為降低緊急醫療救護患者的死亡及失能，儘快到達現場施行急救處置是重要的關鍵。因救護資源有限，故救護資源的配置需考量事故的時間及空間特性。本研究目的即在於探討不同時段之救護案件的空間特性。
本研究資料取自臺南市緊急醫療救護系統97年1月1日至97年12月31日之救護案件資料，計21,760件。本研究先以變異數分析檢定不同星期、時間之事故案件數量是否具有顯著差異；針對不同時段，運用Ripley’s K值來證明救護案件是否有群集現象。針對有群集的時段，以兩階段群集分析法--最近鄰接分層空間群集模式（Nnh）及K組平均數法K-Means）來辨識事故之群集分佈位置。最後利用卡方一致性檢定及群集分析，將空間分佈相同之時段合併，以減少組數，並分別再辨識其群集位置，以利實務上應用。
本研究結果發現，在救護案件數量的變異數分析部分，不同時間的救護案件數量具有顯著的差異，亦即每日之救護案件具有尖離峰的趨勢；星期間則無顯著差異。本研究依據案件數量將資料分為星期一～星期日，每日24小時再區分為四個時段。利用Ripley’s K分析知，28個時段救護案件之空間分佈皆具有群集特性。在救護案件群集分析部份，大部份之群集皆集中於臺南市人口較為密集的區域（北區、中西區、東區，及南區與中西區交界處）。最後以卡方檢定及群集分析，將28個時段合併為8組，再分別辨識其群集位置及分析群集中心點分佈狀況，發現8組資料中，除臺南市南區外，其餘五區之群集中心點分佈皆相當近似。
本研究明確辨識出不同時段之救護案件群集位置，可提供消防、衛生、交通及社會等相關政府單位管理者，研擬相關對策，以提高救護患者存活率。

To decrease the morbidity and mortality of casualties, response time is one of the most important factors in Emergency Medical Services (EMS). Because of limited resource, EMS resources must be deployed based on spatial and temporal distributions of calls. The objective of the present study was to analyze the spatial characteristic of EMS calls in different time periods.
The Tainan EMS system was the subject of the study. We collected data from 21,760 cases from January through December 2008. This study used Analysis of variance to test if there is any significant difference in number of calls between weekdays and between hours. For each duration, the study used Ripley’s K function to analyze whether the case locations were spatially aggregated or not. We also adopted two-step clustering analysis to identify the locations of clusters. Finally, we used Chi square test for homogeneity and hierarchical clustering analysis to aggregate the durations without significant differences in spatial distributions.
There were no significant differences in number of cases between weeks, but the differences existed between hours. According to the amounts of cases, the data were divided into day of the week, and a 24-hour day was divided into four durations (23:00-07:00; 07:00-13:00; 13:00-17:00; 17:00-23:00). Using Ripley’s K, the locations of all 28 durations were spatially aggregated. Most clusters were concentrated in the densely-populated districts. By using test for homogeneity and clustering analysis, 28 durations were aggregated into 8 groups. And for all 8 groups, the distributions of cluster centroids were quite similar, except for the clusters in Southern district of Tainan City.
The study explicitly identifies the locations of clusters for all durations. Based on the results, the authority can propose coping strategies to improve the patients’ survival.

Alsalloum, O. I., and Rand, G. K. (2006). Extensions to emergency vehicle location models. Computers and Operations Research, 33(9), 2725-2743.
Anderson, T. K. (2009). Kernel density estimation and K-means clustering to profile road accident hotspots. Accident Analysis and Prevention, 41(3), 359-364.
Batta, R., Dolan, J. M., and Krishnamurthy, N. N. (1989). The maximal expected covering location problem: Revisited. Transportation Science, 23(4), 277-287.
Berman, O., Larson, R. C., and Parkan, C. (1987). The stochastic queue p-median problem. Transportation Science, 21(4), 207-216.
Brotcorne, L., Laporte, G., and Semet, F. (2003). Ambulance location and relocation models. European Journal of Operational Research, 147(3), 451-463.
Chiang, W.-C., Ko, P. C.-I., Wang, H.-C., Yang, C.-W., Shih, F.-Y., Hsiung, K.-H., et al. (2009). EMS in Taiwan: Past, present, and future. Resuscitation, 80(1), 9-13.
Dale, M. R. T., Dixon, P., Fortin, M.-J., Legendre, P., Myers, D. E., and Rosenberg, M. S. (2002). Conceptual and mathematical relationships among methods for spatial analysis. Ecography, 25(5), 558-577.
De Maio, V. J., Stiell, I. G., Wells, G. A., and Spaite, D. W. (2003). Optimal defibrillation response intervals for maximum out-of-hospital cardiac arrest survival rates. Annals of Emergency Medicine, 42(2), 242-250.
Dixon, P. M. (2002). Ripley's k function. In A. H. El-Shaarawi and W. W. Piegorsch (Eds.), Encyclopedia of Environmetrics (Vol. 3, pp. 1796-1803). Chichester: John Wiley and Sons.
Eisenberg, M. S., Bergner, L., and Hallstrom, A. P. (1979). Cardiac resuscitation in the community. Importance of rapid provision and implications for program planning. Journal of the American Medical Association, 241(18), 1905-1907.
Erdoğan, S., Yilmaz, I., Baybura, T., and Gullu, M. (2008). Geographical information systems aided traffic accident analysis system case study: City of afyonkarahisar. Accident Analysis and Prevention, 40(1), 174-181.
Garson, G. I., and Moser, E. B. (1995). Aggregation and the Pearson Chi-square statistic for homogeneous proportions and distributions in ecology. Ecology, 76(7), 2258-2269.
Hanson, C. E., and Wieczorek, W. F. (2002). Alcohol mortality: A comparison of spatial clustering methods. Social Science and Medicine, 55(5), 791-802.
Harries, K. (2006). Extreme spatial variations in crime density in Baltimore county, md. Geoforum, 37(3), 404-416.
Henderson, S. G., and Mason, A. J. (2004). Ambulance service planning: Simulation and data visualization. In F. Sainfort, M. L. Brandeau and W. P. Pierskalla (Eds.), Operations Research and Health Care: A Handbook of Methods and Applications (Vol. 70, pp. 77-102). Boston: Kluwer Academic.
Karlaftis, M. G., and Tarko, A. P. (1998). Heterogeneity considerations in accident modeling. Accident Analysis and Prevention, 30(4), 425-433.
Lambe, S., Washington, D. L., Fink, A., Herbst, K., Liu, H., Fosse, J. S., et al. (2002). Trends in the use and capacity of California's emergency departments, 1990-1999. Annals of Emergency Medicine, 39(4), 389-396.
Lancaster, J., and Downes, B. J. (2004). Spatial point pattern analysis of available and exploited resources. Ecography, 27, 94-102.
Levine, N. (2004). Crimestat III: A spatial statistics program for the analysis of crime incident locations. Texas: Ned Levine and Associates, and Washington, DC: the National Institute of Justice.
Levine, N., Kim, K. E., and Nitz, L. H. (1995). Spatial analysis of Honolulu motor vehicle crashes: I. Spatial patterns. Accident Analysis and Prevention, 27(5), 663-674.
Marcon, E., and Puech, F. (2003). Evaluating the geographic concentration of industries using distance-based methods. Journal of Economic Geography, 3(4), 409-428.
Mendonça, F. C., and Morabito, R. (2001). Analysing emergency medical service ambulance deployment on a Brazilian highway using the hypercube model. Journal of the Operational Research Society, 52(3), 261-270.
Ohshige, K. (2008). Reduction in ambulance transports during a public awareness campaign for appropriate ambulance use. Academic Emergency Medicine, 15(3), 1-5.
Ong, M. E. H., Tan, E. H., Yan, X., Anushiac, P., Lim, S. H., Leong, B. S.-H., et al. (2007). An observational study describing the geographic-time distribution of cardiac arrests in Singapore: What is the utility of geographic information systems for planning public access defibrillation? (PADs phase I). Resuscitation, 76(3), 388-396.
Peleg, K., and Pliskin, J. S. (2004). A geographic information system simulation model of EMS: Reducing ambulance response time. American Journal of Emergency Medicine, 22(3), 164-170.
Pell, J. P., Sirel, J. M., Marsden, A. K., Ford, I., and Cobbe, S. M. (2001). Effect of reducing ambulance response times on deaths from out of hospital cardiac arrest: Cohort study. British Medical Journal, 322(9), 1385-1388.
Perry, J. N., Liebhold, A. M., Rosenberg, M. S., Dungan, J., Miriti, M., Jakomulska, A., et al. (2002). Illustrations and guidelines for selecting statistical methods for quantifying spatial pattern in ecological data. Ecography, 25(5), 578-600.
Ripley, B. D. (1976). The second-order analysis of stationary point processes. Journal of Applied Probability, 13(2), 255-266.
Ripley, B. D. (1977). Modelling spatial patterns. Journal of the Royal Statistical Society. Series B, 39(2), 172-212.
Ripley, B. D. (1979). Tests of `randomness' for spatial point patterns. Journal of the Royal Statistical Society. Series B, 41(3), 368-374.
Schneider, R. J., Ryznar, R. M., and Khattak, A. J. (2004). An accident waiting to happen: A spatial approach to proactive pedestrian planning. Accident Analysis and Prevention, 36(2), 193-211.
Singer, M., and Donoso, P. (2008). Assessing an ambulance service with queuing theory. Computers and Operations Research, 35, 2549-2560.
Sohn, S. Y. (1999). Quality function deployment applied to local traffic accident reduction. Accident Analysis and Prevention, 31, 751-761.
Spooner, P. G., Lunt, I. D., and Briggs, S. V. (2004). Spatial analysis of anthropogenic disturbance regimes and roadside shrubs in a fragmented agricultural landscape. Applied Vegetation Science, 7, 61-70.
Weisburd, D., and Green, L. (1995). Policing drug hot spots: The jersey city drug market analysis experiment. Justice Quarterly, 12(4), 711-735.
White, R. D., Asplin, B. R., Bugliosi, T. F., and Hankins, D. G. (1996). High discharge survival rate after out-of-hospital ventricular fibrillation with rapid defibrillation by police and paramedics. Annals of Emergency Medicine, 28(5), 480-485.
White, R. D., Bunch, T. J., and Hankins, D. G. (2005). Evolution of a community-wide early defibrillation programme experience over 13 years using police/fire personnel and paramedics as responders. Resuscitation, 65(3), 279-283.
Zhang, C., Luo, L., Xu, W., and Ledwith, V. (2008). Use of local moran's i and gis to identify pollution hotspots of pb in urban soils of galway, ireland. Science of the Total Environment, 398(1-3), 212-221.
江博煌、謝顯堂、陳筱蕙、詹大千、劉德明、溫啟邦 (2006)，利用地理資訊系統評估台南安順污染廠址周圍之土壤污染，臺灣公共衛生雜誌， 25(5)， 363-371。
林均雅 (2005)，恆春山茶之族群內遺傳結構，國立台灣大學，臺北市。
林嘉韋 (2008)，運用空間資料挖掘技術於產業群聚之研究－以台中都會區製造業為例，逢甲大學，臺中市。
胡勝川 (1992)，車禍與意外死亡–漸受重視的疾病，台灣醫界， 35， 255-258。
唐世智 (2002)，救災資訊系統之建置與應用─以pda運用為例，大葉大學，彰化縣。
張淑貞 (2007)，街頭搶奪犯罪之空間與時間群聚性研究—以台中市西屯區為例，逢甲大學，臺中市。
張嫺茹 (2001)，肇事特性分析暨車禍處理小組區位配置之模擬研究－以台南市為例，國立成功大學，台南市。
張學聖、廖晉賢、李佳蓁、黃輝林 (2006)，緊急醫療救護案件區位特性與救援設施空間檢討之研究－以台灣台南市為例。第九屆城市地理資訊系統學術論壇 2006 年年會。
陳致元、朱子豪 (2004)，以空間群聚分析探討單一物流中心車輛途程問題，地理學報， 37， 123-137。
陳順宇 (2004)，多變量分析 (第三版)，台北市：華泰書局。
黃國平、吳青翰 (2006)，緊急醫療救護案件發生時間機率研究，中央警察大學災害防救學報， 7， 245-262。
黃國平、吳青翰 (2007)，緊急醫療救護系統資源配置及績效評估之模擬研究—以台南市為例，臺灣公共衛生雜誌， 26(3)， 184-195。
黃國平、吳青翰、洪慈佑 (2005)，緊急醫療救護案件區位模型分析，規劃學報， 32， 13-30。
黃國平、張嫺茹、吳青翰 (2004, 11月12日)，都市行車事故發生時間機率研究，第一屆台灣作業研究學會學術研討會暨2004年科技與管理學術研討會，臺北市。
董建明 (2006)，群聚特性對群聚績效影響之研究-以大陸台商為例，國立成功大學，臺南市。
蘇喜、石崇良、陳麗華 (2002)，創傷病患之到院前緊急救護模式模擬及成本效益分析，行政院國家科學委員會專題研究計畫。