Design

Walkable neighborhoods aren’t just trendy. They’re the backbone of a more affordable, connected, and equitable city. It’s time we rethink how we build. Walkable neighborhoods aren’t a lifestyle trend or a luxury. They are a strategy for community wealth, affordability, and resilience. Here’s why: -Households in walkable communities save up to 20% on transportation costs. That’s $5K–$10K per year back in their pockets. -Local businesses see more foot traffic and thrive when customers can walk instead of drive. -Lower emissions. Safer streets. More public space. -People actually know their neighbors. Yet, we still build for cars first. We prioritize parking lots over parks, highways over homes. If we want cities that work for everyone, we have to start with small-scale, mixed-use, walkable development. The kind that builds wealth at the community level, not just for big developers. This isn’t about trends. It’s about priorities. What’s one thing you think every city should do to build more walkable, affordable neighborhoods?

Researchers at the Universitat Politècnica de Catalunya trained neural networks on thousands of archival sketches and photographs of Gaudí’s buildings. The models can now “guess” how an unfinished curve might wrap into a column or how a roofline could rise if the architect had kept sketching. In blind tests, some architects rated these AI completed designs as more imaginative than Gaudí’s published drawings. Apple’s recent GAUDI research project also converts a single line drawing or a short text prompt into an immersive 3D scene you can walk through in VR. The system combines two AI techniques: - Diffusion models to fill in missing visual detail, and - NeRFs (Neural Radiance Fields) to map those details onto a volumetric space. The result is a room or an entire facade that you can orbit around, inspect from new angles, and even relight. Digital artist Sofia Crespo recently projected AI generated marine patterns onto the undulating facade of Casa Batlló in Barcelona. Interactive projections and VR tours make a 19th century visionary feel contemporary to new audiences. Gaudí sketched in silence; today, algorithms can let those sketches speak - and even improvise. If you could breathe new life into any historical artwork or structure with AI, what story would you retell? #innovation #technology #future #management #startups

Could you build an entire town from scratch? Casey Roloff did. Here's how Seabrook survived when other developments crashed (without over-leveraging). I just talked with Casey Roloff about Seabrook, Washington. A town he built from scratch over 20 years. His approach flips everything we know about development upside down. Here's why it matters: He moved slowly when others rushed. He avoided high leverage when banks laughed. He lived in his own development when most developers don't. And it worked. The town survived the 2008 crash when others failed. Now it's 85% complete and thriving. Why this matters: He's not building a vacation spot. He's creating a blueprint for how we'll all live next. Casey calls it "The New American Dream": Old way: • 2,000 sq ft houses • Huge lots • Long commutes • Nobody knows neighbors New way: • 1,400 sq ft townhomes • Walk to everything • No car needed • Real community "People walk around here asking why everyone is smiling," Casey told me. "They think we've hired actors." But it's just good design at work. The money guys have noticed: Top research now shows buyers want to walk to coffee shops. Big developers are scrambling to figure this out. And Casey's 20-year "experiment" suddenly looks like the future. The big shift is happening now: We're moving from: • 6-8 homes per acre • Car-dependent sprawl  • Isolation by design To: • 15-25 homes per acre • Walkable communities • Built-in connections With 70% of non-homeowners thinking they'll never own a home, we need this shift. We need density that doesn't feel cramped. We need walkability that creates joy. We need towns, not subdivisions. This isn't just nice architecture. It's the answer to our housing crisis, loneliness epidemic, and climate challenges. What do you think? Would you trade square footage for walkability? Full story is linked in the comments.

One of the biggest challenges in data visualization is deciding 𝘸𝘩𝘪𝘤𝘩 chart to use for your data. Here’s a breakdown to guide you through choosing the perfect chart to fit your data’s story: 🟦 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 𝗖𝗵𝗮𝗿𝘁𝘀 If you’re comparing different categories, consider these options: - Embedded Charts – Ideal for comparing across 𝘮𝘢𝘯𝘺 𝘤𝘢𝘵𝘦𝘨𝘰𝘳𝘪𝘦𝘴, giving you a comprehensive view of your data. - Bar Charts – Best for fewer categories where you want a clear, side-by-side comparison. - Spider Charts – Great for showing multivariate data across a few categories; perfect for visualizing strengths and weaknesses in radar-style. 📈 𝗖𝗵𝗮𝗿𝘁𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗢𝘃𝗲𝗿 𝗧𝗶𝗺𝗲 When tracking changes or trends over time, pick these charts based on your data structure: - Line Charts – Effective for showing trends across 𝘮𝘢𝘯𝘺 𝘤𝘢𝘵𝘦𝘨𝘰𝘳𝘪𝘦𝘴 over time. Line charts give a sense of continuity. - Vertical Bar Charts – Useful for tracking data over fewer categories, especially when visualizing individual data points within a time frame.    🟩 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽 𝗖𝗵𝗮𝗿𝘁𝘀 To reveal correlations or relationships between variables: - Scatterplot – Best for displaying the relationship between 𝘵𝘸𝘰 𝘷𝘢𝘳𝘪𝘢𝘣𝘭𝘦𝘴. Perfect for exploring potential patterns and correlations. - Bubble Chart – A go-to choice for three or more variables, giving you an extra dimension for analysis. 🟨 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗖𝗵𝗮𝗿𝘁𝘀 Understanding data distribution is essential for statistical analysis. Use these to visualize distribution effectively: - Histogram – Best for a 𝘴𝘪𝘯𝘨𝘭𝘦 𝘷𝘢𝘳𝘪𝘢𝘣𝘭𝘦 with a few data points, ideal for showing the frequency distribution within a dataset. - Line Histogram – Works well when there are many data points to assess distribution over a range. - Scatterplot – Can also illustrate distribution across two variables, especially for seeing clusters or outliers. 🟪 𝗖𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 𝗖𝗵𝗮𝗿𝘁𝘀 Show parts of a whole and breakdowns with these: - Tree Map – Ideal for illustrating hierarchical structures or showing the composition of categories as part of a total. - Waterfall Chart – Perfect for showing how individual elements contribute to a cumulative total, with additions and subtractions clearly represented. - Pie Chart – Suitable when you need to show a single share of the total; use sparingly for clarity. - Stacked Bar Chart & Area Chart – Both work well for visualizing composition over time, whether you’re tracking a few or many periods. 💡 Key Takeaways - Comparing across categories? Go for bar charts, embedded charts, or spider charts. - Tracking trends over time? Line or bar charts help capture time-based patterns. - Revealing relationships? Scatter and bubble charts make variable correlations clear. - Exploring distribution? Histograms or scatter plots can showcase data spread. - Showing composition? Use tree maps, waterfall charts, or pie charts for parts of a whole.

Want more leads from your website? Your homepage is probably missing these six crucial elements. The data doesn't lie: most startups are leaving money on the table with homepages that fail to answer prospects' actual questions. Recently, I read some research where Ekaterina (Sam) Howard analyzed 26 startup homepages reviewed on Wynter’s podcast and discovered most failed to answer these essential questions/concerns: → "How is your product different?" (19/26 failed) → "What does your product actually do?" (16/26 failed)   → "Can you back up these claims?" (15/26 failed) → "Will it really work?" (13/26 failed) → "How does it work?" (13/26 failed) → "Show us your product!" (12/26 failed) These aren't just random questions. They're the exact objections preventing your visitors from converting. I've found the most effective homepages address each of these concerns head-on: 1. Differentiation: Frame the problem, then introduce your solution as the breakthrough alternative. Show the before/after transformation.     2. Functionality: Clearly describe your category, primary use case, and main capability in plain language. If first-time visitors can't explain what you do in one sentence, simplify it.     3. Proof: Include relevant testimonials and quantifiable results. Real customers solving real problems will always outperform empty claims.     4. Credibility: Use social proof, customer logos, and case studies to overcome the "will it work for me?" objection.     5. Simplicity: Demonstrate how easy it is to implement your solution with a simple 1-2-3 process.     6. Visibility: Show the actual product through interactive demos, video walkthroughs, or (at minimum) real screenshots. Which of these six elements is your homepage missing?

At Maven Analytics, we see a TON of reports, dashboards and infographics designed for our monthly data challenges. Here are 5 of the most common data viz mistakes that we see: 🙅♂️ Pies & donuts with too many segments Humans are bad at comparing angles. Use bar or column charts instead, or limit donuts to 2-3 slices max. 🙅♂️ Line charts for categorical comparisons Line charts should be used to show trends. Using them for categorical data (vs. time-series) is misleading, and can suggest relationships or patterns that don't exist. 🙅♂️ Treemaps for non-hierarchical data Treemaps are designed to show hierarchies (like subcategories within categories). For simple categorical comparisons, use a bar or column chart instead. 🙅♂️ Unsorted data Don't expect viewers to make their own visual comparisons. Use intuitive sorting rules to organize your data and tell a clear story. 🙅♂️ Too much noise, too little focus While it's tempting to add background images, complex custom visuals or crazy 3-D effects, remember that effective data visualization is all about minimizing noise and maximizing clarity. Datafam, what other common visualization mistakes have you seen? #data #datavisualization #businessintelligence

Human Inclusion As the political landscape in the U.S. increasingly targets DEI (Diversity, Equity, and Inclusion) initiatives, I find myself reflecting on what this means for people like my daughter Sara, who was born with #Downsyndrome. These debates aren’t just abstract policy discussions—they have real, chilling consequences for individuals who rely on inclusive practices to access the world around them. But here’s the thing: #accessibility isn’t just about including people with disabilities. It’s about including humans. All of us. We are beautifully fragile beings, and our needs evolve over time. Whether it’s navigating technology, using apps, banking, or accessing AI, accessibility ensures that everyone—regardless of ability, age, or circumstance—can participate fully in society. Think about it: as we age, our vision, mobility, and cognitive abilities change. What works for us today might not work tomorrow. Accessibility isn’t a niche concern—it’s a universal one. It’s about designing products, services, and systems that work for all humans, not just some. This isn’t just about disability inclusion or DEI. It’s about human inclusion. It’s about ensuring that everyone can use your product, download your app, bank with your institution, and benefit from the transformative power of AI and technology. When we prioritize accessibility, we’re not just checking a box—we’re building a world where everyone belongs. Let’s not lose sight of what truly matters: creating a future where no one is left behind. Because accessibility isn’t a privilege—it’s a right. And it’s one that benefits us all. 💙 #Accessibility #HumanInclusion #WeAreBillionStrong #HumanInclusion #TechForAll #DisabilityInclusion #A11Y Puneet Singh Singhal Eng. Nabil Eid Jasmin Ambiong Richard Streitz Antonio Vieira Santos Neil Milliken

8 out of 10 analysts struggle with delivering impactful data visualizations. Here are five tips that I learned through my experience that can improve your visuals immensely: 1. Know Your Stakeholder's Requirements: Before diving into charts and graphs, understand who you're speaking to. Tailor your visuals to match their expertise and interest levels. A clear understanding of your audience ensures your message hits the right notes. For executives, I try sticking to a high-level overview by providing summary charts like a KPI dashboard. On the other hand, for front-line employees, I prefer detailed charts depicting day-to-day operational metrics. 2. Avoid Chart Junk: Embrace the beauty of simplicity. Avoid clutter and unnecessary embellishments. A clean, uncluttered visualization ensures that your message shines through without distractions. I focus on removing excessive gridlines, and unnecessary decorations while conveying the information with clarity. Instead of overwhelming your audience with unnecessary embellishments, opt for a clean, straightforward line chart displaying monthly trends. 3. Choose The Right Color Palette: Colors evoke emotions and convey messages. I prefer using a consistent color scheme across all my dashboards that align with my brand or the narrative. Using a consistent color scheme not only aligns with your brand but also aids in quick comprehension. For instance, use distinct colors for important data points, like revenue spikes or project milestones. 4. Highlight Key Elements: Guide your audience's attention by emphasizing critical data points. Whether it's through color, annotations, or positioning, make sure your audience doesn't miss the most important insights. Imagine presenting a market analysis with a scatter plot showing customer satisfaction and market share. By using bold colors to highlight a specific product or region, coupled with annotations explaining notable data points, you can guide your audience's focus. 5. Tell A Story With Your Data: Transform your numbers into narratives. Weave a compelling story that guides your audience through insights. A good data visualization isn't just a display; it's a journey that simplifies complexity. Recently I faced a scenario where I was presenting productivity metrics. Instead of just displaying a bar chart with numbers, I crafted a visual story. I started with the challenge faced, used line charts to show performance fluctuations, and concluded with a bar chart illustrating the positive impact of a recent strategy. This narrative approach helped my audience connect emotionally with the data, making it more memorable and actionable. Finally, remember that the goal of data visualization is to communicate complex information in a way that is easily understandable and memorable. It's both an art and a science, so keep experimenting and evolving. What are your go-to tips for crafting effective data visualizations? Share your insights in the comments below!

At some point in our lives, each of us will likely experience some form of disability, whether it’s temporary or permanent. Whether through aging, injury, illness, or other life events, disability is a natural part of the human experience. Which is why accessibility isn’t just a nice-to-have; it’s a necessity. Building accessibility into our environments, products, and services from the get-go ensures that everyone can participate fully, without barriers. Waiting to make things accessible only when a need arises is too late. It’s also very challenging to make things accessible when they’re already in their final state. When we design with accessibility in mind from the beginning, we create spaces and experiences that work better for everyone. Think of curb cuts, closed captions, and voice-activated technology—tools originally designed for disabled people that benefit us all. Additionally, by prioritizing accessibility now, we’re future-proofing our businesses, products, and services. As the population ages and more people experience disabilities, the demand for accessible solutions will only increase. It’s time for every organization and leader to prioritize accessibility as a non-negotiable part of design. What are your thoughts on this? #Accessibility #Inclusion #Disability