52 research outputs found
Poverty issues and policies in China: the case of Luliang District in Sanxi Province
No full textThe huge gap in economic development between rural and urban areas in China is gradually being eroded by the introduction of light industries into the traditional agricultural economies of rural areas. This has been especially evident in coastal and suburban regions but there has, as yet, been very little change in the mountainous areas of southwest and northwest China. This study focuses on Luliang District in Shanxi Province in northwest China where the economy remains underdeveloped and where poverty (as defined by daily food consumption and household expenditure on basic needs) is widespread. Data collected at the household level yielded a whole range of indices with which to assess the level of poverty (for example, income, expenditure, cultivable land, taxes paid, subsidies received, employment and education). This has allowed the incidence of poverty to be explored further. The author has related its incidence in Luliang District to three basic factors: the intrinsic nature of the rural households themselves, environmental conditions/geographical location, and the impact of the State's macroeconomic policies. In terms of the last factor, Shanxi Province suffers from the fact that its industrial structure is biased towards heavy industry (based on coal supplies). This has undermined any balanced growth of the regional economy and slowed the industrialization of agricultural enterprises. The farmers' per capita income in the Province is far below the levels of those in most other provinces and the local governments lack adequate funds to attempt any modernization of traditional agriculture. The problem is further exacerbated by the continuing growth of the population. Surplus rural labour is unable to move freely to find non-agricultural work and increasing numbers of households find themselves confronting the prospect of poverty. The author assesses the impact of national economic policies and the State's own anti-poverty policies. In short, China simply cannot support a large population in poverty. The material payment policy, credit funds and tax reduction schemes have had mixed results so far, but the effect of increased prices for agricultural and side-line products has had a more far- 11l reaching effect on poverty. Nevertheless, many households trapped in the vicious cycle of poverty still find their conditions worsening and will need some protection from further harmful readjustments occurring within the national macroeconomy. Unless the government acts carefully in implementing its anti-poverty policies, it may find its scarce resources draining away in ineffectual schemes. ----- ---- ---- -- - - -- -
Exploring the Impact of Localized COVID-19 Events on Intercity Mobility during the Normalized Prevention and Control Period in China
No full textUncontrolled, large-scale human mobility can amplify a localized disease into a pandemic. Tracking changes in human travel behavior, exploring the relationship between epidemic events and intercity travel generation and attraction under policies will contribute to epidemic prevention efforts, as well as deepen understanding of the essential changes of intercity interactions in the post-epidemic era. To explore the dynamic impact of small-scale localized epidemic events and related policies on intercity travel, a spatial lag model and improved gravity models are developed by using intercity travel data. Taking the localized COVID-19 epidemic in Xi’an, China as an example, the study constructs the travel interaction characterization before or after the pandemic as well as under constraints of regular epidemic prevention policies, whereby significant impacts of epidemic events are explored. Moreover, indexes of the quantified policies are refined to the city level in China to analyze their effects on travel volumes. We highlight the non-negligible impacts of city events and related policies on intercity interaction, which can serve as a reference for travel management in case of such severe events
Hierarchical Clustering Algorithm for Multi-Camera Vehicle Trajectories Based on Spatio-Temporal Grouping under Intelligent Transportation and Smart City
No full textWith the emergence of intelligent transportation and smart city system, the issue of how to perform an efficient and reasonable clustering analysis of the mass vehicle trajectories on multi-camera monitoring videos through computer vision has become a significant area of research. The traditional trajectory clustering algorithm does not consider camera position and field of view and neglects the hierarchical relation of the video object motion between the camera and the scenario, leading to poor multi-camera video object trajectory clustering. To address this challenge, this paper proposed a hierarchical clustering algorithm for multi-camera vehicle trajectories based on spatio-temporal grouping. First, we supervised clustered vehicle trajectories in the camera group according to the optimal point correspondence rule for unequal-length trajectories. Then, we extracted the starting and ending points of the video object under each group, hierarchized the trajectory according to the number of cross-camera groups, and supervised clustered the subsegment sets of different hierarchies. This method takes into account the spatial relationship between the camera and video scenario, which is not considered by traditional algorithms. The effectiveness of this approach has been proved through experiments comparing silhouette coefficient and CPU time
Paleoproterozoic crustal growth in the North China Craton: evidence from the Luliang Complex
No full textAbstract not availableM. Santosh, Qiong-Yan Yang, Xueming Teng, Li Tan
CTCD-Net: A Cross-Layer Transmission Network for Tiny Road Crack Detection
No full textCrack detection is essential for the safety maintenance of road infrastructure. However, there are two major limitations to detecting road cracks accurately: (1) tiny cracks usually possess less distinctive features and are more susceptible to noises, so they are apt to be ignored; (2) most existing methods extract cracks with coarse and thicker boundaries, which needs further improvement. To address the above limitations, we propose CTCD-Net: a Cross-layer Transmission network for tiny road Crack Detection. Firstly, we propose a cross-layer information transmission module based on an attention mechanism to compensate for the disadvantage of unobvious features of tiny cracks. With this module, the feature information from upper layers is transmitted to the next one, layer by layer, to achieve information enhancement and emphasize the feature representation of tiny crack regions. Secondly, we design a boundary refinement block to further improve the accuracy of crack boundary locations, which refines boundaries by learning the residuals between the label images and the interim coarse maps. Extensive experiments conducted on three crack datasets demonstrate the superiority and effectiveness of the proposed CTCD-Net. In particular, our method largely improves the accuracy and completeness of tiny crack detection
Research and application of space-time behavior maps: a review
No full textToday, in the space-time big data environment, the research into behavior maps, as an important spatial analysis and behavior visualization method, has gradually increased in dimension, depth and breadth. In this study, we used the China National Knowledge Infrastructure (CNKI) database and the Web of Science (WOS) Core Collection database as the main literature search engines, through the literature review, the behavior maps analysis type, visual expression, and its method evolution are summarized. And we used CiteSpace and VOSviewer scientific knowledge mapping software to reveal the hotspots, development trends, and field dynamics of space-time behavior maps research in the collected documents. We find that the current research literature on space-time behavior maps shows three areas of clustering: post-occupancy evaluation, public open space, and time geography. By establishing the typical application cases in the three fields, the applications and development frontiers are summarized and considered
Object-Based Convolutional Neural Networks for Cloud and Snow Detection in High-Resolution Multispectral Imagers
No full textCloud and snow detection is one of the most significant tasks for remote sensing image processing. However, it is a challenging task to distinguish between clouds and snow in high-resolution multispectral images due to their similar spectral distributions. The shortwave infrared band (SWIR, e.g., Sentinel-2A 1.55–1.75 µm band) is widely applied to the detection of snow and clouds. However, high-resolution multispectral images have a lack of SWIR, and such traditional methods are no longer practical. To solve this problem, a novel convolutional neural network (CNN) to classify cloud and snow on an object level is proposed in this paper. Specifically, a novel CNN structure capable of learning cloud and snow multiscale semantic features from high-resolution multispectral imagery is presented. In order to solve the shortcoming of “salt-and-pepper” in pixel level predictions, we extend a simple linear iterative clustering algorithm for segmenting high-resolution multispectral images and generating superpixels. Results demonstrated that the new proposed method can with better precision separate the cloud and snow in the high-resolution image, and results are more accurate and robust compared to the other methods
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