• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.4
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• pp.343-350
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• 2006

For integrating and utilizing the statistical data, which is summarized by arbitrary areal unit such as demographics, with stellite imagery or other GIS data, areal unit of both data should be accorded. Dasymetric mapping is proposed as a useful method fur disaggregating the aggregated statistical data to finer areal unit or generating surface model from object data such as polygonal area. This research evaluate the effectiveness of dasymetric mapping by 1) summarizing the yellow page information by administrative district, 2) modeling the business density using dasymetric mapping, and 3) comparing the business densities of raw data and that of spatial interpolation result.

• Journal of the Korean association of regional geographers
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• v.10 no.2
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• pp.389-406
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• 2004

with the increasing popularity of regional studies, the importance of regional data has been recognized dramatically in recent years. However, due to potential problems from the intrinsic characteristics of aggregate regional data for the research, and incompatible regional units between source and target regional data units, the method for reorganizing the regional data units for a given research analysis should be required. In this regard, the purpose of this research is to review the significant interpolation methods for reorganizing the data units and, based on it, to propose the population weighted interpolation method. For the first purpose, areal weighted interpolation method, pycnophylactic method, dasymetric method, area-to-point method were reviewed. The proposed population-weighted interpolation method was applied to the case study of population census regional data in Erie County, NY, compared with areal weighted interpolation method, pycnophylactic method in terms of several statistical characteristics.

• Journal of the Korean Geographical Society
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• v.46 no.1
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• pp.67-80
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• 2011

Choropleth mapping of population distribution is based on the assumption that people are uniformly distributed throughout each enumeration unit. Dasymetric mapping technique improves choropleth mapping by refining spatially aggregated data with residential information. Further, pycnophylactic interpolation can upgrade dasymetric mapping by considering population distribution of neighboring areas, while preserving the volumes of original units. This study proposed a combined solution of dasymetric mapping and pycnophylactic interpolation to improve the accuracy of population density distribution. Specifically, the dasymetric method accounts for the spatial distribution of population within each census unit, while pycnophylactic interpolation considers population distribution of neighboring area. This technique is demonstrated with 1990 census data of the Athens, GA. with land use land cover information derived from remotely-sensed imagery for the areal extent of populated areas. The results are evaluated by comparison between original population counts of smaller census units (census block groups) and population counts of the grid map built from larger units (census tracts) aggregated to the same areal units. The estimated populations indicate a satisfactory level of accuracy. Population distribution acquired by the suggested method can be re-aggregated to any type of geographic boundaries such as electoral boundaries, school districts, and even watershed for a variety of applications.

• Journal of the Korean association of regional geographers
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• v.22 no.1
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• pp.251-266
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• 2016

Intelligent interpolation methods such as dasymetric mapping are considered to be the best way to disaggregate zone-based population data by observing and utilizing the internal variation within each source zone. This research reviews the advantages and problems of the dasymetric mapping method, and presents a geographically weighted regression (GWR) based method to take into consideration the spatial heterogeneity of population density - land cover relationship. The locally adaptive intelligent interpolation method is able to make use of readily available ancillary information in the public domain without the need for additional data processing. In the case study, we use the preclassified National Land Cover Dataset 2011 to test the performance of the proposed method (i.e. the GWR-based multi-class dasymetric method) compared to four other popular population estimation methods (i.e. areal weighting interpolation, pycnophylactic interpolation, binary dasymetric method, and globally fitted ordinary least squares (OLS) based multi-class dasymetric method). The GWR-based multi-class dasymetric method outperforms all other methods. It is attributed to the fact that spatial heterogeneity is accounted for in the process of determining density parameters for land cover classes.

• Journal of the Korean association of regional geographers
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• v.12 no.5
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• pp.614-630
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• 2006

Improved estimates of populations at risk for quick and effective response to natural and man-made disasters require spatial disaggregation of zonal population data because of the spatial mismatch problem in areal units between census and impact zones. This paper implements a dasymetric surface model to facilitate spatial disaggregation of the population of a census block group into populations associated with each constituent pixel and evaluates the performance of the surface-based spatial disaggregation model visually and statistically. The surface-based spatial disaggregation model employed geographic information systems (GIS) to enable dasymetric interpolation to be guided by satellite-derived land use and land cover data as additional information about the geographic distributor of population. In the spatial disaggregation, percent cover based empirical sampling and areal weighting techniques were used to objectively determine dasymetric weights for each grid cell. The dasymetric population surface for the Atlanta metropolitan area was generated by the surface-based spatial disaggregation model. The accuracy of the dasymetric population surface was tested on census counts using the root mean square error (RMSE) and an adjusted RMSE. The errors related to each census track and block group were also visualized by percent error maps. Results indicate that the dasymetric population surface provides high-precision estimates of populations as well as the detailed spatial distribution of population within census block groups. The results also demonstrate that the population surface largely tends to overestimate or underestimate population for both the rural and forested and the urban core areas.

• Spatial Information Research
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• v.22 no.3
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• pp.35-46
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• 2014

Population data are usually provided at administrative spatial units in Korea, so areal interpolation is needed for fine-grained analysis. This study aims to compare various methods of areal interpolation for population subgroups rather than the total population. We estimated the number of elderly people and single-person households for small areal units from Dong data by the different interpolation methods using 2010 census data of Seoul, and compared the estimates to actual values. As a result, the performance of areal interpolation methods varied between the total population and subgroup populations as well as between different population subgroups. It turned out that the method using GWR (geographically weighted regression) and building type data outperformed other methods for the total population and households. However, the OLS regression method using building type data performed better for the elderly population, and the OLS regression method based on land use data was the most effective for single-person households. Based on these results, spatial distribution of the single elderly was represented at small areal units, and we believe that this approach can contribute to effective implementation of urban policies.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.3
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• pp.145-153
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• 2015

This study aims to accurately estimate population distribution more specifically than administrative unites using a RK (Regression-Kriging) model. The RK model is the areal interpolation technique that involves linear regression and the Kriging model. In order to estimate a population’s distribution using a sample region, four different models were used, namely; a regression model, RK model, OK (Ordinary Kriging) model and CK (Co-Kriging) model. The results were then compared with each other. Evaluation of the accuracy and validity of evaluation analysis results were the basis RMSE (Root Mean Square Error), MAE (Mean Absolute Error), G statistic and correlation coefficient (ρ). In the sample regions, every statistic value of the RK model showed better results than other models. The results of this comparative study will be useful to estimate a population distribution of the metropolitan areas with high population density

• Journal of the Korean Geographical Society
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• v.47 no.5
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• pp.755-774
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• 2012

The main objective of the study is to propose GIS-based methods to assess the population distribution criteria for undesirable facilities such as nuclear power plants. First of all, a review of the relevant criteria was conducted for the official documents compiled by such institutions as IAEA (International Atomic Energy Agency), U.S. NRC (Nuclear Regulatory Commission), and some national institutes including the Korea Institute of Nuclear Safety. It is informed from the review that the fundamental principle underlying the various criteria is to maximize the distance between a plant and the nearest population center. It is realized that two interrelated GIS-based techniques need to be devised to put the principle into practice; sophisticated ways of representing population distribution and identifying population centers. A dasymetric areal interpolation is proposed for the former and cell-based and area-based critical density methods are introduced. Grid-based population distributions at various spatial resolutions are created by means of the dasymetric areal interpolation. By applying the critical density methods to the gridded population distribution, some population centers satisfying the population size and density criteria can be identified. These methods were applied to the case of the Gori-1 nuclear power plant and their strengths and limitations were discussed. It was revealed that the assessment results could vary depending upon which method was employed and what values were chosen for various parameters. This study is expected to contribute to foster the applications of methods and techniques developed in geospatial analysis and modeling to the site selection and evaluation.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.3
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• pp.146-155
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• 2006

A handful of previous studies have attempted to integrate socioeconomic data and remotely sensed data for urban quality of life assessment with their spatial dimension in a zonal unit. However, such a zone-based approach not only has the unrealistic assumption that all attributes of a zone are uniformly spatially distributed throughout the zone, but also has resulted in serious methodological difficulties such as the modifiable areal unit problem and the incompatibility problem with environmental data. An alternative to the zone-based approach can be a pixel-based approach which gets its spatial dimension through a pixel. This paper proposes a pixel-based approach to linking remotely sensed data with socioeconomic data in GIS for urban quality of life assessment. The pixel-based approach uses dasymetric mapping and spatial interpolation to spatially disaggregate socioeconomic data and integrates remotely sensed data with spatially disaggregated socioeconomic data for the quality of life assessment. This approach was implemented and compared with a zone-based approach using a case study of Fulton County, Georgia. Results indicate that the pixel-based approach allows for the calculation of a microscale indicator in the urban quality of life assessment and facilitates efficient data integration and visualization in the assessment although it costs an intermediate step with more processing time such as the disaggregation of zonal data. The results also demonstrate that the pixel-based approach opens up the potential for the development of new database and increased analytical capabilities in urban analysis.