Geostatistics deals with continuous variation over space and emphasizes the idea of spatial correlation via covariance. It is widely used for spatial interpolation. We will use ArcGIS and R to explore and develop an understanding of variogram and kriging and how they can be used for robust and unbiased interpolation of data over space. The workshop will also highlight computational aspects involved in implementing geostatistical models for relatively large data.
This is the first workshop in a series of three workshops that will cover spatial modeling of three broad classes of data: (i) spatial point pattern, (ii) discrete spatial variation on areal units, and (iii) continuous spatial variation.
Spatial point (and marked point) process models help us analyze the geometrical pattern of points in space and find application in a variety of fields including image processing, public health, forestry, ecology, and business. This workshop will provide an introduction to the point process model focusing on the conceptual aspects and implementation in R.
Google Earth Engine (GEE) combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities. This workshop will provide an introduction to GEE. We will cover data models in GEE, basic vector and raster operations, and classification in both feature and image space.
CSCAR announces a reading and discussion group Data science in understanding and addressing climate change that will meet on the third or fourth (depending on the preferences of participants) Friday of every month between 3 and 5 pm. We will discuss reports and significant papers that illuminate fundamental issues in climate change science, policy, and management. The suggested format at this stage is that we discuss one science and one policy (or management) paper or chapter. The focus will be on the spatial (and temporal) dimensions of the issue and we will concentrate more on methods and techniques keeping the requirement for domain knowledge relatively low. We will lay emphasis on the conceptual part of the tools and techniques so that it is accessible to a wider set of participants, but will also get into the technical details.
This is an effort to bring people involved in climate change together from a data science perspective. The idea is to learn together in a fun environment and foster dialogue with a focus on how data science can provide the common ground for mutual learning and understanding.
We will meet in Rackham, but we will be open to rotating the location. You will be able to participate remotely, if you choose to.
If you are interested send an email to Manish Verma at email@example.com
If you have any suggestion for discussion and reading let us know. We will include chapters from the IPCC and US global change science programs in our discussion.
High-resolution satellite data from NASA’s Orbiting Carbon Observatory-2 are revealing the subtle ways that carbon links everything on Earth – the ocean, land, atmosphere, terrestrial ecosystems and human activities. Scientists using the first 2 1/2 years of OCO-2 data have published a special collection of five papers today in the journal Science that demonstrates the breadth of this research. In addition to showing how drought and heat in tropical forests affected global carbon dioxide levels during the 2015-16 El Niño, other results from these papers focus on ocean carbon release and absorption, urban emissions and a new way to study photosynthesis. A final paper by OCO-2 Deputy Project Scientist Annmarie Eldering of NASA’s Jet Propulsion Laboratory in Pasadena, California, and colleagues gives an overview of the state of OCO-2 science.
Manish Verma, a Geospatial/Data Science Consultant at the University of Michigan’s Consulting for Statistics, Computing and Analytics Research (CSCAR) unit, contributed as a coauthor to an article on a new way to measure photosynthesis over time and space.
Using data from the OCO-2, Verma’s analysis helped expand the utility of measurements of solar induced fluorescence (SIF), which indicates active photosynthesis in plants. Verma’s work showed that SIF data collected from the OCO-2 satellite provides reliable information on the variability of photosynthesis at a much smaller scale — down to individual ecosystems.
This can, in turn, “lead to more reliable estimates of carbon sources — that is, when, where, why and how carbon is exchanged between land and atmosphere — as well as a deeper understanding of carbon-climate feedbacks,” according to the Science article.
For more, see the NASA press release (https://www.nasa.gov/feature/jpl/new-insights-from-oco-2-showcased-in-science) and the Science article (http://science.sciencemag.org/content/358/6360/eaam5747.full)