Grist28%

Desperate for shade on your walk? There’s (almost) an app for that. 44%

By Matt Simon44%

6/18/2026, 8:30:00 AM

BS Summary: This article contains 22 faulty reasoning types, including Attempt to Sell a Product or Service, Hasty Generalization, and Appeal to Authority, with Optimism Bias as the most egregious example at 30.8% saturation with 230 hits. Analysis detected 1,695 faulty-reasoning hits from 746 analyzed words, generating a BS Score of 47.2% and a BS Rank of 44% (12,286 of 21,887 articles). This article is better (less manipulative) than 56.10% of the article peer group.

It’s getting increasingly unbearable, even downright dangerous, to walk in cities. 
That’s because of the urban heat island effect: Buildings, sidewalks, and roads absorb the sun’s energy and radiate it back at pedestrians, raising temperatures far above that you’d find in the surrounding countryside. 
If a city like Phoenix doesn’t have enough shade, people can’t move safely by foot, when really we need to help folks do more of that, because ambling improves public health and reduces vehicle traffic. 
If you boot up a maps app on your phone, it provides the most efficient way of getting from point A to point B in a metropolis, but it tells you nothing about the blast furnace you’ll endure along the way. 
A new research project from Arizona State University hopes to fix that with Cool Routes, an online tool that calculates the heat that you might feel along active mobility paths. 
In addition to finding the shortest path, it determines the coolest and shadiest  and therefore safest, thermally speaking  one. 
Though it’s limited at the moment to the ASU Tempe campus, the researchers are open-sourcing the tool for any city to use, and maybe one day popular map apps will incorporate such data too. 
Your weather app isn’t lying about the warmth, but it doesn’t provide a full picture about the heat load on the human body. 
Air temperature is just one component of how comfortable you feel: Add high humidity, and 80 degrees Fahrenheit feels more like 100, because the extra atmospheric moisture makes sweating less efficient at cooling the body. 
A lack of shade makes the heat feel even worse. 
“That is what makes being in a hot environment dangerous, because we see lower numbers on our phones,” said Isaac Buo, an urban informatics scientist at ASU who co-led Cool Routes with Ariane Middel, director of the university’s SHaDE Lab. 
Take a diversion into the shade and you reduce the heat load by half. 
Instead of simply referencing a thermometer, Cool Routes calculates “mean radiant temperature.” 
It, for instance, determines the shade available from buildings and trees. 
(That’s possible thanks to the United States Geological Survey, which has used lidar technology to map landscapes in extreme detail.) 
“It takes a high-resolution representation of the urban environment, and also the forecasted weather data, for us to simulate the thermal conditions at any given time,” Buo said. 
That time is an important consideration, because walking to work at 8 a.m. will feel quite different than walking to lunch at noon, and not just because temperatures rise throughout the day. 
At high noon, for example, skyscrapers might not provide much shade because the sun is directly overhead, but trees will continue to because they create an overhanging canopy. 
This complexity of the urban environment is why heat exposure differs dramatically not just neighborhood to neighborhood, but even block to block. 
To account for this, Cool Routes factors in heat coming at pedestrians from six directions at any given point and time: From north, south, east, and west, and from above and below. 
The ASU researchers ground-truthed these calculations with a “mobile human-biometeorological cart” called MaRTy (MRT coming from mean radiant temperature), which they rolled along courses suggested by Cool Routes on typical hot summer days. 
In addition to individuals using Cool Routes to find the most comfortable course, cities might use the platform to determine where to prioritize tree planting and create parks. 
Maybe there’s a particularly popular route that people follow from a subway station to, say, a financial district, where adding vegetation might reduce temperatures by several degrees. 
(Really, trees are one of the simplest yet most powerful tools for any city: In addition to providing cooling, green patches provide habitat for animals and absorb stormwater to reduce the risk of flooding.) 
And even though at its core it’s meant to encourage walking, the platform could further support public transportation systems by identifying where bus stops could use some shade for people to wait under.  
While it’s early days for Cool Routes, a future update for map apps might incorporate this kind of data. 
Like they already suggest walking routes with fewer hills, for example, they might one day suggest ones with less heat. 
“If you're willing to make a detour of, say, two extra minutes,” Buo said, “we can get you through a route that is well-shaded.” 
How cool is that? 
Article reasoning-pattern comparisonThis article: 11.4%Matt Simon: 2.9%Grist: 2.2%Confirmation Bias11.4%This article: 0.0%Matt Simon: 0.5%Grist: 0.8%Anchoring Bias0.0%This article: 8.0%Matt Simon: 2.8%Grist: 3.0%Availability Heuristic8.0%This article: 0.0%Matt Simon: 0.8%Grist: 1.0%Representativeness Heuristic0.0%This article: 0.0%Matt Simon: 0.0%Grist: 0.5%Hindsight Bias0.0%This article: 6.3%Matt Simon: 4.7%Grist: 1.3%Overconfidence Bias6.3%This article: 8.6%Matt Simon: 5.6%Grist: 5.0%Framing Effect8.6%This article: 0.0%Matt Simon: 0.5%Grist: 0.7%Loss Aversion0.0%This article: 4.6%Matt Simon: 0.9%Grist: 0.5%Status Quo Bias4.6%This article: 0.0%Matt Simon: 0.0%Grist: 0.3%Sunk Cost Effect0.0%This article: 30.8%Matt Simon: 11.3%Grist: 3.1%Optimism Bias30.8%This article: 2.3%Matt Simon: 0.6%Grist: 2.0%Pessimism Bias2.3%This article: 7.8%Matt Simon: 2.2%Grist: 6.8%Negativity Bias7.8%This article: 0.0%Matt Simon: 1.9%Grist: 1.0%Self-Serving Bias0.0%This article: 0.0%Matt Simon: 0.6%Grist: 0.6%Fundamental Attribution Error0.0%This article: 0.0%Matt Simon: 0.0%Grist: 0.1%Actor-Observer Bias0.0%This article: 0.0%Matt Simon: 0.2%Grist: 0.8%In-Group Bias0.0%This article: 0.0%Matt Simon: 0.0%Grist: 0.2%Out-Group Homogeneity Bias0.0%This article: 7.9%Matt Simon: 2.8%Grist: 1.0%Halo Effect7.9%This article: 0.0%Matt Simon: 0.0%Grist: 0.0%Horn Effect0.0%This article: 0.0%Matt Simon: 0.0%Grist: 0.0%Dunning-Kruger Effect0.0%This article: 2.5%Matt Simon: 1.1%Grist: 1.2%Recency Bias2.5%This article: 0.0%Matt Simon: 0.0%Grist: 0.3%Primacy Effect0.0%This article: 0.0%Matt Simon: 0.0%Grist: 0.1%Blind-Spot Bias0.0%This article: 0.0%Matt Simon: 0.0%Grist: 0.3%Ad Hominem0.0%This article: 0.0%Matt Simon: 0.3%Grist: 0.1%Straw Man0.0%This article: 15.1%Matt Simon: 7.3%Grist: 4.1%Appeal to Authority15.1%This article: 8.6%Matt Simon: 2.8%Grist: 1.4%False Dilemma8.6%This article: 7.1%Matt Simon: 1.0%Grist: 0.9%Slippery Slope7.1%This article: 0.0%Matt Simon: 0.5%Grist: 0.1%Circular Reasoning0.0%This article: 22.4%Matt Simon: 7.2%Grist: 3.9%Hasty Generalization22.4%This article: 0.0%Matt Simon: 0.0%Grist: 0.1%Red Herring0.0%This article: 0.0%Matt Simon: 1.9%Grist: 0.6%Bandwagon0.0%This article: 12.3%Matt Simon: 3.6%Grist: 4.0%Appeal to Emotion12.3%This article: 0.0%Matt Simon: 0.9%Grist: 0.7%Begging the Question0.0%This article: 5.4%Matt Simon: 3.1%Grist: 3.1%Post Hoc (False Cause)5.4%This article: 0.0%Matt Simon: 0.0%Grist: 0.0%Tu Quoque0.0%This article: 0.0%Matt Simon: 0.0%Grist: 0.3%Burden of Proof0.0%This article: 4.6%Matt Simon: 0.4%Grist: 0.3%Appeal to Nature4.6%This article: 0.0%Matt Simon: 1.1%Grist: 0.3%Composition/Division0.0%This article: 0.0%Matt Simon: 0.6%Grist: 2.0%Anecdotal0.0%This article: 0.0%Matt Simon: 0.0%Grist: 0.0%No True Scotsman0.0%This article: 9.1%Matt Simon: 2.4%Grist: 1.7%Ambiguity (Equivocation)9.1%This article: 0.0%Matt Simon: 0.0%Grist: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Matt Simon: 0.0%Grist: 0.1%Middle Ground0.0%This article: 0.0%Matt Simon: 0.0%Grist: 0.1%Personal Incredulity0.0%This article: 0.0%Matt Simon: 0.0%Grist: 0.1%Special Pleading0.0%This article: 0.0%Matt Simon: 0.0%Grist: 0.0%Genetic Fallacy0.0%This article: 5.4%Matt Simon: 1.1%Grist: 0.8%Unattributed Quote5.4%This article: 14.1%Matt Simon: 0.9%Grist: 1.0%Quote-first Misdirection14.1%This article: 7.4%Matt Simon: 6.4%Grist: 2.5%Biased Writer Voice7.4%This article: 0.0%Matt Simon: 2.7%Grist: 1.4%Indoctrination0.0%This article: 0.0%Matt Simon: 0.4%Grist: 1.0%Politically Left Leaning Bias0.0%This article: 0.0%Matt Simon: 0.0%Grist: 0.1%Politically Right Leaning Bias0.0%This article: 25.6%Matt Simon: 1.9%Grist: 0.7%Attempt to Sell a Product or S…25.6%

746 words analyzed.

Speakers

3speakers23%attributed speech571writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 6 words • 100.0% coverageWriter's voice • 6 words • 100.0% coverageWriter's voice • 11 words • 100.0% coverageWriter's voice • 33 words • 0.0% coverageWriter's voice • 35 words • 0.0% coverageWriter's voice • 41 words • 100.0% coverageArizona State University • 30 words • 100.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 34 words • 100.0% coverageWriter's voice • 23 words • 0.0% coverageWriter's voice • 35 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageIsaac Buo • 40 words • 100.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageUnited States Geological Survey • 20 words • 0.0% coverageIsaac Buo • 28 words • 0.0% coverageWriter's voice • 32 words • 0.0% coverageWriter's voice • 28 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 32 words • 0.0% coverageArizona State University • 33 words • 0.0% coverageWriter's voice • 28 words • 100.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 34 words • 100.0% coverageWriter's voice • 33 words • 100.0% coverageWriter's voice • 19 words • 100.0% coverageWriter's voice • 20 words • 0.0% coverageIsaac Buo • 24 words • 100.0% coverageWriter's voice • 4 words • 100.0% coverage
Selected voice

Isaac Buo

100%flagged-word coverage
92 attributed words53% of attributed speech75% writer coverage
0%35.0%70.0%Quote-first Misdirection+62.4 ptsWriter: 7.2%Isaac Buo: 69.6%69.6%Unattributed Quote+43.5 ptsWriter: 0.0%Isaac Buo: 43.5%43.5%Attempt to Sell a Product -28.2 ptsWriter: 28.2%Isaac Buo: 0.0%0.0%Biased Writer Voice-9.6 ptsWriter: 9.6%Isaac Buo: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.