Have you ever wished you had your own geoportal like ArcGIS Online within your own ArcGIS Online Organization, now you can with Esri Arcgis Hub. ArcGIS Hub and the Sites it can create will allow you to do just that. Now within UCANR we can create sub sites within our organization account. We have the ability to create sub sites for other groups in UCANR like Integrated Pest Management (IPM), 4H, Master Gardeners, to name but a few. I look forward to rolling out these Sites to other groups and team within UCANR.
Day 1 at the User Conference was dominated by the Plenary talks of ESRI Owner Jack Dangermond and others. The morning plenary by Mr. Dangermond and other ESRI Staff is where they highlight the newest technology that we now have access to from ESRI. In the past I have heard of the User Conference as the “Show” and it continues to live up to that name. This year they highlighted the new machine learning and AI tools that have been integrated into ArcGIS and the new capabilities of ArcGIS Online, ArcGIS Portal, and ArcGIS Pro. Over the coming days I hope to highlight these technologies and more in greater detail.
At UC Berkeley and at UC ANR, my outreach program involves the creation, integration, and application of research-based technical knowledge for the benefit of the public, policy-makers, and land managers. My work focuses on environmental management, vegetation change, vegetation monitoring, and climate change. Critical to my work is the ANR Statewide Program in Informatics and GIS (IGIS), which I began in 2012 and is now really cranking with our crack team of IGIS people. We developed the IGIS program in 2012 to provide research technology and data support for ANR’s mission objectives through the analysis and visualization of spatial data. We use state-of-the-art web, database and mapping technology to provide acquisition, storage, and dissemination of large data sets critical to the ANR mission. We develop and delivers training on research technologies related to important agricultural and natural resource issues statewide. We facilitate networking and collaboration across ANR and UC on issues related to research technology and data. And we deliver research support through a service center for project level work that has Division-wide application. Since I am off on sabbatical, I have decided to take some time to think about my outreach program and how evaluate its impact.
There is a great literature about the history of extension since its 1914 beginnings, and specifically about how extension programs around the nation have been measuring impact. Extension has explored a variety of ways to measure the value of engagement for the public good (Franz 2011, 2014). Early attempts to measure performance focused on activity and reach: the number of individuals served and the quality of the interaction with those individuals. Through time, extension began to turn their attention to program outcomes. Recently, we’ve been focusing on articulating the Public Values of extension, via Condition Change metrics (Rennekamp and Engle 2008). One popular evaluation method has been the Logic Model, used by extension educators to evaluate the effectiveness of a program through the development of a clear workflow or plan that links program outcomes or impacts with outputs, activities and inputs. We’ve developed a fair number of these models for the Sierra Nevada Adaptive Management Program (SNAMP) for example. Impacts include measures of changes in learning, behavior, or condition change across engagement efforts. Recently, change in policy became an additional measure to evaluate impact. I also think measuring reach is needed, and possible.
So, just to throw it out there, here is my master table of impact that I try to use for measuring and evaluating impact of my outreach program, and I’d be interested to hear what you all think of it.
- Change in reach: Geographic scope, Location of events, Number of users, etc.
- Change in activity: Usage, Engagement with a technology, New users, Sessions, Average session duration
- Change in learning; Participants have learned something new from delivered content
- Change in action, behavior, method; New efficiencies, Streamlined protocols, Adoption of new data, Adoption of best practices
- Change in policy; Evidence of contributions to local, state, or federal regulations
- Change in outcome: measured conditions have improved = condition change
I recently used this framework to help me think about impact of the IGIS program, and I share some results here.
Measuring Reach. The IGIS program has developed and delivered workshops throughout California, through the leadership of Sean Hogan, Shane Feirer, and Andy Lyons (http://igis.ucanr.edu/IGISTraining). We manage and track all this activity through a custom data tracking dashboard that IGIS developed (using Google Sheets as a database linked to ArcGIS online to render maps - very cool), and thus can provide key metrics about our reach throughout California. Together, we have delivered 52 workshops across California since July 2015 and reached nearly 800 people. These include workshops on GIS for Forestry, GIS for Agriculture, Drone Technology, WebGIS, Mobile Data Collection, and other topics. This is an impressive record of reach: these workshops have served audiences throughout California. We have delivered workshops from Humboldt to the Imperial Valley, and the attendees (n=766) have come from all over California. Check this map out:
Measuring Impact. At each workshop, we provide a feedback mechanism via an evaluation form and use this input to understand client satisfaction, reported changes in learning, and reported changes in participant workflow. We’ve been doing this for years, but I now think the questions we ask on those surveys need to change. We are really capturing the client satisfaction part of the process, and we need to do a better job on the change in learning and change in action parts of the work.
Having done this exercise, I can clearly see that measuring reach and activity are perhaps the easiest things to measure. We have information tools at our fingertips to do this: online web mapping of participant zip-codes, google analytics to track website activity. Measuring the other impacts: change in action, contributions to policy and actual condition changes are tough. I think extension will continue to struggle with these, but they are critical to help us articulate our value to the public. More work to do!
Franz, Nancy K. 2011. “Advancing the Public Value Movement: Sustaining Extension During Tough Times.” Journal of Extension 49 (2): 2COM2.
———. 2014. “Measuring and Articulating the Value of Community Engagement: Lessons Learned from 100 Years of Cooperative Extension Work.” Journal of Higher Education Outreach and Engagement 18 (2): 5.
Rennekamp, Roger A., and Molly Engle. 2008. “A Case Study in Organizational Change: Evaluation in Cooperative Extension.” New Directions for Evaluation 2008 (120): 15–26.
We've just wrapped up #DroneCamp2018, hosted at beautiful UC San Diego.
This was an expanded version from last year's model, which we held in Davis. We had 52 participants (from all over the world!) who were keen to learn about drones, data analysis, new technology, and drone futures.
Day 1 was a flight day from half our participants: lots of hands-on with takeoffs and landings, and flying a mission;
Day 2 covered drone safety and regulations, with guest talks from Brandon Stark and Dominique Meyer;
Day 3 covered drone data and analysis;
Day 4 was a flight day for Group 2 and a repeat of Day 1.
We had lots of fun taking pics and tweeting: here is our wrapup on Twitter for #DroneCamp2018.
I’ve been away from the blog for awhile, but thought I’d catch up a bit. I am in beautiful Madison Wisconsin (Lake Mendota! 90 degrees! Rain! Fried cheese curds!) for the NASA LP DAAC User Working Group meeting. This is a cool deal where imagery and product users meet with NASA team leaders to review products and tools. Since this UWG process is new to me, I am highlighting some of the key fun things I learned.
What is a DAAC?
A DAAC is a Distributed Active Archive Center, run by NASA Earth Observing System Data and Information System (EOSDIS). These are discipline-specific facilities located throughout the United States. These institutions are custodians of EOS mission data and ensure that data will be easily accessible to users. Each of the 12 EOSDIS DAACs process, archive, document, and distribute data from NASA's past and current Earth-observing satellites and field measurement programs. For example, if you want to know about snow and ice data, visit the National Snow and Ice Data Center (NSIDC) DAAC. Want to know about social and population data? Visit the Socioeconomic Data and Applications Data Center (SEDAC). These centers of excellence are our taxpayer money at work collecting, storing, and sharing earth systems data that are critical to science, sustainability, economy, and well-being.
What is the LP DAAC?
The Land Processes Distributed Active Archive Center (LP DAAC) is one of several discipline-specific data centers within the NASA Earth Observing System Data and Information System (EOSDIS). The LP DAAC is located at the USGS Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota. LP DAAC promotes interdisciplinary study and understanding of terrestrial phenomena by providing data for mapping, modeling, and monitoring land-surface patterns and processes. To meet this mission, the LP DAAC ingests, processes, distributes, documents, and archives data from land-related sensors and provides the science support, user assistance, and outreach required to foster the understanding and use of these data within the land remote sensing community.
Why am I here?
Each NASA DAAC has established a User Working Group (UWG). There are 18 people on the LP DAAC committee, 12 members from the land remote sensing community at large, like me! Some cool stuff going on. Such as...
Two upcoming launches are super interesting and important to what we are working on. First, GEDI (Global Ecosystem Dynamics Investigation) will produce the first high resolution laser ranging observations of the 3D structure of the Earth. Second, ECOSTRESS (The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station), will measure the temperature of plants: stressed plants get warmer than plants with sufficient water. ECOSTRESS will use a multispectral thermal infrared radiometer to measure surface temperature. The radiometer will acquire the most detailed temperature images of the surface ever acquired from space and will be able to measure the temperature of an individual farmer's field. Both of these sensors will be deployed on the International Space Station, so data will be in swaths, not continuous global coverage. Also, we got an update from USGS on the USGS/NASA plan for the development and deployment of Landsat 10. Landsat 9 comes 2020, Landsat 10 comes ~2027.
Other Data Projects
We heard from other data providers, and of course we heard from NEON! Remember I posted a series of blogs about the excellent NEON open remote sensing workshop I attended last year. NEON also hosts a ton of important ecological data, and has been thinking through the issues associated with cloud hosting. Tristin Goulden was here to give an overview.
NASA staff gave us a series of demos on their WebGIS services; AppEEARS; and their data website. Their webGIS site uses ArcGIS Enterprise, and serves web image services, web coverage services and web mapping services from the LP DAAC collection. This might provide some key help for us in IGIS and our REC ArcGIS online toolkits. AppEEARS us their way of providing bundles of LP DAAC data to scientists. It is a data extraction and exploration tool. Their LP DAAC data website redesign (website coming soon), which was necessitated by the requirement for a permanent DOI for each data product.
LP DAAC is going full-force in user engagement: they do workshops, collect user testimonials, write great short pieces on “data in action”, work with the press, and generally get the story out about how NASA LP DAAC data is used to do good work. This is a pretty great legacy and they are committed to keep developing it. Lyndsey Harriman highlighted their excellent work here.
Grand Challenges for remote sensing
Some thoughts about our Grand Challenges: 1) Scaling: From drones to satellites. It occurs to me that an integration between the ground-to-airborne data that NEON provides and the satellite data that NASA provides had better happen soon; 2) Data Fusion/Data Assimilation/Data Synthesis, whatever you want to call it. Discovery through datasets meeting for the first time; 3) Training: new users and consumers of geospatial data and remote sensing will need to be trained; 4) Remote Sensible: Making remote sensing data work for society.
A primer on cloud computing
We spent some time on cloud computing. It has been said that cloud computing is just putting your stuff on “someone else’s computer”, but it is also making your stuff “someone else’s problem”, because cloud handles all the painful aspects of serving data: power requirements, buying servers, speccing floor space for your servers, etc. Plus, there are many advantages of cloud computing. Including: Elasticity. Elastic in computing and storage: you can scale up, or scale down or scale sideways. Elastic in terms of money: You pay for only what you use. Speed. Commercial clouds CPUs are faster than ours, and you can use as many as you want. Near real time processing, massive processing, compute intensive analysis, deep learning. Size. You can customize this; you can be fast and expensive or slow and cheap. You use as much as you need. Short-term storage of large interim results or long-term storage of data that you might use one day.
Image courtesy of Chris Lynnes
We can use the cloud as infrastructure, for sharing data and results, and as software (e.g. ArcGIS Online, Google Earth Engine). Above is a cool graphic showing one vision of the cloud as a scaled and optimized workflow that takes advantage of the cloud: from pre-processing, to analytics-optimized data store, to analysis, to visualization. Why this is a better vision: some massive processing engines, such as SPARC or others, require that data be organized in a particular way (e.g. Google Big Table, Parquet, or DataCube). This means we can really crank on processing, especially with giant raster stacks. And at each step in the workflow, end-users (be they machines or people) can interact with the data. Those are the green boxes in the figure above. Super fun discussion, leading to importance of training, and how to do this best. Tristan also mentioned Cyverse, a new NSF project, which they are testing out for their workshops.
Image attribution: Corey Coyle
Super fun couple of days. Plus: Wisconsin is green. And warm. And Lake Mendota is lovely. We were hosted at the University of Wisconsin by Mutlu Ozdogan. The campus is gorgeous! On the banks of Lake Mendota (image attribution: Corey Coyle), the 933-acre (378 ha) main campus is verdant and hilly, with tons of gorgeous 19th-century stone buildings, as well as modern ones. UW was founded when Wisconsin achieved statehood in 1848, UW–Madison is the flagship campus of the UW System. It was the first public university established in Wisconsin and remains the oldest and largest public university in the state. It became a land-grant institution in 1866. UW hosts nearly 45K undergrad and graduate students. It is big! It has a med school and a law school on campus. We were hosted in the UW red-brick Romanesque-style Science Building (opened in 1887). Not only is it the host building for the geography department, it also has the distinction of being the first buildings in the country to be constructed of all masonry and metal materials (wood was used only in window and door frames and for some floors), and may be the only one still extant. How about that! Bye Wisconsin!