Getting started with mapping and GIS for free (Tips from a non-expert)

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Sometimes it is useful to see results on a map. Maybe you want to see where your participants are coming from or show survey results geographically.

Mapping and GIS (geographic information system) are skills that I had been interested in learning for awhile. I never seemed to have the time to really delve into it and so these interests took the back shelf while other priorities popped up.

This past year I have been working on a project where geography and location are key and so I finally had the push I needed to get up to speed. I had a minimal budget (read: $0) for software. Although there are pretty fancy GIS programs out there (that require minimal know-how) those weren’t in the cards.

Although there are other resources out there, these are the two that I used. They require no coding, making them very beginner-friendly.

1. Google Maps

If you are looking for very basic mapping, Google Maps can actually do quite a bit. You can draw polygons/boundaries, add points, add in directions, and import data (although I think data imports are limited at 50 rows).

Here is a fictitious example of a program location (the purple star) and where the program participants live (the green dots).


The nice thing about Google is that everything is saved on the cloud and you can access your maps from anywhere (and easily share them with others).



I needed to do more complex mapping than Google Maps allows and so I turned to QGIS, an open source GIS tool. I will warn you that it has a steep learning curve but there are many tutorials online (I found QGIS Tutorials and Tips extremely helpful!) and a community over at StackOverflow if you get stuck.

Here is another fictitious example of program locations (the grey circles) mapped in relation to income (red being the lowest income and the darker green being the highest income):


I’d love to hear more from others about this subject. Do you know of a great mapping/GIS tool? Have you used mapping in evaluation? Let me know in the comments!

Switching from SPSS to R: Save scripts, not workspaces!


I’m back with a quick lesson that I have learned while switching to R for data analysis (if you are curious about why I am doing so, I have a list of reasons here). This was a bit of a painful lessons that cost me a lot of time: SAVE SCRIPTS, NOT WORKSPACES.

What does that mean?

I am going to explain this in non-technical terms (sorry R experts), mainly because I don’t know the technical lingo.

In SPSS, your data file is a tangible thing. You can make changes to it and save it and then go back to the actual file and boom, there is the data just as you left it.

In R things work a bit differently. All changes to data (and analysis, and charts, and everything else) are executed through scripts. You write a block of code that does something. You save this script and each time you open R, you should re-load the script. Objects and dataframes aren’t “real” as they are in SPSS.

Like most R users, I use R Studio. R Studio is amazing and awesome and I love it. But it has a default setting that was allowing me to keep a bad habit I learned from SPSS (i.e., not re-loading a script each time to make sure that it included everything my analysis needed and treating objects as “real”). R Studio has a default setting that will automatically save your workspace and re-load it next time you start the program. Amazing! Or so I thought.

I have been working through the book R for Data Science (a great book which is FREE by the way) and in the workflow section the authors make this point very clearly: save scripts, not workspaces.  I didn’t really get why this was so important. It was so much easier just to open R Studio and have my previous workspace waiting for me.

Unfortunately I learned first-hand why this is so important.

I manually cleared my workspace because I thought I was done with my analysis (and I was sure my script had everything the analysis needed). Turns out my script was missing something pretty important. When I had to go back to my analysis to change something, lo and behold a few objects were missing from my script. I had to manually re-create them from memory.

It wasn’t the end of the world since I was able to do that, but it cost me a lot of time. And what if I had to go back to that analysis a year later? My memory would have certainly faded. If I had been working solely from scripts the entire time this error would have been caught right away (or not have occurred in the first place).

Thankfully you can change the default setting on R Studio so that it doesn’t save your workspace and enable this bad habit. Instructions are here. Don’t repeat my mistake!