Browsing by Author "Lee, Monghyeon"
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Item Impacts of Location Uncertainty on Statistical Modeling of Georeferenced Data(2017-12) Lee, Monghyeon; Griffith, Daniel A; Chun, YongwanUncertainty in data analysis has been a critical topic in numerous fields, such as public health, medicine, civil engineering, ecology and other natural sciences, and many of the social sciences, including geospatial information sciences. It may occur in any step of a study, such as collecting, recording, and analyzing data, and interpreting analysis results. Uncertainty is often propagated to analysis outcomes. The outcomes to which serious uncertainties are transferred likely yield misleading conclusions about a phenomenon, and constitute inaccurate results. Locational uncertainty, which is the difference between a true and a represented location, is a unique source of uncertainty in a spatial data analysis. Furthermore, locational uncertainty may interact with uncertainties from other sources (e.g., measurement, specification, sampling, or stochastic noise), and makes outcomes more unreliable. Propagation of uncertainty has been widely investigated. However, locational uncertainty propagation and combining uncertainties from different sources merit more attention, because the propagation and combination of uncertainties are quite complicated and can seriously corrupt analysis outcomes. This research examines uncertainty in spatial data analysis using two sources of public health data: Florida cancer data and Syracuse blood lead level data. The research 1) presents a study about how locational uncertainty propagates through an analysis involving an urban hierarchy in terms of spatial relationships between poverty and cancer using the Florida cancer data, 2) explores relationships and propagations of location and measurement uncertainties using pediatric blood lead level data for Syracuse, New York, and 3) examines a reverse transformation (i.e., a geometric centerline recovery method) from a kernel density surface to points using the Florida cancer data.Item Uncertainty in the Effects of the Modifiable Areal Unit Problem under Different Levels of Spatial Autocorrelation: A Simulation Study(Taylor & Francis Ltd, 2018-11-13) Lee, Sang-Il; Lee, Monghyeon; Chun, Yongwan; Griffith, Daniel A.; 0000-0002-4957-1379 (Chun, Y); 0000-0001-5125-6450 (Griffith, DA); 297769863 (Chun, Y); 14855602 (Griffith, DA); Chun, Yongwan; Griffith, Daniel A.The objective of this paper is to investigate uncertainties surrounding relationships between spatial autocorrelation (SA) and the modifiable areal unit problem (MAUP) with an extensive simulation experiment. Especially, this paper aims to explore how differently the MAUP behaves for the level of SA focusing on how the initial level of SA at the finest spatial scale makes a significant difference to the MAUP effects on the sample statistics such as means, variances, and Moran coefficients (MCs). The simulation experiment utilizes a random spatial aggregation (RSA) procedure and adopts Moran spatial eigenvectors to simulate different SA levels. The main findings are as follows. First, there are no substantive MAUP effects for means. However, the initial level of SA plays a role for the zoning effect, especially when extreme positive SA is present. Second, there is a clear and strong scale effect for the variances. However, the initial SA level plays a non-negligible role in how this scale effect deploys. Third, the initial SA level plays a crucial role in the nature and extent of the MAUP effects on MCs. A regression analysis confirms that the initial SA level makes a substantial difference to the variability of the MAUP effects.