Sep 19, 2007 the need to combine spatial data representing sociodemographic information across incompatible spatial units is a common problem for demographers. Eicher, c, brewer, c, 2001, dasymetric mapping and areal interpolation. Cartography and geographic information science 28 2, 12538. Using geographically weighted regression to solve the areal. Poddar, the methods i need are not available in geostatistical analyst extension. Data may be collected for individual households, as shown by the four blocks on the left. Jan 01, 2014 areal interpolation and dasymetric modeling are similar in that both are methods for downscaling spatial data. 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 twodimensional 2d land use or onedimensional 1d road network information as ancillary data to give insight on the underlying distribution of a variable. Ijgi free fulltext areal interpolation using parcel and census. Google scholar crossref fisher, p f, langford, m, 1995, modelling the errors in areal interpolation between zonal systems by monte carlo simulation environment and.
Dasymetric spatiotemporal interpolation request pdf. If population counts were taken at the county level, a demographer may need to downscale the data to predict the population of census blocks. A third type of simple areal interpolation is based on pointbased areal interpolation lam 1983. In recent years, dasymetric interpolation techniques have witnessed fast development given new data sources, improved statistical assessment for estimating accuracy, and advancing multiple areal. Dasymetric maps of six socioeconomic variables were produced fm a study area of 159 counties in the eastern u. Areal interpolation differs from dasymetric modeling. Validating the dasymetric areal interpolation method to inform health policy chieko maene, ms social sciences computing services, university of chicago monica e.
Geographic information system software to remodel population data using dasymetric mapping methods. Brewer, 2001, dasymetric mapping and areal interpolation. Estimation of spatial distribution of heavy metals in groundwater using interpolation methods and multivariate statistical techniques. Areal interpolation of population counts using preclassified. Usgs techniques and methods 11c2 usgs publications. The simplest form of areal interpolation is areal weighting, in which a homogeneous distribution of population is assumed to occur throughout each original choropleth map zone. 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 twodimensional 2d land use or onedimensional 1d road network information as ancillary data to give insight on the underlying distribution of a. The process of dasymetric mapping is the transformation of data from a set of arbitrary source zones to a dasymetric map via the overlay of the source zones with an ancillary data set. Modeling sensitivity to accuracy in classified imagery. To that end, this study provides an example of dasymetric areal interpolation using the pre. Areal weighting is a type of an areal interpolation used to transform geographic data from one set of boundaries to another. When the geometry of a research area differs signifi.
Though dasymetric mapping has existed for well over a century, the methods for producing these maps have not been thoroughly examined. The approaches considered are the weighted areal interpolation in the text interpolazione areale ponderata, iap and the intelligent dasymetric mapping. Jan 22, 2020 tobler is a python package for areal interpolation, dasymetric mapping, and change of support. Pdf intelligent dasymetric mapping and its application to areal. A local polycategorical approach to areal interpolation. Validating the dasymetric areal interpolation method to. To overcome inconsistent spatial units between hospital discharge data and health policy needs in chicago, we developed and evaluated a dasymetric areal interpolation method, which disaggregates and allocates incidence rates from zip code to chicago community area. Areal interpolation differs from dasymetric modeling in that it assumes the boundaries of the target regions are arbitrary and that the variable being interpolated varies smoothly across the boundaries of the source and target regions.
Implementation and evaluation cartography and geographic information science 28 125 8. As part of a larger, primary study to determine a possible association between travel barriers to pediatric cancer facilities and adolescent cancer mortality across the united states, we examined methods to estimate mortality within zones at varying distances from these facilities. Areal interpolation and dasymetric mapping methods using. Transforming spatial data from one scale to another is a challenge in geographic analysis. Citeseerx dasymetric mapping and areal interpolation. Pdf intelligent dasymetric mapping and its application. Areal interpolation involves the transfer of data often socioeconomic statistics and especially population data from one zonation of a region to another, where the two zonations are geographicall. Dasymetric maps are used instead of choropleth maps because they represent underlying data distributions more accurately.
Data interpolation food and agriculture organization. Intelligent dasymetric mapping and its application to. It is arguably the simplest and most common approach to areal interpolation, though it does have some drawbacks see the areal weighted interpolation vignette for details. Jun 06, 2017 for more on the use of interpolation in water quality, see. A pointbased intelligent approach to areal interpolation. When boundaries collide public opinion quarterly oxford. Choropleth map top and dasymetric map bottom of astoria, oregon from figure. Areal interpolation transfers data into a common dataset for use in analysis and comparison mennis, 2003. Community level rates of diabetesrelated hospitalizations were. We thus wish to judge the relative accuracy of the four methods of areal weighting, dasymetric mapping, point kriging, and krigingbased areal interpolation in our use case so we can proceed with the best estimation method for overlaying demographic and voting data. Modelling the errors in areal interpolation between zonal. Dasymetric mapping depicts quantitative areal data using boundaries that divide the area into zones of relative homogeneity with the purpose of better portraying the population distribution.
A particular concern is computing small area trends when aggregation zone boundaries change during the trend interval. As part of a larger, primary study to determine a possible association between travel barriers to pediatric cancer facilities and adolescent cancer mortality across the united states, we examined methods to estimate mortality within zones at varying distances from these. Many areal interpolation methods can be incorporated into dasymetric mapping methods to improve the detail of a choropleth map below the level of the enumeration unit fisher. Mrozinski and cromley 1999 provide a helpful typology of areal interpolation within which dasymetric mapping may be placed. Other methods and approaches in dasymetric mapping include areal interpolation, filtered areal weighting using the binary method, filtering with land cover data, and cadastralbased expert dasymetric system. Although this system is appropriate for the dissemination of large amounts of national demographic data, often the boundaries of the enumeration units do not reflect the distribution of the underlying statistical. Areal interpolation and dasymetric modeling are similar in that both are methods for downscaling spatial data. Implementation and evaluation, abstract dasymetric maps display statistical data in meaningful spatial zones. In practice, dasymetric mapping is often considered a particular type of areal interpolation technique where source zone data are excluded from certain classes in a categorical ancillary data set. Areal interpolation is needed to estimate attribute information for different geographic. The typology delineates methods for combining choropleth and areaclass maps.
Areal weighting assigns to each grid cell population value based on its percentage area of the host areal units mennis 2003. Intelligent dasymetric mapping and its application to areal interpolation article pdf available in cartography and geographic information science 333. In the entire areal interpolation workflow, this step takes the most time and is the most critical for obtaining accurate predictions. There are six types of areal interpolation models included in this study.
Review of arealinterpolation and dasymetricmapping approaches. Intelligent dasymetric mapping outperforms areal weighting, and. Areal interpolation extension for arcgis geonet, the esri. Geographic information system software to remodel population data using dasymetric mapping methods by rachel sleeter and michael gould 2007 choropleth map top and dasymetric map bottom of astoria, oregon from figure 11. In most gis literature, areal interpolation specifically means the reaggregation of data from one set of polygons the source polygons to another set of polygons the target polygons. Dasymetric mapping for disaggregating coarse resolution. Census bureau provides decadal demographic data collected at the household level and aggregated to larger enumeration units for anonymity purposes.
This paper proposes the application of a combination of dasymetric mapping and areal interpolation techniques to overcome the issues of. Tobler is a python package for areal interpolation, dasymetric mapping, and change of support. Volumepreserving methods called the areabased areal interpolation approach overcome the aboveshortcoming of nonvolumepreserving methods because no point interpolation process is required. Fundamentals of geographic information systems 0 citeseerx. The development of an areal interpolation arcgis extension. Integrating areal interpolation and dasymetric refinement to resolve temporal incompatibilities in zoning systems sarah ehresman and matt ruther, ph. Wright in a 1936 map of cape cod cartographers use dasymetric mapping for population density over. Using a hybrid methodology of dasyametric mapping and.
Areal interpolation in the arcgis geostatistical analyst extension is a geostatistical interpolation technique that extends kriging theory to data averaged or aggregated over polygons. Dasymetric modelling of population distribution large data. Creating an extension that overcomes the obstacles inherent in dasymetric mapping, the method becomes transferable to a wider spectrum of users. Areal interpolation, dasymetric mapping, address points, cadastral data cartography and geographic information science, vol. The method can be used for area itself, or for of any other quantity that needs massconservative interpolation, including cortical volumes and possibly other measurements. Diagram showing how dasymetric mapping redistributes ancillary information to better represent population distribution. Comparison of population distribution models using areal. Of different methods fallen within this approach, the simplest method is the overlay method called by lam 1983. Kentucky state data center, university of louisville. Integrating areal interpolation and dasymetric refinement to. In the context of the discussion above areal interpolation based on dasymetric mapping has a number of attractive qualities that suggest it is well placed to overcome the inertia associated with simple areal weighting. Areal interpolation and dasymetric mapping methods using local ancillary data sources. In recent years, dasymetric interpolation techniques have witnessed fast development given new data sources, improved statistical assessment for. Geographic information system software to remodel population.
Areal interpolation refers to the procedures for transferring attribute data from one partitioning of geographic space a set of source units to another a set of target units goodchild and lam 1980. Inspired by this work, we tested five dasymetric mapping methods, including methods derived from work on areal interpolation. 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. It guides the redistribution of source zone values to target zones using auxiliary information as a spatial control. Intelligent dasymetric mapping and its comparison to other. The approaches considered are the weighted areal interpolation in the text interpolazione areale ponderata, iap and the intelligent dasymetric mapping idm as implemented in a plugin for.
Areal interpolation refers to interpolation using polygons or areas. With the automated tool, we have the ability to map and analyze any area of interest provided the appropriate data sources. Despite numerous recent developments in intelligent areal interpolation methods, and studies that have demonstrated their clear. Modeling the errors in areal interpolation between zonal systems by monte carlo simulation.
To that end, this study provides an example of dasymetric areal interpolation using the preclassified land cover data available. A study of areal interpolation by dasymetric mapping. Peek, md, mph, msc section of general internal medicine, university of chicago chicago center for diabetes translation research. Areal interpolation between one partitioning of geographical space and another remains an important topic, particular in terms of population counts and related statistics which are often required in order to compute an incidence ratio.
Mar 29, 2019 areal weighting is a type of an areal interpolation used to transform geographic data from one set of boundaries to another. Areal weighting is the most basic form of areal interpolation whereby a homogeneous data distribution is assumed to occur within each choropleth map zone. Here a software implementation of the steps to perform areal pycnophylactic interpolation of brain surface area is available, using octavematlab. 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. Dasymetric mapping, on the other hand, divides mapped area into zones of relative homogeneity 7.
Demographic data is usually represented by a choropleth map, where the statistical data is aggregated to areal units. Areal interpolation is the data transfer from one zonal system to another. In contrast, research on areal interpolation has been more thorough and has examined methods of transferring data from one set of map zones to another, an issue that is applicable to dasymetric mapping. For example, demographers frequently need to downscale or upscale the administrative units of their data. In reply to hoang pham nov 24, 2004 just last night i downloaded the trial version of genuine fractals to test against the photoshop 7 standard bicubic enlargement. Proceedings of the 12th annual gis research uk conference, university of east anglia, uk. System software to remodel population data using dasymetric mapping methods. In the context of dasymetric mapping, where data are redistributed to subchoropleth map zone units via overlay with an area. Dasymetric method of thematic mapping, which uses areal symbols to spatially classify volumetric data developed and named in 1911 by benjamin petrovich semenovtyanshansky popularized by j. Intelligent dasymetric mapping and its application to areal astro. Enhancing areal interpolation frameworks through dasymetric.
Such maps can be preferable to choropleth maps that show data by enumeration zones, because dasymetric zones more accurately represent underlying data distributions. Areal interpolation is mostly an areal weighting procedure and does not take ancillary sources into consideration when the spatial distribution of data is refined. Using a hybrid methodology of dasyametric mapping and data. The goal is to change the parameters on the right so that most empirical covariances blue crosses fall within the confidence intervals red bars.
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