Development of an approach to forecast future takeaway outlet growth around schools and population exposure to takeaways in England.

IF 3 2区 医学 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH International Journal of Health Geographics Pub Date : 2024-11-10 DOI:10.1186/s12942-024-00383-6
Bochu Liu, Oliver Mytton, John Rahilly, Ben Amies-Cull, Nina Rogers, Tom Bishop, Michael Chang, Steven Cummins, Daniel Derbyshire, Suzan Hassan, Yuru Huang, Antonieta Medina-Lara, Bea Savory, Richard Smith, Claire Thompson, Martin White, Jean Adams, Thomas Burgoine
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Abstract

Background: Neighbourhood exposure to takeaways can contribute negatively to diet and diet-related health outcomes. Urban planners within local authorities (LAs) in England can modify takeaway exposure through denying planning permission to new outlets in management zones around schools. LAs sometimes refer to these as takeaway "exclusion zones". Understanding the long-term impacts of this intervention on the takeaway retail environment and health, an important policy question, requires methods to forecast future takeaway growth and subsequent population-level exposure to takeaways. In this paper we describe a novel two-stage method to achieve this.

Methods: We used historic data on locations of takeaways and a time-series auto-regressive integrated moving average (ARIMA) model, to forecast numbers of outlets within management zones to 2031, based on historical trends, in six LAs with different urban/rural characteristics across England. Forecast performance was evaluated based on root mean squared error (RMSE) and mean absolute scaled error (MASE) scores in time-series cross-validation. Using travel-to-work data from the 2011 UK census, we then translated these forecasts of the number of takeaways within management zones into population-level exposures across home, work and commuting domains.

Results: Our ARIMA models outperformed exponential smoothing equivalents according to RMSE and MASE. The model was able to forecast growth in the count of takeaways up to 2031 across all six LAs, with variable growth rates by RUC (min-max: 39.4-79.3%). Manchester (classified as a non-London urban with major conurbation LA) exhibited the highest forecast growth rate (79.3%, 95% CI 61.6, 96.9) and estimated population-level takeaway exposure within management zones, increasing by 65.5 outlets per capita to 148.2 (95% CI 133.6, 162.7) outlets. Overall, urban (vs. rural) LAs were forecast stronger growth and higher population exposures.

Conclusions: Our two-stage forecasting approach provides a novel way to estimate long-term future takeaway growth and population-level takeaway exposure. While Manchester exhibited the strongest growth, all six LAs were forecast marked growth that might be considered a risk to public health. Our methods can be used to model future growth in other types of retail outlets and in other areas.

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开发一种方法来预测英格兰学校周围未来外卖店的增长情况以及人口接触外卖的情况。
背景:附近居民接触外卖会对饮食和与饮食相关的健康结果产生负面影响。英格兰地方当局(LA)的城市规划者可以通过拒绝为学校周边管理区的新外卖店颁发规划许可来改变外卖暴露程度。地方当局有时将其称为外卖 "禁区"。了解这一干预措施对外卖零售环境和健康的长期影响是一个重要的政策问题,需要有方法来预测未来的外卖增长和随后的外卖人口接触情况。在本文中,我们介绍了一种新颖的两阶段方法来实现这一目标:方法:我们使用外卖店位置的历史数据和时间序列自动回归综合移动平均模型(ARIMA),根据历史趋势预测英格兰六个具有不同城乡特点的洛杉矶管理区内到 2031 年的外卖店数量。预测性能根据时间序列交叉验证中的均方根误差 (RMSE) 和平均绝对缩放误差 (MASE) 分数进行评估。利用 2011 年英国人口普查的上班出行数据,我们将这些对管理区内外卖数量的预测转化为家庭、工作和通勤领域的人口级暴露:根据均方根误差(RMSE)和最大误差(MASE),我们的ARIMA模型优于指数平滑模型。该模型能够预测到 2031 年所有六个洛杉矶地区外卖数量的增长情况,各区域协调委员会的增长率各不相同(最小-最大:39.4%-79.3%)。曼彻斯特(被归类为非伦敦市区和主要城市群的洛杉矶)的预测增长率最高(79.3%,95% CI 61.6,96.9),估计管理区内的外卖人口数量也最高,人均增加了 65.5 家,达到 148.2 家(95% CI 133.6,162.7)。总体而言,城市(相对于农村)洛杉矶的预测增长更快,人口风险更高:我们的两阶段预测方法为估计未来长期外卖增长和人口层面的外卖暴露提供了一种新方法。虽然曼彻斯特的外卖增长最为强劲,但所有六个洛杉矶的外卖增长都很明显,可能会对公众健康造成威胁。我们的方法可用于模拟其他类型零售店和其他地区的未来增长。
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来源期刊
International Journal of Health Geographics
International Journal of Health Geographics PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH -
CiteScore
10.20
自引率
2.00%
发文量
17
审稿时长
12 weeks
期刊介绍: A leader among the field, International Journal of Health Geographics is an interdisciplinary, open access journal publishing internationally significant studies of geospatial information systems and science applications in health and healthcare. With an exceptional author satisfaction rate and a quick time to first decision, the journal caters to readers across an array of healthcare disciplines globally. International Journal of Health Geographics welcomes novel studies in the health and healthcare context spanning from spatial data infrastructure and Web geospatial interoperability research, to research into real-time Geographic Information Systems (GIS)-enabled surveillance services, remote sensing applications, spatial epidemiology, spatio-temporal statistics, internet GIS and cyberspace mapping, participatory GIS and citizen sensing, geospatial big data, healthy smart cities and regions, and geospatial Internet of Things and blockchain.
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Development of an approach to forecast future takeaway outlet growth around schools and population exposure to takeaways in England. Using spatial video and deep learning for automated mapping of ground-level context in relief camps. The influence of malaria control interventions and climate variability on changes in the geographical distribution of parasite prevalence in Kenya between 2015 and 2020. Understanding Ixodes ricinus occurrence in private yards: influence of yard and landscape features. Accessibility, neighborhood socioeconomic disadvantage and expenditures on electronic gambling machines: a spatial analysis based on player account data.
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