Leveraging the Google Cloud to Estimate Individual Level CO2 Emissions Linked to the School Commute

在国际上，上学的主动交通（如骑自行车或步行）比例下降，必然转向可持续性较差的出行方式，这与环境的负面影响有关，包括排放增加、学校周围交通拥堵加剧以及与体力活动水平降低或污染物暴露有关的负面健康影响。在英国，学校占公共部门总排放量的15%（DCFS，2010年），在英格兰，估计相当于每年约940万吨二氧化碳（SDC，2006年）。其中7%（65.8万吨）与学生往返学校的交通有关，因此，学生采取更可持续的出行行为对环境有显著的好处。该研究项目创建了一个覆盖全国的、地理敏感的与学校通勤有关的二氧化碳排放模型。这涉及整合各种公共部门的“大数据”，包括约750万学生的来源地、目的地和交通方式选择，以及对在非常小的地理区域内登记的汽车排放特征的小区域估计。这些数据被整合，以创建一个地理敏感的估计措施，个别学生的二氧化碳贡献与他们的旅程。处理如此大的数据的计算负担，特别是在交通网络级别（公路，铁路等）估计到学校的路线是巨大的。在这项研究中使用谷歌云环境来减少这种计算负担。考虑到人口特征和环境的空间多样性，以及当地基础设施和政策的差异，“一刀切”的方法来解决与学校通勤相关的排放问题不太可能像针对当地情况的干预措施那样富有成效。随着云计算在公共部门“大数据”时代的日益普及，社会科学越来越容易使用本地化和地理智能建模方法。然而，除了技术挑战之外，还需要解决与数据披露和隐私有关的关键道德问题。因此，该项目既建立了技术程序，也提出了关于在上下文敏感的个人层面数据中道德使用云技术的建议。这项研究中提出的雄心勃勃的空间建模技术首次将地理定位的输入参数和地理环境控制集成到与学校通勤相关的排放校准中，使产出能够深入到个人层面。这项研究将绘制模式选择和排放的地理图，并测量对这些模式的影响在空间上是如何变化的。

Internationally, the rates of active transport (e.g. cycling or walking) to school are in decline and the corollary switch to less sustainable modes of travel are linked with negative effects on the environment in terms of increased emissions, increasing traffic congestion around schools and negative health impacts related to lower physical activity levels or pollutant exposure. In a UK context, schools account for 15% of total public sector emissions (DCFS, 2010), which in England is estimated to be the equivalent of around 9.4 million tonnes of CO2 per year (SDC, 2006). 7% (658k tonnes) of this total is associated with the pupil-school commute, and as such, there are significant environmental benefits of pupils adopting more sustainable travel behaviours.This research project creates a national coverage and geographically sensitive model of CO2 emissions linked with the school commute. This involves the integration of a variety of public sector "big data", including the origin destination and mode choices for around 7.5 million pupils, and small area estimates of the emission characteristics of cars registered within very small geographic areas. These data are integrated to create a geographically sensitive estimate measure of an individual pupils contribution of CO2 related to their journey. The computational burden of processing such large data, and especially in estimating routes to school at a transport network level (road, rail etc) are great. The Google cloud environment is utilised in this research to reduce this computational burden.Given the spatial diversity of population characteristics and circumstance, alongside differences in local infrastructure and policy; a 'one size fits all' approach to tacking the issue of emissions linked to the school commute is unlikely to be as fruitful as interventions tailored to local context. With the increasing availability of cloud computing in an era of public sector "big data", localised and geographically intelligent modelling approaches are increasingly accessible to the social sciences. However, technical challenges aside, there are also critical ethical concerns that need to be addressed related to data disclosure and privacy. As such this project establishes both technical procedures and also makes recommendations about the ethical use of cloud technology within the context sensitive individual level data.For the first time, the ambitious spatial modelling techniques presented in this research integrate geographically localised input parameters and control for geographical context in the calibration of emissions linked to the school commute, enabling outputs to be explored down to the level of an individual. This research will map the geography of mode choice and emissions, also measure how influences on these patterns vary spatially.