MapBrief™

“Why Map Portals Don’t Work” is a five-part exploration of why the dominant visual grammar of GIS interfaces serves its public audience so poorly and continues to diverge from the best practices found most everywhere else on the web. Read Part II, Part III, Part IV, Part V.  On February 27th, I will be joining James Fee for an online conversation about this series at SpatiallyAdjusted.com.

It’s been six months since my most popular post How the Public Actually Uses Local Government Web Maps: Metrics from Denver; I’ve been gratified by the feedback.  But despite laying out detailed metrics showing that single-topic maps garner 3x the traffic of traditional portals, that user-friendly text search is critical to the map experience, and that users don’t spend time fiddling with default viewer parameters, I’ve found two particular reactions troubling:

• No one has widely circulated their own web metrics that shine a more positive light on map portals.
• People still keep building public-facing map portals. And writing press releases about them.

            The road to mediocre web experiences is paved with good intentions

For the next few posts, then,  we’ll lay out some major drawbacks of standard web portals as well as suggest a few alternatives along the way.  While the baseline scenario I have in mind are public-facing government mapping portals, those rolling corporate intranet solutions would do well to take heed.

Problem #1: Map Portals Over-Focus on the Map, Under-focus on Text-Based Search and Discovery

The dominant finding from Denver’s metrics is that the public approaches maps to retrieve particular bits of information, and then leave.  And how does everyone do Search & Discovery?  Think of your favorite search engine: you start typing into an auto-suggest box, then you get a text list of possibilities.  Both Google Maps and Bing Maps use a similar visual grammar–search text-box across the top, listings on the left, map on the right.

            Users are familiar with a map that contextualizes text-based search

GIS people all-too-easily lose sight how unfamiliar map navigation is for the general user, especially when confronted with an “immersive” experience.  The text box, then, featured prominently and with auto-complete, is a life-raft of familiarity.  Contrast that to this typical setup that we find in the Lancaster County GIS Property Search site:

          Ye shall be known by your Property Account number

The search box is on the left side of the map, with Account search separate from Address search.  OK, what’s an Account number? Address is familiar, but why is House Number separate from Street Name?  Because, as we can all guess, that’s how it’s stored in the database.  And none of this is auto-complete, so my address-lookup is really an address-guess.  Being a clever sort, I enter “100” “Main” and get 15 possibilities popping up in a Results window on the other side of the map. And only two fields are clearly visible, the Property Account and the Address (which I already entered).  Now this business with the Account number being featured so prominently is starting to make me wonder if this site is really meant for the public or the clerks in the Assessor’s Office who work with Account numbers all day long.  Finally, instead of plotting the properties in the map as obvious, clickable placemarks, they’re rendered as polygons, which, at the zoom level necessary to encompass all 15 possibilities, makes them barely visible.  Which I figured out when I went to remove a smudge from my monitor.

We’re not pointing fingers at Lancaster County here, because these are the same choices GIS people inflict on the public every day: unfamiliar interface layouts, text fields that don’t auto-complete, and results windows that pop up in other parts of the screen.

Takeaway #1: Users Want and Need a Large, Obvious, Auto-Complete Text Box to Drive Search & Discovery

Coming Soon: All Those Layers are Getting in the User’s Way

—Brian Timoney

Who’s up for another Nate Silver post?

You know, the guy who single-handedly save America from the pox of Triumphalist Innumeracy. As both a post-Election victory lap as well as promotion tour for his new book The Signal and the Noise, Silver gave a number of interviews that I enjoyed including the one below at Google.

At the 48:10 mark he mentions a project on rating New York neighborhoods, and observes in passing that the per-sqaure-foot cost variation in apartment prices can largely be explained by distance to midtown Manhattan, proximity of (quality) schools, and proximity to parks.  According to Silver using just these three variables yielded a r-squared value of 0.93 i.e. 93% of the variation in per-square-foot cost can be explained by the variation in these three variables (a very robust result, by the way).  Those of us who work in the geospatial realm typically have a nodding familiarity with distance weighting, hotspot analysis, spatial autocorrelation, etc.  But using spatially-derived measures in more standard statistical techniques such as multiple regression strike me as a more likely analytic scenario in the day-to-day “data science” work that we’re all promised is the future.

Nate Silver used spatial measures to determine Park Slope is the most desirable neighborhood in New York

Your “Data Science” is Worth Little Without Clearly Communicated Reasoning

A couple of weeks ago Marc Andressen made headlines by stating that English majors and other humanities types were sentencing themselves to professional futures in shoe stores.  As an all-too-typical prejudice of the engineer-centric tech scene, it summarily ignores a piece of the Nate Silver phenomenon every bit as important as his statistical modelling savvy:  his writing ability.  What set Silver apart was that he was explaining his quantitative reasoning clearly, discussing possible weaknesses of his model, and addressing criticisms of his work all on a daily basis.  As a prominent financial blogger noted, the combination of a robust, well-reasoned model combined with a narrative fluency is the true sweet spot.  So by all means, sign up for a Data Analysis course and get your feet wet with R, but keep your Chekhov and Munro within easy reach as well. The world is already plenty full of ineffectual factotums bearing scatter-plots getting steamrolled by those comfortably reliant on their lifetime of hunches.

It’s Not About the Size of your Hadoop Cluster

 
Yes, this.

—Brian Timoney

So some Colorado folk have rustled up a get-together geared towards those who make maps using Javascript APIs.

JS.geo January 14-15, 2013 Denver CO

It’s not a “conference” and no one is making money: we’re charging $11.54.

There will be no “tracks”: Monday the 14th we’ll have talks (full talks, lightning rounds, etc.) in a single room.

The second day, the 15th, we’ll have office space for small-group work, collaborative coding, or free-association white-boarding.

REGISTER HERE

So…what’s the point of this again?  Well, everybody knows that at most conferences the most valuable take-aways are the conversations among attendees. So let’s get-together and make the content of those hallway conversations the focus: particularly the “who’s doing what” and “wouldn’t it be cool if…” bits.

Who’s In?  The registration page has the RSVP list–you might recognize a few names. We expect folks from OpenGeo, Vizzuality, MapBox, Stamen, ESRI, Google, et al.

What’s the catch?  Ah, the catch.  We’re capping attendance at 75 or so.  And we’re already halfway to that quota.

If you’re a sleeves-up web mapper working with Javascript front-ends, this might be the best $11.54 you ever spent.

 
—Brian Timoney

ADDENDUM: First-day facility is being provided courtesy of the Facility for Advanced Spatial Technology (FAST) at the University of Colorado Denver. We are grateful for their support that enables us to keep the cost of this event to a minimum.