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GovHack 2016 – Mapping Innovation

So I got involved in GovHack again, this time with some of new colleagues in Sydney!

In spite of a big event the Friday night severely impacting our abilities Saturday morning, this is what we pulled off!

Project page and video below.

 

Mapping Innovation

The printing press, the pencil, the flush toilet, the battery–these are all great ideas. But where do they come from? What kind of environment breeds them? What sparks the flash of brilliance? How do we generate the breakthrough technologies that push forward our lives, our society, our culture?

                                                                                                                                  – Steven Johnson, Where Good Ideas Come From

Innovation is a key measure of a country’s success and one that is of key importance to Australia in our current climate. In 2015, Australia was ranked number 17 in the Global Innovation Index with a score of 55.2 for 2015. Australia has maintained its 17th position from the Index in 2014 and increased two ranks from 2013 where Australia was ranked 19.

This project allows the user to journey through the innovation datasets available to Govhack and explore the trademarks, patents and design applications that have been submitted.

This tool will take you on a journey from the national level, where we explore the country and the innovation hubs as they have emerged through time, and then stepping into the state level datasets and exploring the spatial positioning of these innovations over time Finally, we step down further into an individual scale where we have developed Australian word cloud mapping that highlights the key innovations as they occurred throughout each decade.

At a National Scale:

Australia has been ranked number 17 in the Global Innovation Index with a score of 55.2 for 2015. Australia has maintained its 17th position from the Index in 2014 and increased two ranks for 2013 where we were ranked 19.

The Global Innovation Index is a culmination of various aspects of the economy including property, human capital, infrastructure, market sophistication, business sophistication, knowledge and technology outputs and creative outputs. Australia excels at a number of these sub-indices and our key strengths include:
• Regulatory quality
• Ease of starting a business
• School life expectancy
• Tertiary enroelment
• QS university ranking
• Overall Infrastructure
• Information and Communications technologies
• Environmental Performance
• Ease of getting credit
• Intensity of local competition
• Global entertainment and media outputs
• Printing and publishing outputs
• Online creativity
To view how Australia ranked in each category- the data can be viewed here:

https://www.globalinnovationindex.org/analysis-economy

This showcases 100 years of innovation using Patent, Design and Trademark data across Australia. On a global scale, you can see Australia emerging with famous ideas such as the Hills Hoist, wifi, Cochlear implants. These ideas are joined by hundreds of thousands on other IP applications. As you watch this application, you can see the Innovation market explode throughout Australia, clearly identifying the city areas as the key place for the Innovative Economy.

We have also geocoded the list of Australian Universities as the correlation between Universities and innovation is a key relationship that has been highlighted.

In this data, trademark data did not have a exact spatial location so the postcode field was used to geocode these postcodes and determine a latitude and longitude for each point. To create the actual variance across the suburb that would occur in the real environment, a variance calculation was used to adjust the spatial location within the suburb to be able to physically see some of the hotspots. Many joins had to be made to relate each of the IP data set, and millions of calculations using our toolkit below.

Datasets used:

At a State Scale:

This map allows you to explore the datasets on a state level for Patents, Designs and Trademarks. This map plays through time and shows the innovative ideas as they emerge.

The Patent and Design datasets are shown as points on the map, whilst due to the huge number of trademarks being registered every year, these are shown as a chloropleth map in the background. This was a conscience decision to highlight the patents and design applications to show their location across NSW.

Datasets used:

On a Projects Scale:

This data set lets you explore the Trademark, Patent and Design database key words, as mentioned in the application description. As you step through time from the 1990s for the trademark data, these word clouds highlight the most common elements of innovation throughout that decade right up to 2015 datasets.

  • The trademark data sets span over 100 years with the first application for trademark within this dataset being from 1906.
  • The design datasets span over 50 years, ranging from 1972 through to 2015.
  • The Patent dataset only spans for 2 decades, with the first data available to download being from 2003.

Method:

This project started by exploring the IP Australia datasets where we explored the trademark, patent and design applications that have been created over the past 100 years. There was enormous amounts of data to explore so our first was to explore what it contain and how we could use it.

Mapping this data quickly became a method that we could easily display the data and enable insights to be drawn. From here, we went to work sorting through the data and extracting the informative information. We divided our project into 3 scopes at this section and set to work creating the final product.

National Scale:

These datasets were cleaned and using D3 and Cartodb, worked on creating an informative and interesting map of Australian Innovative history.

In this data, trademark data did not have a exact spatial location so the postcode field was used to geocode these postcodes and determine a latitude and longitude for each point. To create the actual variance across the suburb that would occur in the real environment, a variance calculation was used to adjust the spatial location within the suburb to be able to physically see some of the hotspots.

State Scale:

The datasets we wanted to explore here were at an SA3 level, however we wanted to also overlay this with more fine grained project coordinates. This culminated in a D3 map and the data.

Project Scale:

This project wanted to explore the individual projects and extract meaningful analysis about the trends of projects. The Trademark and Design application descriptions were a great resource to extract all of the project titles and run a correlation. This involved processing of millions of individual words.

Tools Used across the whole Project:

  • ·         D3
  • ·         Python
  • ·         React
  • ·         Processing and various libraries
  • ·         FME
  • ·         QGIS
  • ·         Animoto
  • ·         Topojson
  • ·         Canopy and Jupyter

Open Datasets Used:

Intellectual Property Government Open Data 2016

The Intellectual Property Government Open Data (IPGOD) includes over 100 years of Intellectual Property (IP) rights administered by IP Australia comprising patents, trademarks, designs and plant breeder’s rights. The data is highly detailed, including information on each aspect of the application process from application through to granting of IP rights. We have published a paper to accompany IPGOD which describes the data and illustrates its use, as well as a technical paper on the firm matching. Links to these papers can be found in the Data Dictionary.

http://portal.govhack.org/datasets.html#ip-australia

SA3 Region Innovation Data 2009-15

This dataset reports on innovation activities (R&D expenditure, patent and trademark counts) and business creation (new businesses) across SA3 regions in Australia.

http://portal.govhack.org/sponsors/department-of-industry-innovation-and-science.html

Global Innovation Index 2015

The Global Innovation Index (GII) aims to capture the multi-dimensional facets of innovation and provide the tools that can assist in tailoring policies to promote long-term output growth, improved productivity, and job growth. The GII helps to create an environment in which innovation factors are continually evaluated.

https://www.globalinnovationindex.org/

Universities Australia: University Profiles

Universities Australia lists the number and location of all universities and the various campuses within Australia as part of the Australia University Profiles 2016.

https://www.universitiesaustralia.edu.au/australias-universities/university-profiles#.V52AfaLbR26

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The Active City : Visualising cultural activity in London

Over the last few months we have been working on a series of visualisations representing the theme of an active city as part of our masters coursework in spatial analysis & visualisation at CASA, UCL.

Our group – consisting of myself, Rowan Blaik, Lyzette Zeno-Cortes and Agata Brok, chose to pursue this with the specific idea of cultural activity and interaction.

This post will summarize it all in one page, but a more comprehensive presentation & folio for this piece can be viewed via the website: cityofcultu.re

Below are some examples of the wide array of visualisations we produced to explore the topic, which covered global, regional, city and street scale.

Global Magnetism

Here we explored global migration to London through a static visualisation Processing, as well as a look into the magnitude of one of London’s many cultural attractors (the V&A Museum) using D3.

va2

Cultural Icons

These visualisations explored how data from monuments of individuals and events can be made more meaningful through crowd-sourced data from sources such as Wikipedia. In particular, we focused on English Heritage Blue Plaques, which have a strong presence in London.

pic03

Plaque Explorer

The most exciting and challenging phase of this visualisation involved generating a browser-based London using threejs. Here we represented cultural phenomena in the form of metaballs in the city – which are divided by category and scaled by their proximity to their own type. The plaques were also included in this city, with their height increasing with amount of page views on their biographies in Wikipedia in the last 90 days.

cityss2

One of the advantages of using threejs was that it allowed us to add our own customization. One of these involved implementing the visualisation so it can be viewed with the Oculus Rift virtual reality headset. While this was a bit of a challenge, it proved an interesting, and of course fun, way to interact with the city in your browser. If you’d like to run this you will need an Oculus Rift with the Oculus bridge plugin running locally.

rift-trans

Icons of reminiscence

This final aspect of the project involved moving to a street scale, which opens up ideas of how these can be represented in a augmented reality/wearable avenue. The people from the plaques were rendered nicely in Adobe After Effects and represented as portraits flying through the streets, with bubbles representing their page views again.

lyz_2

If you have any questions feel free to leave a comment below or tweet any of us at @oc_lock , @land_lab, @urbanjuicing, @cityremade.

If you’d like to learn how to do these kind of visualisations, take a look at these courses that CASA run.

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Quantified Self & Mapping

The quantified self (QS) movement is intrinsically about knowing more about your most important asset in order to make better decisions. There are many applications out there that allow you to record daily activities – be it mood, food consumed or movement. For the great majority of these they probably, in truth, gain little more actual utility than simply writing these down with pen and paper would provide.

When can these quantified self applications be effective? Some applications such as ‘OptimizeMe‘ use integrated approach of these measurements and give intelligent suggestions such as ‘If you get more sleep on Sunday you will have a better mood on Monday’ ; with these technologies emerging, it feels as if the next step, integration of self-data with real data will be combined – such as the application suggesting : ‘Today the sunniest morning of the week – maybe you should walk to work’. Can having these kind of suggestions available make people, en masse, happier, healthier or more active than they already are?

As part of our MRes coursework we are working on creating visual representations of ‘the active city’. One element my group deduced was physical activity and we are currently exploring different QS datasets and how these might be involved.

At the moment I am testing out the ‘Moves‘ application. This application seamlessly records your movement throughout the day which can then be extracted and visualised using the Moves API. So far it has been impressive in its ability to deduce when I am walking by foot, using transport and which venues I spend hours on the computer.

unnamed

There is also a GitHub example of visualising this data through Processing available!

screenshot_1 More information about this viz.

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