I have been gaining interest in data visualization lately. I especially enjoy articles with narrative driven data-viz like the ones in http://polygraph.cool/ for example.
What would be a great 'hello world' project to learn about conveying information effective through data viz? I'm not sure where to start.
Thanks!
Two subreddits come to mind. Here you can find some nice applications of data visualizations, and here you can keep up to date datasets that get published. Put those two together and you can come up with some novel ideas. Looking forward to seeing your stuff in /r/dataisbeautiful!
How about starting with a data density app?
If you search on my name and "data density" you'll find some routines on the web, but that would be cheating. The way the system works is to take reciprocal of squared distance plus a fudge factor to prevent 1/d when the sample pixel point is very close to a data point. So you get the density of a 2D scatterplot.
You then need a nice visual representation of a linear scale, using colours to represent value changes. I'll give you those, I have several colour palettes at
http://www.malcolmmclean.site11.com/www/datadensity/colourschemes.c
http://www.malcolmmclean.site11.com/www/datadensity/colourschemes.h
This question somewhat overlaps knowledge on geospatial information systems, but I think it belongs here rather than GIS.StackExchange
There are a lot of applications around that deal with GPS data with very similar objects, most of them defined by the GPX standard. These objects would be collections of routes, tracks, waypoints, and so on. Some important programs, like GoogleMaps, serialize more or less the same entities in KML format. There are a lot of other mapping applications online (ridewithgps, strava, runkeeper, to name a few) which treat this kind of data in a different way, yet allow for more or less equivalent "operations" with the data. Examples of these operations are:
Direct manipulation of tracks/trackpoints with the mouse (including drawing over a map);
Merging and splitting based on time and/or distance;
Replacing GPS-collected elevation with DEM/SRTM elevation;
Calculating properties of part of a track (total ascent, average speed, distance, time elapsed);
There are some small libraries (like GpxPy) that try to model these objects AND THEIR METHODS, in a way that would ideally allow for an encapsulated, possibly language-independent Library/API.
The fact is: this problem is around long enough to allow for a "common accepted standard" to emerge, isn't it? In the other hand, most GIS software is very professionally oriented towards geospatial analyses, topographic and cartographic applications, while the typical trip-logging and trip-planning applications seem to be more consumer-hobbyist oriented, which might explain the quite disperse way the different projects/apps treat and model the problem.
Thus considering everything said, the question is: Is there, at present or being planned, a standard way to model canonicaly, in an Object-Oriented way, the most used GPS/Tracklog entities and their canonical attributes and methods?
There is the GPX schema and it is very close to what I imagine, but it only contains objects and attributes, not methods.
Any information will be very much appreciated, thanks!!
As far as I know, there is no standard library, interface, or even set of established best practices when it comes to storing/manipulating/processing "route" data. We have put a lot of effort into these problems at Ride with GPS and I know the same could be said by the other sites that solve related problems. I wish there was a standard, and would love to work with someone on one.
GPX is OK and appears to be a sort-of standard... at least until you start processing GPX files and discover everyone has simultaneously added their own custom extensions to the format to deal with data like heart rate, cadence, power, etc. Also, there isn't a standard way of associating a route point with a track point. Your "bread crumb trail" of the route is represented as a series of trkpt elements, and course points (e.g. "turn left onto 4th street") are represented in a separate series of rtept elements. Ideally you want to associate a given course point with a specific track point, rather than just giving the course point a latitude and longitude. If your path does several loops over the same streets, it can introduce some ambiguity in where the course points should be attached along the route.
KML and Garmin's TCX format are similar to GPX, with their own pros and cons. In the end these formats really only serve the purpose of transferring the data between programs. They do not address the issue of how to represent the data in your program, or what type of operations can be performed on the data.
We store our track data as an array of objects, with keys corresponding to different attributes such as latitude, longitude, elevation, time from start, distance from start, speed, heart rate, etc. Additionally we store some metadata along the route to specify details about each section. When parsing our array of track points, we use this metadata to split a Route into a series of Segments. Segments can be split, joined, removed, attached, reversed, etc. They also encapsulate the method of trackpoint generation, whether that is by interpolating points along a straight line, or requesting a path representing directions between the endpoints. These methods allow a reasonably straightforward implementation of drag/drop editing and other common manipulations. The Route object can be used to handle operations involving multiple segments. One example is if you have a route composed of segments - some driving directions, straight lines, walking directions, whatever - and want to reverse the route. You can ask each segment to reverse itself, maintaining its settings in the process. At a higher level we use a Map class to wire up the interface, dispatch commands to the Route(s), and keep a series of snapshots or transition functions updated properly for sensible undo/redo support.
Route manipulation and generation is one of the goals. The others are aggregating summary statistics are structuring the data for efficient visualization/interaction. These problems have been solved to some degree by any system that will take in data and produce a line graph. Not exactly new territory here. One interesting characteristic of route data is that you will often have two variables to choose from for your x-axis: time from start, and distance from start. Both are monotonically increasing, and both offer useful but different interpretations of the data. Looking at the a graph of elevation with an x-axis of distance will show a bike ride going up and down a hill as symmetrical. Using an x-axis of time, the uphill portion is considerably wider. This isn't just about visualizing the data on a graph, it also translates to decisions you make when processing the data into summary statistics. Some weighted averages make sense to base off of time, some off of distance. The operations you end up wanting are min, max, weighted (based on your choice of independent var) average, the ability to filter points and perform a filtered min/max/avg (only use points where you were moving, ignore outliers, etc), different smoothing functions (to aid in calculating total elevation gain for example), a basic concept of map/reduce functionality (how much time did I spend between 20-30mph, etc), and fixed window moving averages that involve some interpolation. The latter is necessary if you want to identify your fastest 10 minutes, or 10 minutes of highest average heartrate, etc. Lastly, you're going to want an easy and efficient way to perform whatever calculations you're running on subsets of your trackpoints.
You can see an example of all of this in action here if you're interested: http://ridewithgps.com/trips/964148
The graph at the bottom can be moused over, drag-select to zoom in. The x-axis has a link to switch between distance/time. On the left sidebar at the bottom you'll see best 30 and 60 second efforts - those are done with fixed window moving averages with interpolation. On the right sidebar, click the "Metrics" tab. Drag-select to zoom in on a section on the graph, and you will see all of the metrics update to reflect your selection.
Happy to answer any questions, or work with anyone on some sort of standard or open implementation of some of these ideas.
This probably isn't quite the answer you were looking for but figured I would offer up some details about how we do things at Ride with GPS since we are not aware of any real standards like you seem to be looking for.
Thanks!
After some deeper research, I feel obligated, for the record and for the help of future people looking for this, to mention the pretty much exhaustive work on the subject done by two entities, sometimes working in conjunction: ISO and OGC.
From ISO (International Standards Organization), the "TC 211 - Geographic information/Geomatics" section pretty much contains it all.
From OGS (Open Geospatial Consortium), their Abstract Specifications are very extensive, being at the same time redundant and complimentary to ISO's.
I'm not sure it contains object methods related to the proposed application (gps track and waypoint analysis and manipulation), but for sure the core concepts contained in these documents is rather solid. UML is their schema representation of choice.
ISO 6709 "[...] specifies the representation of coordinates, including latitude and longitude, to be used in data interchange. It additionally specifies representation of horizontal point location using coordinate types other than latitude and longitude. It also specifies the representation of height and depth that can be associated with horizontal coordinates. Representation includes units of measure and coordinate order."
ISO 19107 "specifies conceptual schemas for describing the spatial characteristics of geographic features, and a set of spatial operations consistent with these schemas. It treats vector geometry and topology up to three dimensions. It defines standard spatial operations for use in access, query, management, processing, and data exchange of geographic information for spatial (geometric and topological) objects of up to three topological dimensions embedded in coordinate spaces of up to three axes."
If I find something new, I'll come back to edit this, including links when available.
My name is John and I am a grad student at the University of Florida. As part of my research one of my tasks is to create a piece of software that is to display a map of the surrounding area, which shows the current location (from a GPS), and to implement a shapefile (as a boundary outline). I am not able to really get enough information to get on the right track on how to do this, and would appreciate any assistance!
The project involves a large-scale robot that will be operated by tele-communication in rough terrain. So this mapping and gps software will need to be entirely offline, but the location in use will be known. It is very preferred to find a cost effective means to doing this process (maybe even a simple API that could do the simple task, dll libraries, or active x.
My initial guess is to use a geo-referenced image (that I would get the lat and long of and know the boundaries of that image). Then from a GPS I then would treat the image as an XY plot somehow and that would provide the current position. Obviously even this step can be a challenge depending on what kind of image, map, kml file, etc that I can find and use.
So I would appreciate any advice, suggestions, or comments.
Suggest you online reference source code, and then modify their own, this project is currently on the Internet, you can through search engines to find. Good luck!
I have developed two differing methods in MATLAB which aim to analyse a pop song and then automatically create a 30 second audio thumbnail (a preview clip) containing part of the chorus section.
Both methods have varying results:
The first method can create a thumbnail for each track, managing to find a chorus section in 40 out of 50 tested songs
The second method only managed to work on 30 out of the 50 songs, and it found the chorus section 21 times out the 30.
Obviously I know which method is superior, but I need to describe and explain the results in a report which requires the demonstration of proper statistical testing.
Other academic papers have previously used an f-test to do this, but because their methods are vastly superior, their aims are usually involve the detection of chorus onset times with 100% accuracy.
My aim is more relaxed as I am just looking for the generated thumbnails to contain any part of the chorus, regardless of onset.
Can anyone suggest some objective tests that I could possibly explore with regards to my project? This is my first time conducting an investigation like this so my experience/knowledge is incredibly low.
Thank you!
Possibly, the way for you is formating your song track with time cuts for relevant information about type of sound(chorus, etc). In sound editor like CoolEdit, you can set time cuts and assign names for theirs like 'chorus', 'pause','music'... Then, you must extract cut information to import in Matlab. For Windows 32 can be used utility Wav2labs from http://www.pallier.org/ressources/wspot/sig2wav/toolswav.html; http://www.pallier.org/ressources/wspot/sig2wav/Wav2labs.exe This program extract cuts to text file and you can read with Matlab textscan function.
After all, only segmentation accuracy must be proceed, like percent time when signal type(chorus/not chorus) was recognized correctly
Or specify your question more exactly
I'm just starting to take an interest in visualization and I'd like to know where I can get my hands on some data, preferably real world, to see what queries and graphics I can draw from it. Its more of a personal exercise to create some pretty looking representations of that data.
After seeing this I wondered where the data came from and what else could be done from Wikipedia. Is there anyway I can obtain data from say, wikipedia?
Also, could anyone recommend any good books? I don't trust the user reviews on the amazon website :-)
You can download the raw Wikipedia data from http://download.wikimedia.org. There are many different views of the data available. The English Wikipedia is by far the largest database, and there isn't a current full dump available, but one is in progress. It will probably take months to finish and be available for download.
The most recent one was 18 GB compressed, which uncompressed to something like 2.5 TB.
A fantastic book is The Visual Display of Quantitative Information by Edward Tufte.