Development of Bayesian Maximum Entropy Method Toolbox on Quantum GIS—An Application of Long-term Exposure Estimation of Particulate Matter in Taiwan
Date Issued
2010
Date
2010
Author(s)
Ku, Shang-Chen
Abstract
This study developed the Quantum Bayesian Maximum Entropy Toolbox (QtBME), which is a spatiotemporal statistics function, can be applied to estimate and map a non-stationary and non-homogeneous spatiotemporal process under the platform of Quantum GIS (QGIS) software. Kernel smoothing method is used to divide the original process into a deterministic trend and a stationary and homogeneous spatiotemporal process, assuming that a spatiotemporal process can be divided into high and low frequency. The covariance model of the process of high frequency is selected objectively by particle swarm optimization (PSO) method and Akaike''s information criterion (AIC). Bayesian maximum entropy method is then applied to spatiotemporal mapping of the variable of interest. By means of ability of geoprocessing as well as graphical computing and mapping in QGIS libraries, QtBME can display the results easily with two types of geographical data format, i.e., raster and vector formats. This study evaluated the long-term township-based exposure estimation of particulate matter (PM10) from 2004 to 2008 in Taiwan. Results showed that PM10 concentration are higher in Taipei, Tainan, and Kaohsiung, and lower in Taidon and Ilan. Moreover, the probability of the high PM10 exposure (i.e., higher than 50μg/m3) has strong seasonality; in general, it decreases from March to July and then increases from August to February. The results of cross validation show that QtBME provides satisfactory predictions for the PM10 with relative errors less than 20%. High relative error seldom occurred because of the particular characteristic of certain stations and lack of information provided from the stations in the estimation neighborhoods.
Subjects
Bayesian maximum entropy (BME)
Particulate matter
Python programming language
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