scholarly journals Economy Estimation of Mainland China at County-Level Based on Landsat Images and Multi-Task Deep Learning Framework

2020 ◽  
Vol 86 (2) ◽  
pp. 99-105
Author(s):  
Bo Yu ◽  
Ying Dong ◽  
Fang Chen ◽  
Yu Wang
Keyword(s):  
Deep Learning ◽  
Local Governments ◽  
Central Government ◽  
Mainland China ◽  
County Level ◽  
Landsat Images ◽  
Learning Framework ◽  
Economic Statistics ◽  
Dense Distribution ◽  
Potential Strategy

The social-economic statistics collected from local governments are the main access for the central government to achieve national economic circumstance, especially for China. However, the statistics of almost 10% of national counties are missing or inconsistent due to the statistical caliber change in the wave of urbanization during economic development. Some researchers proposed to apply a night luminosity product to solve such issue. However, it lacks the ability to distinguish between the wealthy populations with a dense distribution and the less developed places. In this paper, the publicly available daytime Landsat images are used to estimate economic statistics. An end-to-end multi-task deep learning framework is constructed to estimate the county-level economy of Mainland China and the overall accuracy of this model achieves higher than 85%. The experiments show that our model provides a potential strategy to make up for the missing statistics and examines the reliability of the statistics collected for the central government.

scholarly journals Will Rural Collective-Owned Commercial Construction Land Marketization Impact Local Governments’ Interest Distribution? Evidence from Mainland China

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 209
Author(s):  
Mingyu Zhang ◽  
Qiuxiao Chen ◽  
Kewei Zhang ◽  
Dongye Yang
Keyword(s):  
Distribution System ◽  
Local Governments ◽  
Census Data ◽  
Central Government ◽  
Causal Effect ◽  
Control Group ◽  
County Level ◽  
Step Method ◽  
Construction Land ◽  
Marketization Reform

To promote the harmonious human-land relationships and increased urban-rural interaction, rural collective-owned commercial construction land (RCOCCL) marketization reform in some pilot areas was a new attempt by the Chinese Central Government in 2015. In this areas, a novel interest distribution system was established with the land right adjustment and the corresponding local governments were likely to benefit through taxation and land appreciation adjustment fund. This study proposed the hypothesis that the RCOCCL marketization reform would improve local government revenue, and explored the actual effect based on panel census data of county-level administrative units from 2010 to 2018. We applied the difference-in-difference (DID) method to analyze the causal effect of this reform on fiscal revenue with 29 pilot areas selected as the treatment group and 1602 county-level units as the control group. The empirical results of the optimized DID robustness test models and the Heckman two-step method showed that the RCOCCL marketization reform does not have a significant impact because of lower land circulation efficiency, the transfer of land transaction costs, and the policy implementation deviations. Thus, weakening the administrative intervention of local governments in the RCOCCL marketization is essential to the land market development in China.

County-Level Determinants of the Spatial Distribution of Taiwanese Direct Investment in Mainland China

Asian Survey ◽  
2015 ◽  
Vol 55 (4) ◽  
pp. 766-792
Author(s):  
Kelan (Lilly) Lu
Keyword(s):  
Spatial Distribution ◽  
Local Governments ◽  
Direct Investment ◽  
Mainland China ◽  
Selection Strategy ◽  
County Level ◽  
Location Selection

This paper investigates whether and why the Taiwanese investors in mainland China are pursuing a different location selection strategy from other foreign direct investors. I find that, compared to the general foreign direct investors, Taiwanese direct investors seem to be more dependent on the autonomy of local governments, especially as investment in a locality increases.

OPTIMALISASI PERAN PEMDA DALAM MENGATASI KENDALA PELAKSANAAN KONSERVASI KEANEKARAGAMAN HAYATI DI DAERAH (STUDI KASUS PROVINSI BENGKULU)

2020 ◽  
Vol 15 (2) ◽  
Author(s):  
Antung Deddy Radiansyah
Keyword(s):  
Local Government ◽  
Biodiversity Conservation ◽  
Local Governments ◽  
Central Government ◽  
Wild Plants ◽  
Regional Government ◽  
Conservation Areas ◽  
Conservation Area ◽  
Conservation Policies

Gaps in biodiversity conservation management within the Conservation Area that are the responsibility of the central government and outside the Conservation Areas or as the Essential Ecosystems Area (EEA) which are the authority of the Regional Government, have caused various spatial conflicts between wildlife /wild plants and land management activities. Several obstacles faced by the Local Government to conduct its authority to manage (EEA), caused the number and area of EEA determined by the Local Government to be still low. At present only 703,000 ha are determined from the 67 million ha indicated by EEA. This study aims to overview biodiversity conservation policies by local governments and company perceptions in implementing conservation policies and formulate strategies for optimizing the role of Local Governments. From the results of this study, there has not been found any legal umbrella for the implementation of Law number 23/ 2014 related to the conservation of important ecosystems in the regions. This regulatory vacuum leaves the local government in a dilemma for continuing various conservation programs. By using a SWOT to the internal strategic environment and external stratetegic environment of the Environment and Forestry Service, Bengkulu Province , as well as using an analysis of company perceptions of the conservation policies regulatary , this study has been formulated a “survival strategy” through collaboration between the Central Government, Local Governments and the Private Sector to optimize the role of Local Government’s to establish EEA in the regions.Keywords: Management gaps, Essential Ecosystems Area (EEA), Conservation Areas, SWOT analysis and perception analysis

Asymmetrical Attribution of Performance in China

Asian Survey ◽  
2020 ◽  
Vol 60 (5) ◽  
pp. 978-1003
Author(s):  
Jacqueline Chen Chen ◽  
Jun Xiang
Keyword(s):  
Economic Development ◽  
Local Governments ◽  
Central Government ◽  
Cultural Theory ◽  
Political Trust ◽  
Mediation Model ◽  
Multilevel Mediation ◽  
Dual Pathway ◽  
Underlying Mechanisms ◽  
The Impact

Existing studies of the impact of economic development on political trust in China have two major gaps: they fail to explain how economic development contributes to the hierarchical trust pattern, and they do not pay enough attention to the underlying mechanisms. In light of cultural theory and political control theory, we propose adapting performance theory into a theory of “asymmetrical attribution of performance” to better illuminate the case of China. This adapted theory leads to dual pathway theses: expectation fulfillment and local blaming. Using a multilevel mediation model, we show that expectation fulfillment mainly upholds trust in the central government, whereas local blaming undermines trust in local governments. We also uncover a rural–urban distinction in the dual pathway, revealing that both theses are more salient among rural Chinese.

A Deep Learning Framework for Prediction of Retinal Disorders

2020 ◽  
Author(s):  
Raniyaharini R ◽  
Madhumitha K ◽  
Mishaa S ◽  
Virajaravi R
Keyword(s):  
Deep Learning ◽  
Learning Framework ◽  
Retinal Disorders

COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image (Preprint)

2020 ◽  
Author(s):  
Jinseok Lee
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Ct Images ◽  
Chest Ct ◽  
University Hospital ◽  
Detection Accuracy ◽  
Ct Image ◽  
Test Dataset ◽  
Learning Framework ◽  
Testing Dataset

BACKGROUND The coronavirus disease (COVID-19) has explosively spread worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) can be used as a relevant screening tool owing to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely busy fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. OBJECTIVE We aimed to quickly develop an AI technique to diagnose COVID-19 pneumonia and differentiate it from non-COVID pneumonia and non-pneumonia diseases on CT. METHODS A simple 2D deep learning framework, named fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning, using one of the four state-of-art pre-trained deep learning models (VGG16, ResNet50, InceptionV3, or Xception) as a backbone. For training and testing of FCONet, we collected 3,993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and non-pneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training and a testing set at a ratio of 8:2. For the test dataset, the diagnostic performance to diagnose COVID-19 pneumonia was compared among the four pre-trained FCONet models. In addition, we tested the FCONet models on an additional external testing dataset extracted from the embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. RESULTS Of the four pre-trained models of FCONet, the ResNet50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100%, and accuracy 99.87%) and outperformed the other three pre-trained models in testing dataset. In additional external test dataset using low-quality CT images, the detection accuracy of the ResNet50 model was the highest (96.97%), followed by Xception, InceptionV3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). CONCLUSIONS The FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing dataset, the ResNet50-based FCONet might be the best model, as it outperformed other FCONet models based on VGG16, Xception, and InceptionV3.

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