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As a geo explorer, I would like to visualize geographic data on maps so that I can better understand the data's spatial dimensions #424

@jasonlally

Description

@jasonlally

Maps are an important component for visualizing the City's open data. About 60% of our currently available data has geographic fields and an additional number reference geographic boundaries (supervisor districts, census tracts, etc.) or lines (street segment ID, block ID).

We made a strategic decision not to pursue building out the mapping features first as there are many dimensions to consider and we didn't want to spread the team too thin building out features. But we'll want to prioritize this as one of the major new features for a future release.

This user story will likely get broken up into more specific user stories as we do user research.

Some considerations for future design sessions:

  1. How to properly handle the user experience around large datasets (hundreds of thousands or millions of points). There are a range of things we can do here that include client side and server side architectures. We'll want to balance the effort with the value we think we can provide
  2. How to handle datasets that simply reference a geographic boundary, but may not have geographic fields. We'll want to design scalable ways to identify specific fields as geographic references and tie them to that reference dataset.
  3. How to handle potentially mapping data by arbitrary boundaries. Depending on the user research, we may want to provide a means to use any boundary dataset to create a choropleth map, for example.
  4. How to gently allow users to switch mapping contexts within and among datasets. We'll want to consider in the designs how one might move smoothly from categorical mapping to visualizing points.
  5. And more...

The key challenge with maps is that the experience can vary based on the underlying data. Despite this, we'll want to make sure that the user experience varies in consistent and predictable ways. We can make this problem smaller through additional user research and prioritization.

Feature requested by

  • DBender

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