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- Author: Sean Hogan
I was honored last week to be a presenter at the AmericaView Winter Business Meeting, in Reston Virginia, as representative of the CaliforniaView section of this nationwide consortium of remote sensing scientists. AmericaView shares many of the same interests as the IGIS program, such as applied remote sensing research, outreach, education, workforce development and technology transfer. At this particular meeting of over 50 remote sensing experts, I spoke about some of the ways that the University of California is using drones to advance environmental and agricultural research; including the use of automated photogrammetry for elevation modeling, thermal imagery for water use efficiency, multispectral cameras for monitoring post-fire vegetation recovery, Lidar for tree structure modeling, and hyperspectral scanners for invasive pest detection.
While my presentation and participation at the AmericaView meeting went very well, the highlight of my trip was being invited to Capitol Hill to meet with California Congressman Ami Bera, and staffers for Senator Diane Feinstein and Congressman Paul Cook. The objective of these meetings was to advance awareness for the importance of remote sensing resources, such as the USGS's provision of data from the Landsat satellite missions. In particular, our meeting with Dr. Ami Bera went fantastic. He is such a great and knowledgeable supporter of the scientific outreach we at the UC Division of Agriculture and Natural Resources undertake, and understands the importance supporting the scientific infrastructure that many of us rely on for our research and educational efforts.
Left to right – Pia Van Benthem, Dr. Ami Bera, myself, and Dr. Susan Ustin
Day 3: I opened the day with a lovely swim with Elizabeth Havice (in the largest pool in New England? Boston? The Sheraton?) and then embarked on a multi-mile walk around the fair city of Boston. The sun was out and the wind was up, showing the historical buildings and waterfront to great advantage. The 10-year old Institute of Contemporary Art was showing in a constrained space, but it did host an incredibly moving video installation from Steve McQueen (Director of 12 Years a Slave) called “Ashes” about the life and death of a young fisherman in Grenada.
My final AAG attendance involved two plenaries hosted by the Remote Sensing Specialty Group and the GIS Specialty Group, who in their wisdom, decided to host plenaries by two absolute legends in our field – Art Getis and John Jensen – at the same time. #battleofthetitans. #gisvsremotesensing. So, I tried to get what I could from both talks. I started with the Waldo Tobler Lecture given by Art Getis: The Big Data Trap: GIS and Spatial Analysis. Compelling title! His perspective as a spatial statistician on the big data phenomena is a useful one. He talks about how data are growing fast: Every minute – 98K tweets; 700K FB updates; 700K Google searches; 168+M emails sent; 1,820 TB of data created. Big data is growing in spatial work; new analytical tools are being developed, data sets are generated, and repositories are growing and becoming more numerous. But, there is a trap. And here is it. The trap of Big Data:
10 Erroneous assumptions to be wary of:
- More data are better
- Correlation = causation
- Gotta get on the bandwagon
- I have an impeccable source
- I have really good software
- I am good a creating clever illustrations
- I have taken requisite spatial data analysis courses
- It’s the scientific future
- Accessibly makes it ethical
- There is no need to sample
He then asked: what is the role of spatial scientists in the big data revolution? He says our role is to find relationships in a spatial setting; to develop technologies or methods; to create models and use simulation experiments; to develop hypotheses; to develop visualizations and to connect theory to process.
The summary from his talk is this: Start with a question; Differentiate excitement from usefulness; Appropriate scale is mandatory; and Remember more may or may not be better.
When Dr Getis finished I made a quick run down the hall to hear the end of the living legend John Jensen’s talk on drones. This man literally wrote the book on remote sensing, and he is the consummate teacher – always eager to teach and extend his excitement to a crowded room of learners. His talk was entitled Personal and Commercial Unmanned Aerial Systems (UAS) Remote Sensing and their Significance for Geographic Research. He presented a practicum about UAV hardware, software, cameras, applications, and regulations. His excitement about the subject was obvious, and at parts of his talk he did a call and response with the crowd. I came in as he was beginning his discussion on cameras, and he also discussed practical experience with flight planning, data capture, and highlighted the importance of obstacle avoidance and videography in the future. Interestingly, he has added movement to his “elements of image interpretation”. Neat. He says drones are going to be routinely part of everyday geographic field research.
What a great conference, and I feel honored to have been part of it.
Day 1: Wednesday I focused on the organized sessions on uncertainty and context in geographical data and analysis. I’ve found AAGs to be more rewarding if you focus on a theme, rather than jump from session to session. But less steps on the iWatch of course. There are nearly 30 (!) sessions of speakers who were presenting on these topics throughout the conference.
An excellent plenary session on New Developments and Perspectives on Context and Uncertainty started us off, with Mei Po Kwan and Michael Goodchild providing overviews. We need to create reliable geographical knowledge in the face of the challenges brought up by uncertainty and context, for example: people and animals move through space, phenomena are multi-scaled in space and time, data is heterogeneous, making our creation of knowledge difficult. There were sessions focusing on sampling, modeling, & patterns, on remote sensing (mine), on planning and sea level rise, on health research, on urban context and mobility, and on big data, data context, data fusion, and visualization of uncertainty. What a day! All of this is necessarily interdisciplinary. Here are some quick insights from the keynotes.
Mei Po Kwan focused on uncertainty and context in space and time:
- We all know about the MAUP concept, what about the parallel with time? The MTUP: modifiable temporal unit problem.
- Time is very complex. There are many aspects of time: momentary, time-lagged response, episodic, duration, cumulative exposure
- How do we aggregate, segment and bound spatial-temporal data in order to understand process?
- The basic message is that you must really understand uncertainty: Neighborhood effects can be overestimated if you don’t include uncertainty.
As expected, Michael Goodchild gave a master class in context and uncertainty. No one else can deliver such complex material so clearly, with a mix of theory and common sense. Inspiring. Anyway, he talked about:
- Data are a source of context:
- Vertical context – other things that are known about a location, that might predict what happens and help us understand the location;
- Horizontal context – things about neighborhoods that might help us understand what is going on.
- B oth of these aspects have associated uncertainties, which complicate analyses.
- Why is geospatial data uncertain?
- Location measurement is uncertain
- Any integration of location is also uncertain
- Observations are non-replicable
- Loss of spatial detail
- Conceptual uncertainty
- This is the paradox. We have abundant sources of spatial data, they are potentially useful. Yet all of them are subject to myriad types of uncertainty. And, the conceptual definition of context is fraught with uncertainty.
- He then talked about some tools for dealing with uncertainty, such as areal interpolation, and spatial convolution.
- He finished with some research directions, including focusing on behavior and pattern, better ways of addressing confidentiality, and development of a better suite of tools that include uncertainty?
My session went well – I got a great question from Mark Fonstad about the real independence of errors – as in canopy height and canopy base height are likely correlated, so aren’t their errors? Why do you treat them as independent? Which kind of blew my tiny mind, but Qinghua stepped in with some helpful words about the difficulties of sampling from a joint probability distribution in Monte Carlo simulations, etc.
Plus we had some great times with Jacob, Leo, Yanjun and the Green Valley International crew who were showcasing their series of Lidar instruments and software. Good times for all!
Our third GIF Spatial Data Science Bootcamp has wrapped! We had an excellent 3 days with wonderful people from a range of locations and professions and learned about open tools for managing, analyzing and visualizing spatial data. This year's bootcamp was sponsored by IGIS and GreenValley Intl (a Lidar and drone company). GreenValley showcased their new lidar backpack, and we took an excellent shot of the bootcamp participants. What is Paparazzi in lidar-speak? Lidarazzi?
Here is our spin: We live in a world where the importance and availability of spatial data are ever increasing. Today’s marketplace needs trained spatial data analysts who can:
- compile disparate data from multiple sources;
- use easily available and open technology for robust data analysis, sharing, and publication;
- apply core spatial analysis methods;
- and utilize visualization tools to communicate with project managers, the public, and other stakeholders.
At the Spatial Data Science Bootcamp we learn how to integrate modern Spatial Data Science techniques into your workflow through hands-on exercises that leverage today's latest open source and cloud/web-based technologies.