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2 edition of Resolving the spatial limitations of parish agricultural census data by areal interpolation found in the catalog.

Resolving the spatial limitations of parish agricultural census data by areal interpolation

Paul Allanson

Resolving the spatial limitations of parish agricultural census data by areal interpolation

by Paul Allanson

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Published by Countryside Change Unit, Dept. of Agricultural Economics & Food Marketing, University of Newcastle upon Tyne in Newcastle upon Tyne .
Written in English


Edition Notes

At head of cover: ESRC Countryside Change Initiative.

StatementPaul Allanson, Ben White.
SeriesCountryside change working paper series -- 20
ContributionsWhite, Ben., ESRC Countryside Change Initiative.
ID Numbers
Open LibraryOL13974883M

  The US Census provides an incredible wealth of data but it’s not always easy to work with it. In the past, working with the tabular and spatial census data generally meant downloading a table from FactFinder and a shapefile from the boundary files site and joining the two, perhaps in a GIS system. These files could also be handled in R but getting the data, reading it into R and, in. Past studies have found associations between land and poverty, however these studies are usually focused on small areas using ground based studies. This research explored and quantified associations between socioeconomic conditions derived from national census data and environmental metrics derived from remotely sensed imagery from Earth observation satellites on an extensive spatial scale.

  As pycnophylactic interpolation works mass-preserving, it was chosen among other existing areal interpolation approaches that solely rely on census data (Qiu, Zhang, & Zhou, ). Version B is considered a major improvement of the overall population redistribution as it removes unrealistic administrative border effects. Areal Representation Issues in Using Census Data for Urban Research: explore the various strengths and limitations that are associated with the formats of the spatial data that they use for research. This thesis explores the effects of different census data formats on spatial analyses within Relating Census Data and Spatial.

GIS and spatial analysis in the dissemination of census data Oswaldo Palma INEGI-México • The main objective in the census data collection processes is: Ensure the complete territorial coverage. – Conteo – Economic censuses – Agricultural census. Keywords: spatial data, spatial analysis, spatial data handling, US Census, demography, R. 1. Introduction The US Decennial Census is arguably the most important data set for social science research in the United States. The US conducts a census of the entire population every ten years to de-.


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Resolving the spatial limitations of parish agricultural census data by areal interpolation by Paul Allanson Download PDF EPUB FB2

Resolving the spatial limitations of parish agricultural census data by areal interpolation By P. Allanson, B White and Newcastle upon Tyne Univ. (United Kingdom). Dept. of Agricultural Economics and Food Marketing. Allanson, P. & White, B. Resolving Spatial Limitations of the Parish Agricultural Census Data by Areal Interpolation.

CCU Working Paper Department of Agricultural Economics and Food Marketing, University of Newcastle Upon Tyne. Google ScholarCited by: available at neighborhood level; thus, areal interpolation is necessary by using the neighborhoods as the target zones.

Three datasets were obtained to assist interpolation: the ACS data, the decennial census data, and the zoning and parcel data.

The ACS data is our source data, the others ancillary : XiaoHang Liu, Alexis Martinez. Investigating the impact of sampling designs on data interpolation A short introduction about the design effect on data interpolation A simulation experiment to assess the impact of the design effect on spatial interpolation Remarks and findings P F, Savage D, and White B Areal interpolation of parish Agricultural Census data.

In Whitby M C (ed) Land Use Change: The Causes and Consequences. London, HMSO (ITE. Population numbers at local levels are fundamental data for many applications, including the delivery and planning of services, election preparation, and response to disasters. In resource-poor settings, recent and reliable demographic data at subnational scales can often be lacking.

National population and housing census data can be outdated, inaccurate, or missing key groups or areas. Spatial Data Analysis: Theory and Practice, first published inprovides a broad ranging treatment of the field of spatial data analysis. It begins with an overview of spatial data analysis and the importance of location (place, context and space) in scientific and policy related research.

Spatial disaggregation and areal interpolation methods have also been previously explored in the context of historical data, for instance to estimate population in one census year (i.e., the.

Spatial disaggregation and areal interpolation methods have also been previously explored in the context of historical data, for instance to estimate population in one census year (i.e., the source regions) within the units of another year (i.e., the target regions), in order to construct temporally consistent small census units [18,19,25,26,   The need to combine spatial data representing sociodemographic information across incompatible spatial units is a common problem for demographers.

A particular concern is computing small area trends when aggregation zone boundaries change during the trend interval. To that end, this study provides an example of dasymetric areal interpolation using the pre-classified land cover data.

AbstractHigh resolution colour mapping using modern technology has allowed us to explore the breadth of census data available to the contemporary social scientist. An examination of methods used to create cartograms, which minimise visual bias involves considering how densities and area boundaries should be mapped.

The Modifiable Area Unit Problem is discussed and the advantages of using three. A comparison of simple areal weighting and intelligent areal interpolation using ancillary data inputs. (a) Target zone t occupies 25% of the area of source zone s. With no other information available, population in s is assumed to be evenly distributed; thus target zone t is estimated to contain 25% of the population of s.

Goodchild MF, Lam N. Areal interpolation: A variant of the traditional spatial problem. Geo-Processing. ; – Gregory IN. The accuracy of areal interpolation techniques: standardising 19th and 20th century census data to allow long-term comparisons.

Computers, Environment and Urban Systems. ; – Gregory IN, Ell PS. The Living Atlas of the World now contains layers about various population and housing topics with data sourced straight from the most recent 5-year estimates offered by the Census Bureau’s American Community Survey (ACS).The layers are updated each year behind the scenes when the newest data is released by the ACS (December), meaning that you are always accessing the most current data.

Population grids can be built from census data through the application of spatial disaggregation methods (Monteiro et al., ), which range in complexity from simple mass- preserving areal weighting, to intelligent dasymetric weighting schemes that leverage regression analysis to combine multiple sources of ancillary data.

sealed surfaces combined with census data and other geospatial data can serve such user needs and offer additional application potential. In order to model the distribution of the residential population recorded at the level of administrative units according to the actually occupied areas, areal interpolation methods are employed.

Popular Articles (12 months)»Turbidity and Secchi disc depth with Sentinel-2 in different trophic status reservoirs at the Comunidad Valenciana views since: »Identification of disturbances in the Colombian tropical rainforest from Landsat satellite image time series using the Landtrendr algorithm views since: »Potential distribution model of Leontochir.

Areal interpolation is used to dis-aggregate census data into areas or zones that are compatible and can be analyzed. In this project, two population distribution models are compared using areal interpolation.

The two distribution models evaluated consist of simple areal weighting and a dasymetric-based approach. Simple areal weighting is used. () explored errors in areal interpolation for temporal census data. All of these studies suggest that the accuracy of areal interpolation depends on a combination of factors, including method used, the nature of the variable being interpolated, the nature of the ancillary data, and the shape and size of both the source and target zones.

Introduction. Accurate spatial data sets that represent the distributions of human populations are critical in many health, economic, and environmental fields across various temporal and spatial scales [1–3].Considering an estimated world population increase of billion people between andwith more than 50% of that growth absorbed into urban areas [], the ramifications of.

Summary. Evaluation of Population Census Data through Demographic Analysis. Demographic analysis is an important tool for evaluating census data, particularly in countries where independent sources of data, such as vital registration and sample surveys, are lacking or where a post-enumeration survey (PES) is not conducted.

Spatial Analysis means to manipulate geographic data to extract new meaningful information. Interpolation is one of such geostatistical methods in which we use known values at sampled points to.Areal interpolation from census blocks Among U.S. census units, blocks are the smallest and most numerous unit by a large margin.

In census data, there are million blocks (excluding Puerto Rico and other territories), 39 times more than the next most numerous unit, block groups.