시문을 통해 본 소쇄원의 공간인식에 관한 기초연구 (A Basic Study on Spatial Recognition through Poet in Soswaewon Garden)
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- 한국전통조경학회지
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- 제33권3호
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- pp.38-49
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- 2015
본 연구는 소쇄원과 관련하여 시문을 남겼던 인물들의 공간 인식을 살펴보고자 소쇄원 관련 시문의 수집과 작자의 교유관계 분석을 기반으로 시문 속 단어의 빈도추출과 내용분석을 수행한 결과 다음과 같은 결과를 도출하였다. 첫째, 소쇄원 관련 시문을 저작한 인물들의 관계성은 원림의 소유주와의 교유관계를 중심으로 형성되었다. 양산보 대에는 송순과 김언거, 김인후 등 인척관계를 중심으로 그들과 교유한 인물들이 소쇄원에서 시문을 남겼다. 특히 김인후는 총 10편의 시문을 남겼으며, 양자정과도 교유하는 등 소쇄원 관련 시문에 주도적 입장을 보이고 있다. 양자정 대에는 임억령, 김성원 등 선대의 교유인물들이 지속적인 관계를 맺고 있다. 양진태 대에는 송시열, 김수항 등 당시 문인들에게 영향을 미치는 유명인사와의 관계를 형성하였으며 적극적인 대외활동에 힘썼다. 이후 양경지, 양채지를 중심으로 교유관계가 나타나는데 조경망, 조정만과 김창흡 등 선대에 형성된 관계가 후손들에게도 이어지는 모습을 보이고 있으며 모임을 개최함에 따라 주변 문인들이 소쇄원에서 시문을 저작하였다. 둘째, 소쇄원 관련 시문을 대상과 내용으로 구분하여 분석한 결과 시문에서 주로 언급되는 관심의 대상은 수목과 점경물을 들 수 있다. 수목의 경우 왕대와 소나무가 높은 빈도를 보이고 있는데 왕대는 소쇄원도나 현존식생에서도 소쇄원을 둘러싸고 있는 대표적 수종으로 나타났으며 소나무는 단목으로서 주요 시문의 대상으로 이용되었다. 또한 점경물 중 시문에 주로 이용된 담장은 김인후의 시문이 남겨져 있어 후손들이 이를 바라보며 다수의 시문을 저작하는 등 오랫동안 유지되어 왔으나 현재는 그 모습을 살펴볼 수 없는 실정이다. 또한 소쇄원에서의 행위로는 원림 내 공간구성요소 각각의 대상을 개별경관으로 감상하는 사례가 가장 높은 비율을 차지하고 있으며 감회와 관련한 내용으로는 과거 선현에 대한 존숭이 주를 이루었는데 이는 김인후의 시가 남겨진 담장을 시문으로 읊는 양상과 관련지을 수 있다. 셋째, 소쇄원 관련 인물관계와 시문의 교차분석 결과 소쇄원에 대한 공간 인식의 양상은 경관 인식, 행위와 관련된 인식, 정서와 관련된 인식으로 구분할 수 있다. 소쇄원에서의 경관인식은 양산보 대에 수목 중심의 장면이 주로 묘사되는 특성을 지니고 있어 소쇄원 조영 초기의 모습은 수목에 위요된 정자로 인식되었을 것으로 판단된다. 이후 양자정 대부터 소쇄원이라는 명칭이 등장하게 되는데 이는 당시 소쇄원을 방문한 이들이 단순한 정자위주의 공간에 전체적인 모습이 갖추어진 원림으로 인식하게 되었다. 행위와 관련된 인식의 경우 소쇄원의 경관을 감상하고 술을 마시는 행위가 주를 이루고 있다. 양산보 대에는 작시, 소요, 망월 등의 행위가 높은 빈도를 보이고 있으나 양자정 대 이후에는 소쇄원에서 술을 마시며 원림을 감상하는 행위 외에는 상대적으로 낮은 빈도를 보이고 있다. 이러한 결과는 초기 혈연관계의 내향적 향유행위가 소쇄원을 찾는 이가 다수로 확장됨에 따라 향유행태의 성격이 외향적으로 변화되면서 단편화 되었다. 정서와 관련된 인식으로는 양산보 대 소쇄원을 떠나는 아쉬움과 그리움이 주로 확인되었으며, 소쇄원에서 느끼는 분위기는 대체로 한가롭다는 인식이 나타났다. 양자정 대에는 소쇄원에서의 즐거웠던 시간을 그리워하는 시문이 주를 이루었으며 양천운 대에는 소쇄원에서의 즐거운 감정, 선대에 대한 존경 등이 수반되고 있다. 양진태와 양택지 대, 양경지와 양채지 대에는 선대에 대한 존경심을 나타내는 시문이 특징적으로 확인되었다.
本文阐述了产业和学术之间的合作项目. 这个合作项目关注美国东北部的一家大型地区连锁百货商店的两个自有品牌服装的营销和产品开发战略发展. 这个项目的目标是通过和学生的想法的合作来振兴产品线. 从而给学生提供真实产业环境中的实践经验. 这个项目中有很多关键者. 在美国东北部的一家私有连锁百货商店为已有的两个自有服装品牌寻求一个学术伙伴. 他们的目标客户是追求休闲, 适中价格的中年消费者. 这个公司想要改变包装和展示的方向, 甚至是产品的设计. 公司的品牌和产品开发部门联系东北一个州立大学的学术部门的教授. 有两位教授认为这个项目非常适合他们的课程-一个是初级的媒介品牌管理课程; 一个是高级的时装产品开发课程. 这些教授认为通过合作项目, 学生在安全的学术学习环境中能进入一个真实的工作场景中在一个多学科协作团队, 提供超出一个学生的能力, 经验和资源优势, 并增加了解决问题的过程中的 "智囊" (Lowman 2000). 这种提高学生的能力目标的方向让每班教师去组织品牌和产品开发类的跨学科团队. 此外, 许多大学都聘请科研和教学的产业伙伴关系, 协作的时间(学期)和环境(教室/实验室)的约束有助于提高学生的知识和对现实世界的经验. 在田纳西大学, 产业服务中心和UT-Knoxville's 工学院和一家公司合作来发展它们美国公司的的设计进步. 本研究中, 因为是和一个自有商标零售品牌, Wickett, Gaskill 和Damhorst's (1999) 指出产品开发和品牌管理团队使用的零售服装产品开发模型. 之所以选择这个框架是因为它从零售这个角度强调了服饰产品开发. 两个班级参与了这个项目: 一个初级品牌管理班级和一个高级时装产品开发班级. 7个团队包括四名学习品牌管理的学生和两名学习产品开发的学生. 这两个课程在同一个学期但是不同的时间. 在学期开始的时候, 每个班级都被介绍给了产业合作伙伴并接受了问题. 一半的团队指定为男士品牌, 另一半是女士品牌. 这些小组负责制定解决问题的方法, 制定自己的工作时间表, 在与业界代表保持接触, 并确保每个小组成员以积极的方式负责任. 这些小组的目标是通过用销售规划进程来计划, 发展和展示一条产品线(遵循Wickett, Gaskill和Damhorst 模型) 并为这条产品线发展新的品牌战略. 这些小组展示了趋势, 色彩, 面料和目标市场调查; 制定一个产品线的草图;编辑了草图, 介绍他们的执行计划书写说明书, 配上合适的模型并最终开发生产样品. 品牌班的学生完成了SWOT分析, 品牌测量研究报告, 品牌心智图和完整综合的营销报告. 这些报告在介绍新产品线时同时发表. 将来如果有更多这样的协作机会而且公司希望同时考虑品牌和产品开发战略, 那么课程应该定在相同的时间, 这样学生有更多的时间在一起讨论时间表和被分配的任务. 像上面的任务, 学生不得不每堂课之外的时间见面. 这使得团队工作变得具有挑战性(Pfaff和Huddleston, 2003). 虽然这项工作的后勤是费时设立和管理, 但教授认为对学生的好处是多种多样的. 根据两堂课的学生的回复, 最重要的好处是和产业专业人士一起工作的机会, 跟进他们的进程, 并看到公司里做决定级别的高层对他们作品的评估. 教员们都感激有一个 "真实的世界" 的案例. 制定的创意和战略扩大和加强了品牌和产品开发两个部门的联系. 通过和来自不同知识领域的学生一起工作并且和产业伙伴联系, 遵守产业活动的框架和时间表, 学生小组在新的环境中完成优秀创新的作品是具有挑战性的. 在产品开发和为 "现实生活" 品牌的品牌工作, 这些品牌都在努力给学生一个机会, 看看他们的课程是如何紧密的与现实世界联系, 以及公司运营中设计和商业方面如何需要创造性, 协作和灵活性. 行业人员对(a)学生的知识水平和深度以及执行力, (b)品牌的新思路的创造性产生了深刻的印象.
The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.