Urban Vitality Area Identification and Pattern Analysis from the Perspective of Time and Space Fusion

Shao-jun, Liu; Ling, Zhenhao; Long, Yi

doi:10.3390/su11154032

Cited by 46 publications

(36 citation statements)

References 40 publications

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“…We found that the vitality of most streets in the main urban area of Xining city was characterized by low intensity and low stability, and that vitality intensity on weekdays and weekends was similar; however, street vitality stability increased significantly on weekends. While previous studies have shown that the spatial concentration and orderliness of land use have a significant impact on vitality [53], we found that land use concentration was positively related to street vitality intensity, and its richness and uniformity were positively related to street vitality stability. Superior shopping, life, catering, and transportation services appeared on these high-intensity vitality streets, which confirm the previous findings that convenient traffic conditions are an important driving force for vitality [11].…”

Section: Discussioncontrasting

confidence: 95%

An Evaluation of Street Dynamic Vitality and Its Influential Factors Based on Multi-Source Big Data

Guo

Hong-fei

Yang

2021

IJGI

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Urban vitality is an important indicator of urban development capacity. Streets’ metrics can depict intro-urban fabrics and physiognomy in detail, and thus street vitality affected by street metrics is a concrete manifestation of urban vitality. However, few studies have evaluated dynamic vitality or explored how it is influenced by land use. To bridge this gap, we fully evaluated street dynamic vitality and explored how to enhance the street dynamic vitality by changing the distribution and combination of land use. Specifically, we examined the street dynamic vitality and land use diversity in the main urban zone of Xining city in China using mobile communication and point of interest data, adopted optimized K-means clustering to identify street dynamic vitality types, evaluated the classification result based on vitality intensity and vitality stability, and explored the link between land use and dynamic vitality. Since vitality intensity limitations were found in describing street dynamic vitality, it was necessary to introduce vitality stability. We also found a positive correlation between the vitality intensity and land use density, there were outstanding traffic facilities in high-intensity vitality streets, and improving the abundance and uniformity of land use was beneficial to increase vitality stability. Overall, describing street vitality from a dynamic perspective can improve resource utilization efficiency and rationally plan layouts.

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Section: Discussioncontrasting

confidence: 95%

An Evaluation of Street Dynamic Vitality and Its Influential Factors Based on Multi-Source Big Data

Guo

Hong-fei

Yang

2021

IJGI

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“…This study also demonstrates how appropriately designed and formulated physical environments can attract and amplify people's attention, although their importance and relevance may differ according to regional characteristics. By individually examining the three city centers, the results indicated that increases in development capacity were not the solution for vitalizing commercial activities or attracting people; rather, development programs that consider high accessibility to public transportation or well-designed pedestrian areas, depending on the region, might be more effective [1,3,4,9,16,19,20,29,41].…”

Section: Discussionmentioning

confidence: 99%

“…The factors described in Table 1 were also used in this analysis and included socioeconomic factors, land use mix, development capacity, access to public transportation, access to green space/attractions, and adjacent roads and terrain conditions, with a total of 15 sub-components. These factors were used in the analysis because several former successful studies also considered these [1,2,4,8,16,17,19,20,35,41]. In addition, these variables were selected as independent of each other, and the VIF values between these variables were confirmed.…”

Section: Data Sources and Variablesmentioning

confidence: 99%

Tourist Attraction and Points of Interest (POIs) Using Search Engine Data: Case of Seoul

2020

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Points of interest (POIs)—areas with a concentration of places that attract people—are important urban planning and tourism policy targets. This study aims to determine the points of interest of urban residents by analyzing big data from search engines to reveal the physical characteristics of POIs. To achieve this, POI data were collected in three city centers in Seoul using a South Korean dominant portal site that includes a search engine. The most popular POIs were determined by using GIS search engine analysis frequency, and correlation and regression analyses were conducted to investigate the relation between POIs and urban elements. The results revealed different POI trends in each city center. While POIs were concentrated in old, narrow streets with small attractions and mixed-use construction near Seoul City Wall (historic downtown district), they also formed around notable architectural landmarks in the newly developed Yeouido and Yeongdeungpo areas. This study found that tourism attraction took different forms in old and new areas, demonstrating that citizens are interested in both historic downtown areas and new areas, as traditional urban theorists suggest. Thus, urban planners and tourism policy makers should consider specific spatial contexts with search engines.

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“…For example, He found that in areas with a high population density and urban function mixing degree, office-type functional areas should be added [ 21 ]. Liu [ 22 ] used mobile phone data collected over a week, combined with the land use characteristics of POIs, and integrated this with the spatial-temporal feature clustering method to calculate the density and spatial entropy of POIs, thereby measuring the urban function mixing degree and finding a strong correlation between it and urban development.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on the Coupling Coordination Relationships between Urban Function Mixing Degree and Urbanization Development Level Based on Information Entropy

Xia

Lin

Ding

et al. 2020

IJERPH

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With the rapid development of urbanization, the blind expansion of urban space has led to a series of social problems. In this process, the degree of urban function mixing affects the urbanization development level, making it particularly important to study the degree of coupling coordination between the two aspects. In this paper, taking Beijing as an example, we use urban point of interest (POI) data and taxi GPS trajectory data to calculate the urban POIs’ spatial entropy and taxis’ temporal entropy, based on the information entropy. We use the POIs’ spatial entropy and taxis’ temporal entropy to measure the urban function mixing degree. Also, the model of coupling coordination degree is used to measure the degree of coupling coordination between the urban function mixing degree and the urbanization development level. The results indicate the following: First, the POIs’ spatial entropy and taxis’ temporal entropy have significant regional imbalances. On the whole, both show a declining pattern when moving from the central urban area to the outer suburbs. The urban function mixing degree and urbanization development level are also higher in the central urban area than in the outer suburbs. Second, the coupling coordination among the urbanization development level, POIs’ spatial entropy, and taxis’ temporal entropy is distributed unevenly across various regions, which means that the three types of coupling coordination are in balanced development in the central urban area, but in unbalanced development in the outer suburbs. Third, from the perspective of spatial correlation characteristics, the higher is the degree of spatial agglomeration, the higher are the urban function mixing degree and urbanization development level, and the higher is the coupling coordination degree among the urbanization development level, POIs’ spatial entropy, and taxis’ temporal entropy. Therefore, relevant departments should plan the construction of urban functional areas reasonably, according to the degree of coupling coordination between the urban function mixing degree and the urbanization development level in different regions, so as to realize the healthy and sustainable development of a city.

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Urban Vitality Area Identification and Pattern Analysis from the Perspective of Time and Space Fusion

Cited by 46 publications

References 40 publications

An Evaluation of Street Dynamic Vitality and Its Influential Factors Based on Multi-Source Big Data

An Evaluation of Street Dynamic Vitality and Its Influential Factors Based on Multi-Source Big Data

Tourist Attraction and Points of Interest (POIs) Using Search Engine Data: Case of Seoul

Research on the Coupling Coordination Relationships between Urban Function Mixing Degree and Urbanization Development Level Based on Information Entropy

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