The housing vacancy rate(HVR)is an important index in assessing the healthiness of residential real estate market.In China,it is hardly to take advantage of the basic data of real estate information due to the opaque ...The housing vacancy rate(HVR)is an important index in assessing the healthiness of residential real estate market.In China,it is hardly to take advantage of the basic data of real estate information due to the opaque of those data.In this paper,the HVR is estimated to two scales.At the grid level,urban area ratio was calculated by nighttime images after eliminating outliers of nighttime images and night light intensity of non-residential pixels in mixed pixels by a proposed modified optimal threshold method,and built-up areas in each pixel were extracted from the land-cover data.Then,the HVR is calculated by comparing the light intensity of specific grid with the light intensity of full occupancy rate regions.At the administrative scale,the GCI(‘ghost city’index)is constructed by calculating the ratio of the total light radiation intensity of a city to the total construction land area of the city.The overall spatial differentiation pattern of the vacant houses in the national prefecture level administrative regions is analyzed.The following conclusions were drawn:vacant housing is rare in certain eastern coastal cities and regions in China with relatively fast economic development.Cities based on exhausted resources,some mountainous cities,and cities with relatively backward economic development more typically showed high levels of housing vacancy.The GCI of prefecture-level administrative units gradually declined from north to south,whereas the east-west distribution showed a parabolic shape.As city level decreased,the GCI registered a gradual upward trend.China’s urban housing vacancy can be divided into five categories:industry or resources driven,government planned,epitaxy expansionary,environmental constraint and speculative activate by combining the spatial distribution of housing vacancy with the factors of natural environment,social economic development level,and population density into consideration.展开更多
Explicitly identifying the spatial distribution of ecological transition zones(ETZs)and simulating their response to climate scenarios is of significance in understanding the response and feedback of ecosystems to glo...Explicitly identifying the spatial distribution of ecological transition zones(ETZs)and simulating their response to climate scenarios is of significance in understanding the response and feedback of ecosystems to global climate change.In this study,a quantitative spatial identification method was developed to assess ETZ distribution in terms of the improved Holdridge life zone(iHLZ)model.Based on climate observations collected from 782 weather stations in China in the T0(1981–2010)period,and the Intergovernmental Panel on Climate Change Coupled Model Intercomparison Project(IPCC CMIP5)RCP2.6,RCP4.5,and RCP8.5 climate scenario data in the T1(2011–2040),T2(2041–2070),and T3(2071–2100)periods,the spatial distribution of ETZs and their response to climate scenarios in China were simulated in the four periods of T0,T1,T2,and T3.Additionally,a spatial shift of mean center model was developed to quantitatively calculate the shift direction and distance of each ETZ type during the periods from T0 to T3.The simulated results revealed 41 ETZ types in China,accounting for 18%of the whole land area.Cold temperate grassland/humid forest and warm temperate arid forest(564,238.5 km~2),cold temperate humid forest and warm temperate arid/humid forest(566,549.75 km~2),and north humid/humid forest and cold temperate humid forest(525,750.25 km~2)were the main ETZ types,accounting for 35%of the total ETZ area in China.Between 2010 and 2100,the area of cold temperate desert shrub and warm temperate desert shrub/thorn steppe ETZs were projected to increase at a rate of 4%per decade,which represented an increase of 3604.2,10063.1,and 17,242 km~2 per decade under the RCP2.6,RCP4.5,and RCP8.5 scenarios,respectively.The cold ETZ was projected to transform to the warm humid ETZ in the future.The average shift distance of the mean center in the north wet forest and cold temperate desert shrub/thorn grassland ETZs was generally larger than that of other ETZs,with the mean center moving to the northeast and the shift distance being more than 150 km during the periods from T0 to T3.In addition,with a gradual increase of temperature and precipitation,the ETZs in northern China displayed a shifting northward trend,while the area of ETZs in southern China decreased gradually,and their mean center moved to high-altitude areas.The effects of climate change on ETZs presented an increasing trend in China,especially in the Qinghai-Tibet Plateau.展开更多
Recent advances in spatially resolved transcriptomics(SRT)have provided new opportunities for characterizing spatial structures of various tissues.Graph-based geometric deep learning has gained widespread adoption for...Recent advances in spatially resolved transcriptomics(SRT)have provided new opportunities for characterizing spatial structures of various tissues.Graph-based geometric deep learning has gained widespread adoption for spatial domain identification tasks.Currently,most methods define adjacency relation between cells or spots by their spatial distance in SRT data,which overlooks key biological interactions like gene expression similarities,and leads to inaccuracies in spatial domain identification.To tackle this challenge,we propose a novel method,SpaGRA(https://github.com/sunxue-yy/SpaGRA),for automatic multi-relationship construction based on graph augmentation.SpaGRA uses spatial distance as prior knowledge and dynamically adjusts edge weights with multi-head graph attention networks(GATs).This helps SpaGRA to uncover diverse node relationships and enhance message passing in geometric contrastive learning.Additionally,SpaGRA uses these multi-view relationships to construct negative samples,addressing sampling bias posed by random selection.Experimental results show that SpaGRA presents superior domain identification performance on multiple datasets generated from different protocols.Using SpaGRA,we analyze the functional regions in the mouse hypothalamus,identify key genes related to heart development in mouse embryos,and observe cancer-associated fibroblasts enveloping cancer cells in the latest Visium HD data.Overall,SpaGRA can effectively characterize spatial structures across diverse SRT datasets.展开更多
Urban-suburban-rural(U-S-R)zones exhibit distinctive transitional characteristics in interaction between human and nature.U-S-R transition zones(U-S-RTZ)are also highlighting the function diversity and landscape heter...Urban-suburban-rural(U-S-R)zones exhibit distinctive transitional characteristics in interaction between human and nature.U-S-R transition zones(U-S-RTZ)are also highlighting the function diversity and landscape heterogeneity across territorial spaces.As a super megacity in western China,Chengdu’s rapid urbanization has driven the evolution of U-S-R spaces,resulting in a sequential structure.To promote the high-quality spatial development of urban-rural region in a structured and efficient manner,it is essential to con-duct a scientific examination of the multidimensional interconnection within the U-S-RTZ framework.By proposing a novel identifica-tion method of U-S-RTZ and taking Chengdu,China as a case study,grounded in a blender of natural and humanistic factors,this study quantitatively delineated and explored the spatial evolutions of U-S-RTZ and stated the optimization orientation and sustainable devel-opment strategies of the production-living-ecological spaces along the U-S-R gradients.The results show that:1)it is suitable for the quantitative analysis of U-S-RTZ by established three-dimensional identification system in this study.2)In 1990-2020,the urban-sub-urban transition zones(U-STZ)in Chengdu have continuously undergone a substantial increase,and the scale of the suburban-rural transition zones(S-RTZ)has continued to expand slightly,while the space of rural-ecological transition zones(R-ETZ)has noticeably compressed.3)The landuse dynamics within U-S-RTZ has gradually increased in 1990-2020.The main direction of landuse transition was from farmland to construction land or woodlands,with the expansion of construction land being the most significant.4)R-ETZ primarily focus on ecological functions,and there is a trade-off relationship between the production-ecological function within the S-RTZ,and in the U-STZ,production-living composite functions are prioritized.This study emphasizes the importance of elastic planning and precise governance within the U-S-RTZ in a rapid urbanization region,particularly highlighting the role of suburbs as landscape corridors and service hubs in urban-rural integration.It elucidates to the practical implications for achieving high-quality development of integrated U-S-R territorial spaces.展开更多
The advanced data mining technologies and the large quantities of remotely sensed Imagery provide a data mining opportunity with high potential for useful results. Extracting interesting patterns and rules from data s...The advanced data mining technologies and the large quantities of remotely sensed Imagery provide a data mining opportunity with high potential for useful results. Extracting interesting patterns and rules from data sets composed of images and associated ground data can be of importance in object identification, community planning, resource discovery and other areas. In this paper, a data field is presented to express the observed spatial objects and conduct behavior mining on them. First, most of the important aspects are discussed on behavior mining and its implications for the future of data mining. Furthermore, an ideal framework of the behavior mining system is proposed in the network environment. Second, the model of behavior mining is given on the observed spatial objects, including the objects described by the first feature data field and the main feature data field by means of the potential function. Finally, a case study about object identification in public is given and analyzed. The experimental results show that the new model is feasible in behavior mining.展开更多
Sequencing-based spatial transcriptomics(ST)is an emerging technology to study in situ gene expression patterns at the whole-genome scale.Currently,ST data analysis is still complicated by high technical noises and lo...Sequencing-based spatial transcriptomics(ST)is an emerging technology to study in situ gene expression patterns at the whole-genome scale.Currently,ST data analysis is still complicated by high technical noises and low resolution.In addition to the transcriptomic data,matched histopathological images are usually generated for the same tissue sample along the ST experiment.The matched high-resolution histopathological images provide complementary cellular phenotypical information,providing an opportunity to mitigate the noises in ST data.We present a novel ST data analysis method called transcriptome and histopathological image integrative analysis for ST(TIST),which enables the identification of spatial clusters(SCs)and the enhancement of spatial gene expression patterns by integrative analysis of matched transcriptomic data and images.TIST devises a histopathological feature extraction method based on Markov random field(MRF)to learn the cellular features from histopathological images,and integrates them with the transcriptomic data and location information as a network,termed TIST-net.Based on TIST-net,SCs are identified by a random walk-based strategy,and gene expression patterns are enhanced by neighborhood smoothing.We benchmark TIST on both simulated datasets and 32 real samples against several state-of-the-art methods.Results show that TIST is robust to technical noises on multiple analysis tasks for sequencing-based ST data and can find interesting microstructures in different biological scenarios.TIST is available at http://lifeome.net/software/tist/and https://ngdc.cncb.ac.cn/biocode/tools/BT007317.展开更多
基金Under the auspices of National Natural Science Foundation of China(No.2071216,41661025)Research Capacity Promotion Program for Young Teachers of Northwest Normal University(No.NWNU-LKQN-16-7)。
文摘The housing vacancy rate(HVR)is an important index in assessing the healthiness of residential real estate market.In China,it is hardly to take advantage of the basic data of real estate information due to the opaque of those data.In this paper,the HVR is estimated to two scales.At the grid level,urban area ratio was calculated by nighttime images after eliminating outliers of nighttime images and night light intensity of non-residential pixels in mixed pixels by a proposed modified optimal threshold method,and built-up areas in each pixel were extracted from the land-cover data.Then,the HVR is calculated by comparing the light intensity of specific grid with the light intensity of full occupancy rate regions.At the administrative scale,the GCI(‘ghost city’index)is constructed by calculating the ratio of the total light radiation intensity of a city to the total construction land area of the city.The overall spatial differentiation pattern of the vacant houses in the national prefecture level administrative regions is analyzed.The following conclusions were drawn:vacant housing is rare in certain eastern coastal cities and regions in China with relatively fast economic development.Cities based on exhausted resources,some mountainous cities,and cities with relatively backward economic development more typically showed high levels of housing vacancy.The GCI of prefecture-level administrative units gradually declined from north to south,whereas the east-west distribution showed a parabolic shape.As city level decreased,the GCI registered a gradual upward trend.China’s urban housing vacancy can be divided into five categories:industry or resources driven,government planned,epitaxy expansionary,environmental constraint and speculative activate by combining the spatial distribution of housing vacancy with the factors of natural environment,social economic development level,and population density into consideration.
基金National Key R&D Program of China,No.2018YFC0507202,No.2017YFA0603702National Natural Science Foundation of China,No.41971358,No.41930647+1 种基金Strategic Priority Research Program(A)of the Chinese Academy of Sciences,No.XDA20030203Innovation Research Project of State Key Laboratory of Resources and Environment Information System,CAS。
文摘Explicitly identifying the spatial distribution of ecological transition zones(ETZs)and simulating their response to climate scenarios is of significance in understanding the response and feedback of ecosystems to global climate change.In this study,a quantitative spatial identification method was developed to assess ETZ distribution in terms of the improved Holdridge life zone(iHLZ)model.Based on climate observations collected from 782 weather stations in China in the T0(1981–2010)period,and the Intergovernmental Panel on Climate Change Coupled Model Intercomparison Project(IPCC CMIP5)RCP2.6,RCP4.5,and RCP8.5 climate scenario data in the T1(2011–2040),T2(2041–2070),and T3(2071–2100)periods,the spatial distribution of ETZs and their response to climate scenarios in China were simulated in the four periods of T0,T1,T2,and T3.Additionally,a spatial shift of mean center model was developed to quantitatively calculate the shift direction and distance of each ETZ type during the periods from T0 to T3.The simulated results revealed 41 ETZ types in China,accounting for 18%of the whole land area.Cold temperate grassland/humid forest and warm temperate arid forest(564,238.5 km~2),cold temperate humid forest and warm temperate arid/humid forest(566,549.75 km~2),and north humid/humid forest and cold temperate humid forest(525,750.25 km~2)were the main ETZ types,accounting for 35%of the total ETZ area in China.Between 2010 and 2100,the area of cold temperate desert shrub and warm temperate desert shrub/thorn steppe ETZs were projected to increase at a rate of 4%per decade,which represented an increase of 3604.2,10063.1,and 17,242 km~2 per decade under the RCP2.6,RCP4.5,and RCP8.5 scenarios,respectively.The cold ETZ was projected to transform to the warm humid ETZ in the future.The average shift distance of the mean center in the north wet forest and cold temperate desert shrub/thorn grassland ETZs was generally larger than that of other ETZs,with the mean center moving to the northeast and the shift distance being more than 150 km during the periods from T0 to T3.In addition,with a gradual increase of temperature and precipitation,the ETZs in northern China displayed a shifting northward trend,while the area of ETZs in southern China decreased gradually,and their mean center moved to high-altitude areas.The effects of climate change on ETZs presented an increasing trend in China,especially in the Qinghai-Tibet Plateau.
基金supported by the National Natural Science Foundation of China(Nos.62303271,U1806202,62103397)the Natural Science Foundation of Shandong Province(ZR2023QF081)Funding for open access charge:the National Natural Science Foundation of China(Nos.62303271,U1806202).
文摘Recent advances in spatially resolved transcriptomics(SRT)have provided new opportunities for characterizing spatial structures of various tissues.Graph-based geometric deep learning has gained widespread adoption for spatial domain identification tasks.Currently,most methods define adjacency relation between cells or spots by their spatial distance in SRT data,which overlooks key biological interactions like gene expression similarities,and leads to inaccuracies in spatial domain identification.To tackle this challenge,we propose a novel method,SpaGRA(https://github.com/sunxue-yy/SpaGRA),for automatic multi-relationship construction based on graph augmentation.SpaGRA uses spatial distance as prior knowledge and dynamically adjusts edge weights with multi-head graph attention networks(GATs).This helps SpaGRA to uncover diverse node relationships and enhance message passing in geometric contrastive learning.Additionally,SpaGRA uses these multi-view relationships to construct negative samples,addressing sampling bias posed by random selection.Experimental results show that SpaGRA presents superior domain identification performance on multiple datasets generated from different protocols.Using SpaGRA,we analyze the functional regions in the mouse hypothalamus,identify key genes related to heart development in mouse embryos,and observe cancer-associated fibroblasts enveloping cancer cells in the latest Visium HD data.Overall,SpaGRA can effectively characterize spatial structures across diverse SRT datasets.
基金Under the auspices of National Natural Science Foundation of China(No.41930651)Sichuan Science and Technology Program(No.2023NSFSC1979)。
文摘Urban-suburban-rural(U-S-R)zones exhibit distinctive transitional characteristics in interaction between human and nature.U-S-R transition zones(U-S-RTZ)are also highlighting the function diversity and landscape heterogeneity across territorial spaces.As a super megacity in western China,Chengdu’s rapid urbanization has driven the evolution of U-S-R spaces,resulting in a sequential structure.To promote the high-quality spatial development of urban-rural region in a structured and efficient manner,it is essential to con-duct a scientific examination of the multidimensional interconnection within the U-S-RTZ framework.By proposing a novel identifica-tion method of U-S-RTZ and taking Chengdu,China as a case study,grounded in a blender of natural and humanistic factors,this study quantitatively delineated and explored the spatial evolutions of U-S-RTZ and stated the optimization orientation and sustainable devel-opment strategies of the production-living-ecological spaces along the U-S-R gradients.The results show that:1)it is suitable for the quantitative analysis of U-S-RTZ by established three-dimensional identification system in this study.2)In 1990-2020,the urban-sub-urban transition zones(U-STZ)in Chengdu have continuously undergone a substantial increase,and the scale of the suburban-rural transition zones(S-RTZ)has continued to expand slightly,while the space of rural-ecological transition zones(R-ETZ)has noticeably compressed.3)The landuse dynamics within U-S-RTZ has gradually increased in 1990-2020.The main direction of landuse transition was from farmland to construction land or woodlands,with the expansion of construction land being the most significant.4)R-ETZ primarily focus on ecological functions,and there is a trade-off relationship between the production-ecological function within the S-RTZ,and in the U-STZ,production-living composite functions are prioritized.This study emphasizes the importance of elastic planning and precise governance within the U-S-RTZ in a rapid urbanization region,particularly highlighting the role of suburbs as landscape corridors and service hubs in urban-rural integration.It elucidates to the practical implications for achieving high-quality development of integrated U-S-R territorial spaces.
基金Supported by the National 973 Program of China(No.2006CB701305,No.2007CB310804)the National Natural Science Fundation of China(No.60743001)+1 种基金the Best National Thesis Fundation (No.2005047)the National New Century Excellent Talent Fundation (No.NCET-06-0618)
文摘The advanced data mining technologies and the large quantities of remotely sensed Imagery provide a data mining opportunity with high potential for useful results. Extracting interesting patterns and rules from data sets composed of images and associated ground data can be of importance in object identification, community planning, resource discovery and other areas. In this paper, a data field is presented to express the observed spatial objects and conduct behavior mining on them. First, most of the important aspects are discussed on behavior mining and its implications for the future of data mining. Furthermore, an ideal framework of the behavior mining system is proposed in the network environment. Second, the model of behavior mining is given on the observed spatial objects, including the objects described by the first feature data field and the main feature data field by means of the potential function. Finally, a case study about object identification in public is given and analyzed. The experimental results show that the new model is feasible in behavior mining.
基金supported by the National Key R&D Program of China(Grant Nos.2020YFA0712403 and 2021YFF1200901)the National Natural Science Foundation of China(Grant Nos.61922047,81890993,61721003,and 62133006)+1 种基金the Beijing National Research Centre for Information Science and Technology Young Innovation Fund,China(Grant No.BNR2020RC01009)the Science and Technology Commission of Shanghai Municipality,China(Grant No.20PJ1408300)。
文摘Sequencing-based spatial transcriptomics(ST)is an emerging technology to study in situ gene expression patterns at the whole-genome scale.Currently,ST data analysis is still complicated by high technical noises and low resolution.In addition to the transcriptomic data,matched histopathological images are usually generated for the same tissue sample along the ST experiment.The matched high-resolution histopathological images provide complementary cellular phenotypical information,providing an opportunity to mitigate the noises in ST data.We present a novel ST data analysis method called transcriptome and histopathological image integrative analysis for ST(TIST),which enables the identification of spatial clusters(SCs)and the enhancement of spatial gene expression patterns by integrative analysis of matched transcriptomic data and images.TIST devises a histopathological feature extraction method based on Markov random field(MRF)to learn the cellular features from histopathological images,and integrates them with the transcriptomic data and location information as a network,termed TIST-net.Based on TIST-net,SCs are identified by a random walk-based strategy,and gene expression patterns are enhanced by neighborhood smoothing.We benchmark TIST on both simulated datasets and 32 real samples against several state-of-the-art methods.Results show that TIST is robust to technical noises on multiple analysis tasks for sequencing-based ST data and can find interesting microstructures in different biological scenarios.TIST is available at http://lifeome.net/software/tist/and https://ngdc.cncb.ac.cn/biocode/tools/BT007317.
