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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 歐陽彥正 | zh_TW |
| dc.contributor.advisor | Yen-Jen Oyang | en |
| dc.contributor.author | 蔡家豪 | zh_TW |
| dc.contributor.author | Jia-Hau Tsai | en |
| dc.date.accessioned | 2023-09-07T16:40:04Z | - |
| dc.date.available | 2025-08-31 | - |
| dc.date.copyright | 2023-09-11 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-04 | - |
| dc.identifier.citation | [1] K. M. Al-Kindi, A. Alkharusi, D. Alshukaili, N. Al Nasiri, T. Al-Awadhi, Y. Charabi, and A. M. El Kenawy. Spatiotemporal assessment of covid-19 spread over oman using gis techniques. Earth Systems and Environment, 4:797–811, 2020.
[2] T. K. Anderson. Kernel density estimation and k-means clustering to profile road accident hotspots. Accident Analysis & Prevention, 41(3):359–364, 2009. [3] C. M. Bishop and N. M. Nasrabadi. Pattern recognition and machine learning, volume 4. Springer, 2006. [4] L. Breiman, W. Meisel, and E. Purcell. Variable kernel estimates of multivariate densities. Technometrics, 19(2):135–144, 1977. [5] C. Brunsdon, J. Corcoran, and G. Higgs. Visualising space and time in crime patterns: A comparison of methods. Computers, environment and urban systems, 31(1):52–75, 2007. [6] D. T.-H. Chang, C.-C. Wang, and J.-W. Chen. Using a kernel density estimation based classifier to predict species-specific microrna precursors. BMC bioinformatics, 9(12):1–10, 2008. [7] M. Chirico. Emergency - 911 calls, 2020. [8] M. Choi and A. Hohl. Investigating spatiotemporal indoor contact patterns using abm and stkde. In Proceedings of the 4th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, pages 1–8, 2021. [9] D. Collett and T. Lewis. Discriminating between the von mises and wrapped normal distributions. Australian Journal of Statistics, 23(1):73–79, 1981. [10] N. Fisher. Smoothing a sample of circular data. Journal of structural geology, 11(6):775–778, 1989. [11] N. I. Fisher. Statistical analysis of circular data. cambridge university press, 1995. [12] R. A. Fisher. Statistical methods for research workers. Springer, 1992. [13] A. Gramacki. Nonparametric kernel density estimation and its computational aspects, volume 37. Springer, 2018. [14] A. Hohl and P. Chen. Spatiotemporal simulation: local ripley’s k function parameterizes adaptive kernel density estimation. In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, pages 16–23, 2019. [15] S. Jiang, J. Ferreira Jr, and M. C. Gonzalez. Discovering urban spatial-temporal structure from human activity patterns. In Proceedings of the ACM SIGKDD international workshop on urban computing, pages 95–102, 2012. [16] J. Lee, J. Gong, and S. Li. Exploring spatiotemporal clusters based on extended kernel estimation methods. International Journal of Geographical Information Science, 31(6):1154–1177, 2017. [17] D. Lemke, V. Mattauch, O. Heidinger, E. Pebesma, and H.-W. Hense. Comparing adaptive and fixed bandwidth-based kernel density estimates in spatial cancer epidemiology. International journal of health geographics, 14:1–10, 2015. [18] Y. Li, M. Abdel-Aty, J. Yuan, Z. Cheng, and J. Lu. Analyzing traffic violation behavior at urban intersections: A spatio-temporal kernel density estimation approach using automated enforcement system data. Accident Analysis & Prevention, 141:105509, 2020. [19] R.-J. Liu. Study on optimal bandwidth settings for adaptive kernel density estimation. Master’s thesis, National Taiwan University, Jan 2022. [20] S. Lloyd. Least squares quantization in pcm. IEEE transactions on information theory, 28(2):129–137, 1982. [21] K. V. Mardia, P. E. Jupp, and K. Mardia. Directional statistics, volume 2. Wiley Online Library, 2000. [22] T. Nakaya and K. Yano. Visualising crime clusters in a space-time cube: An exploratory data-analysis approach using space-time kernel density estimation and scan statistics. Transactions in GIS, 14(3):223–239, 2010. [23] Y.-J. Oyang, S.-C. Hwang, Y.-Y. Ou, C.-Y. Chen, and Z.-W. Chen. Data classification with radial basis function networks based on a novel kernel density estimation algorithm. IEEE Transactions on Neural Networks, 16(1):225–236, 2005. [24] E. Parzen. On estimation of a probability density function and mode. The annals of mathematical statistics, 33(3):1065–1076, 1962. [25] K. Pearson. X. on the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 50(302):157–175, 1900. [26] B. Romano and Z. Jiang. Visualizing traffic accident hotspots based on spatial-temporal network kernel density estimation. In Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pages 1–4, 2017. [27] M. Rosenblatt. Remarks on some nonparametric estimates of a density function. The annals of mathematical statistics, pages 832–837, 1956. [28] D. W. Scott. Multivariate density estimation: theory, practice, and visualization. John Wiley & Sons, 2015. [29] L. M. Scott and M. V. Janikas. Spatial statistics in arcgis. In Handbook of applied spatial analysis: Software tools, methods and applications, pages 27–41. Springer, 2009. [30] H. Setzler, C. Saydam, and S. Park. Ems call volume predictions: A comparative study. Computers & Operations Research, 36(6):1843–1851, 2009. [31] S. J. Sheather and M. C. Jones. A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society: Series B (Methodological), 53(3):683–690, 1991. [32] B. W. Silverman. Density estimation for statistics and data analysis, volume 26. CRC press, 1986. [33] L. Srikanth and I. Srikanth. A case study on kernel density estimation and hotspot analysis methods in traffic safety management. In 2020 International Conference on COMmunication Systems & NETworkS (COMSNETS), pages 99–104. IEEE, 2020. [34] C. C. Taylor. Automatic bandwidth selection for circular density estimation. Computational Statistics & Data Analysis, 52(7):3493–3500, 2008. [35] M. P. Wand and M. C. Jones. Kernel smoothing. CRC press, 1994. [36] L. Xu, M.-P. Kwan, S. McLafferty, and S. Wang. Predicting demand for 311 non-emergency municipal services: An adaptive space-time kernel approach. Applied geography, 89:133–141, 2017. [37] C.-C. Yang. Kernel density based probability estimation for data classifiers. Master’s thesis, National Taiwan University, Jan 2019. [38] M.-H. Yang, Y.-J. Oyang, and J.-L. Fuh. Application of density estimation algorithms in analyzing co-morbidities of migraine. In 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), pages 578–585. IEEE, 2011. [39] Z. Zhou and D. S. Matteson. Predicting ambulance demand: A spatio-temporal kernel approach. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pages 2297–2303, 2015. [40] Z. Zhou, D. S. Matteson, D. B. Woodard, S. G. Henderson, and A. C. Micheas. A spatio-temporal point process model for ambulance demand. Journal of the American Statistical Association, 110(509):6–15, 2015. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/89356 | - |
| dc.description.abstract | 核心密度估計方法(Kernel Density Estimation)現今廣泛的運用於熱點分析進行空間上的分佈研究。但在緊急醫療事件中，除了距離之外，發生時間也是重要的考量。在本研究中，我們提出了叫做ERAKDE-ST (Elevated Relaxed Adaptive Kernel Density Estimation-Spatio-Temporal)的演算法進行空間加上環形時間的研究，針對美國賓州蒙哥馬利縣(Montgomery County, Pennsylvania, United States)2015到2020年，所有求救電話(9-1-1-calls)中的緊急醫療事件進行分析。利用ERAKDE-ST求得的機率密度和K-means找出在時空間中的集群。對於集群中不同的緊急事件，使用費雪正確性檢定(Fisher's Exact test)和卡方檢定(χ2 test)去分析集群之間是否有明顯不同的分布。我們認為這個研究可以影響緊急救護醫療資源的分配，能夠更有效的利用資源去拯救生命。 | zh_TW |
| dc.description.abstract | Kernel Density Estimation (KDE) is widely used in Hotspot Analysis to research spatial patterns. But in Emergency Medical Services (EMS) incidents, the occurrence time is a critical factor, too. In this study, we proposed a method called Elevated Relaxed Adaptive Kernel Density Estimation-Spatio-Temporal (ERAKDE-ST) to analyze the spatial and circular temporal patterns of the EMS events in 9-1-1-calls from Montgomery County, Pennsylvania, United States, from 2015 to 2020. We use the probability density obtain from ERAKDE-ST and K-means to find spatial-temporal clusters. We later apply Fisher's exact test and the χ2 test to examine and analyze the EMS incidents distribution differences between clusters. We think this study might have the chance to change the allocation of EMS resources to save more lives. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-09-07T16:40:04Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-09-07T16:40:04Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Contents
Page Verification Letter from the Oral Examination Committee i Acknowledgements ii 中文摘要 iii Abstract iv Contents v List of Figures viii List of Tables ix Denotation xiii Chapter 1 Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 Kernel Density Estimation . . . . . . . . . . . . . . . . . . . . . . 2 1.1.2 Hotspot Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2 Aim of the study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Chapter 2 Literature Review 6 2.1 Silverman’s Rule of thumb . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Relaxed Variable Kernel Density Estimator(RVKDE) . . . . . . . . . 7 2.3 Spatio-Temporal Kernel Density Estimation (STKDE) . . . . . . . . 8 2.3.1 Bandwidth Selection for STKDE . . . . . . . . . . . . . . . . . . . 9 2.4 Circular Kernel Density Estimation . . . . . . . . . . . . . . . . . . 10 2.5 Elevated Relaxed Adaptive Kernel Density Estimator (ERAKDE) . . 11 2.6 Elevated Relaxed Adaptive Kernel Density Estimator-3D (ERAKDE-3D) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Chapter 3 Method 14 3.1 Elevated Relaxed Adaptive Kernel Density Estimator - Spatio-Temporal - linear (ERAKDE-ST-linear) . . . . . . . . . . . . . . . . . . . . . 14 3.2 Elevated Relaxed Adaptive Kernel Density Estimator - Spatio-Temporal - circular (ERAKDE-ST-circular) . . . . . . . . . . . . . . . . . . . 15 3.3 K-means . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.4 Performance Measurement . . . . . . . . . . . . . . . . . . . . . . . 17 3.4.1 Log-likelihood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.4.2 Mean Integrated Squared Error . . . . . . . . . . . . . . . . . . . . 18 Chapter 4 Experiment 20 4.1 Data and Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.2 Data Preprocess . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.2.1 Outlier detection and exclude . . . . . . . . . . . . . . . . . . . . . 21 4.2.2 Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.3 Identify Spatio-Temporal Clusters and Analysis . . . . . . . . . . . . 23 4.3.1 Training Spatio-Temporal Kernel Density Estimation Model . . . . 23 4.3.2 K-means . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.4 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Chapter 5 Discussion 35 5.1 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 References 37 Appendix A — p-value 42 Appendix B — performance 58 B.1 Log-likelihood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 B.2 MISE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 | - |
| dc.language.iso | en | - |
| dc.subject | 熱點分析 | zh_TW |
| dc.subject | 核密度估計 | zh_TW |
| dc.subject | 緊急醫療事件 | zh_TW |
| dc.subject | Kernel Density Estimation | en |
| dc.subject | Hotspot Analysis | en |
| dc.subject | Emergency Medical Services | en |
| dc.title | 使用核密度估計分析緊急醫療事件的時空聚集 | zh_TW |
| dc.title | Characterizing the Spatio-Temporal Clusters of Emergency Medical Incidents with Kernel Density Estimation | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 楊孟翰;黃乾綱;溫在弘 | zh_TW |
| dc.contributor.oralexamcommittee | Meng-Han Yang;Chien-Kang Huang;Tzai-Hung Wen | en |
| dc.subject.keyword | 核密度估計,熱點分析,緊急醫療事件, | zh_TW |
| dc.subject.keyword | Kernel Density Estimation,Hotspot Analysis,Emergency Medical Services, | en |
| dc.relation.page | 64 | - |
| dc.identifier.doi | 10.6342/NTU202302756 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2023-08-08 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 資訊工程學系 | - |
| dc.date.embargo-lift | 2025-08-31 | - |
| 顯示於系所單位： | 資訊工程學系 | |
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