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Projects of the Center

by esa — last modified 2007-10-19 12:00

Contributors: deleeuw

Mapping a Watershed: In this GIS based project we will map the Cuddy Valley - Grapevine watershed. The map will use well data from the Department of Water Resources, as well as precipitation data and stream flow measurements. We will try to get as much information as possible for the depth-to-water levels in the about 400 wells in the area, and the change in these levels over time. Data are very sparse.
Ambulance Response Times in Southern California: The data are one year of ambulance response times to 911 calls in Santa Barbara, Kern, and Los Angeles Counties. There is both location and time information in the data sets because we know where and when the call originates. We will predict ambulance response times using temporal and spatial information, as well as call characteristics, and present the results in GIS maps. Geographically Weighted Regression and point process models will be used.
Air Pollution in the Southern San Joaquin Valley: Data are ARB measurements of multiple pollutants over time at about 10 monitoring stations in the Southern San Joaquin. Data are rather sparse, because some stations are only on line for short periods, others provide measurements over 20 years. And some monitoring stations only look at a few pollutants, while others measure many more. We will try imputation techniques to eatimate missing information, use filtering and interpolation techniques, and look at other space-time methds that have been used in the literature.
Car and Truck Traffic in the I-5 Corridor: We study car and truck traffic in the I-5 corridor from the intersection with SR-14 in northern Los Angeles County to the split with SR-99 in southern Kern County. Aggregated and smoothed data from Caltrans are used (average annual daily traffic and average annual daily truck traffic) at about 20 intersections over up to 50 years. Hourly loop counts are available over one full year at 4 locations. Imputation, time series, and repeated measures techniques qill be used to analyze the data. Graphical representations of various sorts will be presented.
The CALSIM Model and Software for the State Water and Central Valley Projects: The State Water Project / Central Valley Project is a huge and complicated system of dams, reservoirs, aqueducts, pumps, and canals that brings water from Northern California to the agriculture of the Central Valley and and the urban regions of Southern California. The Department of Water Resources and the Bureau of Land Management have constructed an equally huge and complicated network model of the system, and mixed integer linear programming techniques are used to optimize deliveries to the clients (water districts and water agencies). The CALSIM-II model, and the corresponding software system, is also used to predict future deliveries of state water to the various agencies. In this project we try to understand both the model and the software, try it out on some smaller artificial systems, investigate various forms of stability and error propagation, and evaluate the software as a predictive tool.
Open Source Software for Environmental Statistics: This project tries to maintain an overview of software relevant for environmental statistics. It will cover GIS, geostatistics, spatial statistics, space-time models, and time series techniques. Emphasis will be on R and on GRASS.
Fire Hazard Estimation Using Point Process Methods: The goal of this project is to develop and assess spatial-temporal models for Los Angeles County wildfires. Currently, fire department officials rely heavily on the Burning Index (BI), which uses weather data recorded at various Remote Automatic Weather Stations, and blends these records together using a complex non-linear dynamic model in order to output a number that is often interpreted as a measure of overall wildfire hazard. However, the BI turns out not to be an effective predictor of wildfire activity in Los Angeles County. We are exploring alternatives, including simple point process models using the same weather variables as those used in the computation of the BI, as well as the history of prior local wildfire activity.
Statistical Evaluation of Earthquake Occurrance Data using Point Process Techniques: This project focuses on space-time marked point process models for California seismicity, such as the widely-used Epidemic-Type Aftershock Sequence (ETAS) models developed by Yosihiko Ogata. Our aim is to develop methods for evaluating the fit of such models to data, and to suggest improvements and refinements to the models currently in use. In particular, detailed data on seismic moment tensors have been available for most earthquakes observed in California for several years, yet other than the scalar seismic moment, which is a crude summary essentially ignoring all directional information in the tensor, the moment tensor is typically not used in ETAS models. We are exploring ways of incorporating this potentially crucial information in order to improve models and forecasts of future seismicity.
The Aerosol Measurement and Processing System (AMAPS): AMAPS is a distributed science computing and data analysis environment for aerosol science. In addition to building the grid infrastructure, this project concentrates on developing statistical methods and data analysis techniques for massive, distributed data sources. Our goal is to exploit both the distributed computing environment and the "virtual" data sets it defines. See http://amaps.jpl.nasa.gov for more information.
Hurricanes: This project concerns the use of multispectral remote sensing observations obtained from two different aircraft instruments flying linear transects through hurricanes, HAMSR (the High MIMC Sounding Radiometer) views a swath some 30km wide along the flight path in 25 spectral channels. EDOP (the ER-2 Doppler Radar) sees a narrow path directly under the aircraft in 15 channels. EDOP's observations are strongly related to rainfall and atmospheric turbulence, and our goal is to infer what EDOP would have seen had it seen the wider HAMSR swath. We are using non-linear multivariate analysis methods based on Gifi (1990) to model the relationship between HAMSR and EDOP directly under the aircraft, and spatial-statistical techniques to transfer that relationship away from the nadir (directly downward looking) view.
Statistics for Planetary Science: Multispectral imagery of Jupiter and Saturn provides a wealth of information about the structure of the atmospheres of these gas giants. Different spectral channels penetrate to different depths of their atmospheres and capture various compositional features. Principle component analysis has been a workhorse of Planetary Science because it can reveal latent structures not visible in the original images. We seek to generalize and extend this idea by applying other linear and non-linear techniques. Data come from the orbiters Galileo (Jupiter) and Cassini (Saturn), from the Hubble Space Telescope, and from ground based telescopes..
Data Fusion for Satellite Remote Sensing: A fundamental problem in the analysis of satellite remote sensing data is that of "merging" two data sets which provide complementary information. Usually, the two data sets will have different observation grids: the pixel footprints are non-nested, of different resolutions, and are oriented differently. The most common ad-hoc solution is to either aggregate the data up to a coarse, common resolution, or to "match-up" pixel centers. In this project, we seek to develop a principled methodology for fusing (merging) the data into a statistically consistent data set suitable for both exploratory and inferential science analysis.

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