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Article
Publication date: 18 October 2011

Malin Song, Shuhong Wang, Jie Wu and Li Yang

This article aims to discuss the binary matrix of spatial association which is suggested by Moran, and proposes a new method of the definition of the w matrix to obtain a new…

1073

Abstract

Purpose

This article aims to discuss the binary matrix of spatial association which is suggested by Moran, and proposes a new method of the definition of the w matrix to obtain a new space‐time correlation coefficient considering the correlation of both time and space.

Design/methodology/approach

From the perspective of the multi‐dimension of space and time, this article proposes a new computational method of a correlation coefficient considering both temporal and spatial factors, based on the analysis of the characteristics of Moran's Global Index and Moran's Local Index. The number of patents granted in mainland China's provinces and municipalities is taken as an example of multi‐dimensional analysis.

Findings

The results of quantitative analysis using this space‐time correlation coefficient show that the outcomes calculated by this new correlation coefficient are not only highly correlated with Moran's Index, but also have advantages in analyzing the trends of both spatial and temporal indicators simultaneously, which is verified by the illustration of the algorithm.

Research limitations/implications

Due to a scarcity of data in China, the algorithm is based on data for the last 20 years, which may not be long enough for this research. Although this does not reduce the value of the conclusions of this article, a closer look should be taken at the effectiveness of the new space‐time correlation coefficient in the future.

Practical implications

The results of space‐time correlation coefficient are highly correlated with Moran's Index. In addition, it can not only analyze the “flow” indicators in a certain period but also analyze the “stock” indicators to reflect both space and time changes. These may reflect superiority of space‐time correlation coefficient to Moran's Index.

Originality/value

This new correlation coefficient that considers both temporal and spatial factors and will provide a more scientific and effective tool for spatial econometric analysis in time and space changes of management on society and the economy.

Details

Management Decision, vol. 49 no. 9
Type: Research Article
ISSN: 0025-1747

Keywords

Article
Publication date: 7 April 2023

Changjun Jiang

Land transactions are a key indicator of urban sustainable development and urban space expansion. Therefore, this paper aims to study the spatial correlation of different types of…

Abstract

Purpose

Land transactions are a key indicator of urban sustainable development and urban space expansion. Therefore, this paper aims to study the spatial correlation of different types of land transactions.

Design/methodology/approach

Based on the big data of land micro transactions in Yangtze River Delta urban agglomeration, this paper uses the generalized forecast error variance decomposition (GFEVD) method to measure the correlation level of urban land markets. Also, social network analysis (SNA) is used to describe spatial correlation network characteristics of an urban agglomeration land market. In the meantime, the factors that influence the spatial correlation of urban land markets are investigated through a quadratic assignment procedure (QAP).

Findings

The price growth rate of urban residential land was higher than that of industrial land and commercial land. The spatial relevance of urban residential land is the highest, while the spatial relevance of the urban commercial land market is the lowest. The urban industrial land market, commercial land market and residential land market all present a typical network structure. Population distance (POD) and Engel coefficient distance (EGD) are negatively correlated with the correlation degree of the urban residential land network; traffic distance (TRD) and economic distance (ECD) are negatively correlated with the correlation degree of the urban industrial land network and commercial land network.

Originality/value

This paper uses a systematically-integrated series of problem-solving models to better explain the development path of urban land markets and to realize the integration of the interdisciplinary methods of geography, statistics and big data analysis.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 11 May 2010

Shyam Adhikari, Eric J. Belasco and Thomas O. Knight

The purpose of this paper is to examine the spatial components of producer heterogeneity in crop insurance product selection among US corn producers and identifies neighborhood…

Abstract

Purpose

The purpose of this paper is to examine the spatial components of producer heterogeneity in crop insurance product selection among US corn producers and identifies neighborhood spillover or agent marketing effects in these decisions.

Design/methodology/approach

County‐level insurance and yield data are used to demonstrate that a gradual shift from yield‐based insurance to revenue‐based insurance has spatial patterns. Conventional risk variables such as yield variability, price variability, prevalence of irrigation, other crops, and yield‐price relationships play an important role in this shift and are consistently estimated only when spatial components are included. A spatial random effects model is used to also identify the impact of spatial lag effects, which include neighborhood spillover and agent marketing effects, on the share of corn acres insured with revenue‐based plans vs yield‐based plans.

Findings

Theoretically consistent variables associated with risk are found to significantly influence the choice between crop revenue and yield insurance. Non‐linear parameters identify the region‐specific effects from changes in irrigation, yield price correlation, and the prevalence of corn production on insurance decisions. In addition, spatial components such as the decisions made by nearby producers and marketing drives are also found to influence decisions. These results may demonstrate the relative influence of trusted sources, such as nearby producers and insurance agents, on insurance decisions.

Originality/value

Traditional risk variables are consistently estimated by controlling for spatial heterogeneity. This study also reveals the propensity of producers to rely on the opinions of other producers or agents that they know.

Details

Agricultural Finance Review, vol. 70 no. 1
Type: Research Article
ISSN: 0002-1466

Keywords

Book part
Publication date: 18 January 2022

Arnab Bhattacharjee, Jan Ditzen and Sean Holly

The authors provide a way to represent spatial and temporal equilibria in terms of error correction models in a panel setting. This requires potentially two different processes…

Abstract

The authors provide a way to represent spatial and temporal equilibria in terms of error correction models in a panel setting. This requires potentially two different processes for spatial or network dynamics, both of which can be expressed in terms of spatial weights matrices. The first captures strong cross-sectional dependence, so that a spatial difference, suitably defined, is weakly cross-section dependent (granular) but can be non-stationary. The second is a conventional weights matrix that captures short-run spatio-temporal dynamics as stationary and granular processes. In large samples, cross-section averages serve the first purpose and the authors propose the mean group, common correlated effects estimator together with multiple testing of cross-correlations to provide the short-run spatial weights. The authors apply this model to the 324 local authorities of England, and show that our approach is useful for modeling weak and strong cross-section dependence, together with partial adjustments to two long-run equilibrium relationships and short-run spatio-temporal dynamics. This exercise provides new insights on the (spatial) long-run relationship between house prices and income in the UK.

Details

Essays in Honor of M. Hashem Pesaran: Panel Modeling, Micro Applications, and Econometric Methodology
Type: Book
ISBN: 978-1-80262-065-8

Keywords

Article
Publication date: 28 March 2023

Lina Zhong and Yingchao Dong

The purpose of this paper is to explore the changes of the scale of urban tourists in mainland China under the impact of COVID-19 and, specifically, the following questions: how…

Abstract

Purpose

The purpose of this paper is to explore the changes of the scale of urban tourists in mainland China under the impact of COVID-19 and, specifically, the following questions: how did the scale of domestic tourists change nationwide and in the seven geographic regions? What are the differences in the changes among the seven geographic regions? What are the changes in the hot spot areas and spatial clustering of domestic tourists across the country?

Design/methodology/approach

Using the data of domestic tourist arrivals in 337 cities in mainland China from 2018 to 2021, this research analyzes the absolute differences and relative differences in the scale of domestic tourists nationwide and in seven geographic divisions with the help of indicators such as range analysis, standard deviation, coefficient of variation and Herfindahl–Hirschman Index and explores the changes in the hot spot areas and spatial concentration degree of the spatial scale of domestic tourists nationwide under the influence of the epidemic using kernel density analysis and spatial auto-correlation analysis.

Findings

The absolute differences in all seven geographical divisions continue to increase during 2018–2021. The domestic tourism in southwest China is extremely uneven. Absolute differences in the northwest and northeast regions are relatively small, and the development in attracting domestic tourists is more balanced. Relative differences in southwest China are comparatively large, with the trend of uneven development being obvious. The northeast, northwest and eastern regions of China are small, and the development is more balanced. The popularity of domestic tourism in the Beijing–Tianjin–Hebei region, as well as the Yangtze River Delta region, continues to decline and then pick up in 2021. The inland southwest region became a new domestic tourism hot spot in 2021. The size of domestic tourists from 2018 to 2021 in mainland China cities shows a significant positive spatial correlation, and there is a spatial agglomeration phenomenon, but some regional agglomeration types change from 2018 to 2021.

Research limitations/implications

The impact of the epidemic on the number and spatial scale of domestic tourism in China has been clarified, which makes up for the comparison of domestic tourism changes before and after the epidemic. A clear understanding of the changes in the number and spatial scale of domestic tourists in different regions after the epidemic is conducive to the development of domestic tourism revitalization strategies in accordance with the actual situation of each province and promotes the internal circulation of Chinese tourism.

Practical implications

This paper tries to clarify the quantitative scale of domestic tourism in different regions after the epidemic, which is conducive to the development of domestic tourism revitalization strategies in cities in different regions according to regional characteristics and the actual situation of each province and to promote the healthy operation of the internal circulation of tourism in China. This paper also tries to show the changes of domestic tourism market hot spots, agglomeration conditions changes before and after the outbreak and the clarity of tourists’ preference space changes.

Originality/value

Scale of domestic tourists; Absolute difference; Relative difference; Spatial hot spot distribution; Spatial agglomeration change

目的

本文旨在探寻疫情影响下中国大陆城市游客规模演化规律, 具体而言, 疫情影响下, 全国及七大地理分区的国内游客量规模变化如何?七大地理地区的变化有何差异?以及疫情影响下, 全国国内游客空间规模的热点区域和空间集聚程度有何变化?

研究设计与方法

利用2018-2021年中国大陆337各城市的国内游客量数据, 借助极差、标准差、变异系数、赫芬达尔指等指标分析全国及七大地理分区国内游客规模的绝对差异和相对差异; 借助核密度分析、空间自相关分析等ArcGIS分析工具, 探寻疫情影响下全国国内游客空间规模的热点区域和空间集聚程度的变化情况。

研究发现

①绝对差异方面, 七大地理分区的绝对差异均持续增大。西南地区的游客量的绝对差异巨大, 国内游发展极不均衡。西北地区、东北地区绝对差异相对较小, 在吸引国内游客方面发展较为均衡。②相对差异方面, 西南地区的国内游发展相对差异较大, 发展不均衡趋势明显; 东北地区、西北地区、华东地区的国内游发展相对差异较小, 发展较为均衡。③热点区域变化方面, 京津冀地区、长三角地区的国内旅游热度持续下降, 在2021年有所回升; 内陆西南地区在2021年成为新的国内游热点区域。④2018年至2021年城市国内游客量规模均呈现出显著的空间正相关的关系, 存在着空间集聚现象, 但部分区域集聚类型在2018到2021年间发生变化。

研究价值

①理论意义:明晰了疫情对中国国内旅游人次的数量规模和空间规模的影响, 弥补了当前疫情前后国内旅游业变化对比的研究; 阐明了疫情前后中国城市国内游客空间格局的变化, 拓展了研究情景, 丰富了中国旅游业时空变化的相关研究。②实践意义:明晰了疫后不同地区国内旅游人次的数量规模和空间规模变化情况, 以及国内旅游市场热点变化和游客空间偏好变化, 有利于各地区城市对症下药, 制定符合各省份实际情况的国内旅游业振兴策略, 促进中国旅游业内循环。

Article
Publication date: 11 July 2016

Shuyun Ren and Tsan-Ming Choi

Panel data-based demand forecasting models have been widely adopted in various industrial settings over the past few decades. Despite being a highly versatile and intuitive…

Abstract

Purpose

Panel data-based demand forecasting models have been widely adopted in various industrial settings over the past few decades. Despite being a highly versatile and intuitive method, in the literature, there is a lack of comprehensive review examining the strengths, the weaknesses, and the industrial applications of panel data-based demand forecasting models. The purpose of this paper is to fill this gap by reviewing and exploring the features of various main stream panel data-based demand forecasting models. A novel process, in the form of a flowchart, which helps practitioners to select the right panel data models for real world industrial applications, is developed. Future research directions are proposed and discussed.

Design/methodology/approach

It is a review paper. A systematically searched and carefully selected number of panel data-based forecasting models are examined analytically. Their features are also explored and revealed.

Findings

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. A novel model selection process is developed to assist decision makers to select the right panel data models for their specific demand forecasting tasks. The strengths, weaknesses, and industrial applications of different panel data-based demand forecasting models are found. Future research agenda is proposed.

Research limitations/implications

This review covers most commonly used and important panel data-based models for demand forecasting. However, some hybrid models, which combine the panel data-based models with other models, are not covered.

Practical implications

The reviewed panel data-based demand forecasting models are applicable in the real world. The proposed model selection flowchart is implementable in practice and it helps practitioners to select the right panel data-based models for the respective industrial applications.

Originality/value

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. It is original.

Details

Industrial Management & Data Systems, vol. 116 no. 6
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 29 October 2020

Jiansheng Qu, Jinyu Han, Lina Liu, Li Xu, Hengji Li and Yujie Fan

The purpose of this paper is to explore the heterogeneity and correlations of agricultural greenhouse gas (GHG) emissions among provinces in China, and then policy implications…

Abstract

Purpose

The purpose of this paper is to explore the heterogeneity and correlations of agricultural greenhouse gas (GHG) emissions among provinces in China, and then policy implications are proposed.

Design/methodology/approach

After agricultural GHG accounting and a pre-analysis of inter-provincial heterogeneity, improved gravity model and the Social Network Analysis (SNA) methods are introduced to construct the network, being carried out from three aspects of the whole network, individual provincial characteristics and cluster analysis.

Findings

(1) There are significant regional variations in agricultural GHG scale among provinces owing to the layout of agricultural production, and the temporal trends show that the direction and speed of agricultural GHG scale change vary among provinces; (2) In terms of inter-provincial correlations, there exists a complex spatial network of agricultural GHG among provinces, which tends to be more complex, intensive and stable, while the status of the provinces in the network also has gradually become more balanced. All provinces played their respective roles in the four clusters of the network with agricultural layout and comparative advantages, and the distribution has continuously optimized.

Practical implications

The inter-provincial network characteristics of agricultural GHG emissions and its evolution have practical implications for differentiated and coordinated agricultural GHG reduction policies at the provincial levels.

Originality/value

This paper innovatively study inter-provincial agricultural GHG correlations in China with the SNA methods used to study economic and social connections in the past. There is some originality in the introduction of network theory and application of the SNA methods, which can provide some reference for researches in similar fields.

Details

China Agricultural Economic Review, vol. 13 no. 1
Type: Research Article
ISSN: 1756-137X

Keywords

Article
Publication date: 2 October 2018

Jing Sun, Jing Wang, Tao Wang and Tao Zhang

Given the recent rapid economic development, the processes of industrialization and urbanization are accelerating. At the same time, the contradiction between environmental…

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Abstract

Purpose

Given the recent rapid economic development, the processes of industrialization and urbanization are accelerating. At the same time, the contradiction between environmental quality and economic development has become increasingly prominent and is likely to restrict the normal pace of China’s economic development and environmental protection. As such, the purpose of this paper is to incorporate the urbanization factor into an analytic framework to discuss the relationship among urbanization, economic development, and environmental pollution.

Design/methodology/approach

A panel data of 31 Chinese provinces from 2004 to 2015 is selected for this research. A spatial correlation test is first conducted on the environmental pollution status, then the spatial Durbin model is used to carry out spatial econometric testing of the relationship among the above three factors.

Findings

Interprovincial environmental pollution in China has significant positive spatial correlation, environmental pollution discharge in most provinces is significantly stable, discharge of environmental pollutants is transitioning from coastal to inland provinces, and urbanization and economic growth can both aggravate environmental pollution, but economic growth can relieve environmental pollution in neighboring provinces.

Originality/value

The relationship between economic growth, urbanization, and environmental quality has always been an important issue for sustainable development. As such, China’s urbanization leads to economic development, while rapid economic growth and environmental pollution are coordinated. This paper focuses on the specific relationship between them. To this end, local governments make concerted efforts to formulate sound environmental regulation policies based on local environmental conditions, where economic development is an effective means of alleviating the contradictory relationship between economic development and environmental protection.

Details

Management of Environmental Quality: An International Journal, vol. 30 no. 2
Type: Research Article
ISSN: 1477-7835

Keywords

Article
Publication date: 25 September 2019

Yuanhua Yang, Dengli Tang and Peng Zhang

Fiscal fund is the key support of carbon emissions control for local governments. This paper aims to analyze the impact of fiscal decentralization on carbon emissions by spatial

Abstract

Purpose

Fiscal fund is the key support of carbon emissions control for local governments. This paper aims to analyze the impact of fiscal decentralization on carbon emissions by spatial Durbin model (SDM), and verify the existence of “free-riding” phenomenon to reveal the behavior of local governments in carbon emissions control.

Design/methodology/approach

Based on the provincial data of carbon emissions from 2005 to 2016 in China, this paper uses spatial exploratory data analysis technology to analyze the spatial correlation characteristics and constructs SDM to test the impact of fiscal decentralization on carbon emissions.

Findings

The results show that carbon emissions exhibits significant spatial autocorrelation in China, and the increasing of fiscal decentralization in the region will increase carbon emissions in surrounding areas and on the whole. Then, by comparing the impact of fiscal decentralization on carbon emissions and industrial solid waste, it is found that “free-riding” phenomenon of carbon emissions control exists in China.

Practical implications

Based on the spatial cluster characteristics of China’s provincial carbon emissions, carbon emissions control regions can be divided into regions and different carbon emission control policies can be formulated for different cluster regions. Carbon emissions indicators should be included in the government performance appraisal policy, and carbon emissions producer survey should be increased in environmental policies to avoid “free-riding” behaviors of local government in carbon emissions control in China.

Originality/value

This paper contributes to fill this gap and fully considers the spatial spillover characteristics of carbon emissions by introducing spatial exploratory data analysis technology, constructs SDM to test the impact of fiscal decentralization on carbon emissions in the perspective of space econometrics, and tests the existence of “free-riding” phenomenon in carbon emissions control for local governments in China.

Details

International Journal of Energy Sector Management, vol. 14 no. 1
Type: Research Article
ISSN: 1750-6220

Keywords

Book part
Publication date: 30 December 2004

Badi H. Baltagi and Dong Li

Baltagi and Li (2001) derived Lagrangian multiplier tests to jointly test for functional form and spatial error correlation. This companion paper derives Lagrangian multiplier…

Abstract

Baltagi and Li (2001) derived Lagrangian multiplier tests to jointly test for functional form and spatial error correlation. This companion paper derives Lagrangian multiplier tests to jointly test for functional form and spatial lag dependence. In particular, this paper tests for linear or log-linear models with no spatial lag dependence against a more general Box-Cox model with spatial lag dependence. Conditional LM tests are also derived which test for (i) zero spatial lag dependence conditional on an unknown Box-Cox functional form, as well as, (ii) linear or log-linear functional form given spatial lag dependence. In addition, modified Rao-Score tests are also derived that guard against local misspecification. The performance of these tests are investigated using Monte Carlo experiments.

Details

Spatial and Spatiotemporal Econometrics
Type: Book
ISBN: 978-0-76231-148-4

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