Yue Pan, Chit Cheong, Eli Blevis
The most recent Sustainably Ours forum addressed the implications of climate change and fostering the sustainable use of resources, especially in the context of digital interactivity and its agency in the world . The article suggested a number of possible approaches. In this issue, we address one of the approaches that we call the Climate Change Habitability Index (CCHI) as a design exercise and catalyst to a program of ongoing research. The idea of the CCHI is to allow ordinary individuals in generalrather than climate scientists in particularto understand the state of the world in terms of habitability at particular places. In the face of climate change, individuals will need to be able to use the CCHI and other measures to answer questions of habitability. The CCHI will need to be stated in a way that is easily understood by nonscientists, similar to how weather forecasting is employed.
Whether the causes are due to human activitiesanthropogenically generatedor not, according to many accounts we are on the precipice of imminent and irreversible climate change. The 2007 Intergovernmental Panel on Climate Change (IPCC) predicts we are under very considerable and likely danger of climate change with unprecedented implications . A new report of the IPCC is due in 2013. Already, much of the climate change data suggests the effects of climate change are worse than even the worst-case scenarios put forward by the IPCC in 2007. To put it simply, changing people’s attitudes toward bottled water and having a hybridelectric car in every driveway is likely not going to be enough to prevent the need for adaptation to certain imminent large-scale changes in terms of where people can live, grow food, obtain fresh water, and create harmony with the natural world.
We need interactive systems that allow access to data about the habitability of particular regions and predictions about the future habitability of particular regions . It will be critical to make such information readily available to ordinary people to ensure that the absorption and evacuation of populations as a response to the effects of climate change are as orderly and peaceful as possible.
To reiterate, the CCHI is defined abstractly as a metric that can be stated in ordinary language and diagrams that allows people to answer the following three questions related to sustainability and adaptation to climate change:
- Can I continue to live where I am living?
- Where can I move if I can’t continue to live where I’m living?
- How many people can the place where I live sustainably support, if where I live continues to be habitable?
By “can I continue to live where I am living,” we mean to ask if a place is habitable enough for people to live in accordance with water and food supplies, health conditions, and the habitability of coastal environments in particular and other environments in general.
By “where can I move if I can’t continue to live where I’m living,” we mean to ask which places are habitable or have better, more sustainable living affordances respective of the five factors identified by the IPCCwater, coasts, food, health, and ecosystems. This question relates to a person’s ability to understand the habitability of where she or he lives now and to make plans for moving in the future, if necessary.
By “how many people can the place where I live sustainably support, if where I live continues to be habitable,” we mean to ask how can we inform policies and decisions related to migration and absorption of immigrants in a manner that promotes peaceful and orderly sustainable practice in the face of the potential and realized effects of climate change.
The CCHI aims to provide a metric to enable everyone from individuals to intergovernmental bodies to plan for the dynamic and orderly migration and absorption of populations as climate change changes the suitability of various regions of the planet for sustainable habitation. Finally, the notion of making the habitability index as highly accessible as a notion like “temperature” is co-requisite to the social imperative to provide for the safety and security of every person and creature whose life or home may be impacted by climate change events and effects.
Possible Data Sources
By focusing on the key impacts caused by increasing global average temperature change, we need access to data that corresponds to changes in food supplies, water availability, health conditions, and even to threats to the habitability of coastal environments. The IPCC and the associated science on which it draws are the main data sources . There are also some visible organizations and agencies working on the impacts of climate change that contribute to IPCC-related data sources or that may provide additional data sources upon which we can base the CCHI. Examples of these data sources include deep explorations and GIS data about ocean, fish, and coasts from the National Oceanic and Atmospheric Administration (NOAA) , and rigorous scientific work relating to very specific individual factors, such as Halpern et al.‘s “A Global Map of Human Impact on Marine Ecosystems,” which contains compelling maps and diagrams that are in fact less accessible to non-scientists than what we have in mind for the CCHI . The idea of the CCHI is to use the best and widest array of data sources available and still provide a means to distill this data into an easily understood and generally accessible form.
Changing people’s attitudes toward bottled water and having a hybrid-electric car in every driveway is likely not going to be enough to prevent the need for adaptation to certain imminent large-scale changes in terms of where people can live, grow food, obtain fresh water, and create harmony with the natural world.
In addition to the scientific data sources listed above, we are also considering enterprise, such as SAP’s carbon-tracking software and crowdsource reporting of data implicated in the potential calculations to support the CCHI. As an example, the USA National Phenology Network uses crowdsource reporting to collect and make available data on the visible impacts of climate change in the U.S. .
Our Initial Design for the CCHI
We are designing a tool to support the summarization of the suitability of any particular place for habitation as a dynamically changing metric corresponding to historical, current, and predicted states of water systems, ecosystems, food supplies, coastal conditions, and health conditions, as well as the likelihood of hazard events. Interactivity of the habitability index may be presented using cloud-based technologies, such as layers implemented as part of the “Google Earth” or “Google Maps” APIs, or other GIS systems. Figure 1 shows the essential interactivity elements we imagine are needed to support interactivity with the CCHI representation as a cascading summary of more detailed data. Our design is deliberately minimalist, and it is easy to image a grayscale version as a matter of accessibility. The regions defined in the map denote a cumulative summary of the risk factors indicated by the risk factor icons.
Three levels. We envision the CCHI will support exploration at three levels of granularity with respect to the map, namely world scale, showing regions defined by countries, country scale, showing regions defined by states, and state scale, showing regions potentially defined by postal codes rather than counties. The choice of three levels of granularity as opposed to many discrete levels of zoom as in Google Maps is deliberate and owes to a concern for presenting the CCHI in as simple a manner as possible. Too many levels of granularity make it hard to think about the world in regional terms that are essential to understanding how to construct policies about absorption of new residents. Too few levels of granularity risk an overly reductive understanding of the CCHI. The postal code as a unit of granularity may be particularly salient, since it is generally highly correlated with population density, which is germane to understanding the capability of a region to absorb new residents.
Risk factors. The IPCC considers five factors in its assessment of climate change scenarios that we have adopted herewater, food, ecosystems, coasts, and health. Looking more closely at how the IPCC defines these factors, we arrived at an initial inventory of subfactors for each factor. Figure 2 shows a design for factors and their column-wise subfactors as an initial iconographic design. These subfactors must be considered carefully. As with the choice of levels, we also need to consider if we have made too few distinctions to actually inform the CCHI. On the other hand, there is a danger that we may make too many distinctionsdoing so may make the CCHI too hard for average people to acquire intuitions about the relationship between the CCHI, its constituent factors and subfactors, and the actual habitability of a particular place.
The CCHI interaction design affords a view of the habitability of a place in accordance with the summarization of the five main risk factors. The CCHI interaction design can also display the habitability of a place in accordance with a sub-selection of the risk factors. For instance, in Figure 3 the map expands only the water risk factor apropos of four detailed risk subfactors, namely freshwater systems, drought, flood, and drinking water availability.
Simple design. In constructing a design for the CCHI and initial notions of interactivity, we want to choose the simplest possible representations we can think of. There is a lot of data available, and assessing the actual habitability of a particular location in terms of risks associated with climate change is a tremendously complex and intricate calculation. Our goal here is to hide all this complexity inasmuch as it is possible to do so. We chose to use color to denote degree of risk, using typical semantics of green through red hues. As we have mentioned, grayscale may also be substituted as a matter of accessibility without loss of semantics.
Time and temperature. Another two essential features of CCHI are time and degrees of warming (or cooling). Our purpose is to allow people to see historical trends and future predictions about how climate change affects our Earth over periods of time. People need to be able to see what the Earth may look like when the average temperature increases by a specific amount, which in the IPCC summary diagrams ranges from 0 to 6 degrees Celsius of warming . Two degrees Celsius of warming is generally considered to be the tipping point. Figure 4 illustrates as a matrix the space of what needs to be represented apropos of time and degrees of warming historically and in prediction as underlying data to support the interactivity. Based on the assumptions of the IPCC reports, we define year 2000 to be the base point and set the temperature change to be 0 for 2000, relative to the period from 19801999 .
Crowdsourcing. One primary goal of the CCHI is to target a wider audience than climate scientists, local policy holders, planners, and managersnamely individuals in the general public and societal groups at various levels, from civic groups and municipal officials to regional, national, and international bodies. We also hope to include formal scientific data sources as well as crowdsource reporting and the wiki-style editing of data about global habitability conditions. The latter form of data collection risks confidence in the data compared to peer-reviewed reporting by the scientific community, but so long as such contributions are clearly demarcated, they also have the benefit of involving the public, raising awareness, and prompting debate.
The program of research and practice that has evolved in our interaction design and HCI community around sustainability is important and promising work. We need to augment our efforts to also focus on adaptation in the face of the likelihood of climate change regardless of our efforts to mitigate such changes. This initial design sketch of the CCHI is one step in this direction.
As always, if you are engaged in interaction design or HCI research related to adaptation to climate change, kindly consider contributing to this forum.
Eli Blevis, Editor
We gratefully acknowledge the valuable discussions about the CCHI we had with Beth Plale, Paul Dantilio, and Shunying Blevis.
2. IPCC. “Summary for Policymakers.” In Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by Parry, M.L., Canziani, O.F., Palutikof, J.P., van der Linden, P.J. and Hanson, C.E. 722. Cambridge: Cambridge University Press, 2007; www.ipcc.ch.
3. NOAA, National Oceanic and Atmospheric Administration; http://www.noaa.gov/
4. Halpern, et. al. “A Global Map of Human Impact on Marine Ecosystems.” Science 319, 5865 (2008): 948952. DOI: 10.1126/science.1149345. This paper is available online; http://www.sciencemag.org/cgi/content/full/319/5865/948. A more generally accessible description of how these authors have created their map is here: http://www.nceas.ucsb.edu/globalmarine/.
5. USA National Phenology Network; http://www.usanpn.org/
Yue Pan is a doctoral student in the Human-Computer Interaction Design program at Indiana University’s School of Informatics and Computing. She has a background in computer science, and her area of research is sustainable interaction design.
Chit Meng Cheong is a master’s student in the Human Computer Interaction Design program in the School of Informatics and Computing, Indiana University Bloomington. He works in the field of sustainable interaction design.
Eli Blevis is an associate professor of informatics in the Human-Computer Interaction Design program of the School of Informatics and Computing at Indiana University, Bloomington. His primary area of researchsustainable interaction designand his core expertise are situated within the confluence of human-computer interaction as it owes to the computing and cognitive sciences, and design as it owes to the reflection of design criticism and the practice of critical design. His research also engages design theory, digital photography, and studio-based learning.
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