreams and all kinds of data to work with. How young and naive we were.

Midpoint Check:

Laura was working on the Python script and sent a well-timed email about what was and wasn’t possible to get done in the time we were given. I find public speaking challenging so that was not my favorite workshop. I would say it went alright.

Final:

We prevailed and presented something that worked in public. Laura wrote a great Python script and cleaned up a lot of the data. You can even find it here. One day in the near future it will be in IDEALS as well where you can already check out projects from our fellow humanities hackers.

Key takeaways:

  • Choose your teammates wisely; try to pick a team of folks you’ve worked with in advance. Working with a mix of new and not-so-new people in a short time frame is hard.
  • Talk to your potential client base! This was definitely something we should have done more of.
  • Go to workshops and ask for help. I wish we had asked for more help.
  • Practicing your presentation in advance as well as usability testing is key. Yes, using the actual Usability Lab at Scholarly Commons is ideal but at the very least take time to make sure the instructions for using what you created are accurate. It’s amazing what steps you will leave off when you have used an app more than twice. Similarly make sure that you can run your program and another program at the same time because if you can’t chances are it means you might crash someone’s browser when they use it.

Overall, if you get a chance to participate in a library hackathon, go for it, it’s a great way to do a cool project and get more experience working with data!

Learn Python Summer 2017

Are you sitting around thinking to yourself, golly, the bloggers at Commons Knowledge have not tried to convince me to learn Python in a few weeks, what’s going on over there? Well, no worries! We’re back with another post going over the reasons why you should learn Python. And to answer your next question no, the constant Python promotion isn’t us taking orders from some sinister serpentine society. We just really like playing with Python and coding here at the Scholarly Commons.

Why should I learn Python?

Python is a coding language with many applications for data science, bioinformatics, digital humanities, GIS, and even video games! Python is a great way to get started with coding and beef up your resume. It’s also considered one of the easier coding languages to learn and whether or not you are a student in LIS 452, we have resources here for you! And if you need help you can always email the Scholarly Commons with questions!

Where can I get started at Scholarly Commons?

We have a small section of great books aimed at new coders and those working on specific projects here in the space and online through the library catalog. Along with the classic Think Python book, some highlights include:

Python Crash Course: A Hands on Project-Based Introduction to Programming

Python Crash Course is an introductory textbook for Python, which goes over programming concepts and is full of examples and practice exercises. One unique feature of this book is that it also includes three multi-step longer projects: a game, a data visualization, and a web app, which you can follow for further practice. One nice thing is that with these instructions available you have something to base your own long term Python projects on, whether for your research or a course. Don’t forget to check out the updates to the book at at their website.

Automate Boring Stuff with Python: Practical Programming for Total Beginners

Automate Boring Stuff with Python is a solid introduction to Python with lots of examples. The target audience is non-programmers who plan to stay non-programmers; the author aims to provide the minimum amount of information necessary so that users can ultimately use Python for useful tasks, such as batch organizing files. It is still a lot of information and I feel some of the visual metaphors are more confusing than helpful. Of course, having a programming background helps, despite the premise of the book.

This book can also be found online for free on this website.

Learn Python the Hard Way: A Very Simple Introduction to the Terrifyingly Beautiful World of Computers and Code

Although focused on Python 2, this is a book about teaching programming skills to newbie coders. Although the author does not specifically use this term this book is based on what is known in psychology as deliberate practice or “the hard way,” which is described in Cal Newport’s blog post “The Grandmaster in the Corner Office” (Newport, 2010).  And Learn Python the Hard Way certainly lives up to the title. Even the basic command line instructions prove difficult. But based on my own learning experiences with deliberate practice, if you follow the instructions I imagine you will have a solid understanding of Python, programming, and from what I’ve read in the book definitely some of your more techie friends’ programming jokes.

Online Resources

If the command line makes you scared or if you want to get started right away, definitely check out PythonAnywhere, which offers a basic plan that allows users to create and run Python programs in their browser. If PythonAnywhere isn’t your speed, check out this article, which lists the 45 best places to learn to code online.

Interested in joining an online Python learning group this summer?

Definitely check out, Advent of Python, an online Python co-learning group through The Digital Humanities Slack. It started Tuesday May 30 with introductions, and every week  there will be Python puzzles for you to help you develop your skills. IT IS NOT TOO LATE TO JOIN! The first check-in and puzzle solutions will be June 6. The solutions and check-ins are going to be every Tuesday, except the Fourth of July — that meeting will be on Wednesday, July 5.  There is a Slack, a Google Doc, and subreddits.

Living in Champaign-Urbana?

Be sure to check out Py-CU a Maker/Hacker group in Urbana welcome to coders with all levels of experience with the next meeting on June 3rd. And obligatory heads up, the Urbana Makerspace is pretty much located in Narnia.

Question for the comments, how did you learn to code? What websites, books and resources do you recommend for the newbie coder? 

Works Cited:

Newport, C. (2010, January 6). The Grandmaster in the Corner Office: What the Study of Chess Experts Teaches Us about Building a Remarkable Life. Retrieved May 30, 2017, from http://calnewport.com/blog/2010/01/06/the-grandmaster-in-the-corner-office-what-the-study-of-chess-experts-teaches-us-about-building-a-remarkable-life/

Telling Your Story With StoryMap JS

Earlier on the blog, we talked about ways to create story maps so we’re following that up with a tutorial for one of the options on there, StoryMapJS.

StoryMapJS is a web-hosted program that lets you create interactive maps by adding Google slides and images onto a map layout from OpenStreetMap. More advanced users can overlay their own map using the Gigapixel mode. But today we are keeping it simple and creating a map giving an around the world tour of cookies.

Getting Started:

  1. Click “Make a Story Map” and sign in with your Google account and come up with a snazzy title!

Name your map

2. If you come up with an even snazzier title you can change it by clicking “My Maps” and choosing the map and the settings option.

arrow pointing to My Maps in top left corner and arrow pointint to gear icon on

Finally, remember to save your work. StoryMapJS can get a little confusing and better to be safe than sorry.

Creating Your Title Slide:

Slide editing mode StoryMapJS

This is the title slide editing page but it’s not that different from the page to create your slides for the map. You can write a title — I’ve chosen “Cookies, Biscuits and Other Circular Treats” — in the “Headline” section and either write or paste your writing on the right side. You can  use the “Media” side to upload an image, but you also change the background of the slide itself by clicking Slide Background and uploading an image into StoryMapJS. To see how your map is coming so far you can check it out in the Preview section as seen here:

cookies in a plastic bag as background image of title page

If you’ve ever wondered who makes sure that cookies leftover from library events get eaten, you can thank the brave and dedicated graduate assistants of Scholarly Commons for providing this service.

Don’t want it to say “Start Exploring” for your title page instructions? Don’t worry — you can change that, too! Click Options and check “Yes” for “Call to Action” and add a custom call. This is also where you can change the type of map (such as creating a Gigapixel) and other important features.

Options in StoryMap JS

Creating Your First Map Point 

Click “Add Slide”

We’re going to start this journey off knowing that various versions of cookies originated in the ancient world, with National Geographic, a trusted source, saying the first cookies appeared in Persia during the 7th century (B. Patrick, & J. Thompson, 2009). Since there’s no mural, painting or visual documentation of “The First Cookie” (like there should be for such a historically significant event), I am not using an image here, making this similar to a title slide. However, this time we’re using a location!  If you’re not sure exactly where something happened simply search the country and click on the correct date.

Adding a location on the map

Creating A Map Point With Images and More!

Unlike many types of cookies, we know exactly where chocolate chip cookies were invented and we can even look up coordinates in Google Maps. Specifically, they were invented by Ruth Graves Wakefield and were originally sold in Whitman, Massachusetts at the Toll House Inn, which has since burned down (Michaud, 2013). However, we have an address! Simply search the address on the map and it will place a point on the map approximating that location.

Typing in Whitman MA address in StoryMapJS

Upload media into your slide:

For this slide, I will be using a picture of a chocolate chip cookie in the GA office that I took myself. Since I took the picture myself I am the copyright holder, please take at least a minute to think about copyright law especially if you are using your StoryMap as part of, or in lieu of, an article. Go to the “Media” section and simply paste a link to your photo from your preferred photo cloud storage (make sure it you have the share settings so that it is public) or the source hosting the photo or upload an image from your computer. You can write where the photo comes from in the next box and can add a caption below that.

Uploaded media demo

Sharing Your Work:

Alright, so you’ve told your story through slides and map points. You’ve moved your slides into the order you want by dragging and dropping them on the side bar. You’ve edited your text, attributed your photos to their sources, and are ready to go. Simply hit “Share” in the top right corner and choose whether you want to embed your map on your website or share the link itself. A word of warning, these sites use aspects through Google and may have issues with link rot so make sure to back up your text and images elsewhere as well!

For Further GIS Assistance

If you’re looking for a more in-depth approach to GIS, please contact James Whitacre, our GIS specialist.

Works Cited:
Drop Cookies – Oxford Reference. 2017. 1st ed. Oxford University Press. Accessed April 17. doi:10.1093/acref/9780199313396.013.0166.

Michaud, Jon. 2013. “Sweet Morsels: A History of the Chocolate-Chip Cookie.” The New Yorker. The New Yorker. December 19. http://www.newyorker.com/culture/culture-desk/sweet-morsels-a-history-of-the-chocolate-chip-cookie.

Olver, Lynne. 2017. “Food Timeline: Food History Research Service.” Accessed April 17. http://www.foodtimeline.org/index.html.

Stradley, Linda. 2015. “History Of Cookies, Whats Cooking America.” What’s Cooking America. June 28. https://whatscookingamerica.net/History/CookieHistory.htm.

Sweets. (2009). In B. Patrick, & J. Thompson, An Uncommon History of Common Things. Washington, DC: National Geographic Society. Retrieved from http://proxy2.library.illinois.edu/login?url=http://search.credoreference.com/content/entry/ngeouc/sweets/0?institutionId=386

Toll house cookie. (2014). In J. F. Mariani, The Encyclopedia of American Food and Drink (2nd ed.). London, UK: Bloomsbury. Retrieved from http://proxy2.library.illinois.edu/login?url=http://search.credoreference.com/content/entry/bloomfood/toll_house_cookie/0?institutionId=386

Register Today for ICPSR’s Summer Program in Quantitative Methods of Social Research

The ICPSR logo.

The Inter-university Consortium for Political and Social Research (ICPSR) is once again offering its summer workshops for researchers! Workshops range from Rational Choice Theories of Politics and Society to Survival Analysis, Event History Modeling, and Duration Analysis. There are so many fantastic choices across the country that we can hardly decide which we’d want to go to the most!

This is what the ICPSR website describes the workshops as:

Since 1963, the Inter-university Consortium for Political and Social Research (ICPSR) has offered the ICPSR Summer Program in Quantitative Methods of Social Research as a complement to its data services. The ICPSR Summer Program provides rigorous, hands-on training in statistical techniques, research methodologies, and data analysis. ICPSR Summer Program curses emphasize the integration of methodological strategies with the theoretical and practical concerns that arise in research on substantive issues. The Summer Program’s broad curriculum is designed to fulfill the needs of researchers throughout their careers. Participants in each year’s Summer Program generally represent about 30 different disciplines from more than 350 colleges, universities, and organizations around the world. Because of the premier quality of instruction and unparalleled opportunities for networking, the ICPSR Summer Program is internationally recognized as the leader for training in research methodologies and technologies used across the social, behavioral, and medical sciences.

Courses are available in 4-week sessions (June 26 – July 21, 2017 and July 24 – August 18, 2017) as well as shorter workshops lasting 3-to-5 days (beginning May 8). More details about the courses can be found here.

Details about registration deadlines, fees, and other important information can be found here.

If you want some help figuring out which workshops are most appropriate for you or just want to chat about the exciting offerings, come on over to the Scholarly Commons, where our social science experts can give you a hand!

Scholarly Smackdown: StoryMap JS vs. Story Maps

In today’s very spatial Scholarly Smackdown post we are covering two popular mapping visualization products, Story Maps and StoryMap JS.Yes they both have “story” and “map” in the name and they both let you create interactive multimedia maps without needing a server. However, they are different products!

StoryMap JS

StoryMap JS, from the Knight Lab at Northwestern, is a simple tool for creating interactive maps and timelines for journalists and historians with limited technical experience.

One  example of a project on StoryMap JS is “Hockey, hip-hop, and other Green Line highlights” by Andy Sturdevant for the Minneapolis Post, which connects the stops of the Green Line train to historical and cultural sites of St. Paul and Minneapolis Minnesota.

StoryMap JS uses Google products and map software from OpenStreetMap.

Using the StoryMap JS editor, you create slides with uploaded or linked media within their template. You then search the map and select a location and the slide will connect with the selected point. You can embed your finished map into your website, but Google-based links can deteriorate over time! So save copies of all your files!

More advanced users will enjoy the Gigapixel mode which allows users to create exhibits around an uploaded image or a historic map.

Story Maps

Story maps is a custom map-based exhibit tool based on ArcGIS online.

My favorite example of a project on Story Maps is The Great New Zealand Road Trip by Andrew Douglas-Clifford, which makes me want to drop everything and go to New Zealand (and learn to drive). But honestly, I can spend all day looking at the different examples in the Story Maps Gallery.

Story Maps offers a greater number of ways to display stories than StoryMap JS, especially in the paid version. The paid version even includes a crowdsourced Story Map where you can incorporate content from respondents, such as their 2016 GIS Day Events map.

With a free non-commercial public ArcGIS Online account you can create a variety of types of maps. Although it does not appear there is to overlay a historical map, there is a comparison tool which could be used to show changes over time. In the free edition of this software you have to use images hosted elsewhere, such as in Google Photos. Story Maps are created through their wizard where you add links to photos/videos, followed by information about these objects, and then search and add the location. It is very easy to use and almost as easy as StoryMap JS. However, since this is a proprietary software there are limits to what you can do with the free account and perhaps worries about pricing and accessing materials at a later date.

Overall, can’t really say there’s a clear winner. If you need to tell a story with a map, both software do a fine job, StoryMap JS is in my totally unscientific opinion slightly easier to use, but we have workshops for Story Maps here at Scholarly Commons!  Either way you will be fine even with limited technical or map making experience.

If you are interested in learning more about data visualization, ArcGIS Story Maps, or geopatial data in general, check out these upcoming workshops here at Scholarly Commons, or contact our GIS expert, James Whitacre!

Spotlight: Shanti Interactive

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If you’re looking for tools that will help you create web-based visualizations, images or maps, Shanti Interactive may have exactly what you need. Shanti Interactive, a suite of tools made available from the University of Virginia’s Sciences, Humanities & Arts Network of Technological Initiatives (SHANTI), is free to use and a helpful resource for individuals seeking to show their data visually.

The Shanti Interactive suite includes five programs: Qmedia, SHIVA, MapScholar, VisualEyes, and VisualEyes 5. Qmedia creates instructional and scholarly videos. SHIVA creates “data-driven visualizations,” such as charts, graphs, maps, image montages and timelines. MapScholar creates geospatial visualizations while VisualEyes — arguably the most well-known tool from the suite — creates historic visualizations by weaving images, maps, charts, video and data into online exhibits. While we could write an entire post on each member of the suite (and maybe someday we will), I will quickly go over some of the main functions of the Shanti Interactive suite.

Qmedia

A screenshot of QMedia's demo video.

A screenshot of Qmedia’s live demo.

Qmedia creates an interactive video experience. The screen is broken up into various, customizable boxes, which the user can then interact with. In its own words, Qmedia “delineraizes” the video, allowing it to be scanned. Tools in Qmedia include table of contents, clickable, searchable transcripts, graphical concept maps, images, live maps, interactive visualizations, web apps and websites! While this list can be a little overwhelming, you can see the incredible results with Qmedia’s live demo.

SHIVA

SHIVA's timeline capability.

SHIVA’s timeline capability.

Think of SHIVA as a multi-faceted data visualization tool. It can create charts, maps, timelines, videos, images, graphs, subway maps, word clouds as well as plain text. SHIVA works with open source and open access web tools, such as Google’s Visualization Toolkit and Maps, YouTube, and Flickr. When a user inputs data, they do so through Google Docs. One fantastic feature in SHIVA is the ability to add on layers of annotations onto your data. For more on SHIVA’s capabilities and partners, see the SHIVA about page.

MapScholar

MapScholar is a great tool for creating what they call digital “atlases,” allowing scholars to use historic maps to compare and contrast how different areas have been depicted by mapmakers through time. For example, here is the base map on the eastern United States:

And here is that map overlayed with a Native American map from 1721:

VisualEyes and VisualEyes 5

VisualEyes is a multi-faceted online exhibit toolkit, which helps create interactive websites to display data. There are two versions: Flash-based VisualEyes, and HTML5-based VisualEyes 5, which is recommended. In many ways, VisualEyes is a combination of the rest of the suite’s tools, providing a platform for some incredible integration of sources. VisualEyes’ current example is a tour of Thomas Jefferson’s life (as the program was created at the University of Virginia), and worth a look if you’re interested in the program’s capabilities! It is far more interactive than one screengrab can communicate.

This project includes historic and modern maps, a timeline, and text, which all work together to create the story of Thomas Jefferson’s life.

Shanti Interactive includes diverse, free resources that can transform the way that you present your data to the world. If you need help getting started, or want to brainstorm ideas, stop by the Scholarly Commons and we’ll have someone ready to chat!