Speaking of simplicity, the easiest tool to create a map is definitely BatchGo. Its Geocoding strategies is very efficient and smart, although there are no commas in my original address data and the zip codes are missing 0’s, BatchGo still did the job and recognized the addresses in less than one second. Google Fusion table caused less confusion to me than MapMe did. Some alteration on the address need to be done in order to be recognized, since FusionTable can only grab address data from one block(while BatchGo can grab address data from multiple columns at a time), and the address must correctly match the format. MapMe had a even more strict restriction on formatting the data. It need the user to insert data in certain models, or it won’t recognize it. But if the data is formatted correctly, MapMe geocoded without trouble.
Speaking of features, I personally love MapMe over the other two tools. Mapme allowed me to insert multiple images associated with the location, and can let the user easily view locations one by one. Both MapMe and BatchGo sorted the locations by type automatically, and allowed me to change the colors and icons of the location by groups. FusionTable did a weaker job on sorting. However, BatchGo limits the information that I can change on each location(only the basic address and links, I cannot find a way to add descriptions on individual location). FusionTable and MapMe all provided flexible info-displaying features.
The portability of the three are almost the same. They can all be easily embedded by iframe, and all the three tools provided html code and links.
All 3 mapping tools have their pros and cons. However, the easiest tool to make a map is definitely BatchGo- you basically copy your data and paste it and it literally convert the data into map for you. The second easiest mapping tool in my opinion is MapMe. MapMe is very user friendly due to its interactive font and easy instructions. Also, there is someone from their website that would chat with you to guide you on how to use it. The hardest one for me is Google Fusion Table. Google Fusion can be really useful if you know how to make use of their features, but it is very confusing because it has a lot of features. It’s not like you can hover your mouse over to the features and the instructions would pop up. You have to google if you are stuck.
In my opinion, MapMe is my favorite because it is very user friendly. It has a good amount of features to create the map you need it. One of my favorite features is that you can customize your location with different colors easily. Another feature that is very cool is that you can give a short description for each location. Unlike Google fusion and BatchGo, their maps look plain. However, I prefer Google Fusion if I have a lot of data and it can be easily sorted if it’s used correctly. I would use BatchMe if I just want a quick and basic map.
Overall, I thought that Mapme was the most detailed Point Map system to use. It gives you the option to manually add each ‘pin’ or import a spreadsheet and automatically track it. It also gives the user much more control over the information that is shown on each pin. Pins can be customized by color and logo, while photos, and websites can be added to the description.
On the other hand, BatchGeo was the least customizable interface to use. There is no way to change the starting map location, nor the pin color or size. However it was the fastest and easiest map system to use. All the user has to do is copy and paste their spreadsheet to a text box on BatchGeo’s website, and BatchGeo does the rest of the mapping.
Google Fusion Tables is somewhere in between. Excel and Google Sheets can be imported and edited in Fusion Tables, the pin size and color can be customized, and the starting map location can also be set. Fusion Tables is not as fast as BatchGeo, nor as customizable as Mapme, but it does have a hybrid advantages from each interface. It also does offer a ‘heat map’ where a user can show the distance between different restaurant locations. This could be used in my project to show food deserts in between grocery stores in Hartford.
Trinity College recently invited faculty to respond to a call for proposals to create massive open online courses (MOOCs) for our edX platform. Here’s a copy of my submission.
Data Visualization For All Proposal to Trinity College edX by Jack Dougherty, Professor of Educational Studies last updated March 1, 2016
1. Course Title and Description: I propose to create Data Visualization For All, an introductory massive open online course for the Trinity edX platform. A growing number of free software tools now allow ordinary users to transform data tables into interactive charts and maps. By learning how to design and embed meaningful visualizations on our own websites, the breadth and depth of our communication becomes more powerful than conventional paper. This course introduces design principles for working with easy-to-learn tools, including step-by-step tutorials, short videos, and real-world examples. The learning objective is best summarized by the course tagline: “Tell your story and show it with data.”
This online course expands on my Data Visualization internship seminar, FORG 210-04, which I first taught in Spring 2014 and am currently teaching in Spring 2016. Trinity students from various majors (including anthropology, computer science, economics, educational studies, environmental science, mathematics, psychology, public policy, and sociology) meet with me for one hour per week and learn to co-create data visualizations with their Hartford community partner organizations, in fields such as public health, economic development, food distribution, policy journalism, and education reform. Currently, we are videorecording the seminar to capture excerpts of student questions and rich examples for possible inclusion in the online course. Learn more and view the syllabus at http://commons.trincoll.edu/dataviz.
This introductory online course has no prerequisites, and is designed to make data visualization more widely accessible to all. While teaching our seminar, Veronica Armendariz (Trinity ‘16) and other contributors and I have begun writing an open-access digital textbook, Data Visualization for All (http://DataVizForAll.org). This book will provide the content for much of the online course, which I envision as six one-week units:
Design Principles for Data Visualizations
Find and Transform Data
Create and Embed Interactive Charts
Point Maps Versus Polygon Maps
Modify Code Templates on the Web
Tell Your Story and Show the Data
First, we begin with design concepts and essential spreadsheet skills (inserting formulas, sorting, pivot tables), which many Trinity students tell me that no one has ever taught them. Second, we create and embed charts and maps using easy-to-learn tools with a graphical user interface, such as BatchGeo, Tableau Public, and Google Fusion Tables. Finally, we learn how to modify existing code templates and host them online with user-friendly GitHub browser tools. Although the course presumes no prior experience with coding, it concludes by teaching students how to “tweak” existing data visualization code that can be found and shared online.
2. Audience: The online course is designed for two audiences. First, Trinity students who enroll in future semesters of the credit-bearing data visualization internship seminar will work through the online instructional content before class, to allow for more interaction and discussion during class. Second, learners from outside Trinity will enroll in the edX course. Trinity students and I have led half-day data visualization workshops for non-profit organization staff, sponsored by the Hartford Foundation for Public Giving. Although I have not publicized Data Visualization for All beyond these workshops, GitBook statistics reveal that nearly 10,000 visitors have discovered the online book-in-progress, and over 500 have downloaded a free e-book, since April 2015.
DataVizBook.org GitBook web traffic from April 2015 to present
3. Distinctiveness: This online course will highlight Trinity’s urban engagement and community learning initiative. Data visualization examples will be drawn from two years of Trinity student partnerships with Hartford non-profit organizations, and our efforts to help them tell their stories and show their data. In our current internship seminar, seven Hartford community partners have granted permission and have been video recorded while working with their Trinity students.
4. Research and Design, and Benefit to Trinity Students: This data visualization course is well-suited to the online format because the core learning objective is to teach students how to communicate more effectively on the web. Also, the online format allows individual students to replay tutorial videos at their own pace, and it also brings together a community of learners to share links and comment on each other’s visualizations. Trinity students in future Data Visualization internship seminars will benefit from online course material before class, to allow more interaction and discussion during our one-hour weekly class sessions. Finally, pending funding, Trinity students who completed the course may be hired to work as “virtual teaching assistants” and respond to off-campus learners’ questions on the edX discussion board.
5. Timing: I propose to launch the edX course in February 2017. This schedule allows me and an instructional technologist to create the course in Fall 2016, and to preview it with the next cohort of Trinity students in the Data Visualization internship seminar in January 2017.
6. Stipend: I plan to request a course stipend rather than a TU release.
After creating point maps of sample lunch data on three different tools, I have learned that BatchGeo is the easiest to use, while MapMe is much more flexible, and Google Fusion Table is the middle ground of the other two.
In less than five minutes, I could create a nice map on BatchGeo by copying the data on the website. However, the downside for this is that I did not have the chance to customize my map. The info windows are fixed, and I cannot change the point size and display. Therefore, BatchGeo is recommended for people who need to make a simple map quickly.
On the other hand, MapMe offers users many choices of map display. I could customize the info windows and symbols for the points on the map. I could decide the map type and even upload a map logo. There are also more tools, which I hope to learn more in the semester. Unfortunately, MapMe is not very user-friendly and efficient. The tool is rigid in terms of importing data. The website did not allow me to create a map until all the locations in my data are formatted correctly. So I had to add one address at a time. Though adding an address is not hard because the site suggested the right location very quickly, this will not be efficient if I’m working with some data with many addresses. Thus, MapMe is suitable for those who want a customized map and do not mind spending some time working on it.
For me, Google Fusion Table is the middle ground between BatchGeo and MapMe. Google Fusion Table is easy to use. I could create a map after uploading data and pick the right column for the geocoding process. I can also customize my info windows and points, though not as flexibly as MapMe. So if an user is looking for an user-friendly and flexible tool, he or she can choose Google Fusion Table to create a map.
