7,478 research outputs found
Remote Sensing/gis Integration for Site Planning and Resource Management
The development of an interactive/batch gridded information system (array of cells georeferenced to USGS quad sheets) and interfacing application programs (e.g., hydrologic models) is discussed. This system allows non-programer users to request any data set(s) stored in the data base by inputing any random polygon's (watershed, political zone) boundary points. The data base information contained within this polygon can be used to produce maps, statistics, and define model parameters for the area. Present/proposed conditions for the area may be compared by inputing future usage (land cover, soils, slope, etc.). This system, known as the Hydrologic Analysis Program (HAP), is especially effective in the real time analysis of proposed land cover changes on runoff hydrographs and graphics/statistics resource inventories of random study area/watersheds
Site participation in the small community experiment
The Small Community Solar Thermal Experiment, planned to test a small, developmental solar thermal power plant in a small community application, is assessed. The baseline plan is to install a field of parabolic dishes with distributed generation to provide 1 MWe of experimental power. Participation by the site proposer is an integral element of the experiment; the proposer will provide a ten-acre site, a connection to the electrical distributional system serving the small community, and various services. In addition to the primary participant, site study efforts may be pursued at as many as five alternative sites
Exponential-family Random Network Models
Random graphs, where the connections between nodes are considered random
variables, have wide applicability in the social sciences. Exponential-family
Random Graph Models (ERGM) have shown themselves to be a useful class of models
for representing com- plex social phenomena. We generalize ERGM by also
modeling nodal attributes as random variates, thus creating a random model of
the full network, which we call Exponential-family Random Network Models
(ERNM). We demonstrate how this framework allows a new formu- lation for
logistic regression in network data. We develop likelihood-based inference for
the model and an MCMC algorithm to implement it. This new model formulation is
used to analyze a peer social network from the National Lon- gitudinal Study of
Adolescent Health. We model the relationship between substance use and
friendship relations, and show how the results differ from the standard use of
logistic regression on network data
Analysis of Partially Observed Networks via Exponential-family Random Network Models
Exponential-family random network (ERN) models specify a joint representation
of both the dyads of a network and nodal characteristics. This class of models
allow the nodal characteristics to be modelled as stochastic processes,
expanding the range and realism of exponential-family approaches to network
modelling. In this paper we develop a theory of inference for ERN models when
only part of the network is observed, as well as specific methodology for
missing data, including non-ignorable mechanisms for network-based sampling
designs and for latent class models. In particular, we consider data collected
via contact tracing, of considerable importance to infectious disease
epidemiology and public health
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