2 edition of choice of estimation technique for the areal interpolation of land use data found in the catalog.
choice of estimation technique for the areal interpolation of land use data
by Countryside Change Unit, Dept. of Agricultural Economics & Food Marketing, University of Newcastle upon Tyne in Newcastle upon Tyne
Written in English
At head of cover: ESRC Countryside ChangeInitiative.
|Statement||Paul Allanson, Andrew Moxey.|
|Series||Countryside change working paper series -- 42|
|Contributions||Moxey, Andrew., ESRC Countryside Change Initiative.|
Drought monitoring is an essential component of drought risk management. Drought indices—functions of precipitation showing the severity of dryness during a particular time period—are often used for . Estimation using TSA is limited by problems associated with edge effects and multicollinearity caused by spatial autocorrelation. TSA assumes errors are independent. In addition to use as a spatial interpolation technique, TSA receives use in the removal of broad trends prior .
Myers, D.E., Interpolation and estimation with spatially located data. Chemometrics and Intelligent Luboratoty Systems, Kriging is a regression method used with irregularly spaced data in l-, 2- or 3-space for the estimation of values at unsampled locations or for the estimation of the spatial average over a length, area or. Van Huyen Do, Christine Thomas-Agnan, and Anne Vanhems, “Accuracy of areal interpolation methods for count data”, Spatial Statistics, vol. 14, November , pp. –
Spatial analysis or spatial statistics is a type of geographical analysis that explains the behavioral patterns of humans, animals, epidemics, etc and their spatial expression in terms of geometry. Examples of spatial analysis are nearest neighbor analysis and Thiessen also deals with any of the formal techniques which study entities using their topological, geometric, or. Lecture: Interpolation and approximation methods and principles Lecturer: Helena Mitasova Course: NCSU GIS/MEA Geospatial Modeling and Analysis Materials.
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Areal interpolation in the ArcGIS Geostatistical Analyst extension is a geostatistical interpolation technique that extends kriging theory to data averaged or aggregated over polygons.
Predictions and standard errors can be made for all points within and between the input polygons, and predictions (along with standard errors) can then be. Ancillary data sources that have been used include raster based land use and topographic data, and vector data such as streets and roads (Reibel and Bufalino ), address point data sets (Moon.
Introduction This exercise demonstrates how to use areal interpolation to take data collected at one set of polygons (the source polygons) and predict the data values for a new set of polygons (the target polygons). The data in this exercise involves obesity rates among fifth grade students in the Los Angeles area (for privacy reasons, the original data has been altered).
preclassified land cover data sets for different areal interpolation methods, but it also examines different enhancements of the land cover data sets for improving the accuracy of these areal interpolation methods.
Study area and data The study area consists of a nine-town region in Hartford County, Connecticut (Figure 1). A Framework for the Areal Interpolation of Socioeconomic Data Data on land cover/use reduces the deviation to %. This technique -areal interpolation or areal weighting -is a form of.
On the other hand, areal interpolation methods that do use supplementary data have also been suggested. Representative examples of these methods are the dasymetric method , which utilizes land- use data, and the method of linear regression based method , which uses aggregated by: 4.
Hi, My question is in regards to using the areal interpolation and data normality. I am currently working on an thesis with the purpose to describe agricultural soils in my region. To do so, a 20 ha grid was superimposed over the region and for each cell a composite soil samples consisting of 20 soil cores was taken in a W pattern over the entire s: 2.
of the rainfall data was investigated using the double mass curve technique. Rainfall data at 68 rainfall stations were shown to be reliable and were therefore used for further analysis. To be able to distinguish the effectiveness of each technique for areal rainfall interpolation, large amounts of rainfall give better results than small File Size: KB.
In contrast to the bottom-up approach of microsimulation, areal interpolation entails distributing administrative level census data across a finer scale to produce a detailed population surface. Areal interpolation is of interest for SAE problems when coarse scale population information is available, but small-scale measurements in areas of Cited by: 6.
types, and land use, areal interpolation needs to be performed because the boundaries of these areal units are seldom spatially congruent with those of the census.
Many areal interpolation algorithms have been proposed in the literature based on different assumptions regarding the underlying distribution of the population. The sim. 9 Preface This Technical Report on Spatial Disaggregation and Small-Area Estimation Methods for Agricultural Surveys: Solutions and Perspectives was prepared within the framework of the Global Strategy to Improve Agricultural and Rural Statistics.
The Global Strategy is an initiative endorsed in by the United Nations Statistical. I would like to be able to upload my own data in ArcGIS Online in one boundary and use the underlying block populations to re-cut the data in another boundary. I know this is possible through enriching data in AGOL for data supplied by Esri, but I would like to be able to upload my own data and have the same functionality.
This paper reports on a research project concerned with the areal interpolation problem — the problem of comparing different data sets when they have been made available for different zonal systems. Our approach is based on using additional information to guide the interpolation by: Control data, which are spatially correlated with the distribution of the attributes being estimated in areal interpolation, are used to improve the quality of the estimation.
Over time the number and variety of control layers used in areal interpolation has increased as Cited by: Bayesian areal interpolation, estimation, and smoothing produces not only a point estimate for each interpolated count, but in fact an estimate of the entire posterior distribution for each count.
In particular, variance estimates asso ciated with each point estimate are. Estimation of Missing Rainfall Data Using Spatial Interpolation and Imputation Methods Noor Fadhilah Ahmad Radia, Roslinazairimah Zakariab and Muhammad Az- zuhri Azmanc a Institute of Engineering Mathematics, Universiti Malaysia Perlis, Taman Bukit Kubu.
Abstract. Spatially distributed measurements of precipitation have gained renewed interest in connection with the spread of distributed hydrological modeling and the increased use of remotely sensed data for a number of tasks such as land use and climate-change impact studies, determination of water budgets at different temporal and spatial scales, etc.
Point precipitation measurements are Cited by: 1. Areal interpolation is the data transfer from one zonal system to another. A survey of previous literature on this subject points out that the most effective methods for areal interpolation are the intelligent approaches, which often take two-dimensional (2-D) land use.
The challenge of transforming spatial data collected at one scale to another scale, often referred to as areal interpolation or cross-area estimation, has long been recognized in spatial analysis .In many cases, geographic boundaries, such as counties, are unsuitable in terms of the units needed for meaningful data by: 6.
Data. Sources of data used in these analyses are listed in Table primary survey data came from the Community Survey, a population based random-digit-dialing telephone survey. 10 Data were collected in from residents in Baltimore City/County, Maryland; Forsyth County, North Carolina; Northern Manhattan and Bronx, New York.
While the Forsyth site was the most rural of the 3 Cited by:. Areal interpolation (or cross-area estimation) enables us to recalculate values from one polygonal delimitation to another. It is understood as “a set of methods that can estimate an aggregate attribute of one areal unit system based on that of another, spatially incongruent, system in which the attribute data were collected” (, p.
). In Author: Pavlína Netrdová, Vojtěch Nosek, Pavol Hurbánek.Evaluate the sample data. Do this to get an idea on how data are distributed in the area, as this may provide hints on which interpolation method to use.
Apply an interpolation method which is most suitable to both the sample data and the study objectives. When you are in doubt, try several methods, if .the ability to work with data from sources of different accuracies; and spatial support, having to do with differences of spatial sampling.
The case of areal interpolation is explored, and it is shown that all known methods of areal interpolation can be brought within a single, unified framework based onFile Size: KB.