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

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.

Who will benefit from this research?

This project is of substantial policy relevance to variety of key stakeholders looking at the impact of policy decisions on levels of CO2 emissions related to the school commute, and additionally, on those drivers of more active forms of transport, given their associated health benefits. For the first time, geographical heterogeneity of the influences and outputs of a CO2 / mode choice model will be explored, giving policy makers a better indication of the geography of likely responses to their interventions. A by no means comprehensive list of those stakeholders who we would envisage this project to be of benefit include: the Department for Education, Department for Transport, local authorities, school management; and from a cyber infrastructure perspective, Google and other cloud based analytical providers.

In addition to beneficiaries of the substantive output of this grant, from a methodological perspective, social science researchers will also gain a better understanding of how sensitive data can be used within a cloud computing (specifically google) environment. Furthermore, researchers interested in routing applications using the Google service will be provided with additional guidance on how these API can be practically exploited in their work.

How will they benefit from this research?

The model output from this research has a wide range of benefits. For example, within the theme 'Health and Wellbeing', given a specific policy scenario, the model would enable exploration of the loss or adoption of active transport; or, given the fine geographic scales in which the model can be applied, it would be possible to examine street level exposure to vehicular pollutants. You might for example use this information to create "walking bus" routes to minimise this exposure. Furthermore, within the context of 'Environment, Energy and Resilience', it would be possible to expand the literature on the influences of mode choice, to examine how these vary geographically. This would be of relevance when designing policies that encourage more sustainable environmental practices in a switch to a low carbon economy.

Specific deliverables which would be of relevance to a wider [non academic] community include:

Policy facing website - will include dissemination of software / code and results
Best practice guide on using sensitive public sector "Big Data" with Google cloud services
Training workshop on using cloud services for social science
R software package enabling the use of the Google transport API within R