At Foodtruck.ai we encounter tons of geospatial data from our business clients. It has never been easier from a technology perspective to throw data on a map however from a visualization perspective the outcomes are not always home runs. Storytelling With Data has a wonderful writeup about some of the challenges with using maps.
Commercial Real Estate (CRE) professionals in particular love maps. They crave all the goodness of Google Maps but with their data layered on.
Real Estate data is naturally geospatial which is good for a map, but there are so many different types of professionals (investors, lenders, borrowers, underwriters, property managers, brokers, appraisers, etc) that understanding the nuances of what each person wants to see or do on that map is an almost never ending task. Complicated by an equally long list of possible data points to display.
A helpful abstraction is to think of geospatial visualization from the perspective of the three spheres of Data Literacy - we can assume that every map user is more skilled\focused (expert) in one of these circles and less skilled\focused (learning) in another.
In each sphere certain characteristics of the data (metadata) will be relevant to the context displayed on the map.
An intern assembling data who may not understand what the data represents - they are usually finding\building a dataset for someone else to reason on. They are exploring. They are rendering the results of a search or filter. The map may inform them that the firm does in fact own a lot of properties in Cincinnati and they want to explore more there. Quality, coverage and reporting frequencies are of paramount concern when crafting a dataset.
An analyst drawing inferences is already intimately familiar with the dataset loaded into the map - they aren't exploring the dataset but rather they are reasoning and looking for patterns. The map will often need to be accompanied by more detailed data\visualizations to give the analyst enough context. This is where Storytelling With Data's MLB team payrolls on Opening Day 2022 example nails it. This is the most common kind of visualization you see in real estate tools and they can be confusing. Many tools will combine a map with sidebars\overlays to include additional context like this one.
An executive looking to prepare easily digestible insights. They already know the story they want to tell and need to express it in the simplest way possible. They want a screenshot that can be included in a deck. Visualizing how values change over time is one feature of executive storytelling that can be tricky to combine with geospatial.
For Real Estate time is one of the most difficult pieces of context to include in a geospatial visualization. The reporting frequencies of datasets for large buildings can be much more varied then many other industries (stocks, marketing, etc). Data will report daily, weekly, monthly, quarterly, yearly, every few years and so lining up datapoints by time to compare apples to apples is paramount.
For example, if my house was appraised yesterday for 200k and my neighbor's house was last appraised for 190k I may draw one conclusion. But when I find out my neighbors house was last appraised three years ago I may draw a different one.
Choose one sphere of data literacy and demonstrate a creative rendering of the sample CRE dataset on a map that is optimized for a user who is highly skilled/focused in that sphere. The visualization must include time as context for the data. Explain your choice in your submission.
Submission deadline is July 13.
Email submissions to email@example.com.
Submissions will be judged based on how well the visualization embodies the spirit of the data literacy sphere in which it targets.
Submissions do not have to include working code - static visualizations are fine.
Winner’s of the June 2022 challenge will receive:
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