As part of the Esri User Conference, Dawn Wright, Esri Chief Scientist, organised a Science Symposium that gave an opportunity for those with interest in scientific use of Esri GIS to come together, discuss and meet.
Dawn Wright opened and mentioned that the science symposium is aimed to bring people people from different areas: hydrology, ecology or social sciences – together. The Esri science programme is evolving – and there is official science communication approach. There are different ways to support science including a sabbatical programme. Esri will launch a specific challenge for applications of data sets for students with focus on land, ocean and population. Esri will provide access to all the data that is available and the students are expected to carry out compelling analysis and communicate it. It is an activity in parallel to the global year of understanding. There are also sessions in the AGU meeting that are support by Esri staff.
Margaret Leinen (president, American Geophysical Union) who is working on marine and oceanography gave the main talk ‘what will be necessary to understand and protect the planet…and us?‘. Her talk was aimed at the audience in the conference – people who’s life focus is on data. What is necessary to understand the planet is data and information – it’s the first step of understanding. There are many issues of protecting and understanding the planet – we need to understand planetary impacts on us. The first example is the way we changed our understanding of climate change on the ocean. When we look at the change in sea surface temperature in the 1990 we can see changes up to 2 degrees F. The data was mostly collected in traditional means – measurements along the paths of ships. Through studies from ship records over the years, we have created a view of ocean heating – with different results between groups and researchers with lots of hand crafted compilation of records. In the last decade things have changed: ARGO floats are going up and down through ocean, and make all the data is available – there are 3839 operational floats, reporting every week. This is a completely new way or seeing the data, with huge scale international collaboration. Now we can see the annual cycle and determined the slope in the change in heat content. We have a 10 years time series for the depth of 0-2000m. We have a much more detailed information of the changes. There is an approach to make these devices that will understand the full planetary budget on heat through the whole depth of the ocean. The EarthScope Facilities also provide a demonstration of detailed sensing data – understanding the Earth and it’s movements. Many seismometers that are used for over a decade – the US array provided a massive increase in the resolution of seismic measurements. In 2011, the network identified the Japanese Honshu earthquake. The measurement provided a new class of earthquake modelling that can be used in engineering and science. GPS also provides new abilities to understand deformation o earth. Permanent GPS receivers – many of them – can provide the resolution and accuracy to notice subtle movement, by using very sophisticated statistical filtering. HPWREN – High Performance Wireless Research and Education Network – provide a way to transfer information from sensors who are very remote, then then linked through line of sight communication, and the network provide a reliable and resilient public safety network. The network support many sensing options. There are fire cameras that are linked to it, that alert to provide real time information to the fire department. WiFire is a programme that aim to deliberately work on this issues. GIS data is used to assess surface fuel. In summary: Earth science is going through huge transformation through collaboration of large groups of researchers who are using dense sensing networks. We can now monitor different processes – from short to long term. We gain new insights, and it is rapidly transform into local, regional, national and global responses.
After her talk, a set of responses was organised from a panel, including: Mike Goodchild, John Wilson, Marco Paniho , Ming Tsou, and Cyrus Shahabi.
John: discussion about GIScience – the examples that we’ve seen point to future challenges. We can train people in the spatial sciences, and insist that they’ll learn another area, or change the earth sciences, so people learn about spatial issues, or somewhere in between, with people becoming aware of each other language. Spatial scientists have little capacity to learn a new areas – and same is true for earth scientists. The only viable path is to work together – it’s about working in interdisciplinary teams and enabling people to work with them. Data acquisition is moving fast and it is a challenge to train graduates in this area. Only recently we start thinking about solutions. Academics are experts in dealing with problems in the world, and instead we need to suggest solutions and test them.
Marco: the principle and ideas are problems that are familiar in GIScience although the specific domain of the problem was not familiar. Issues of resolution and scale are familiar in GIScience. We have a long way to go in terms of details of describing a phenomena. We need to see how systematic are we now in acquiring data? We need details of the maps of the heating of the ocean, and understanding what is going on. What is the role of remote sensing in helping us in monitoring global phenomena? We need to think about down-scaling – get from aggregate data to more detailed understanding something locally. What is the role of citizens in providing highly local information on phenomena?
Ming: we need to remembers about ‘how to lie with maps?’ – we need to be very careful about visualisations and cartographic visualisation. Each map is using projections, cartographic representation, and we need to think if it is the appropriate way to ask if that is the appropriate way to present the information? How can we deliver meaningful animation. Cartography is changing fast, but today we need to look at 2000-5000 scale, but we are using now levels and not scale. The networks and models of wildfire are raising questions about which model is appropriate, how many variables we need and which sources of information, as well as the speed of the modelling. Need to think which model is appropriately used.
Cyrius: there are more and more sensors in different context, and with machine learning we have an increased ability to monitor cities. In case of existing models – we have cases of using more data analysis in computer science.
Margaret: we have new ability to move from data, model, analysis and keep the cycle going. In the past, there was gulf between modelling or observations, we don’t see a divide any more and see people going between the modelling and the data.
Discussion points: We need to consider what is the messages that we want to communicate in our maps – we need to embrace other disciplines in improving communication. We need to implement solutions – how much uncertainty you are willing to accept. Every single map or set of data is open and other people can look and change it – this is a profound change.
The earth system is an interrelated system – but we tend to look at specific variables, but data is coming in different resolutions, and details that make it difficult to integrate. Spatial statistics is the way to carry out such integration, the question is how do we achieve that.
It’s not enough to have data as open but the issue is how to allow people to use it – issues of metadata, making it able to talk with other data sets. Esri provide a mechanism to share and address the data.
There is uncomfortable relationships between science and policy – the better the models, there is more complex the issue of discussing them with the public. How to translate decimal points to adjectives for policy making. This creates an issue to communicate with the public and policy makers. There is a need to educate scientists to be able to communicate with the wider public.
Another issue of interdisciplinarity – encouraged as graduate, but not when it come to the profession. There are different paths. Once you land a job, it is up to how you behave and perform.
Considering the pathways of integration, the challenge between the modellers and the observationalists. We can think about identifying a path.
Machine learning might need to re-evaluate how we learn and know something. There is also need to think about which statistics we want to use.
Margaret: what is different now – a growing sense of lack of being able to characterised the things that are going on. The understanding about our ignorance: in the past we had simple linear expectations of understanding. We finding that we don’t understand, and the biosphere and what is does to the world. There are so many viruses in the sea air, and we don’t know what it does to the world. The big revolution is the insights into the complexity of the earth system. How not to simplify beyond the point that we will loose important insight!