By Tyler Pullen, M.S. Candidate, Civil & Environmental Engineering, Stanford
Perhaps the most prominent overarching challenge for holistic action against global warming is (especially American) civilians’ resistance to trust high level science and conclusions based on abstract, difficult to conceptualize data. Incredibly distant and face-less scientists from around the world are agreeing on world-scale changes in climate patterns that are practically imperceptible on a daily - and human - scale. Furthermore, even in the case of those non-technical professionals who believe and begin to understand the macro causes and effects of climate change, developing a clear sense of how to act and what goals to set based on these predictions is a leviathan task. Particularly in regards to the decision makers at the city scale, the translation of such complex, comprehensive climate models into more intuitive and interactive tools is one of the primary barriers to productive policy proactively mitigating global environmental damage.
A primary problem in communicating climate-related goals and effects is in the inherently abstract concept of global emissions. This is combined with the fact that the effects of these values if not curbed by meaningful and decisive carbon-cutting actions and policies are difficult to observe on a human scale. In this way, effectively communicating the urgency and importance of cutting emissions is understandably difficult. Thus, the first goal for putting climate change into perspective for civilians and policy makers is translating the data and implications of climate change into metrics that are more relatable. Depending on the particular factor(s) being discussed, this could be the delay in minutes due to traffic during rush hour as a result of low public transit usage, the visibility distance impacted by smog in the local atmosphere, or the amount of rain in inches from storms of increasing magnitude as the sea rises. Possibly most relevant to citizens and politicians alike, however, is the simple metric of dollars. In one way or another, most consequences of climate change impacts can be described - at least roughly - in human currency. A large scale effort to do exactly this was already undertaken by the US EPA in attempting to define the “social cost of carbon”. This could refer to the amount of property damage in the case of storms of high severity, or the rising collective cost of electricity if energy efficiency measures are not taken, or the amount of individual productivity lost for long commutes due to traffic from single occupancy vehicles. The effort is understandably limited due to ambiguity over the scoping of the problem and inaccuracy or unavailability of data, but it still a laudable attempt. Because regardless of the specific intervention being discussed to counter these effects, putting the theoretical cost of action - and inaction - into relatable terms to decision makers is critical if they are to use this data to design effective policies and measure their progress.
With the relevant data in relevant terms, the next critical challenge for engineers and scientists is to succinctly visualize the information in ways that are digestable to those who likely do not have backgrounds in advanced data analytics. Graphs, tables, and other diagrams must be comprehensive (at least in regards to the aspect of climate action being discussed) but concise. Sufficiently simplified but not so much so as to be inaccurate. And more abstractly: in this process of simplification, it is paramount to not insert or exaggerate existing biases. For example, a dataset may suggest that a municipality with very low car ownership rates is pro-environmental (an image said municipality would embrace, presumably), but perhaps residents in the region take a substantial amount of flights that more than offset the lack of vehicle usage. These are by no means easy considerations to balance for the eclectic sources and implications of global warming, but the process of organizing and presenting information can be as critical as collecting it in the first place, especially if they are meant to persuade those into decisive action.
One of the less-explored aspects of climate change and, more broadly, data analytics, is the importance of making the models and the data presented adjustable. Even data sets used for the most parochial subjects require a massive amount of variables and assumptions before being used to model the potential of an intervention (which is obviously critical for predicting the effect of certain policies into the future). These assumptions are necessary in order to bound the problem, make the databases reasonably efficient, and make predictions tenable; but they’re also inherently subjective. What equation did you use to model population projection over the next 20 years? How will this theoretical incoming population disperse throughout the different municipalities in a region over time? There are no correct answers to these questions. And though, ideally, policy makers will be involved from the first stage of data usage and model creation to help make these assumptions in a sensible and ethical manner, it is also vital to allow for adjustment of the major original numeric assumptions so that it can be tested for accuracy and so that potential interventions can be modeled as well. Due to the often-enormous backend of data to support these models, however, it is very challenging to allow for these system levers to less technical users who likely do not know how to use the software supporting the analysis. Thus, building them with easy-to-use points of interaction to vary initial assumptions or change values over time could be immensely helpful in getting decision makers to understand and play around with large models to be creative with potential interventions and better assess their effects.
The last factor I consider to be central in data analysis and modeling for action is building in a framework for updating them with the most recent data. Governments - especially municipal ones - don’t historically have the bandwidth or resources to sustain massive data streams over time. And even the best models, once created, are mere snapshots of a once-current state of the system being studied (especially when they use data that were already a few years old at the time of simulation). And so, however revelational they may be, they are instantaneously outdated and increasingly irrelevant as the city actually implements interventions based on its original insights, unless formatted to automatically update. Even the comprehensive and beautifully simplified set of visualizations provided by Shaun Fernando from PwC (who presented at the SUS Seminar this quarter), for instance, would be more chronically useful to San Jose if it was updated live or at least semi-regularly. If cities are to truly and permanently augment their decision making with holistically-managed data streams and respective models, then keeping this information current is mandatory. This is even more necessary in order for cities to be able to track the progress towards long term goals and consequently measure the effectiveness of interventions over time.
The overarching theme for better utilization of data in the public sector of city management is this: we need to do better. Cities are almost-impossibly complex systems that we have effectively no chance of modeling perfectly. With that established: more collaborative conversations and interactions absolutely need to happen for data scientists and engineers at large to better align their work with the desperate need cities have for more informed decision making.