Based on remote sensing data of the Yangtze River Delta(YRD) in the years of 1991, 2001 and 2008, the paper built an index system of land use potential restraint factors in YRD, according to geological condition, terr...Based on remote sensing data of the Yangtze River Delta(YRD) in the years of 1991, 2001 and 2008, the paper built an index system of land use potential restraint factors in YRD, according to geological condition, terrain condition, water area, natural reserve area and basic farmland, and evaluated construction land potential based on the platform of GIS spatial analysis model. The results showed that:(1) the construction land increased rapidly since 1991 and reached 24,951.21 km2 in 2008, or 21.27% of the total area. Among all the cities in the YRD, Shanghai took the greatest percentage, followed by Jiangsu and Zhejiang. Spatially, areas where government departments are located became the growth center of construction land. Prefecture-level cities were the fastest growth region and the changing trend showed circle layered characteristics and significant increase with Shanghai and Suzhou as the core.(2) The higher the quality of construction land potentials(CLP), the smaller the number of CLP units. High sensitive area accounted for the largest percentage(40.14%) among all types of constraint regions and this was followed by medium sensitive region(31.53%) of the whole region.(3) The comprehensive CLP in the YRD was 24,989.65 km2, or 21.76% of the total YRD. The land use potential showed spatial distribution imbalance. CLP of Zhejiang was obviously larger than that of Jiangsu. CLP was insufficient in regional central city. Moreover, CLP in the YRD formed a circle layered spatial pattern that increasingly expanded centered in prefecture-level cities. Low potential area expanded from north to south. High potential area was mainly located in south YRD. Areas with zero potential in the YRD formed a northwest-southeast "Y-shaped" spatial pattern in north Hangzhou Bay.(4) CLP per capita in YRD was 0.045 ha/person and also unevenly distributed. Some 25.57% of the study units at county level nearly had no construction land and 8.24% of the units had CLP per capita below the national average level. CLP per capita in less than 25% of the county-level units was larger than the YRD average level, which were mainly located in Zhejiang. Therefore, research on the construction potential area in YRD was favorable for analysis of the development status and potential space of this region under the background of rapid urbanization and industrialization.展开更多
Selectivity estimation is crucial for query optimizers choosing an optimal spatial execution plan in a spatial database management system.This paper presents an Annular Bucket spatial histogram(AB histogram)that can e...Selectivity estimation is crucial for query optimizers choosing an optimal spatial execution plan in a spatial database management system.This paper presents an Annular Bucket spatial histogram(AB histogram)that can estimate the selectivity in finer spatial selection and spatial join operations even when the spatial query has more operators or more joins.The AB histogram is represented as a set of bucket-range,bucket-count value pairs.The bucket-range often covers an annular region like a sin-gle-cell-sized photo frame.The bucket-count is the number of objects whose Minimum Bounding Rectangles(MBRs)fall between outer rectangle and inner rectangle of the bucket-range.Assuming that all MBRs in each a bucket distribute evenly,for every buck-et,we can obtain serial probabilities that satisfy a certain spatial selection or join conditions from the operations' semantics and the spatial relations between every bucket-range and query ranges.Thus,according to some probability theories,spatial selection or join selectivity can be estimated by the every bucket-count and its probabilities.This paper also shows a way to generate an updated AB histogram from an original AB histogram and those probabilities.Our tests show that the AB histogram not only supports the selectivity estimation of spatial selection or spatial join with "disjoint","intersect","within","contains",and "overlap" operators but also provides an approach to generate a reliable updated histogram whose spatial distribution is close to the distribution of ac-tual query result.展开更多
基金National Natural Youth Science Foundation of China,No.41201168
文摘Based on remote sensing data of the Yangtze River Delta(YRD) in the years of 1991, 2001 and 2008, the paper built an index system of land use potential restraint factors in YRD, according to geological condition, terrain condition, water area, natural reserve area and basic farmland, and evaluated construction land potential based on the platform of GIS spatial analysis model. The results showed that:(1) the construction land increased rapidly since 1991 and reached 24,951.21 km2 in 2008, or 21.27% of the total area. Among all the cities in the YRD, Shanghai took the greatest percentage, followed by Jiangsu and Zhejiang. Spatially, areas where government departments are located became the growth center of construction land. Prefecture-level cities were the fastest growth region and the changing trend showed circle layered characteristics and significant increase with Shanghai and Suzhou as the core.(2) The higher the quality of construction land potentials(CLP), the smaller the number of CLP units. High sensitive area accounted for the largest percentage(40.14%) among all types of constraint regions and this was followed by medium sensitive region(31.53%) of the whole region.(3) The comprehensive CLP in the YRD was 24,989.65 km2, or 21.76% of the total YRD. The land use potential showed spatial distribution imbalance. CLP of Zhejiang was obviously larger than that of Jiangsu. CLP was insufficient in regional central city. Moreover, CLP in the YRD formed a circle layered spatial pattern that increasingly expanded centered in prefecture-level cities. Low potential area expanded from north to south. High potential area was mainly located in south YRD. Areas with zero potential in the YRD formed a northwest-southeast "Y-shaped" spatial pattern in north Hangzhou Bay.(4) CLP per capita in YRD was 0.045 ha/person and also unevenly distributed. Some 25.57% of the study units at county level nearly had no construction land and 8.24% of the units had CLP per capita below the national average level. CLP per capita in less than 25% of the county-level units was larger than the YRD average level, which were mainly located in Zhejiang. Therefore, research on the construction potential area in YRD was favorable for analysis of the development status and potential space of this region under the background of rapid urbanization and industrialization.
基金Supported by the Innovation Project of IGSNRR (No. O9V90220ZZ)the Research Plan of LREIS (O88RA700KA),CAS
文摘Selectivity estimation is crucial for query optimizers choosing an optimal spatial execution plan in a spatial database management system.This paper presents an Annular Bucket spatial histogram(AB histogram)that can estimate the selectivity in finer spatial selection and spatial join operations even when the spatial query has more operators or more joins.The AB histogram is represented as a set of bucket-range,bucket-count value pairs.The bucket-range often covers an annular region like a sin-gle-cell-sized photo frame.The bucket-count is the number of objects whose Minimum Bounding Rectangles(MBRs)fall between outer rectangle and inner rectangle of the bucket-range.Assuming that all MBRs in each a bucket distribute evenly,for every buck-et,we can obtain serial probabilities that satisfy a certain spatial selection or join conditions from the operations' semantics and the spatial relations between every bucket-range and query ranges.Thus,according to some probability theories,spatial selection or join selectivity can be estimated by the every bucket-count and its probabilities.This paper also shows a way to generate an updated AB histogram from an original AB histogram and those probabilities.Our tests show that the AB histogram not only supports the selectivity estimation of spatial selection or spatial join with "disjoint","intersect","within","contains",and "overlap" operators but also provides an approach to generate a reliable updated histogram whose spatial distribution is close to the distribution of ac-tual query result.
