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The Generic Annular Bucket Histogram for Estimating the Selectivity of spatial Selection and spatial join

Geo-spatial Information Science 2011年第4期14卷 262-273页

作者： Changxiu Cheng Chenghu Zhou Rongguo Chen State Key Laboratory of Resources and Environmental Information Systems Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Selectivity estimation is crucial for query optimizers choosing an optimal spatial execution plan in a spatial database management *** 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 *** AB histogram is represented as a set of bucket-range,bucket-count value *** bucket-range often covers an annular region like a sin-gle-cell-sized photo *** bucket-count is the number of objects whose Minimum Bounding Rectangles(MBRs)fall between outer rectangle and inner rectangle of the *** 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 ***,according to some probability theories,spatial selection or join selectivity can be estimated by the every bucket-count and its *** paper also shows a way to generate an updated AB histogram from an original AB histogram and those *** 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.

关键词： selectivity estimation AB histogram annular bucket spatial selection spatial join

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VID join： Mapping Trajectories to Points of Interest to Support Location-Based Services

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Journal of Computer Science & Technology 2015年第4期30卷 725-744页

作者：商烁谢珂心郑凯刘家俊文继荣 Department of Computer Science China University of Petroleum Beijing 102249 China School of Information Technology and Electrical Engineering The University of Queensland Brisbane QLD 4072 Australia School of Computer Science and Technology Soochow University Suzhou 215006 China Commonwealth Scientific and Industrial Research Organisation Kenmore QLD 4069 Australia Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Beijing 100080 China

Variable influence duration （VID） join is a novel spatio-temporal join operation between a set T of trajectories and a set P of spatial points. Here, trajectories are traveling histories of moving objects （e.g., travelers）, and spatial points are points of interest （POIs, e.g., restaurants）. VID join returns all pairs of （τs, p） if τs is spatially close to p for a long period of time, where τs is a segment of trajectory τ ∈ T and p ∈ P. Each returned （τs, p） implies that the moving object associated with τs stayed at p （e.g., having dinner at a restaurant）. Such information is useful in many aspects, such as targeted advertising, social security, and social activity analysis. The concepts of influence and influence duration are introduced to measure the spatial closeness between τ and p, and the time spanned, respectively. Compared to the conventional spatio-temporal join, the VID join is more challenging since the join condition varies for different POIs, and the additional temporal requirement cannot be indexed effectively. To process the VID join e？ciently, three algorithms are developed and several optimization techniques are applied, including spatial duplication reuse and time duration based pruning. The performance of the developed algorithms is verified by extensive experiments on real spatial data.

关键词： trajectory spatial database spatial join spatio-temporal join

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Intelligent Identification of Building Patches and Assessment of Roof Greening Suitability in High-density Urban Areas:A Case Study of Chengdu

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Journal of Resources and Ecology 2022年第2期13卷 247-256页

作者： LUO Luhua CHEN Mingjie DONG Lulu SU Wei LI Xin HU Xiaodong ZHANG Xin LI Chen CHENG Weiming SHI Hanning LUO Jiancheng Faculty of Geomatics Lanzhou Jiaotong UniversityLanzhou 730070China National-Local joint Engineering Research Center of Technological and Application for National Geographic State Monitoring Lanzhou 730070China Gansu Provincial Engineering Laboratory for National Geographic State Monitoring Lanzhou 730070China College of Geoscience and Surveying Engineering China University of Mining&Technology(Bejing)Beijing 100083China School of Earth Science and Engineering Hebei University of EngineeringHebei 056000China Tianjin University Urban Planning and Design Institute Co.Ltd Tianjin 300072China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China The ImageSky International Co.Ltd SuzhouJiangsu 215000China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100101China School of Architecture Tianjin UniversityTianjin 300072China

With the expansion of a city,the urban green space is occupied and the urban heat island effect is *** the roof surfaces of urban buildings is an effective way to increase the area of urban green space and improve the urban ecological *** provide effective data support for urban green space planning,this paper used high-resolution images to(1)obtain accurate building spots on the map of the study area through deep learning assisted manual correction;and(2)establish an evaluation index system of roof greening including the characteristics of the roof itself,the natural environment and the human society *** weight values of attributes not related to the roof itself were calculated by Analytic Hierarchy Process(AHP).The suitable green roof locations were evaluated by spatial join,weighted superposition and other spatial analysis *** the areas within the Chengdu city’s third ring road as the study area,the results show that an accurate building pattern obtained by deep learning greatly improves the efficiency of the *** roof surfaces unsuitable for greening can be effectively classified by the method of feature extraction,with an accuracy of 86.58%.The roofs suitable for greening account for 48.08%,among which,the high-suitability roofs,medium-suitability roofs and low-suitability roofs represent 45.32%,38.95%and 15.73%.The high-suitability green buildings are mainly distributed in the first ring district and the western area outside the first ring district in *** paper is useful for solving the current problem of the more saturated high-density urban area and allowing the expansion of the urban ecological environment.

关键词： deep learning roof greening suitability assessment spatial join weighted overlay

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