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Pages : 193-209    整合數值航測與空間統計技術於遺址土地利用變遷與文化層破壞相關因子偵測之研究
論文名稱 整合數值航測與空間統計技術於遺址土地利用變遷與文化層破壞相關因子偵測之研究
Title Integrating Digital Photogrammetry Techniques and Spatial Statistics to Explore the Related Factors of Land-Use Change and Destruction Degree of Cultural Layer in Archaeological Sites
作者 莊永忠、何彥陞
Author Yung-Chung Chuang, Yen-Sheng Ho
中文摘要 本研究以臺灣三處遺址範圍為研究區,整合1976~1978 與2016~2017 年航空照片與數值航測技術,進行土地利用與地形變遷對遺址破壞程度偵測之研究,並分析影響文化層擾動之空間關聯顯著因子。研究結果顯示各遺址於1976 年至2017 年間普遍受到高密度土地使用影響,人為屯墾、風水災害與工程建設所造成之地形變動為遺址文化層受破壞主因,其中又以私有土地為甚。基於此,以都市計畫手段進行土地徵收與土地管制有其必要性。未來將可應用此技術提高遺址管理成效並有效針對問題因子進行檢討。
Abstract Current situation and accumulated development of archaeological sites have very high impacts on the preservation value of the site. This research set 3 archaeological sites in Taiwan as study areas. Assessment of the degree of destruction of cultural layers due to land use change and morphological change were conducted with aerial photographs (1976~1978; 2016~2017) and digital aerial survey technology. The results showed that the archaeological sites were all seriously influenced due to the high land use intensity between 1976~2017. Morphological changes caused by human cultivation and engineering construction were main causes of site destruction, especially in private lands. Therefore, urban planning methods for land acquisition or land regulation are necessary. The prediction model of the destruction degree of the cultural layers of the site developed by the research team has also achieved good predictive ability. In the future, this technology can be applied to improve the effectiveness of archaeological sites management and effectively review the problem factors.
關鍵字 遺址、數值航測、文化層、地形變動、都市計畫
Keywords Archaeological Sites, Digital Aerial Survey Technology, Cultural Layers, Morphological Changes, Urban Planning
Pages : 211-225    車禍發生與都市地表溫度之相關性探討
論文名稱 車禍發生與都市地表溫度之相關性探討
Title A Study on the Correlation between Car Accidents and Urban Surface Temperature
作者 劉蕙慈、徐逸祥
Author Hui-Tzu Liu, Yi-Shiang Shiu
中文摘要 本研究分析環境溫度與車禍發生次數之關聯,環境溫度來源為MODIS 衛星影像,而車禍紀錄則取自政府開放資料平台 (open data)。而為了確認道路型態是否也與車禍發生有關,因此以空間句法 (space syntax) 分析相對不對稱性 (relative asymmetry, RA) 指標。之後以雙變數空間自相關 (bivariate local indicators of spatial association, BiLISA) 及卜瓦松迴歸 (Poisson regression),分別對地表溫度、RA 值與和車禍次數進行空間及非空間關聯性分析。結果顯示雙變數空間自相關分析或是卜瓦松迴歸分析都證明車禍發生次數與地表溫度呈現正相關,後續可提供給都市計畫以及地方警政單位做為未來通盤檢討、都市設計、車禍防治、市容整理及交通執法等方面做參考。
Abstract This study analyzed the relationship between the environmental temperature and the number of car accidents from the perspectives of space and time. The environmental temperature was retrieved by satellite images and the car accident records were based on the open data provided by the government open data platform. After sorting out the location data, three grid cells of different sizes were used to calculate the average surface temperature of the city and the number of car accidents. In order to confirm whether other factors are also related to the occurrence of car accidents, we added index related to road types, and analyzed relative asymmetry (RA) indicators through the concept of space syntax using road network diagrams to calculate the integration of the road. After the above pre-processing was completed, the bivariate local indicators of spatial association (BiLISA) and Poisson regression were used to analyze the spatial and non-spatial correlations between the surface temperature and the RA value and the number of accidents. The results help us to clarify whether there is a correlation between the location of the car accident and the urban surface temperature or road pattern. The results showed that both BiLISA and Poisson regression proved that the number of car accidents is positively correlated with the surface temperature, which can be provided to urban planning and local police units for future comprehensive review, urban design, car accident prevention, city appearance arrangement and traffic law enforcement etc. as the reference.
關鍵字 MODIS影像、地表溫度、開放資料、雙變數空間自相關、卜瓦松迴歸
Keywords MODIS Images, Surface Temperature, Open Data, Bivariate Local Indicators of Spatial Association, Poisson Regression
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