“Such. A cool. Analysis. ?” -Brittni Donaldson, Director of Coaching Analytics for the Detroit Pistons

“Nobody, and I mean nobody, makes better charts than Todd.” -Ian Levy, Creative Editorial Director for Fansided

“This is a fantastic, fantastic way to display a graphic.” -Chris Herring, Sports Illustrated Senior Writer and NYT Best Selling Author of Blood in the Garden: The Flagrant History of the 1990s New York Knicks

The above quotes aren’t referencing anything that you may see on television during a marquee NBA matchup or a splash page on ESPN. No, they’re just a smattering of the hundreds and thousands of retweets, quote tweets, and mentions racked up by Todd Whitehead (a.k.a. @crumpledjumper on Twitter) of Synergy Sports and former writer at Nylon Calculus, a flagship NBA Analytics blog. Just imagine the level of polish and verve someone’s work needs to be able to force a pause on any Lebron versus Jordan internet debates in their tracks. And then imagine the skill and versatility needed to repeat that feat, over and over again, whether it’s in advance for the NBA Finals, during the NBA Draft, or ahead of a pivotal WNBA matchup.

The use of analytics in sports has now been around for a few decades, and it takes a better historian than myself to track it in its entirety, but it’s safe to say that it hit an inflection point in the public consciousness with Michael Lewis’ seminal 2003 book Moneyball (not to mention the ensuing Brad Pitt and Jonah Hill motion picture vehicle of the same name). With baseball as a sort of torch-bearer for how the use of data could be transformative to a team’s ability to construct a roster and win games, the analytics era had arrived. It is not uncommon today to see professional baseball teams employ entire 20+ persons analytics research groups, resembling something that is usually seen at a hedge fund or tech company. Basketball’s own analytics movement is often, or even popularly referred to as “Moreyball,” a moniker that credits both Moneyball as well as Daryl Morey, current President of the Philadelphia 76ers, who is seen as a sort of figurehead for analytics in the NBA from his time at the Houston Rockets.

Whether fans realize it or accept it, analytics has (to borrow a perhaps tired phrase) changed the game. Many of the most successful teams in the league invest in their sports analytics group. The spread out offensive style of play that we see today is a consequence of analytically-derived insights. However, with more data comes more responsibility (thanks, Uncle Ben). Basketball analytics is, at the end of the day, in service to the game of basketball, which means the translation of analytical concepts into actionable basketball insights is the premium skill. As Seth Partnow, Director of North American Sports at StatsBomb and former Director of Basketball Research with the Milwaukee Bucks, frames it in his book, The Midrange Theory, “the goal is a distillation of basketball concepts from what can at first appear to be a jumble of data.”

That’s where data visualization comes in. What better bridge between the data and the consumer, whether they be a front office, a media outlet, or a fan. The best practitioners of data visualization understand how to communicate advanced concepts to diverse audiences, and optimize for the right balance between engagement and conveying new, unique insights. Undoubtedly, the most ubiquitous form of data visualization in basketball is the shot chart, a mapping of a player or team’s shot attempts onto the layout of the basketball court. Running the gamut from point by point recordings to heat maps, they’ve fast become an invaluable way to summarize some of the most critical patterns of a player or a game for analysts, media, and fans alike.

But data visualization in basketball, and all sports more broadly, encompasses so much more than shot charts (in fact, around the launch of Nightingale several years ago, we profiled another celebrated dataviz practitioner in sports, Daren Willman — you can find that article right here). Folks like Peter Beshai, Andrew Patton, and Todd Whitehead (among many others) are elevating the scope and possibilities of dataviz in basketball. Done right, basketball data visualization in the public sphere can become itself a vessel for growing the sport’s audience and engagement, as opposed to some opaque barrier of ones and zeros. And few are doing it better right now than Todd Whitehead. So dear reader, let’s pick his brains and introduce a heavyweight champion of basketball data visualization to the rest of the dataviz world.

Senthil Natarajan (SN): Hey Todd! Let’s get some quick context for readers. Can you tell us a little bit about yourself and your background? How did you get started doing data visualization, and more pertinently in basketball dataviz?

Todd Whitehead (TW): Until recently, I was working in the School of Public Health at Cal-Berkeley, first as a PhD student then as a postdoc and ultimately as an assistant researcher. Academic work calls for writing scientific papers and making conference abstracts, most of which tend to contain a graph or two, so I’ve had some dataviz practice along those lines. At the same time, I was making freelance contributions to Nylon Calculus, a basketball analytics site for nerdy NBA fans.

That experience gave me a chance to bounce dataviz ideas off the likes of yourself, Bo Schwartz-Madsen, and Seth Partnow in an environment that encouraged creativity and experimentation under Ian Levy’s guidance. It also gave me a platform to share my early dataviz work with the public to get feedback so I could iterate and improve. Then, last summer, I started a new career at Synergy Sports, a division of Sportradar, where I am helping coaches and scouts to develop winning team strategies as part of the Analytics and Insights Team. I’m still using my dataviz skills to surface actionable insight but now it’s in the field of sports instead of the field of public health.

SN: Of course now with your job at Synergy, there’s a lot more, let’s call it “standard”, types of data visualization that you have to do for your job. But we’re not here to focus on that. You’re really popular for doing very creative dataviz on Twitter. What are some of the favorite visualizations you’ve done?

TW: Last year, I had a lot of fun experimenting with physical dataviz. One of my favorites was a 3-D model of Steph Curry’s record-breaking 2974 career three-pointers, inspired by the shot charts Nathan Yau created with a 3-D printer on his Flowing Data blog. I stacked up one tiny plastic tray for each trey Curry had hit in his career to break the NBA record. I wanted to pay a proper tribute to his achievement because I’ve had so much fun watching him play over the years. It was Curry and the Warriors that pulled me back into following the NBA with their joyful style of play and that led me to Nylon Calculus and eventually to a job in sports! The project ended up being the most time-consuming dataviz I’ve ever tackled — I was trimming wire hangers for rebar, laying down a model basketball court, and lovingly arranging each tray in its precise spot — it took me weeks to get the whole thing set up and I was worried about it all toppling over the whole time!

SN: Do these public dataviz differ from ones you do more privately for your job? Or are they similar? How does your thought process differ when considering possibly different audiences?

TW: Definitely. I think knowing your audience is key. I try to organize my dataviz process along two axes, from practical to artistic, and from simple-to-read to intricate. Viz that was meant to be practical but ended up being intricate is what I consider the stuff of first drafts — a project that needs to be improved before it’s finalized. Projects that are artistic and simple-to-read, like the 3-D Curry shot chart was meant to be, are perfect for Twitter. They’re fun to look at and they will catch a viewer’s attention as they scroll through their feed.

Projects that are artistic and intricate also have a time and place, but they ask a lot of the viewer. So, chances are you’ll be left with a smaller, niche audience that is passionate about the topic you’re covering. It may take a second to unpack a more intricate viz like this but there’s ultimately potential for a bigger payoff (these projects can be rewarding for the viewer and the creator). But the practical and simple-to-read space is the place I want to live at Synergy. I want to make visuals that provide actionable insight to coaches which are engaging and intuitive. At work, it’s less about pushing the boundaries and being eccentric, and more about making something that is reproducibly useful.

SN:  How do you push yourself to new frontiers of creativity or visual communication? What spurs the continuous process of growth and learning as a dataviz practitioner for you?

TW: I really like to tinker and I get a kick out of trying new things with my charts. Sometimes the tinkering is bad and I make what could have been a simple chart into an overwrought, self-indulgent mess. That’s always a bummer. But, hopefully, sometimes the tinkering can be good or, at least, point in the direction of something that could be good. And that’s a really invigorating feeling! I find it a drag to make the exact same chart over and over again. So I chase that buzz you get from creating something good that is a little bit distinct from what has been tried before.

SN: The theme of this edition is “inspiration.” I recall you having a series of physical visualizations, made by stacking coins, cutting neck ties, etc. What inspires you to these ideas? What’s your muse?

TW: I really enjoy collecting my own data to visualize, so my 40 Years of Draft Fits viz is another of my favorites. For that project, I watched every NBA draft selection show since 1982 and recorded the features of each lottery pick’s outfit: the color and trim of his jacket, the number of buttons, and what type of tie he was wearing. Then I used fabric and buttons to create charts that showed the NBA fashion trends over the last 40 years.

That particular series of charts was inspired by Mona Chalabi. She has this one fantastic visual that stuck in my brain titled For every $1 a white man earns… which was a bar chart made out of folded dollar bills. It’s a physical representation of data that is so unique and fresh. In general, I love her hand drawn aesthetic as a way to draw a viewer in, and make the experience more personal. I wanted to see if I could capture some of that feeling with physical viz on my favorite topic — basketball!

SN: Speaking of sources of inspiration, are there other people or resources you look towards or have been able to learn from? Do you sometimes try to incorporate their various styles into your own work?

TW: Definitely. I have had the good fortune to write a few articles for the FiveThirtyEight sports section. They have a great graphics team and a style that I really appreciate. Their fonts and layouts are always super clean looking and when you go through the editorial process with them you begin to notice the consistency of their format, like their headlines and subheadings. That’s something that I borrowed from them. I know that sounds really basic but it’s actually a super helpful practice!

I like to follow what people are doing in other sports and bring back my favorite pieces of their charts into the basketball world. I’ve also taken inspiration from a few particularly great books with fun visualizations: FreeDarko Presents – The Undisputed Guide to Pro Basketball History has an awesome mix of playful data viz-slash-illustration put together by Jacob Weistein. Dear Data is a lovely data viz postcard correspondence between Stefanie Posavec and Giorgia Lupi that experiments with data collection and data viz in creative ways. I always like flipping through both of those books to look for ideas. Generally, I just try to be open to visual inspiration. I think, once you start making a lot of charts, you sort of get tuned into finding elements that could go into future charts, whether that’s at a trip to the museum or just in watching a TV commercial or reading a magazine ad or whatever. You just start paying more attention, and it becomes a habit.

SN:  Let’s close out with a more philosophical consideration of data visualization in sports. What do you think is the next frontier for data visualization in sports (or basketball, if you want to be more specific) media?

TW: I think there is a lot of exciting stuff on the horizon for dataviz in sports media. I think when you look at the popularity and ubiquitousness of baseball’s launch angle and exit velocity graphics to characterize home runs, you can see there is an appetite for new types of data among sports fans, assuming that the information is interesting and that it adds something to the viewing experience.

Oracle’s Ariel Kelman talked at the Sloan Sports Analytics Conference about how the future of media is personalized fan experiences where viewers can decide for themselves how much data they want to see on their screens. I think you can already see that type of personalization happening in the NBA coverage, whether it’s Danny Leroux and Nate Duncan creating their own simultaneous version of the broadcast or Nekias Duncan and Steve Smith with theirs. In the future, people will be able to control how they watch the game, and some of those people will want the most in-depth, data-heavy version of a broadcast possible. I think player tracking data (and in a few years, joint-tracking data) will create a ton of fun content for that type of fan. I’m excited to see what sort of viz we can make with that new data!

The post Meet the Data Visualization King of Basketball Twitter: Todd Whitehead appeared first on Nightingale.

Visualizations, in the form of charts, graphs, or pictures, can help communicate abstract or complex ideas to the public in a simple form. Visualizations can be leveraged to provide direct links between people’s daily lives and climate change. This may help to motivate the public to take action towards more sustainable behavior. Research has demonstrated that using past events, we can help pre-experience possible future events. We can leverage visualizations to provide examples of past climate change and help reconstruct what the climate will look in the future. A hands-on approach where people can build visualizations themselves, or interact with them, may help convince them to take climate change mitigation more seriously. In this article, I will demonstrate potential visualizations that may help to shrink the psychological distance of climate change. 

Carbon dioxide concentrations

Carbon dioxide is the major greenhouse gas responsible for increasing the average temperature of the Earth. Its concentration has continued to increase since the pre-industrial era, thanks to the burning of fossil fuels. One climate-change visualization that is widely popular among the public is the Keeling Curve, which depicts the increase of carbon-dioxide concentrations in the atmosphere from 1958 to the present day. The curve is named after the famous scientist Charles David Keeling, who began the monitoring program on which it is based. The Keeling Curve has often been hailed as one of the most important scientific works of the 20th century. The curve depicts daily atmospheric carbon-dioxide concentration data collected by Hawaii’s Mauna Loa Observatory. This data is available as a public service to those who are interested in climate change. I was able to make use of this data to create my own version of the Keeling Curve in R. Click here to learn how to create the Keeling Curve in R.

Evidence of climate change in glaciers

There is much evidence of climate change, but its effects are most prominently seen in glaciers. Glaciers are huge masses of ice that move slowly due to the influence of their weight and gravity. Because of climate change, many glaciers around the globe are retreating (reduction of size by melting) at an unprecedented rate, which leads to sea-level rise. The United States Geological Survey (USGS) estimates that the sea level would rise by approximately 70 meters if all the glaciers of the globe were to melt. Thanks to satellites such as Landsat, which is operated by the National Aeronautics and Space Administration (NASA) of the United States, it is possible to compare changes in the size and volume of individual glaciers over time. Almost all of the glaciers in the United States’ Glacier National Park have lost a significant portion of their surface area, with some having lost close to 80 percent of their area. Using publicly-available spatial data from the USGS, I created an application where users can manipulate a slide bar to see the changes in individual glaciers at Glacier National Park between 1966 and 2015. Click here to interact with the application.

Land-use and land-cover change

There are many actions that lead to emissions of greenhouse gases into the atmosphere, and one of the most prominent of these is land-use and land-cover change. The IPCC’s 2014 report estimated that land-use change is responsible for about a quarter of human-induced greenhouse gas emissions. Forests, grasslands, and peatlands sequester a massive amount of carbon from the atmosphere, and cutting or clearing them for human development releases trapped greenhouse gases into the atmosphere. Over the progression of time, forest land has been cleared at an alarming rate to make room for more human settlement and agriculture. While many imagine land-use clearing as taking place in developing countries with dense rainforests, this effect is also pronounced in developed countries such as the United States. Using ArcGIS online and a publicly-available dataset, I created an application that shows the change in land cover around the globe. The map is zoomed in to the city of Phoenix in Arizona, one of the fastest-growing cities in North America, but you can pan anywhere in the world to see how land cover has changed historically. Click here to interact with the visualization.

Projected future increase in temperature

The Intergovernmental Panel on Climate Change (IPCC) report (2018) mentioned with high confidence that global warming is likely to reach more than 1.5° C between 2030 and 2052. The IPCC recommended limiting global warming to 1.5° C and stressed the importance of taking unprecedented actions in all aspects of society to do so. While global warming means that the average temperature of the Earth is increasing, not all places will bear this rise in temperature equally. This is due to many factors including the diverse geography (oceans, mountains, and polar regions) of the earth. Climate scientists use Representative Concentration Pathways (RCP) in their models, to estimate future increases in global temperature. RCP is the ratio of energy absorbed by the Earth’s atmosphere over energy reflected back into the atmosphere. Increases or decreases in greenhouse gases in the atmosphere cause fluctuations in RCP. Climate scientists use different scenarios of greenhouse-gas emissions to estimate the future change in the Earth’s temperature. Using an available raster dataset from ESRI, I created an application that shows projected increases in the temperature of the Earth at different locations. Users can search their address in the address bar and learn about projected temperature increases in that place. Click here to interact with the visualization.

Natural calamities

The impacts caused by extreme global warming will be long-lasting and could be irreversible, causing the loss of many important ecosystems around the world. Natural disasters have already claimed the lives of thousands of people and destroyed billions of dollars’ worth of property. Scientists are worried that the frequency and intensity of natural disasters will increase in the future due to climate change, and that this will severely impact human life and property. Using the publicly-available dataset from the National Oceanic and Atmospheric Administration website, I created a dashboard in Tableau that depicts the estimated total number of deaths, injuries, and total costs of natural disasters from 2011 to 2020 in the United States. Users can filter out the data by State, Year, or both. Dashboards like this one can help the general public understand how much is lost because of natural calamities and encourage more action to mitigate climate change. To interact with the dashboard, click here.

Conclusion

Climate change is a global issue that needs to be addressed as soon as possible. Carbon dioxide is the main greenhouse gas responsible for climate change and its emissions should be reduced drastically to mitigate climate damage. Many people, however, are not concerned about climate change. This lack of concern may be caused either by a bogus understanding, or a lack of concern about the potential consequences of its effects. Even among those individuals who believe in climate change, there are many who do not perceive it as an immediate threat. To address this issue, I experimented with a range of data visualization approaches to illustrate and personalize the impacts of climate change.

I used the Keeling Curve to illustrate the consistent upward climb of carbon dioxide concentrations in the atmosphere. Similarly, I applied a slider in an application to demonstrate glacier retreat in a much-beloved national park where users can compare the past and the present status of glaciers. This will help in educating themselves about the consequences of climate change. I personalized a time-series application of land cover change to allow users to “see” prospective impacts on geographies of importance to them. I also quantified the cost of lost lives and property in financial terms in a Tableau dashboard. These interactive and personalized visualizations attempt to make climate change individually meaningful — more “real” — and motivate planet-saving action.

The post Using Infographics to Make Climate Change More Visible to the Public appeared first on Nightingale.

Embracing empathy in data and data visualization is a key dimension for people working with data to help put their work into the hands of policymakers, stakeholders, and community members who can use it to affect change. Inclusive and thoughtful data visualization that respectfully reflects the people and communities of focus can also help researchers build trust with those communities.

We think of empathy as it applies to communicating data across six main themes:

1. Put people first. First and foremost, we need to remember and communicate that the data shown reflect the lives and experiences of real people. Data communicators must help readers understand and recognize the people behind the data.

2. Use personal stories to help readers and users better connect with the material. Pairing data-driven charts with personal stories centered on individual experiences can help readers understand and identify with the people represented in the research and data visualizations. Techniques that can be used in tandem with data visualizations to help lift up personal stories include photography, illustrations, pull quotes, and oral histories.

3. Use a mix of quantitative and qualitative approaches to telling a story. Most charts and graphs are built on top of spreadsheets or databases of quantitative data. However, focusing on numbers alone without any context can overlook important aspects of a story including the “why” and the “how.”

4. Create a platform for engagement. This can take the form of interactivity in which users are able to manipulate buttons, sliders, tooltips, and other elements to make selections, filter the dataset, or create customized views of a chart. Such engagement can be leveraged as a way to allow users to find themselves in the data or discover the stories that most interest them. Another form of engagement is offering audiences a means of providing feedback about a data tool or visualization.

5. Consider how your framing of an issue can create a biased emotional response. Carefully consider how the data you visualize presents a particular perspective on the content. Take the examples ProPublica journalist Lena Groeger discusses in this post on different ways to visualize the impact of crime on local communities. Maps that show the locations of where crimes occurred versus maps that show the percentage of residents in a neighborhood who were in prisons are two different ways to visualize data related to the criminal justice system. What data we choose to focus on and what we choose to ignore can bias our audiences’ perceptions of the issues about which we are communicating.

6. Recognize the needs of your audience. Taking an empathetic view of the readers’ needs as they read or perceive information is an important step to better data communication. This kind of empathy can also be couched in terms of producing visualizations that are accessible by people with vision, physical, or intellectual impairments; reducing overly technical or jargon-laden language; and translating your work into languages most used by your target audiences.

Being empathetic to the people and communities of focus does not imply sacrificing the data and methods used in responsible, in-depth, sophisticated research. In fact, the opposite is true: high-quality research and empathy for people and communities can be complementary. Effective research necessarily means understanding someone else’s point of view nonjudgmentally and recording that perspective as accurately and truthfully as possible. Empathy underlies research and data visualizations that uphold diversity, equity, and inclusion, so data communicators should seek to find ways to help their audiences understand and connect with the people that the data represent.

 Read the full Do No Harm guide here.

The post Six Ways to Bring Empathy into your Data appeared first on Nightingale.

It may seem like semantics, but it is a matter of focus that broadens the scope of your practice. This outcome-oriented mindset is a different lens through which to examine your work. The desired outcome should always be kept in mind, especially when it’s time to design projects about hyperobjects. Coined by the philosopher Timothy Morton, the term is a neologism to refer to all those phenomena which are too complex to be fully grasped by humans:

“I can’t see it. I can’t touch it. But I know it exists, and I know I’m part of it. I should care about it.” — Timothy Morton, 2014

Among the hyperobjects, Morton identifies Global Warming — he refuses to call it Climate Change — as the most significant. Its global and multi-layered nature makes it feel incredibly hard to impact, especially as an individual.

Then Morton continues describing hyperobjects:

“On the one hand, we have all this incredible data about them. On the other hand, we can’t experience them directly. [..]. So we need philosophy and art to help guide us, while the way we think about things gets upgraded.” — Timothy Morton

It is true. We have plenty of climate data if you bear in mind that, for instance, the first data collection on CO2 production started in 1958 due to Charles Keeling’s work. Still, it is only in the last few years that the climate issue has been highly debated and discussed publicly, thanks also to the availability of climate open data. We are not artists, nor philosophers, but as data designers, we have the opportunity and the responsibility to metaphorically open the open data, making it accessible and understandable to a wider, non-practitioner audience, utilizing it as a resource for broader climate awareness.

Data enabling

I am drawn to dataviz that moves beyond mere visualization, which relies on storytelling and interaction design hybridization. I call those data-enablingviz. These viz produce experiences that are not limited to seeing data, but which aim to connect the audience to the phenomena, build new knowledge, and finally raise their graphicacy level, explaining what the viz is showing. In other words, data enabling helps audiences approach hyperobjects.

Over the last few years, there have been three climate dataviz that especially impressed me. The first was the 2006 epic Al Gore elevator chart designed for the documentary An Inconvenient Truth, who remembers it?

Then, my list continues with the NYTimes project “How much hotter is your hometown,” followed by the Ed Hawkins stripe visualisation. I love these last two because readers can get personalized visual stories about their places. For instance, in the NYTimes project, “How much hotter is your hometown,” readers are asked to enter their own information to see the climate trends of their places, places they are connected to, such as their birthplace or the place their grew up, studied, or where they had fond memories. It is a clever user engagement strategy. First, it establishes an emotional connection between readers and their places, and then it builds a personalized narrative upon that connection. People, or at least me ?, are more open to continuing to read once they realize you are talking about something they care about.

Glocal Climate Change

In my role as designer at Sheldon.studio, we had the chance to design a project on climate change, thanks to our partnership with the European Data Journalism Network (EDJNet). They asked us to visualize Copernicus’s EU program UERRA dataset, which features 40 years of temperatures from all over the European territory. Our design goal was to raise awareness among EU citizens.

We were inspired by the techniques used in the projects above to tap into the nostalgia audiences have for their places, by revealing the impact of climate change in those places. Upon review, we realized that the original dataset was about air temperatures, without a direct link to the municipalities ?‍. Then, EDJNet did incredible work to map land temperatures to the 100,000+ European municipalities, and they also published an interesting article about their methodology. Once we visualised the resulting dataset, we were impressed by its granularity; we had an incredible data storytelling weapon in our hands! An incredible climate portrait emerged from hundreds of thousands of dots. Traditional climate maps tend to flatten data, clustering it according to countries, regions, or provinces.

Relying on granularity instead, allowed us to identify some red hot spots, usually surrounded by yellow/orange ones. It was clear that each place’s climate results from many variables, such as the land use, the population amount and density, the altitude, the terrain, the proximity to water, and so on.

We spent hours exploring the dot-verse we created ??.
Apart from that data-porn moment ?, we realised the map was impressive, especially highlighting specific situations that would risk being flattened in traditional average-based visualizations. At the same time, we worried that readers might get lost in the climate dot-verse, distracted by the aesthetic experience instead of looking for and caring about their places’ climate. To address this concern, we thought of connecting a further information layer, supplying the climate change behind every single dot. We designed an informative page that metaphorically tour guides them through the story as soon as they click on the chosen dot. The page, which relies on a mix of data-generated storytelling and scroll-based interactions, reveals how the climate of a determined municipality changed over the last 40 years. The narration connects the location with the global climate. First, it compares the selected municipality climate with others nearby, then with the province, and finally, with its region. To foster a deeper reader immersion, we also designed a Wikipedia-like approach. All the places in the narration cross link to their respective pages on the platform, with the idea to foster associative navigation across places.

Some screens of example informative pages.

Activate your readers

Once we finished designing the informative page, we conducted internal tests to evaluate its effectiveness. It worked, perhaps too well ?. We experienced a shared feeling of discouragement after reading that the climate of the places we cared about was increasing. Unfortunately, there are no immediate and effective solutions to climate change other than activating better-informed debate and broader awareness. Reflecting on that realization, we thought of supporting readers’ digital activism, providing them with tools to enable climate conversations or raise awareness among their social bubbles. We built every page to close with an informative thumbnail, ready to be shared across social networks. We generated more than 100,000 pictures that contained the name of the place, followed by the amount the climate has changed in the last 40 years. Readers were invited to share them to raise awareness among their social networks.

The impact

Despite our ironic and funny campaign to promote the project, none of the international politicians, VIPs, and influencers we tagged in our Instagram posts answered our calls… ??

Luckily, the data-enabling project we designed worked better than our social campaign ?. In fact, we started collecting posts from people or local activist groups, all over Europe, who were sharing their places’ climates on their profiles. This trend was also confirmed by our online analytics: there wasn’t one page that was more visited than others, but thousands of visits spread across as many thousands of pages, each with few visits, if any at all. This trend indicates the behavior we were trying to drive: every single municipality is a page, so thousands of visitors were searching for their individual places ?!

What’s next?

I hope to have conveyed the most important lessons we learned during our Glocal Climate Change design process. Based on its effectiveness, we will prioritize developing similar strategies to “hook” the readers in the next projects or leave them some tools to support them in their digital debates and promote the project. Obviously, there are opportunities for improvement on future projects. We would extend the reach with more languages. We would also update the data yearly, to turn Glocal Climate Change into a real digital resource to support broader awareness and more informed debate.

Moreover, it would be great to add a further layer of information to visualize different data connected to each place, data representative of other factors which contribute to its climate situation. The population amount and density, the cementing, the altitude, the land use, or the geographical configuration are some of the several factors that determine climate change, and that we should care about, as citizens, political leaders, or simply humans, for our future. Tomorrow will be like today, but 0.001 degrees hotter, probably ?.

The post Data Enabling: the Glocal Climate Change Design Process appeared first on Nightingale.