Less visually striking than my last project, this visualization shows the voting patterns of Canadian Members of Parliament. It uses a Principal Component Analysis (or PCA) transformation to convert the multidimensional voting record of each MP to a 2D (or Cartesian) form.
Each point on the chart represents an MP. The color of every MP follows their party affiliation. They are tightly clustered because of party discipline : in Canada, MPs normally vote in accordance to directions given by the Prime Minister.
When I worked in a survey firm, I was tasked with building a VOIP system to cut costs and to raise productivity. The biggest productivity drain in an outbound call center is the dialing time and getting someone on the line. By implementing an Asterisk server, we could control and expand the server to our needs. Furthermore, this meant we could have remote workers. We saved a bundle of money in long distance and in fixed costs. The hosted server and the bandwidth itself cost about 80$ a month, while the connectivity to the phone network was negligible and, more importantly, flexible. In other words, if it was a slow month, the cost was low, and conversely, if it was a very busy month, the costs were higher but the money was coming in.
Incoming call popup
Since it was my server and I was billing the company for it, I figured I could use the same server for my personal phones. So I decided to connect my PSTN numbers to this system. I could now use the server as my private VOIP server.
After configuring the VOIP server to my liking, I started to explore the Asterisk API and related Java and Python bindings. My first module was an interactive IVR system to manage callbacks from the survey outbound number. The callers could know who called them and remove their number from our calling lists.
Update (2012): Modified code for asterisk 1.6
Update (2014): Added code (end of page) for KDE
While Limesurvey is a very nice tool to create and manage web surveys, it’s a bit lacking in the reporting area. The functions are a limited and even if you want a quick and dirty, but presentable report, you must export the data and use other tools to format and present the answers to the survey.
While Limesurvey is a very nice tool to create and manage web surveys, it’s a bit lacking in the reporting area. The functions are a limited and even if you want a quick and dirty, but presentable report, you must export the data and use other tools to format and present the answers to the survey.
Limesurvey results imported in Crystal Reports
The problem is the way how Limesurvey stores its data, more specifically the recorded answers. Every column in the table contains theanswer for the question identified by the column and each line (or record) contains a respondent. It seems like a very sensible way to store the respondent’s answers, but the trouble is that it’s very difficult to construct a generic report template in reporting tools with this kind of table schema. To be of any use, the table data have to be converted in a usable form. Furthermore, the schema used by Limesurvey is less than optimal and doesn’t use foreign keys to link tables. This complicates everything.
To convert the answer table data, I’ve written a little python script. It should be pretty plug-and-play. To use it, you have to change the database access variables. The script only takes one argument, the survey id. You can get the survey id in Limesurvey’s administration panel. It should follow the survey title when you select a survey in the administration panel. Continue reading “Using Crystal Reports with Limesurvey”
Less visually striking than my last project, this visualization shows the voting patterns of Canadian Members of Parliament. It uses a Principal Component Analysis (or PCA) transformation to convert the multidimensional voting record of each MP to a 2D (or Cartesian) form.
