Now in its second decade, Google SketchUp has certainly come of age. In a past post I looked at importing SketchUp models into ESRIs ArcGIS and now a look at working with Lumion 2. This demo is looking at an indoor scene aiming to introduce the viewer to an office and environment whilst highlighting some of the work being conducted.
The first image is a view of the office as it is being created in Sketchup and it is evident that the overall image quality is good but nowhere near realistic. Though this may be the case, the modelling process is so efficient that this was created in under and hour.
The next phase is the export from SketchUp and import into Lumion. This process is particularly simple and relies on the use of the COLLADA file format. Using the menu File > Export > 3D Model. In the save menu it is then important to set save options to include 2 sided faces, export edges and triangulate all faces.
Once in Lumion the import process is fairly simple. In the objects menu select import file and then select the COLLADA file. Important consideration at this point is whether to create new textures with Lumion or use the default Google SketchUp.
The video above has been created using the Lumion video engine and exported at 720 dpi. The video maker allows a simple screen-grab based approach to creating a path and then intelligently renders a path between them. The video editor has significant functionality and allows people, vehicle and object animations to be included.
A few faults evident in the video was the flickering on the Mac and tv displays which was a result of multiple textures on the same plane. This may be alleviated by lifting the primary texture.
Processing is an open-source graphic, animation and interaction generating applications which allows a code based environment for development. The example displayed in the video below visualises tweets around London and allows the user to navigate and interrogate the data.
As is evident in the video the main aspect of this code is the keyboard based interaction of the scene. Panning around the scene is available with the traditional A,W,S,D navigation scene and zooming and tilting available using ‘z’ and ‘l’.
The navigation is created through the use of the processing keyPressed() function. This code is in its own .pde file within the main sketch to simplify the sketch structure. In order to create the pan and zoom feature, the camera function is used.
By setting both eyeX and centerX (and the same for Y) to be the same variable the camera will always remain directly above the image. Consequently by adding or subtracting from the cameraX and cameraY variable the scene appears to move.
So it’s been about 2 months since we created the map and now seems an appropriate time to tell its story. Like all good projects, the idea came up in conversation and quickly got put into motion. With the generous support of CASA’s Andy Hudson-Smith the two canvases where purchased and all of a sudden the project was in motion.
With the help of Ian Morton (@visualmetro) it was decided to start the project off with the outline of London and also the river Thames sketched onto the canvas. The reason behind this being to create a framework and point of reference in which to guide the early contributors. At this time we also chose the rough locations to showcase the canvas for contributions and also the time frame.
Over the period of the week Ian and I spent about 15 hours collecting contributions to the map around the UCL campus, assisting contributors with words of advice as to locations and also chatting about the ideas that many of the contributors held about space in London. Many stories emerged including an interesting invitation to a 4am gathering on Hampstead Heath on the 1st of May each year.
All in all there where over 270 contributions to the map (based upon the number of signatures around the map) and a vast number of associated stories. As far as content was concerned the coverage around the central London region was very high though not always accurate (often due to congestion or prior spatial inconsistencies). Adding to the content about London was the interest by international student keen to add direction arrows to home countries or cities in some cases including graphic representations or exact(ish) distances.
A quick post just to highlight my first attempt at creating a GIS related tutorial. This tutorial looks at the creation of 3D models in ArcScene, modelling in Google Sketch up and finally bringing the two together.
This tutorial provides a very basic introduction to this functionality however should be fairly easy to expand upon. If you have any questions please send them through the comment box below.
So here it is. The culmination of 16 hours and several hundred contributions. The UCL map of London has provided a fascinating insight into how people remember, communicate and translate spatial information. Before I write up the full project, I suggest you start to explore some of the contribution and come up with your own conclusions as to the success of the map.
Full screen available here (Higher resolution image to follow)
Inspired by the work of Stephen Walter in his collection of hand drawn maps I have put forward a proposal to conduct a mental mapping exercise, creating a hand drawn map of London by the students and staff at UCL. Though I foresee a number of potential challenges I believe the potential outcome could be hugely impressive. I have included below the project proposal and would be grateful for any insight that may be offered.
Following the work of Stephen Walter who produced the famous London Island map, I would like to propose a similar project which was built upon the knowledge held within UCL. The foundation, being an outline of Greater London and the content being provided on a voluntary basis by members of the university. The work, then being attributed towards those who contributed.
Feasibility/Requirements: Due to the nature of the project the map canvas will need an area of high footfall with reasonably easy access. The project would be run over 7 days allowing access to the majority of interested parties. In addition, to document the production of the map, a webcam would be used to record the development of the map at set time intervals (5 to 10 minutes). In order to promote participation a completion element could be included.
Objectives/desired result: The goal over the week period would be to create a fairly comprehensive map of London combining the knowledge of many participants. The end product would be a digital reproduction which could be displayed in a web format allowing users to explore the map through panning and zooming. In addition the time-lapse data collected would provide insight into how people transfer mental knowledge from mind to paper. For example, do major networks i.e rivers and roads develop first and then the map is filled in around these?
A number of headlines have broadcast recently featuring the introduction of Google’s new indoor street view product, but is this really the answer? The product is aimed at creating a more comprehensive product and has been trialled in a number of museums previously as part of the Google Arts program. The museum project is an excellent use of the street-view technology and allows easy navigation around the museum and view/bookmarking of art pieces of interest.
So there would appear to many benefits to the idea, but, what are the downsides? Firstly, how often are the images updated? The majority of street-view imagery was collected within the last year, and while this may be suitable in slow changing environments is it going to be suitable for the rapidly changing retail market. For example, retailers appearing to stock a product now unavailable and consequently appearing out of date.
Secondly the present system requires user to access the service through Google places rather than through the current street-view interface. This does reduce the smooth transition between services although there is some speculation that this is just a matter of time with a critical mass of indoor-view services available.
I look forward to hearing people’s own views as to this development.
A new discovery for me, StatPlanet by http://www.sacmeq.org/ provides an excellent visualization platform for a reasonable number of per-loaded datasets. The standard GUI provides a number of supporting statistics including temporal change. The temporal change graph is placed above the data an works intuitively displaying the line graph based upon the current mouse position. The included feature ability to select a feature and have it remain graphed for later comparison helps to improve visual data extraction.
Screen Capture of Statpack map (source http://www.sacmeq.org/)
The StatPlanet package is one of a number provided by http://www.sacmeq.org/ and includes a number of graphical data visualizations packages which may be used to display aspatial data with equal interactivity.
The StatPlanet further increases its value through the ability to export the viewed data, not only as images but also the underlying data allowing more personal research to be conducted on the data. All in all making StatPlanet and excellent data discovery and principle testing service.
The country is bleeding. This title was first used on the flowing data Blog when displaying unemployment figures in the United States (available http://goo.gl/iMnH2). In the case of the flowing data map the unemployment data is displayed on a standard projection of the states.
US Unemployment (source Flowing Data)
In my opinion the layout of the United States makes the data fairly easy to visualise as very crudely the states could be seen as a rectangle. The UK however does not have this luxury and as a result visualising similar datasets cannot be performed so easily.
When looking to carry out a similar process on the UK using data from the Guardian data Blog and Wikipedia. In order to better visualise this data it has been structured in such a way that it may be displayed using a rectangular cartogram. The data is divided firstly by region and then electoral constituency before visualising unemployment using a similar colour ramp to that of Flowing Data. The tree map produced in R does a good job of displaying the data though it would be beneficial if spatial integrity could be included. In addition to this each constituency cell is labelled only by number and congruently either a lookup table or call-out function would be required to explore the data further.
Mapping Zombies… Though not the first idea that comes to mind when considering the power of Google. This map highlights some of the potential of the service. The map was created by Dr Mark Graham, Taylor Shelton, Monica Stephens and Matthew Zook of the Oxford Internet Institute.
Zombies Mapped. Source: Oxford Internet Institute
The map creators make the observations that the concentration of searches are reasonably well correlated with the locations of English speaking countries. An interesting further development may be to identify the corresponding words in a number of different languages in order to look at the trend on a more language inclusive level.
The Oxford Internet Institute provides many more excellent data visualisations including a large number of tree maps, twitter and Google visualizations. Below I have included an example of a tree map create by the OII.
Treemap of Academic Knowledge and Language. source Oxford Internet Institute