Skip to main content

3D Map in Excel

3D Maps lets you see five dimensions: latitude, longitude, color, height, and time. Using it is a fascinating way to visualize large data sets.

3D Maps can work with simple one-sheet data sets or with multiple tables added to the Data Model. Select the data. On the Insert tab, choose 3D Map. (The icon is located to the right of the Charts group.) If you have Excel 2013 you might have to download Power Map Preview from Microsoft to use the feature.

3D Map is a new icon to the right of the Charts group on the Insert tab.

Next, you need to choose which fields are your geography fields. This could be Country, State, County, Zip Code, or even individual street addresses.

You can choose to map by Street Address, Zip Code, State, Country, and so on.

You are given a list of the fields in your data set and drop zones named Height, Category, and Time.

The Height drop zone is sales amount. The Category drop zone contains housing allotment. The Time drop zone contains Sales Date.

Hover over any point on the map to get details such as last sale date and amount.

In the default state of 3D Maps, each data point occupies about one city block. To be able to plot many houses on a street, select the Gear Wheel, Layer Options and change the thickness of the point to 10%.

To get the satellite imagery, open the Themes dropdown and use the second theme.

3D Maps provides a completely new way to look at your data. It is hard to believe that this is Excel.

Here is a map of Merritt Island, Florida. The various colors are different housing allotments. Each colored dot on the map is a house with a dock, either on a river or one of many canals dredged out in the 1960s and 1970s.

14 housing allotments are plotted in different colors. In this view from directly overhead, you can't really make out the height of the columns. This view is from March 2013.

Using the time slider, you can go back in time to any point. Here is the same area at the time when NASA landed the first man on the Moon. The NASA engineers had just started building waterfront homes here, a few miles south of Kennedy Space Center.

Drag the time scrubber back to December 1969 and only 2 of the housing allotments are fully built out. A few scattered houses in newer allotments are just starting to appear.

Use the wheel mouse to scroll in. You can actually see individual streets, canals, and driveways.

Zooming in, you can start to make out individual houses and see some of the sales price height.

Hold down the Alt key and drag sideways to rotate the map. Hold down the Alt key and drag up to tip the map so your view is closer to the ground.



Comments

Popular posts from this blog

Why Every BI Professional Needs to Learn Agentic AI in 2026

Meta Description:  Agentic AI is transforming business intelligence. Learn why BI professionals must embrace autonomous AI agents to stay relevant — with practical examples, skills to build, and a BI Lead's honest perspective on the shift. Tags:   Agentic AI  ·  Business Intelligence  ·  Power BI  ·  AI Agents  ·  Data Analytics  ·  Future of BI  ·  Career Growth Let me be blunt: if you're a BI professional in 2025 and you haven't started paying attention to agentic AI, you're already behind. I'm not saying that to scare you. I'm saying it because I've spent over a decade building dashboards, tuning SQL queries, and wrangling Power BI data models — and nothing in my career has shifted the landscape as fast as agentic AI. Not self-service analytics. Not cloud migration. Not even the first wave of AI/ML. This is different. And here's why. What Exactly Is Agentic AI? Forget the chatbot hype for a second. Agentic AI refer...

From Dashboards to Decisions: The Evolution Toward Agentic AI

  For years, dashboards and predictive models have played a critical role in helping organizations understand what happened and anticipate what might happen . They remain essential for visibility and strategic insight. However, decision-making in many cases is still manual—requiring teams to interpret data and take action separately. This is where we’re seeing the emergence of Agentic AI. 👉 The shift is not about replacing dashboards, but extending them into action . Agentic AI brings together: 🧠 AI reasoning (LLMs) to understand context 📊 Mathematical optimization to ensure decisions are feasible ⚙️ Autonomous agents to execute tasks across systems For example, instead of only highlighting a supply chain delay, an agent-enabled system can: ✔ Recalculate plans ✔ Adjust resources ✔ Update operational systems ✔ Notify stakeholders —all with minimal manual intervention. 💡 The evolution looks like this: Dashboards → Visibility Optimization → Better decisions Agentic A...

Agentic AI in Enterprise Decisions: From Insights to Autonomous Execution

  Agentic AI in Enterprise Decisions: From Insights to Autonomous Execution For a long time, organizations have depended on dashboards to understand past performance and predictive models to estimate future outcomes. While these tools improved visibility, they stopped short of actually making decisions. Humans still needed to interpret results and manually apply changes within operational systems like ERP or scheduling platforms. This disconnect between insight and execution often introduced delays and errors. In today’s fast-moving environment, identifying an issue is no longer sufficient—organizations must respond instantly and effectively. We are now witnessing a shift toward a more advanced form of decision intelligence, where systems don’t just analyze but also act. By 2026, enterprise operations—especially in supply chain, manufacturing, and workforce management—are increasingly driven by systems that detect disruptions, compute optimal responses using mathematical logic, and...