Using Local Moran’s I Statistics to Estimate Spatial Autocorrelation of Urban Economic Growth in Shandong Province, China

Zhao, Jun; Wang, Yue; Wenxiu, Shi

doi:10.1007/978-981-13-0893-2_4

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…Attitudes and the resulting behaviours, especially those related to mitigating the negative environmental or social effects of an event, can in turn encourage organizers to take specific actions (Alananzeh et al, 2022;Mueller et al, 2018). Therefore, studying the attitudes of participants in mass events towards various aspects of these events is a topic of interest for researchers and practitioners (Bagiran, Kurgun, 2016; Wang et al, 2018;Marinova, Fox, 2019).…”

Section: Study Of Participants' Attitudes Towards Eventsmentioning

confidence: 99%

Untitled

2023

SPSUTOM

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The papers presented in the number concentrate on many topics connected with organization and management. There are in the number papers about: service management, environmental management, financial management, sustainability, ethical aspects of management, lean management, green economy, impact of COVID-19 pandemic on management, information management, human resource management, supply chain management, quality management, leadership, Industry 4.0, Smart City, production management, public management and business analytics.

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Section: Study Of Participants' Attitudes Towards Eventsmentioning

confidence: 99%

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2023

SPSUTOM

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“…Spatial autocorrelation is interpreted as the greatest similarity of closer objects values in proximity of each other in comparison to the distant objects values which causes the emergence of spatial clusters (Zhao et al, 2017). The most common statistic which is utilized for the detection of spatial autocorrelation is Moran's I statistic.…”

Section: Moran's I Statisticsmentioning

confidence: 99%

Do Spatial Spillover Effects of Crime Matter in Urbanization and Crime Nexus? (Case Study: Iranian Provinces)

Khodapanah

Shabani²,

Shojaeian³

et al. 2022

International Journal of New Political Economy

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However, urbanization can have positive effects on the labor force and economic growth in urban areas the unplanned and rapid pace of urbanization process can ultimately lead to unpleasant consequences including marginalization, unemployment, poverty, the emergence of slums and shanty towns and the prevalence of crime in urban areas. Therefore, the objective of this study was to investigate the spatial spillover effects of urbanization on crime in 30 Iranian provinces during the period from 2006 to 2015 using dynamic spatial panel data models. The results of the study revealed that urbanization had a positive and statistically significant impacts on crime per capita in Iranian provinces indicating an increase in urbanization enhances the crime level. In addition, unemployment rate, and human capital had significant and negative effects on crime in Iranian provinces, respectively. Finally, the crime per capita in a previous period exerted a significant positive effect on the current crime which stablished the dynamic nature of this phenomenon. Moreover, the results of the study demonstrated that increasing crime per capita in neighboring provinces could elevate the crime in a given province.

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“…The local Moran's I index seems to be the most popularly used for hotspot or spatial cluster identification [24]. It has been successfully applied in hotspot identification of urban climate [25], economics [26], environmental monitoring [27], as well as in disease investigation [28].…”

Section: Introductionmentioning

confidence: 99%

Seasonal Variations of Daytime Land Surface Temperature and Their Underlying Drivers over Wuhan, China

et al. 2021

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Rapid urbanization greatly alters land surface vegetation cover and heat distribution, leading to the development of the urban heat island (UHI) effect and seriously affecting the healthy development of cities and the comfort of living. As an indicator of urban health and livability, monitoring the distribution of land surface temperature (LST) and discovering its main impacting factors are receiving increasing attention in the effort to develop cities more sustainably. In this study, we analyzed the spatial distribution patterns of LST of the city of Wuhan, China, from 2013 to 2019. We detected hot and cold poles in four seasons through clustering and outlier analysis (based on Anselin local Moran’s I) of LST. Furthermore, we introduced the geographical detector model to quantify the impact of six physical and socio-economic factors, including the digital elevation model (DEM), index-based built-up index (IBI), modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), population, and Gross Domestic Product (GDP) on the LST distribution of Wuhan. Finally, to identify the influence of land cover on temperature, the LST of croplands, woodlands, grasslands, and built-up areas was analyzed. The results showed that low temperatures are mainly distributed over water and woodland areas, followed by grasslands; high temperatures are mainly concentrated over built-up areas. The maximum temperature difference between land covers occurs in spring and summer, while this difference can be ignored in winter. MNDWI, IBI, and NDVI are the key driving factors of the thermal values change in Wuhan, especially of their interaction. We found that the temperature of water area and urban green space (woodlands and grasslands) tends to be 5.4 °C and 2.6 °C lower than that of built-up areas. Our research results can contribute to the urban planning and urban greening of Wuhan and promote the healthy and sustainable development of the city.

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Using Local Moran’s I Statistics to Estimate Spatial Autocorrelation of Urban Economic Growth in Shandong Province, China

Cited by 5 publications

References 7 publications

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Do Spatial Spillover Effects of Crime Matter in Urbanization and Crime Nexus? (Case Study: Iranian Provinces)

Seasonal Variations of Daytime Land Surface Temperature and Their Underlying Drivers over Wuhan, China

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