Regional diffusion of smart city service in South Korea investigated by spatial autocorrelation: focused on safety and urban management

Kim, Hong-kwang; Yi, Mi-Sook; Shin, Dong‐Bin

doi:10.1007/s41324-017-0150-2

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…Further research could also seek for a comprehensive framework which combine HEC with potential locations for energy production (similar to the study by [57]). The last, in this study the determinant of gas and electricity consumption have been independently studied, the further studies could investigate the spatial autocorrelation between the two (similar to the methodology used by [58]).…”

Section: Resultsmentioning

confidence: 99%

Local determinants of household gas and electricity consumption in Randstad region, Netherlands: application of geographically weighted regression

Mashhoodi

Timmeren

2018

Spat. Inf. Res.

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The previous studies on household energy consumption (HEC) are based on an implicit assumption: the impact of geographic determinants on HEC is uniform across a given region, and such impacts could be unveiled regardless of geographic location of households in question. Consequently, these studies have searched for global determinants which explain HEC of all areas. This study aim at examining validity of this assumption in Randstad region by putting forward a question regarding households' gas and electricity consumption: are the determinants global, stationary across all the areas of the region, or local, varying from one location to another? By application of geographically weighted regression, impact of socioeconomic, housing, land cover and morphological indicators on HEC is studied. It is established that the determinants of HEC are local. This result led to second question: what are the main determinants of gas and electricity consumption in different neighborhoods of Randstad? The results show that variety of factors could be the most effective determinant of gas consumption in different neighborhoods: building age, household size and inhabitants' age, inhabitants' income and private housing tenure, building compactness. Whereas, in case of electricity consumption the picture is more deterministic: in most of the neighborhoods the most effective factors are inhabitants' income and private tenure.

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Section: Resultsmentioning

confidence: 99%

Local determinants of household gas and electricity consumption in Randstad region, Netherlands: application of geographically weighted regression

Mashhoodi

Timmeren

2018

Spat. Inf. Res.

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“…Region analysis is mainly based on "region" as the object of analysis and operation (Kim et al, 2017), in order to more clearly see the trends and trajectories found in urban management cases at the macro level. The standard deviation of kernel density at different scales of each sub-district office can be clearly seen by regional analysis, so as to judge the occurrence of cases in each sub-district and the change of kernel density in different scales.…”

Section: Region Analysismentioning

confidence: 99%

The Spatial Distribution Pattern Analysis of City Infrastructure Cases of Urban Management

Hou¹,

Liu²,

Li³

et al. 2018

GEP

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The effective disposal of daily city infrastructure cases is an important issue for urban management. To more effectively utilize a large amount of historical cases data collected and accumulated in the urban grid management system, and to analyze their spatial distribution pattern information for city managers, this study used the comparative kernel density analysis method in two types of cases (i.e. power facilities and traffic guardrail) in Xicheng District, Beijing for the year 2016 and 2017. This research analyzes them at different scales (300 m, 600 m, 1,200 m), and the experiment results show that the method of comparative kernel density analysis is able to provide an intuitively spatial visualization distribution analysis of city infrastructure related cases. The quantitative information of spatial agglomeration degree is helpful for city managers making decision.

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“…More notably, the usefulness of spatial autocorrelation analysis in urban management research has also received recognition. Kim et al used spatial autocorrelation analysis to interpret the regional spatial relationship of smart city services in South Korea ( 46 ). Balducci et al used it to interpret the spatial pattern of the intelligent management of major towns in Italy ( 47 ).…”

Section: Introductionmentioning

confidence: 99%

Quality of life assessment and its spatial correlation in impoverished districts and counties: A case study of Guizhou Province

Yang

Heng³

et al. 2023

Front. Public Health

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China’s rapid urbanization has greatly boosted the quality of life of its traditionally impoverished regions. Research into the spatial distribution characteristics, evolution and spatial correlation of the quality of life in impoverished regions can help illuminate the experience of successful development and construct a knowledge base for authorities to devise development strategies. This study focuses its attention on the historically impoverished districts and counties (which are designated as parallel administrative units in China) of Guizhou Province in southwestern China. Extensively citing official statistics on districts and counties released by China’s National Bureau of Statistics and local governments, it assesses the quality of life of those places in the 3 years of 2000, 2010, and 2020 from the four dimensions of economy, society, culture, and environment. The aim is to illustrate the distribution characteristics and the evolution of quality of life in Guizhou’s historically impoverished districts and counties. In order to understand the characteristics of spatial clustering as well as the patterns of evolution of the quality of life of Guizhou’s impoverished districts and counties, the study incorporates spatial autocorrelation analysis into a spatio-temporal analysis of local quality of life. It could presumably help enrich the knowledge base that local authorities draw on to formulate development strategies that are scientific and adapted to local conditions. The study found that while the overall quality of life in all the impoverished districts and counties of Guizhou Province has improved, large gaps in quality of life between eastern and western regions of the province persisted. In addition, the driving force behind the evolution in the overall quality of life of those places changed with time, as did the characteristics of the spatial aggregation in quality of life.

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Regional diffusion of smart city service in South Korea investigated by spatial autocorrelation: focused on safety and urban management

Cited by 4 publications

References 7 publications

Local determinants of household gas and electricity consumption in Randstad region, Netherlands: application of geographically weighted regression

Local determinants of household gas and electricity consumption in Randstad region, Netherlands: application of geographically weighted regression

The Spatial Distribution Pattern Analysis of City Infrastructure Cases of Urban Management

Quality of life assessment and its spatial correlation in impoverished districts and counties: A case study of Guizhou Province

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