Alcohol and Personal Security in the Built Environment: Student Engagement in the Night-time Economy of Kingston upon Thames, London, UK

Gant, Robert; Walford, Nigel; Terry, Philip

doi:10.1080/03058034.2020.1842578

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…"Night-time economy" in the early research in the UK refers to a mixture of social science studies of alcohol and social science studies of the night-time leisure industry that have come to dominate how we understand nighttime cities [9]. Although there are is lot of geographical research on the night economy, researchers are still mainly focused on alcohol and violence, such as the concentration of potential nighttime crime zones [10], the impact of the nighttime economy on sexual violence [11], the relationship between the nighttime economy and alcohol-related violence [12], the costs of alcohol-related violence [13], and the dominant role of university students in bars [14]. Other scholars focused on ecological issues arising from the rapid growth of the nighttime economy, such as "light pollution" [15].…”

Section: Introductionmentioning

confidence: 99%

Research on Temporal and Spatial Distribution Pattern of the Night-Time Economy Supported by Multi-Source Data—A Case Study of Xiamen City, Fujian Province, China

Zeng,

Liu,

et al. 2024

Remote Sensing

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The increasing proportion of night consumption in the total daily consumption means the night economy has become an indispensable part of national economic development. However, early social science research on alcohol in the night economy and social science research on the night leisure industry dominated our understanding of the night city. Few researchers have built effective mathematical statistical models to explore the spatio-temporal distribution and regional interactions of the night economy. This paper presents a method to analyze the spatial and temporal distribution pattern of the night economy based on multi-source data. Firstly, K-Means++ and DBSCAN were used to cluster OD points to identify the gathering areas of night activities. Then, the local L-function in “flow space” was used to extract the aggregated flow of each aggregation area and analyze the regional interactions. Finally, the correlation between night activity and night service facilities was calculated by using geographic detector, and night activity and lighting were coupled by the profit and loss value. The research shows that this method can identify the main areas of night activity, dig out the interrelationships at the community level, and find the new night activity gathering points and night economic growth areas in the future. This study extends the current situation that the night economy is limited to theoretical research and research in central urban areas, provides the temporal and spatial distribution of night activities and night lighting supplies from the perspective of big data, and provides a basis for future night economy research, urban planning, and relevant policy issuance.

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Section: Introductionmentioning

confidence: 99%

Research on Temporal and Spatial Distribution Pattern of the Night-Time Economy Supported by Multi-Source Data—A Case Study of Xiamen City, Fujian Province, China

Zeng,

Liu,

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…In addition, others have scientifically analyzed public safety issues in the night-time economy from a social perspective [12,13] and the environmental protection brought about by the light-pollution question [14]. It can be seen that the current research on the night-time economy in Western countries mainly focuses on hidden economic factors, such as night city safety [15] and urban crime [16]. There are few research perspectives, which mainly use qualitative research methods, and a lack of quantitative research on the spatial and temporal distribution of urban night-time economy.…”

Section: Introductionmentioning

confidence: 99%

Spatial Matching Analysis and Development Strategies of County Night-Time Economy: A Case of Anning County, Yunnan Province

2022

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The Chinese government regards the night-time economy as one of the essential means to expand domestic demand and enhance sustainable economic development. Scientifically choosing the night-time economic development path of the suburban counties of the Chinese metropolis (SCCM) and proposing a reasonable spatial matching planning strategy is an urgent problem for Chinese local governments. This study takes Anning county, a suburban Kunming metropolitan area, as the research area. Using Python to capture multi-spatial data, such as POI and Baidu heatmap, we use ArcGIS spatial analysis and statistical tools to show the spatial distribution characteristics of the night-time economic formats in Anning County. At the same time, the spatial coupling coordination model is used to calculate the coupling coordination degree of the night-time economic formats distribution and comprehensive traffic distribution (D1), night-time economic formats distribution and night-crowd vitality (D2), and the spatial coupling coordination of the three (D3). It is divided into five spatial matching levels and analyzes the shortage of night-time economic development in each subdistrict. The research results show that the spatial development of the night-time economy in Anning county is unbalanced at the current stage. The northern part of the county has a good development trend, and the Lianran subdistrict has the highest coupling coordination degree (0.995). In contrast, the southern part of the county has the lowest coupling coordination degree due to a lack of economic formats and traffic restrictions (0.115). According to the subdistricts’ differences, the sustainable development strategy of the county’s night-time economy should be formulated from the perspective of the long-term development of metropolitan areas. We hope that this research can provide valuable inspiration and a development reference for relevant countries and regions to stimulate the sustainable power of the night-time economy.

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Sensing the Nighttime Economy–Housing Imbalance from a Mobile Phone Data Perspective: A Case Study in Shanghai

2022

Remote Sensing

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Sensing the nighttime economy–housing imbalance is of great importance for urban planning and commerce. As an efficient tool of social sensing and human observation, mobile phone data provides an effective way to address this issue. In this paper, an indicator, mobile phone data-based nighttime economy–housing imbalance intensity, is proposed to measure the degree of the nighttime economy–housing imbalance. This indicator can distinguish vitality variations between sleep periods and nighttime activity periods, which are highly related to the nighttime economy–housing imbalance. The spatial pattern of the nighttime economy–housing imbalance was explored, and its association with the built environment was investigated through city-scale geographical regression analysis in Shanghai, China. The results showed that the sub-districts of Shanghai with high-positive-imbalance intensities displayed structures with superimposed rings and striped shapes, and the sub-districts with negative imbalance intensities were distributed around high positive-intensity areas. There were significant linear correlations between imbalance intensity and the built environment. The multiple influences of built environment factors and related mechanisms were explored from a geographical perspective. Our study utilized the social sensing data to provide a more comprehensive understanding of the nighttime economy–housing imbalance. These findings will be useful for fostering the nighttime economy and supporting urban renewal.

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Alcohol and Personal Security in the Built Environment: Student Engagement in the Night-time Economy of Kingston upon Thames, London, UK

Abstract: Part-funded by Kingston Town Centre Management Ltd (KingstonFirst) and its stakeholders.

Cited by 3 publications

References 40 publications

Research on Temporal and Spatial Distribution Pattern of the Night-Time Economy Supported by Multi-Source Data—A Case Study of Xiamen City, Fujian Province, China

Research on Temporal and Spatial Distribution Pattern of the Night-Time Economy Supported by Multi-Source Data—A Case Study of Xiamen City, Fujian Province, China

Spatial Matching Analysis and Development Strategies of County Night-Time Economy: A Case of Anning County, Yunnan Province

Sensing the Nighttime Economy–Housing Imbalance from a Mobile Phone Data Perspective: A Case Study in Shanghai

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