How did industrial land supply respond to transitions in state strategy? An analysis of prefecture-level cities in China from 2007 to 2016

Zhou, Lin; Tian, Li; Gao, Yuan; Ling, Yingkai; Fan, Chenjing; Hou, Deyi; Shen, Tiantian; Zhou, Wentong

doi:10.1016/j.landusepol.2019.05.028

Cited by 59 publications

(21 citation statements)

References 43 publications

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“…A host of spatial econometric models, such as the spatial lag model (SLM), the spatial error model (SEM), and the spatial Durbin model (SDM), can address the spatial autocorrelation issue, and they have been widely used to explain the relationship between property prices and property characteristics [54][55][56][57][58]. The SLM and the SEM are two basic spatial econometric models and focus on the endogenous interaction relationship (or spatial interaction in the dependent variable) and the correlated relationship (or spatial interaction in the error term), respectively.…”

Section: Methodsmentioning

confidence: 99%

Impacts of Tourism Demand on Retail Property Prices in a Shopping Destination

2020

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Understanding the relationship between tourism demand and retail property prices is of great significance to tourist destinations, especially shopping destinations. The increase in tourism demand may alter the implicit prices of certain retail property characteristics (e.g., age and accessibility to transit). This study examines how tourism demand (measured by tourist volume) affects retail property prices in the tourist precinct of a shopping destination, namely Hong Kong. The implementation of the policy Individual Visit Scheme (IVS) in 2003 in Hong Kong has substantially increased tourist shoppers from Mainland China, and it is used as a quasi-natural experiment of the increased tourist volume. Spatial and non-spatial hedonic pricing models are developed based on the ground-floor retail property transaction data of Causeway Bay, Hong Kong before and after the IVS (1993–2011). The findings of this study are as follows. (1) Accessibility to transit has a larger positive price effect after the implementation of the IVS. (2) The implicit price of accessibility to accommodation facilities is not significantly altered by the implementation of the IVS. (3) Age has a larger negative price effect after the implementation of the IVS. The first two outcomes are related to the economic concerns of tourist shoppers, while the last can be explained by their hometown experience. Finally, practical implications are discussed.

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Section: Methodsmentioning

confidence: 99%

Impacts of Tourism Demand on Retail Property Prices in a Shopping Destination

2020

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“…Because of the characteristics of human networking, the transmission of disease is spatially heterogeneous. Spatially localized mass treatment is a crude approach compared to detailed contact tracing [31][32][33], but it may be implemented more quickly in practice. However, its broad nature poses problems: the number of individuals and regions affected by the intervention may be larger, with corresponding burdens that must be accommodated; if the intervention is harmful at the individual level (such as isolation), individuals and economic development will suffer unnecessary losses.…”

Section: Different Emergency Management and Control Plans For Differementioning

confidence: 99%

The Relationship between the Migrant Population’s Migration Network and the Risk of COVID-19 Transmission in China—Empirical Analysis and Prediction in Prefecture-Level Cities

Fan

Cai²,

Gai

et al. 2020

IJERPH

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The outbreak of COVID-19 in China has attracted wide attention from all over the world. The impact of COVID-19 has been significant, raising concerns regarding public health risks in China and worldwide. Migration may be the primary reason for the long-distance transmission of the disease. In this study, the following analyses were performed. (1) Using the data from the China migrant population survey in 2017 (Sample size = 432,907), a matrix of the residence-birthplace (R-B matrix) of migrant populations is constructed. The matrix was used to analyze the confirmed cases of COVID-19 at Prefecture-level Cities from February 1-15, 2020 after the outbreak in Wuhan, by calculating the probability of influx or outflow migration. We obtain a satisfactory regression analysis result (R 2 = 0.826-0.887, N = 330). (2) We use this R-B matrix to simulate an outbreak scenario in 22 immigrant cities in China, and propose risk prevention measures after the outbreak. If similar scenarios occur in the cities of Wenzhou, Guangzhou, Dongguan, or Shenzhen, the disease transmission will be wider.(3) We also use a matrix to determine that cities in Henan province, Anhui province, and Municipalities (such as Beijing, Shanghai, Guangzhou, Shenzhen, Chongqing) in China will have a high risk level of disease carriers after a similar emerging epidemic outbreak scenario due to a high influx or outflow of migrant populations.

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“…We first used the global autocorrelation method to derive the spatial patterns of health status at the provincial level by means of Moran's I index [48]. Subsequently, we used the local spatial autocorrelation method to measure the association of health status in each province with its neighboring provinces and adopt Local Moran's I index to identify the specific spatial agglomeration pattern [49,50,51]. The formula is as follows:Local Moran's I= n(Yi−trueY¯)∑j=1mWij(Yj−trueY¯)∑i=1n(Yi−trueY¯)2, (i≠j) where Yi and Yj are the average ill-health scores of province i and province j ; n is the number of provinces; Wij is the spatial weight matrix, which is established based on the common side or common point of each province using the queen contiguity adjacency standard—i.e., when province i and j are adjacent, Wij = 1, otherwise Wij…”

Section: Methodsmentioning

confidence: 99%

Elderly Health Inequality in China and its Determinants: A Geographical Perspective

Fan

Ouyang

Tian

et al. 2019

IJERPH

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Inter-regional health differences and apparent inequalities in China have recently received significant attention. By collecting health status data and individual socio-economic information from the 2015 fourth sampling survey of the elderly population in China (4th SSEP), this paper uses the geographical differentiation index to reveal the spatial differentiation of health inequality among Chinese provinces. We test the determinants of inequalities by multilevel regression models at the provincial and individual levels, and find three main conclusions: 1) There were significant health differences on an inter-provincial level. For example, provinces with a very good or good health rating formed a good health hot-spot region in the Yangtze River Delta, versus elderly people living in Gansu and Hainan provinces, who had a poor health status. 2) Nearly 2.4% of the health differences in the elderly population were caused by inter-provincial inequalities; access (or lack of access) to economic, medical and educational resources was the main reason for health inequalities. 3) At the individual level, inequalities in annual income served to deepen elderly health differences, and elderly living in less developed areas were more vulnerable to urban vs. rural-related health inequalities.

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How did industrial land supply respond to transitions in state strategy? An analysis of prefecture-level cities in China from 2007 to 2016

Cited by 59 publications

References 43 publications

Impacts of Tourism Demand on Retail Property Prices in a Shopping Destination

Impacts of Tourism Demand on Retail Property Prices in a Shopping Destination

The Relationship between the Migrant Population’s Migration Network and the Risk of COVID-19 Transmission in China—Empirical Analysis and Prediction in Prefecture-Level Cities

Elderly Health Inequality in China and its Determinants: A Geographical Perspective

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