Welcome to the kellylab page!

Our motto is "mapping for a changing California", and we use a range of mapping techniques - remote sensing, object-based image analysis, geospatial modeling, lidar analysis, participatory webGIS and field-based monitoring - to answer applied questions about how and why California landscapes are changing, and what that means for the people who live on, derive sustenance from, and manage them. Here you will find information on people in our lab, our projects, and some connections to other groups and sites of interest. For more on geospatial technology on campus, check out the geospatial innovation facility (GIF) and the GIS@Berkeley website. Enjoy, check out the blog, and stay in touch.


Spatial Data Science: a new map for the 21st century

We live in a great time for geospatial technology. Developments in virtual globes have created a spatially enabled society; the role of the citizen in creating and sharing spatial data has revolutionized data collection; new imagery streams from micro-satellites is poised to transform the way we think about remote sensing and imagery restrictions; data from the past is being digitized and archived at record rates; and we can now go inside forests, cities and oceans like never before to map, monitor and record. We have a new and complicated mapping toolkit today. How do we integrate data from such a diverse stream of sources to answer key questions about sustainabilty?

Through discussions with Kevin Koy, Jenny Palomino and others, I have been developing ideas around core concepts of Spatial Data Science. To my mind, many of the challenging natural resource challenges that we face today – water supply, food scarcity, invasive species, fire, climate change – are large in spatial scale and impact diverse public groups. Addressing these challenges requires a number of things: coordinated monitoring, data collection and synthesis, novel analytical tools, and increased communication and cooperation between scientists and citizens. Maps and mapping are at the heart of this. Spatial Data Science is the field that unites the data, analytics and communication aspects of these solutions. Spatial Data Science revolves around the integration of data – from aircraft, satellites, mobile phones, historic collections, the web; with core spatial concepts – spatial data is characterized by location or neighborhood, by format: field, object, network, event, and by scale and accuracy; application of methods – understanding spatial density, pattern and distribution, coincidence or interactions of factors across space, probability or risk of an event occurring in space, and measures of interconnectedness; and of collaborations of people - scientists, policy-makers, and the public.

We need targeted training (and we are developing a new workshop for this purpose); we need frameworks for data synthesis; we need to help natural resource managers navigate this complex terrain of data and analytics; and we need to continue to network and keep abreast of the rapidly changing field.

Thoughts for the new semester. Welcome back to Fall 2014.