Data Visualization Strategies Using Tile-Grid Maps

BY ZACHARY ROMANO
The United States: the country that moved east to west and got a bit less creative with state boundaries as we got closer to an even more creative idea of Manifest Destiny. What we as a nation are left with today is a national map that gives more visual weight to these larger, dare I say squarer, Western states than their Eastern counterparts. The Northeastern and Mid-Atlantic have a highly populated urban corridor from Boston to Washington D.C. which contain nearly 42 million people while the “Western” states have a combined 72 million people. Journalists at NPR provide a solution to better visually represent the dense populace that is the Northeast and Mid-Atlantic states while, for the first time ever, making Alaska and Hawaii the same size.

Cartograms are a way to geographically represent data of any kind by replacing the area or distance with this new variable. The result would distort the real proportions of U.S. states according to this measure. Similarly, a choropleth map uses shading to proportionally represent data. When these two methods are combined, one can create a map that shows two variables simultaneously. Adam Cole of NPR used this method to show state electoral votes against the amount of ad spending for political parties. These types of maps are useful, depending on the story the data visualizer wants to tell.

A new mapping method has emerged with high-profile news outlets like The New York Times and Bloomberg Business which are using tile-grid maps, which represent each state with a congruent square. It allows for a quick scan of the information and prevents the reader from having to closely inspect the smaller states on the East Coast to decipher the data. Brian Boyer from NPR felt that this could be enhanced and found the square shape to be limiting. His proposal was to generate the same tile-grid template, only this time using hexagons instead of squares as the unit.

SQUARE TILE-GRID MAP. MAP BY DANNY DEBELIUS AND ALYSON HURT.

Hexagons, Boyer argued, allow the data visualizer to maintain the integrity of the U.S. national borders and would make state placement easier. His final hexagon-tile map seems to alleviate his aesthetic concerns of the square map but remains imperfect to the very localized placement of states. In his map, North and South Carolina are side-by-side and Washington D.C. has a full coastline. But in this present big data boom, these strategies are helpful for journalists to tell a clear and concise story visually.

FIREARM DEATH RATE PER THOUSAND MAPPED AS A CHOROPLETH AND A CIRCLE TILE-GRID MAP. MAP: SLEMMA BLOG.

References
Let’s Tesselate: Hexagons For Tile Grid Maps, By Danny DeBelius, NPR Visuals Team


Firearms Deaths, Education, Poverty and The Tile Grid Map, Slemma Blog

Source: GIS Lounge - Maps and GIS

From Big Data to Big Action: Tackling Poverty and Inequality Using Data Visualization

By Matthew Tyler.



“It’s not about big data, it’s about big understanding… Understanding strengthens the link between data tools and policy action.” These were the reflections of Jack Dangermond, Founder and President of the Esri, at the 14th convening of the Project on Municipal Innovation Advisory Group (PMI-AG). Dangermond was joined by leaders from America’s largest cities to discuss how municipal Chief Data Officers can impact policy, emphasizing ways that data – and in particular, data visualization – might be used in the fight against poverty and inequality.

Big data analytics and data visualization have made great strides in recent years. Building and food inspections are better targeted; congestion on roads and public transport is monitored in real time; natural disasters are preemptively simulated; and city “story maps” are used to engage citizens in municipal service delivery, small business decisions, and land use planning. However, the palpable sense of anticipation among PMI participants suggested that using big data and data visualization to tackle deeper systemic issues proves more difficult.


“Understanding strengthens the link between data tools and policy action,” Dangermond said.

With regards to poverty and inequality, progress in data visualization has been largely limited to what several participants described as “sad maps.” Maps for crime rates, school retention, and infant mortality measures all tell similar geographical stories: the darkest shades of the map, indicating the most severe disparities, represent the same few neighborhoods. Participants agreed that all too often these maps prompt concern, but offer few suggestions on how to shape policy.

Over the course of the panel, however, it became clear that several emerging data-orientated approaches have the potential to shape policies that address poverty and inequality:

• Overlay “sad maps” with the existing geographical distribution of service delivery to inform future budget allocations. Several participants are in the process of using maps to determine whether there is underlying discrimination in infrastructure investments.
• Use spatial correlation analysis to identify and understand positive deviance. That is, given what we know about a neighborhood’s context, which areas are performing better than they ought to be? For instance, what can we learn from high poverty neighborhoods where obesity is low?
• Use regression over space and time to identify root causes. In doing so, the most important lead indicators can be used to identify those who may benefit most from early intervention. In Chicago, intensive academic tutoring is targeted at those who fail Algebra 1; the best predictor of school dropout. A similar approach is being employed to analyze patterns of eviction in New York that are most likely to lead to homelessness.

These approaches are only a starting point. To create real change, a symbiotic dance must take place between policy officers and data officers. Policy officers must identify the information products needed to inform decision making. Conversely, data officers need to help point out caveats and suggest how additional analysis could tease out further conclusions. In turn, policy officers complement data analyses with their nuanced research knowledge to build a causative narrative of what are almost always endogenous problems – and this narrative can spring to life with data visualizations or story maps developed in data shops. This is the back and forth of problem solving that translates analysis to action.

Similarly, getting the best out of big data requires engagement with the public. Several Chief Data Officers explained that they spend a lot of time talking with the public to understand what is happening “on the ground.” Data is only one piece of the puzzle. In a bid to share costs and broaden capabilities, a growing number of cities are partnering with leading research universities through the White House backed MetroLab network. It is hoped that these partnerships will underpin the transition of big data and data visualization across mayoral administrations.

Like e-mail, big data and data visualization will likely become ubiquitous. For systemic policy issues though, moving from data to information products and ultimately to policy action is no easy task. The ideas raised during the PMI-AG discussion suggests big data and data visualization have the potential to improve policies targeting poverty and inequality. With that said, there is much work and discovery to come before we can make firm conclusions about their efficacy. In the words of Thomas Edison – “the value of an idea lies in the using of it.”

Source: Public CEO