I have recently stumbled upon a presentation and paper by Duncan Smith exploring geovisualization of 2001 area classification of output areas using building level resolution of urban area (London in this example).

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Two questions:

Could you point me to any other examples of such applications using social/economic/health data?

What are the advantages and disadvantages of your examples?

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    Interactive example following similar principles: casa.oobrien.com/booth/# – radek Feb 21 '12 at 14:01
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    You should make that an answer! I missed it and accidently referred to Booth again in my recent answer. – Andy W Feb 28 '12 at 18:22

Noise mapping a good example of such visualization at the building-level (it is even more detailed, at the facade level). The advantage is obvious: mapping such phenomenon is not possible in 2D. The disadvantage is that is is hard to see the overall noise repartition.

Noise mapping Noise mapping

Sources: Noise map of Paris municipality (left) and MITHRA-SIG (right).

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    Nice example. Just curious though - what is the source? Do you know how they calculated the orientation of the facades too? – djq Mar 22 '11 at 18:49
  • I have added the sources. The facade orientations are certainly calculated using their normal vector. – julien Mar 22 '11 at 20:08
  • I like, but disagree that mapping noise in 2d is not possible. tinyurl.com/bup8q24 I put some 2D noise maps for Defra awhile back. We also did some KML, but TBH it was easier to see patterns in 2D – Simon Mar 29 '12 at 9:49

Building footprints in the Boston area coded according to their proximity to liquor licenses:

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(Source and more info)

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    Every realtor should have this map for the entire U.S. Seriously. – Chad Cooper Feb 28 '12 at 19:20

It's not quite to the building level, but there is a parcel based landuse forecast model for the Houston area.

2035 Lanuse

When I see such fine-grained modeling I get the feeling that GIS needs some sort of uncertainty principle.


I forgot to mention that this model was created using UrbanSIM.


This paper, inspired by UrbanSim, Interactive Design of Urban Spaces using Geometrical and Behavioral Modeling, describes how "building envelopes" can be generated:

Our system determines the building type, which implicitly reflects land use, by inspecting the ratio of number of jobs to the population size and chooses a building from a small database of procedural building styles.

  • Especially since Tobler's First Law makes less and less sense these days. – mvexel Mar 23 '11 at 15:05

Two more examples on building energy/heat issues.

National Heat Map from UK showing heat demand from buildings with pretty nice interactive tool for calculating stats for user defined areas. (More info, source)

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Estimated Total Annual Building Energy Consumption at the Block and Lot Level for NYC. (More info, paper, source).

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CartoDB blog points to an interesting project: Welcome to 1940s New York.

Excellent example of marrying the output of 1943 New York City Market Analysis

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with modern visualization tools:

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Watching episode of BBC's Filthy Cities I stumbled upon one more classic example, simmilar to Booth's map pointed by Andy W.

It's a Sanitary Map and Social Chart of the Fourth Ward of the City of New York that accompanied 1865 Report of the Council of Hygiene and Public Health of the Citizens' Association of New York upon the sanitary condition of the City.

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    Cool beans. I suspect one could find other historical examples in David Rumsey's map collection, but in a quick search I could not find any. (will have to probe deeper) Also this NYC map reminds me of this article on NYC prostitution houses circa 1870. I wonder off-hand if areas of the city that are in disorder even so long ago are still correlated with disorder today. – Andy W Mar 16 '12 at 13:54
  • @AndyW: Thanks for sharing The Gentleman’s Directory - haven't heard of that story. In similar vein - story from XIX century Japan. – radek Mar 16 '12 at 14:44

An few interesting applications can be found on James Chesire's blog in this post, Deceptive in their Beauty?. In that particular example he gives both positive comments about using building parcel data to attempt to more accurately visualize population location data (mainly referring to the work on dasymetric mapping versus just symbolizing census geographies), but also gives some critical comments. In particular, the mapping of the population attributes to the building parcels implies a much greater level of precision in the data than it warrants (a sentiment that fits in well with Kirk's comment about an uncertainty principle). It is a similar critique to many dot density maps (which the blog mentions as well).

He has a few other examples, and points to the work of Charles Booth. A relevant historical figure in mapping such small units. An example of his work (original link available from blog post) is below.

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The NY Times created yet another nice interactive map of building (parcel?) level Sandy's damages:

A Survey of the Flooding in N.Y.C. After the Hurricane

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(via Google maps mania)

And IDV Solutions' blog points to an examples of static and dynamic maps of Sandy'd damage in NY:

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