Which is the best way to visualize effectively a train timetable? Well probably there is a lot of ways to visualize this kind of data, and one of them is using the stem-and-leaf plot (called also stemplot). Using this technique the amount of data to display (hours and minutes) can be reduced.
Why are the stem-and-leaf plots useful in that case? This kind of plot is a method for showing the frequency with which certain classes of values occur. You could achieve the same by making a frequency distribution table or a histogram for the values, or you can use a stem-and-leaf plot and let the numbers themselves to show pretty much the same information.
Lately I’m pleasantly surprised due to two great initiatives which demonstrate the interest that has Catalunya for the world of the open data and the data visualization.
The first one demonstrates how the catalan government is sensible about the value of providing data to the general public, businesses and other organisations so they would be able to re-use public sector data and create value.
The second one is from my point of view such an awesome project that needs an entire post but for the moment, remember that name and its concept: Impure, a visual programming interface designed to give non-programmers access to professional tools for data visualization. Users can use it to process and display data from social media feeds, financial information and more.
The government of Catalonia has launched Gencat Open Data, an open government data portal where information of a public nature is published with the goal of fostering the use and reuse of information that comes from the administration. This portal groups together all the Government’s open data initiatives into a single catalogue, and adds the most important information associated with them for reuse purposes.
Today I’ve attended the MadeInFlex onsite IV, an event where some Adobe Platform evangelists have been talking about the last features of AIR 2.5, Flash Player 10.1 for Android, the integration between Flex 4 and Flash professional and other interesting stuff.
I’ve really got impressed about the posibilities that Flash and P2P can offer together. Specially interesting it has been the demo of Mark Doherty, showing how comunicate via p2p a laptop with an Android mobile device, using a second Android mobile device as a net access point. It was just a demo built with the AIR 2.5 beta but it shows what this technology can offer in reponse to other similiar technologies (the video chat FaceTime for Iphone). Mark shows this in a video on his Flash Mobile Blog.
First things first, what’s a jagged array?
A jagged array is an array whose elements are arrays. The elements of a jagged array can be of different dimensions and sizes. A jagged array is sometimes called an “array of arrays.”
Recently I’ve been playing with FLARTooKit and I found this function into the class ArrayUtils.as.
That’s the book I’m currently reading and if you are interested in the field of the Data Visualization, this book is a good starting point.
The author is Ben Fry, the founder (with Casey Reas) of Processing, a programming language, and development environment. Initially created to serve as a software sketchbook and to teach fundamentals of computer programming within a visual context, Processing quickly developed into a tool for creating finished professional work as well.
Due to my attempt to create a perceptual interface using OpenCV, this post is the first in a series to explain briefly some of the capabilities of the OpenCV library, an open-source computer-vision library.
OpenCV comes with over 500 functions that cover many areas in vision, and its goal is to provide a simple-to-use computer vision infraestructure to build fairly sophisticated vision application quickly. The library is written in C and C++ and runs under Linux, Windows and MAC OS X. There is active development on interfaces for Phyton, Ruby, Matlab, and other languages.
How to find faces
Finding faces means finding complex objects, so OpenCV uses a statistical model (often called classifier), which is trained to find the object we are looking for. The training consists in a set of images, divided into “positive” samples and “negative” samples. The positive samples are instances of the object class of interest and the “negative”, images that don’t contain the object of interest.
Freelance interactive developer and multimedia engineer. I'm interested in data visualization, RIA's and interactive applications. More info here or also at the following sites:
