2 edition of Spatial land-use inventory, modeling, and projection found in the catalog.
Spatial land-use inventory, modeling, and projection
|Statement||Craig Tom, Lee D.Miller and Jerrold W.Christenson.|
|Series||Technical memorandum / National Aeronautics and Space Administration -- 79710|
|Contributions||Miller, Lee D., Christenson, Jerrold W., United States. National Aeronautics and Space Administration.|
|The Physical Object|
|Pagination||xv, 210p. :|
|Number of Pages||210|
Durand MG., Le Berre M. () Spatial Systems Modelling: From Land Use Planning to a Geographical Theory Approach. In: Hauer J., Timmermans H., Wrigley N. (eds) Urban Dynamics and Spatial Choice Behaviour. Theory and Decision Library (An International Series in the Philosophy and Methodology of the Social and Behavioral Sciences), vol Author: Marie-Genevieve Durand, Maryvonne Le Berre. Spatial Modeling of Land Use and Land Cover Change Daniel G. Brown School of Natural Resources and Environment. University of Michigan. Synthesis Products Modeling Chapter in LCLUC book Modeling Section in CCSP Land Use Land Cover Change Science Plan Reference: Brown, D.G., Walker, R., Manson, S., Seto, K. Projection Spatial Extent.
Spatial Sequential Modeling and Predication of Global Land Use and Land Cover Changes by Integrating a Global Change Assessment Model and Cellular Automata during the projection . The inventory will identify datasets and note their location, ownership, format, and appraise coverage and confidence in the data quality. The full results of the audit are presented in Natura in Wales Inventory of Spatial Data Inventory (MS Excel spreadsheet). The results are summarised in this report. Key findings from compiling the.
Recent Updates to Spatial Surrogates for Modeling U.S. Emissions Sources Z. Adelman, B. Naess, M. Omary, L. Ran Center for Environmental Modeling for Policy Development University of North Carolina –Institute for the Environment A. Bar-Ilan, T. Shah ENVIRON International Corporation R. Mason, A. Eyth, A. Zubrow Emissions Inventory and. A Spatial Ensemble Model for Rockfall Source Identification From High Resolution LiDAR Data and GIS were examined. This is based on the selected best subset of conditioning factors and inventory dataset. Stacking LR-RT (the best fit model) was then utilized to produce the probabilities of different landslide types. In order to reduce Cited by:
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Get this from a library. Spatial land-use inventory, modeling and projection: Denver metropolitan area, with inputs from existing maps, airphotos, and Landsat imagery.
[C Tom; L D Miller; J W Christenson; Geological Survey (U.S.); Goddard Space Flight Center.]. Seven multivariate land-use projection models predicting spatial land-use changes achieved accuracies from 42 to 57 percent.
A final modeling strategy was designed, which combines both Markov trend and multivariate spatial projection : C. Tom, J.
Christenson and L. Miller. This text does not contain tutorials - it is a collection of chapters detailing various aspects of modeling and spatial analysis with respect to geographic information systems. Any graduate student or researcher looking into hydrologic or land-use transition modeling will find this text particularly useful.3/5(5).
This book addresses spatial analysis with an emphasis on the integration of different spatial analysis functions within GIS. It focuses on developing advanced GIS functions in order to achieve the zenith in spatial analysis functions for problem solving, prediction and : Hardcover.
This book provides an overview of recent developments and applications of the Land Use Scanner model, which has been used in spatial planning for well over a decade. Internationally recognized as among the best of its kind, this versatile model can be applied at a national level for trend.
Applied Spatial Modelling and Planning shows how much geographical research is policy relevant to a wide variety of agencies modeling the use of GIS and spatial modelling in applied geography. The book’s chapters contain a cross-section of innovative applications and approaches to problem solving within five major domains of the dynamics of economic space, housing and settlements, population.
The book is the first reference to provide methods and applications for combining the use of R and GIS in modeling spatial processes. It is an essential tool for students and researchers in earth and environmental science, especially those looking to better utilize GIS and spatial modeling.
The chapters include detailed examples of the use of spatial information in land use management. The book begins with the technological methods, examines applications in a variety of environments, and describes the ways in which issues of scale, uncertainty, linkage of models and GIS, and problem solution have been addressed.
GIS, Spatial Analysis, and Modeling - Google Books. A guide for geographic analysts, modelers, software engineers, and GIS professionals, this book discusses agent-based modeling, dynamic feedback and simulation modeling, as well as links between models and GIS software.
This collection also presents a state-of-the-art understanding of applications based on environmental, atmospheric, 3/5(2). Attribution — You must attribute the work in the following manner: Based on An Introduction to Mapping and Spatial Modelling R by Richard Harris ().
Noncommercial — You may not use this work for commercial purposes. Use for education in a recognised higher education institution (a College or University) is permissible.
Spatial analysis and modelling are used for spatial statistical analysis, site selection, and identification of planning action areas, land suitability analysis, land use transport modeling, and impact assessment. The following is a brief description, presented in form of stages, on how GIS is used as an urban planning tool.
In this paper, a modeling strategy (hereinafter referred to as GCAM‐CA) that combines a global change assessment model (GCAM) with cellular automata (CA) is proposed. This modeling strategy is designed to sequentially spatialize global LUCCs with 1‐km spatial resolution and 5‐year temporal resolution from to Cited by: 1.
Temporal and spatial modeling of extreme precipitation in urban areas is a major challenge due to sparse data availability and huge spatial nonuniformity in precipitation.
High uncertainties are associated with the short-duration precipitation events, which need to be modeled and further to be considered in the design and risk analyses. The specific information and modeling method of the land use model are described in the supporting information Section S4.
Notably, nonagriculture emissions have always been neglected in ammonia inventory studies at a large spatial scale due to their relatively small contribution of total emissions (in this study, nonagriculture emissions.
Spatial Autocorrelation in Multi-Scale Land Use Models Article in Ecological Modelling () June with Reads How we measure 'reads'. However, refinements are required to properly represent the spatial heterogeneity of land use and land cover in global‐scale LUCC models.
The spatial heterogeneity of land use and land cover plays an important role in global impact assessments (Li et al., ; Meiyappan et al., ). For example, regions may have the same climate zone Cited by: 1. Statement of Problem: Land-use and land-cover modeling is a critical component for analysis and potential mitigation of the consequences of landscape change on ecological processes.
Land managers and researchers require spatially and thematically consistent land-cover data to assess historical, current, and potential future interactions among land use, climate, and a host of ecologically and. Spatial Sequential Modeling and Predication of Global Land Use and Land Cover Changes by Integrating a Global Change Assessment Model and Cellular Automata Min Cao et al-Calibration and analysis of the uncertainty in downscaling global land use and land cover projections from GCAM using Demeter (v) Min Chen et al-Cited by: CHAPTER SPATIAL ANALYSIS AND MODELING Michael F.
Goodchild University of California, Santa Barbara Introduction In the previous chapters we have seen how a wide variety of types of geographic data can be created and stored.
Methods of digitizing and scanning allow geographic data to be created from paper maps and Size: 86KB. A Spatial Econometric Analysis of Land-Use Change With Land Cover Trends Data Introduction Plot-level empirical land-use models are widely used in environmental and resource economics for policy analysis of the effects of land-use change on urban sprawl, for-est land loss, ecosystem services, and biological diversity.
Many of these analysesCited by: 4. We will load a digital elevation model (DEM) from SRTM (provided in the Data folder of Chapter10). Compute the slope from this DEM. Reproject each file to the same projection system so that we can compare one with another.
Extract the elevation and slope values corresponding to the landslide location using the DEM file and the computed slope.This book aims to demonstrate how computer methods of spatial analysis and modeling, integrated in a GIS environment, can be used to better understand reality and give rise to more informed and, thus, improved planning.Spatial optimization models allow companies to spatially plan and optimize economic activities and land use over various time periods in accordance with the company’s strategy.
Scenarios can be created and compared, and the models allow clients to analyze and determine the economic potentials of .