A Parallel Algorithm and Implementation to Compute Spatial Autocorrelation (Hotspot) Using MATLAB
Date of Award
Master of Science (MS)
Being a spatial autocorrelation visualization tool in recent years, hotspot is often used in various fields, such as disease analysis, crime analysis, and weather conditions analysis and prediction in a certain area. Most of the research in hot spot analysis is in applying the concept to a variety of fields and to gain insights on the statistical significance prevalent in the clustering of data. Only a few of them discussed the efficiency and optimization of the algorithm. Commonly, these kinds of analyses would be based upon a huge dataset about space and time, and the conventional algorithm would take too much time to get the results. This paper mainly discusses whether the algorithm can be processed in parallel with MATLAB and how to further optimize the algorithm to shorten the calculation time and obtain accurate outcomes faster. I will use the toolbox ‘parpool’ in MATLAB on a multi-core node to parallelize the conventional algorithm, and then take advantage of the basic idea of the 'R-tree' to further optimize the parallel algorithm. In the end, the results are satisfactory, because the conventional serial algorithm can be parallelized in MATLAB, and the time consumption was saved about five times compared to the original algorithm. When the algorithm was further optimized, its time consumption is saved about ten times. This paper will be helpful in saving time when doing similar computations and analyses in the future.