Analysis of Urban Vitality and Its Determinant Factors Using POI Bigdata in Seoul, Korea

Cao, Yue; Lee, Sugie

doi:10.17208/jkpa.2021.12.56.7.87

Cited by 5 publications

(2 citation statements)

References 44 publications

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“…Structural equation modeling was subsequently utilized to determine relationships between urban landscapes and business models, resulting in spatial patterns manifested by four types of city landscapes to reveal the commercial characteristics of such landscapes from various perspectives [8]. Guided by Jane Jacobs' theory of development, Yue Cao and Sugie Lee (2021) utilized the GPS-based data of de facto population and POIs to investigate the correlation between surroundings affecting urban vibrancy, which, as shown in results, was closely linked to POI-based landuse variables and other physical environmental indicators [20]. To explore urban shrinkage, Yining Zhang et al (2022) examined the data on supply, demand, and consumption and the multisource POIs of residents.…”

Section: Literature Reviewmentioning

confidence: 99%

Big Data Application in Urban Commercial Center System Evaluation

Liu

Guan

et al. 2023

Sustainability

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Big data has provided new opportunities, directions, and methods for research on urban commercial center systems. Based on a quantitative assessment of big data and public participation, the “big data + public feedback” evaluation model can objectively and scientifically quantify the scale and structural characteristics of urban commercial center systems. In this paper, socioeconomic and material spatial attributes were considered in the selection of four indexes, including commercial agglomeration centrality, commercial facility service level, commercial industry status, and industry attraction. Specifically, we based our selection on the big data of the point-of-interest network, housing price, and population. ArcGIS, SPSS, and other analytical tools were employed to conduct a comparative analysis, cluster analysis, spatial network analysis, and correlation analysis. Using these data, we constructed an assessment index system, which was then utilized to comprehensively evaluate the current commercial land use in Nanjing’s main urban area and measure the degree of commercialization. The commercial center system in the main urban area of Nanjing was found to be consistent with the spatial structure system of “one main core, five secondary cores, multiple district cores, three horizontal axes, and one vertical axis.” Meanwhile, a public questionnaire was used to evaluate the public’s perception of the commercialization level in Nanjing. Finally, the results obtained were used for comparison with the structure of the commercial center system of Nanjing commercial network planning. We discovered that the results of the public’s perception of the commercialization level in Nanjing were similar to those of the big data analysis, which confirmed the credibility of big data analysis results. In conclusion, the findings of this study provide a basis for developing urban commercial center-level positioning and propose a method for data-assisted planning research.

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Section: Literature Reviewmentioning

confidence: 99%

Big Data Application in Urban Commercial Center System Evaluation

Liu

Guan

et al. 2023

Sustainability

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“…Based on the initial 18 categories of POI provided by Kakao Map, the data used in the analysis were simplified to 10 different categories to address the redundancy and crossovers between them. This process was guided by the previous studies conducted in a similar urban context [43,47], and the final classification of POI is shown in Table 1. Figure 2 provides an example of the type and distribution of POI in Gangnam, a highly concentrated district in Seoul Metropolitan City.…”

Section: Poi Big Datamentioning

confidence: 99%

Identifying Relationship between Regional Centrality and POI Facilities: A Case Study of Seoul Metropolitan Area

Lee,

Seo

2023

IJGI

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While understanding the dynamic urban network through the concept of regional centrality has provided various implications on the structure and hierarchy of cities, the macroscopic focus of previous studies has largely overlooked the small-scale physical and social urban entities in central places. Meanwhile, recent advances in real-time Point-of-Interest (POI) data have quickly replaced much of traditional urban facility data, emerging as a new representation of urban activities and demands. Therefore, this study proposes a method to identify the relationship between regional centrality and the distribution of POI facilities, particularly focused on the Seoul metropolitan area of South Korea. To this end, this study conducts a correlation analysis between regional centrality results derived from social network analysis and POI indices obtained from POI distribution analysis. The results indicate that a statistically significant relationship exists between regional centrality and the distribution of urban facilities, with a particularly strong correlation exhibited in specific POI categories. The results also demonstrate the effectiveness of the method in capturing disparities in the provision of facilities concerning growing commuting centers. The findings of the study provide pragmatic implications for prioritization and planning of facility development, as well as making informed decisions in real estate and facility investment.

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Urban Spatio-Temporal Weighted Population Vitality Detection and Its Influencing Factor Analysis Based on Multi-Source Data

曹

2024

GSER

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Analysis of Urban Vitality and Its Determinant Factors Using POI Bigdata in Seoul, Korea

Cited by 5 publications

References 44 publications

Big Data Application in Urban Commercial Center System Evaluation

Big Data Application in Urban Commercial Center System Evaluation

Identifying Relationship between Regional Centrality and POI Facilities: A Case Study of Seoul Metropolitan Area

Urban Spatio-Temporal Weighted Population Vitality Detection and Its Influencing Factor Analysis Based on Multi-Source Data

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