scholarly journals Detecting systemic change in a land use system by Bayesian data assimilation

2016 ◽  
Vol 75 ◽  
pp. 424-438 ◽  
Author(s):  
Judith A. Verstegen ◽  
Derek Karssenberg ◽  
Floor van der Hilst ◽  
André P.C. Faaij
Keyword(s):  
Land Use ◽  
Data Assimilation ◽  
Systemic Change ◽  
Land Use System ◽  
Bayesian Data Assimilation

scholarly journals Estimation of land-use change using a Bayesian data assimilation approach

2017 ◽  
Author(s):  
Peter Levy ◽  
Marcel Van Oijen ◽  
Gwen Buys ◽  
Sam Tomlinson
Keyword(s):  
Land Use ◽  
Data Assimilation ◽  
Land Use Change ◽  
Census Data ◽  
Likelihood Function ◽  
Skewed Distribution ◽  
Data Sources ◽  
Modelling Uncertainty ◽  
Bayesian Data Assimilation ◽  
Posterior Estimate

Abstract. We present a method for estimating land-use change using a Bayesian data assimilation approach. The approach provides a general framework for combining multiple disparate data sources with a simple model. This allows us to constrain estimates of gross land-use change with reliable national-scale census data, whilst retaining the detailed information available from several other sources. Eight different data sources, with three different data structures, were combined in our posterior estimate of land-use and land-use change, and other data sources could easily be added in future. The tendency for observations to underestimate gross land-use change is accounted for by allowing for a skewed distribution in the likelihood function. The data structure produced has high temporal and spatial resolution, and is appropriate for dynamic process-based modelling. Uncertainty is propagated appropriately into the output, so we have a full posterior distribution of output and parameters. The data are available in the widely used netCDF file format from http://eidc.ceh.ac.uk/ (doi pending).

Identifying a land use change cellular automaton by Bayesian data assimilation

2014 ◽  
Vol 53 ◽  
pp. 121-136 ◽  
Author(s):  
Judith A. Verstegen ◽  
Derek Karssenberg ◽  
Floor van der Hilst ◽  
André P.C. Faaij
Keyword(s):  
Land Use ◽  
Data Assimilation ◽  
Land Use Change ◽  
Cellular Automaton ◽  
Bayesian Data Assimilation

A Bayesian data assimilation approach to estimating land-use change

2021 ◽  
Author(s):  
Peter E. Levy
Keyword(s):  
Land Use ◽  
Data Assimilation ◽  
Land Use Change ◽  
Census Data ◽  
Joint Probability ◽  
Data Cube ◽  
Joint Probability Distribution ◽  
Multiple Sources ◽  
Time Data ◽  
Bayesian Data Assimilation

<p>The aim of this work was to make improved estimates of land-use change in the UK, using multiple sources of data. We applied a method for estimating land-use change using a Bayesian data assimilation approach. This allows us to constrain estimates of gross land-use change with national-scale census data, whilst retaining the detailed information available from several other sources. We produced a time series of maps describing our best estimate of land-use change given the available data, as well as the full posterior distribution of this space-time data cube. This quantifies the joint probability distribution of the parameters, and properly propagates the uncertainty from input data to final output. The output data has been summarised in the form of land-use vectors. The results show that we can provide improved estimates of past land-use change using this method. The main advantage of the approach is that it provides a coherent, generalised framework for combining multiple disparate sources of data, and adding further sources of data in future is straightforward.</p>

scholarly journals Estimation of gross land-use change and its uncertainty using a Bayesian data assimilation approach

2018 ◽  
Vol 15 (5) ◽  
pp. 1497-1513 ◽  
Author(s):  
Peter Levy ◽  
Marcel van Oijen ◽  
Gwen Buys ◽  
Sam Tomlinson
Keyword(s):  
Land Use ◽  
Data Assimilation ◽  
Land Use Change ◽  
Census Data ◽  
Likelihood Function ◽  
Skewed Distribution ◽  
Data Sources ◽  
Modelling Uncertainty ◽  
Bayesian Data Assimilation ◽  
Posterior Estimate

Abstract. We present a method for estimating land-use change using a Bayesian data assimilation approach. The approach provides a general framework for combining multiple disparate data sources with a simple model. This allows us to constrain estimates of gross land-use change with reliable national-scale census data, whilst retaining the detailed information available from several other sources. Eight different data sources, with three different data structures, were combined in our posterior estimate of land use and land-use change, and other data sources could easily be added in future. The tendency for observations to underestimate gross land-use change is accounted for by allowing for a skewed distribution in the likelihood function. The data structure produced has high temporal and spatial resolution, and is appropriate for dynamic process-based modelling. Uncertainty is propagated appropriately into the output, so we have a full posterior distribution of output and parameters. The data are available in the widely used netCDF file format from http://eidc.ceh.ac.uk/.

Transportation Economic and Land Use System

1998 ◽  
Vol 1617 (1) ◽  
pp. 84-89 ◽  
Author(s):  
Louis J. Pignataro ◽  
Joseph Wen ◽  
Robert Burchell ◽  
Michael L. Lahr ◽  
Ann Strauss-Wieder
Keyword(s):  
Land Use ◽  
Information System ◽  
Management Tool ◽  
Improvement Program ◽  
Transportation Projects ◽  
Accessible Information ◽  
Surface Transportation ◽  
Land Use System ◽  
Land Use Effects ◽  
Computer Based

The purpose of the Transportation Economic and Land Use System (TELUS) is to convert the transportation improvement program (TIP) into a management tool. Accordingly, the system provides detailed and easily accessible information on transportation projects in the region, as well as their interrelationships and impacts. By doing so, TELUS enables public-sector agencies to meet organizational, Intermodal Surface Transportation Efficiency Act, state, and other mandates more effectively. The objectives are accomplished by providing the computer-based capability to analyze, sort, combine, and track transportation projects in or under consideration for a TIP; assessing the interrelationships among significant transportation projects; estimating the regional economic and land use effects of transportation projects; and presenting project information in an easily understood format, including geographic information system formats.

Sign in / Sign up

Export Citation Format

Share Document