Cross-regional locality research reflects the influences of natural environment and the human activities due to the abundant land types and the multiple landscape combinations in related regions. The Chinese farming-p...Cross-regional locality research reflects the influences of natural environment and the human activities due to the abundant land types and the multiple landscape combinations in related regions. The Chinese farming-pastoral ecotone is a typical large-scale region but few studies were conducted. This research contributed to the understanding of cross-regional locality of the Chinese farming-pastoral ecotone from different scales, including national, sectional, and provincial administrative units by utilizing geotagged photos（GTPs） obtained from the Panoramio website. The major results were as follows：（1） the locality elements of the Chinese farming-pastoral ecotone included 52 free nodes classified into 8 types of scene attributes;（2） there were huge differences between locality elements of different regions, and there was a negative correlation between the similarity degree of elements of different provinces and their spatial distances;（3） the Chinese farming-pastoral ecotone could be divided into the northern, central and southern sections, whose localities had differences in element constitution, association structure and the strength of elements, system stability and the anti-interference capability; and（4） the evolution of the localities of the northern and central sections was mainly influenced by human activities, while the locality of southern section retained more natural features. On a theoretical level, this research aimed to establish the research methodology of locality from the perspective of open data on the web with strong operability and replicability. On a practical level, this research could enrich the structuring recognition of the locality of the Chinese farming-pastoral ecotone and the comprehension of its dynamic mechanism. The results provide a reference for locality differentiation protection and the development of a cross-regional scale.展开更多
When travelling,people are accustomed to taking and uploading photos on social media websites,which has led to the accumulation of huge numbers of geotagged photos.Combined with multisource information(e.g.weather,tra...When travelling,people are accustomed to taking and uploading photos on social media websites,which has led to the accumulation of huge numbers of geotagged photos.Combined with multisource information(e.g.weather,transportation,or textual information),these geotagged photos could help us in constructing user preference profiles at a high level of detail.Therefore,using these geotagged photos,we built a personalised recommendation system to provide attraction recommendations that match a user’s preferences.Specifically,we retrieved a geotagged photo collection from the public API for Flickr(Flickr.com)and fetched a large amount of other contextual information to rebuild a user’s travel history.We then created a model-based recommendation method with a two-stage architecture that consists of candidate generation(the matching process)and candidate ranking.In the matching process,we used a support vector machine model that was modified for multiclass classification to generate the candidate list.In addition,we used a gradient boosting regression tree to score each candidate and rerank the list.Finally,we evaluated our recommendation results with respect to accuracy and ranking ability.Compared with widely used memory-based methods,our proposed method performs significantly better in the cold-start situation and when mining‘long-tail’data.展开更多
The relationship between cross-regional cultural landscapes does not currently receive enough attention in cultural landscape conservation. Cultural landscapes in China are faced with the crisis of fragmentation and i...The relationship between cross-regional cultural landscapes does not currently receive enough attention in cultural landscape conservation. Cultural landscapes in China are faced with the crisis of fragmentation and islanding, which makes it necessary to strengthen the idea of integrated conservation. The Great Wall cultural belt in Beijing is a cross-regional linear cultural landscape in the northwest mountainous area of Beijing. With the Great Wall as a link connecting the surrounding natural, social, and cultural resources to form a banded structure, it has the typical and practical significance of integrated conservation. Based on the theory of the cultural landscape security pattern and its ‘node – setting – connection' model, this research studies the spatial structure and shaping mechanism of the Great Wall cultural belt in Beijing from the perspective of tourist flow, using Flickr geo-tagged photos and Wikiloc tracks. The results identify the core nodes and intermediate nodes of the Great Wall cultural belt in Beijing and show that it has a multicore, multi-group hierarchical nesting structure differing from the geographic space, which has groups following the evolution orbit of ‘linear string – circumnuclear star – complete network.' To explain the differences between the cultural association network(CAN) and the geospatial network(GSN), we find that the cultural associations between nodes rely on geophysical channels, including heritage corridors and traffic passages, as physical carriers, but the nodes' attraction and their setting elements, containing natural geography environments, settlements, administrative divisions, etc., change the channel resistance, so CAN and GSN present different characteristics. From the perspective of systematic and spatial network thoughts, this study has made an attempt to adopt new analysis and research methods to achieve the integrated conservation and inheritance of linear cultural landscapes in mountainous areas.展开更多
基金supported by the Sino-German Center (the National Natural Science Foundation of China and the German Science Foundation GZ1201)the Postgraduate Courses Project of Peking University (2014-40)
文摘Cross-regional locality research reflects the influences of natural environment and the human activities due to the abundant land types and the multiple landscape combinations in related regions. The Chinese farming-pastoral ecotone is a typical large-scale region but few studies were conducted. This research contributed to the understanding of cross-regional locality of the Chinese farming-pastoral ecotone from different scales, including national, sectional, and provincial administrative units by utilizing geotagged photos（GTPs） obtained from the Panoramio website. The major results were as follows：（1） the locality elements of the Chinese farming-pastoral ecotone included 52 free nodes classified into 8 types of scene attributes;（2） there were huge differences between locality elements of different regions, and there was a negative correlation between the similarity degree of elements of different provinces and their spatial distances;（3） the Chinese farming-pastoral ecotone could be divided into the northern, central and southern sections, whose localities had differences in element constitution, association structure and the strength of elements, system stability and the anti-interference capability; and（4） the evolution of the localities of the northern and central sections was mainly influenced by human activities, while the locality of southern section retained more natural features. On a theoretical level, this research aimed to establish the research methodology of locality from the perspective of open data on the web with strong operability and replicability. On a practical level, this research could enrich the structuring recognition of the locality of the Chinese farming-pastoral ecotone and the comprehension of its dynamic mechanism. The results provide a reference for locality differentiation protection and the development of a cross-regional scale.
基金supported by grants from the National Key Research and Development Program of China[grant number 2017YFB0503602]the National Natural Science Foundation of China[grant number 41771425],[grant number 41625003],[grant number 41501162]the Beijing Philosophy and Social Science Foundation[grant number 17JDGLB002].
文摘When travelling,people are accustomed to taking and uploading photos on social media websites,which has led to the accumulation of huge numbers of geotagged photos.Combined with multisource information(e.g.weather,transportation,or textual information),these geotagged photos could help us in constructing user preference profiles at a high level of detail.Therefore,using these geotagged photos,we built a personalised recommendation system to provide attraction recommendations that match a user’s preferences.Specifically,we retrieved a geotagged photo collection from the public API for Flickr(Flickr.com)and fetched a large amount of other contextual information to rebuild a user’s travel history.We then created a model-based recommendation method with a two-stage architecture that consists of candidate generation(the matching process)and candidate ranking.In the matching process,we used a support vector machine model that was modified for multiclass classification to generate the candidate list.In addition,we used a gradient boosting regression tree to score each candidate and rerank the list.Finally,we evaluated our recommendation results with respect to accuracy and ranking ability.Compared with widely used memory-based methods,our proposed method performs significantly better in the cold-start situation and when mining‘long-tail’data.
基金supported by the SinoGerman Center(the National Natural Science Foundation of China and the German Science Foundation)(Grant No.GZ1201)the PekingUniversity President’s Undergraduate Research Fellowship(PURF,2016–2017)
文摘The relationship between cross-regional cultural landscapes does not currently receive enough attention in cultural landscape conservation. Cultural landscapes in China are faced with the crisis of fragmentation and islanding, which makes it necessary to strengthen the idea of integrated conservation. The Great Wall cultural belt in Beijing is a cross-regional linear cultural landscape in the northwest mountainous area of Beijing. With the Great Wall as a link connecting the surrounding natural, social, and cultural resources to form a banded structure, it has the typical and practical significance of integrated conservation. Based on the theory of the cultural landscape security pattern and its ‘node – setting – connection' model, this research studies the spatial structure and shaping mechanism of the Great Wall cultural belt in Beijing from the perspective of tourist flow, using Flickr geo-tagged photos and Wikiloc tracks. The results identify the core nodes and intermediate nodes of the Great Wall cultural belt in Beijing and show that it has a multicore, multi-group hierarchical nesting structure differing from the geographic space, which has groups following the evolution orbit of ‘linear string – circumnuclear star – complete network.' To explain the differences between the cultural association network(CAN) and the geospatial network(GSN), we find that the cultural associations between nodes rely on geophysical channels, including heritage corridors and traffic passages, as physical carriers, but the nodes' attraction and their setting elements, containing natural geography environments, settlements, administrative divisions, etc., change the channel resistance, so CAN and GSN present different characteristics. From the perspective of systematic and spatial network thoughts, this study has made an attempt to adopt new analysis and research methods to achieve the integrated conservation and inheritance of linear cultural landscapes in mountainous areas.
