The assessment of the spatiotemporal evolution of habitat quality caused by land use changes can provide a scientifc basis for the ecological protection and green development of mining cities.Taking Yanshan County as ...The assessment of the spatiotemporal evolution of habitat quality caused by land use changes can provide a scientifc basis for the ecological protection and green development of mining cities.Taking Yanshan County as an example of a typical mining city,this article discussed the spatial pattern and evolution characteristics of habitat quality in 2000 and 2018 based on the ArcGIS platform and the InVEST model.The conclusions are as below:from 2000 to 2018,the area of farmland and construction land changed the most in the study area.Among them,the area of farmland decreased by 3.48%,and the area of industrial and mining land and construction land increased by 53.25%.Areas of low,relatively low and high habitat quality expanded,and areas of medium and relatively high habitat quality shrank,which is closely related to the distribution of land use.The areas with high habitat degradation degrees appear around cities,mining areas and watersheds,while the areas with low habitat degradation degrees are mainly distributed in the southern woodland.The distribution of cold and hot spots in the habitat quality distribution of Yanshan County presents a pattern of“hot in the south and cold in the north”.The results are of great signifcance to the precise implementation of ecosystem management decisions in mining cities and the creation of a landscape pattern of“beautiful countrysides,green cities,and green mines”.展开更多
With the widespread adoption of location- aware technology, obtaining long-sequence, massive and high-accuracy spatiotemporal trajectory data of individuals has become increasingly popular in various geographic studie...With the widespread adoption of location- aware technology, obtaining long-sequence, massive and high-accuracy spatiotemporal trajectory data of individuals has become increasingly popular in various geographic studies. Trajectory data of taxis, one of the most widely used inner-city travel modes, contain rich information about both road network traffic and travel behavior of passengers. Such data can be used to study the microscopic activity patterns of individuals as well as the macro system of urban spatial structures. This paper focuses on trajectories obtained from GPS-enabled taxis and their applications for mining urban commuting patterns. A novel approach is proposed to discover spatiotemporal patterns of household travel from the taxi trajectory dataset with a large number of point locations. The approach involves three critical steps： spatial clustering of taxi origin-destination （OD） based on urban traffic grids to discover potentially meaningful places, identifying thresh- old values from statistics of the OD clusters to extract urban jobs-housing structures, and visualization of analytic results to understand the spatial distribution and temporal trends of the revealed urban structures and implied household commuting behavior. A case study with a taxi trajectory dataset in Shanghai, China is presented to demonstrate and evaluate the proposed method.展开更多
空间并置(co-location)模式挖掘是指在大量的空间数据中发现一组空间特征的子集,这些特征的实例在地理空间中频繁并置出现.传统的空间并置模式挖掘算法通常采用逐阶递增的挖掘框架,从低阶模式开始生成候选模式并计算其参与度(空间并置...空间并置(co-location)模式挖掘是指在大量的空间数据中发现一组空间特征的子集,这些特征的实例在地理空间中频繁并置出现.传统的空间并置模式挖掘算法通常采用逐阶递增的挖掘框架,从低阶模式开始生成候选模式并计算其参与度(空间并置模式的频繁性度量指标).虽然这种挖掘框架可以得到正确和完整的结果,但是带来的时间和空间开销非常大.此外传统方法对于空间并置模式的最小频繁性阈值较为敏感,当最小频繁性阈值改变时整个挖掘过程需要重新进行.因此,本文提出一种基于极大团和哈希表的空间并置模式挖掘算法CPM-MCHM(Co-location Pattern Mining based on Maximal Clique and Hash Map)来发现完整并且正确的频繁空间并置模式.CPM-MCHM算法不仅避免逐阶候选-测试框架带来的巨大开销问题,还降低了算法对最小频繁性阈值的敏感.首先,采用基于位运算的分区Bron–Kerbosch算法生成给定空间数据集的所有极大团,并将其存储在哈希表中.然后,提出一种两阶段挖掘框架计算所有模式的参与度并过滤所有频繁空间并置模式.最后,在真实和合成数据集上进行了大量的对比实验.与经典的传统算法和近两年内学者提出的两种算法相比,当实验数据的规模达到20万实例数时,本文提出的CPM-MCHM算法的挖掘时间和空间耗费分别降低了90%和70%以上,当实验数据量进一步加大时CPM-MCHM算法的优势更加明显.展开更多
基金was funded by the Jiangxi Provincial Social Science Foundation“the 14th Five-Year Plan”(2021)regional project(21DQ44)Science and Technology Research Project of Jiangxi Provincial Department of Education(GJJ210723)+1 种基金the Doctoral Research Initiation fund of East China University of Technology(DHBK2019184)the Graduate Innovation Fund of East China University of Technology(DHYC-202123).
文摘The assessment of the spatiotemporal evolution of habitat quality caused by land use changes can provide a scientifc basis for the ecological protection and green development of mining cities.Taking Yanshan County as an example of a typical mining city,this article discussed the spatial pattern and evolution characteristics of habitat quality in 2000 and 2018 based on the ArcGIS platform and the InVEST model.The conclusions are as below:from 2000 to 2018,the area of farmland and construction land changed the most in the study area.Among them,the area of farmland decreased by 3.48%,and the area of industrial and mining land and construction land increased by 53.25%.Areas of low,relatively low and high habitat quality expanded,and areas of medium and relatively high habitat quality shrank,which is closely related to the distribution of land use.The areas with high habitat degradation degrees appear around cities,mining areas and watersheds,while the areas with low habitat degradation degrees are mainly distributed in the southern woodland.The distribution of cold and hot spots in the habitat quality distribution of Yanshan County presents a pattern of“hot in the south and cold in the north”.The results are of great signifcance to the precise implementation of ecosystem management decisions in mining cities and the creation of a landscape pattern of“beautiful countrysides,green cities,and green mines”.
基金This research is sponsored by the National High Technology Research and Development of China （No. 2013AA 12A402）, the National Natural Science Foundation of China （Grant Nos. 40771138, 41101371, and 41301484） and the Zhejiang Province Key Scientific and Technological Project （No. 2013C01124）. Thanks to Dr. Zhongwei Deng for providing taxi trajectory data of Shanghai, China.
文摘With the widespread adoption of location- aware technology, obtaining long-sequence, massive and high-accuracy spatiotemporal trajectory data of individuals has become increasingly popular in various geographic studies. Trajectory data of taxis, one of the most widely used inner-city travel modes, contain rich information about both road network traffic and travel behavior of passengers. Such data can be used to study the microscopic activity patterns of individuals as well as the macro system of urban spatial structures. This paper focuses on trajectories obtained from GPS-enabled taxis and their applications for mining urban commuting patterns. A novel approach is proposed to discover spatiotemporal patterns of household travel from the taxi trajectory dataset with a large number of point locations. The approach involves three critical steps： spatial clustering of taxi origin-destination （OD） based on urban traffic grids to discover potentially meaningful places, identifying thresh- old values from statistics of the OD clusters to extract urban jobs-housing structures, and visualization of analytic results to understand the spatial distribution and temporal trends of the revealed urban structures and implied household commuting behavior. A case study with a taxi trajectory dataset in Shanghai, China is presented to demonstrate and evaluate the proposed method.
文摘空间并置(co-location)模式挖掘是指在大量的空间数据中发现一组空间特征的子集,这些特征的实例在地理空间中频繁并置出现.传统的空间并置模式挖掘算法通常采用逐阶递增的挖掘框架,从低阶模式开始生成候选模式并计算其参与度(空间并置模式的频繁性度量指标).虽然这种挖掘框架可以得到正确和完整的结果,但是带来的时间和空间开销非常大.此外传统方法对于空间并置模式的最小频繁性阈值较为敏感,当最小频繁性阈值改变时整个挖掘过程需要重新进行.因此,本文提出一种基于极大团和哈希表的空间并置模式挖掘算法CPM-MCHM(Co-location Pattern Mining based on Maximal Clique and Hash Map)来发现完整并且正确的频繁空间并置模式.CPM-MCHM算法不仅避免逐阶候选-测试框架带来的巨大开销问题,还降低了算法对最小频繁性阈值的敏感.首先,采用基于位运算的分区Bron–Kerbosch算法生成给定空间数据集的所有极大团,并将其存储在哈希表中.然后,提出一种两阶段挖掘框架计算所有模式的参与度并过滤所有频繁空间并置模式.最后,在真实和合成数据集上进行了大量的对比实验.与经典的传统算法和近两年内学者提出的两种算法相比,当实验数据的规模达到20万实例数时,本文提出的CPM-MCHM算法的挖掘时间和空间耗费分别降低了90%和70%以上,当实验数据量进一步加大时CPM-MCHM算法的优势更加明显.
