The Age of Buildings in the City of Chicago

Following my last post, on the geography of New York City, I've been exploring other building-level datasets to see what they can offer us in relation to telling us more about the fabric of the cities we live in. This time, I've focused on Chicago's 'Building Footprints' dataset. It's not nearly as detailed as New York's PLUTO data but it does include variables on (e.g.) number of floors and year built. As with the NYC data, it is not perfect but we can still make good use of it to understand the development of the city and its structure. I've mapped the city using number of floors as a proxy for height and shaded it by building age to produce the following overview (blues = older buildings, reds = newer).

Even bigger version

Besides looking relatively interesting, the above graphic also reveals something about the phased construction of the City of Chicago and - possibly - something more about the data itself. As with the New York City PLUTO data, I produced a chart of the 'year built' column just to give me some idea of its distribution. It looks better than the New York City chart but I'm still not convinced it is 100% accurate (the year built data run from 1852 to 2010). 

Were there really nearly 15,000 buildings constructed in 2006 and only 78 in 2000? Possibly, but it would be good to know more about the accuracy of the data. In total there are 820,154 building footprints in the dataset and there are a range of different columns - which you can read more about in the metadata file. Once again, it's pretty cumbersome to work with in a normal desktop GIS setting but my machine can just about handle it. 

The Geography of New York City

In June 2013, the city of New York released as open data one of the most detailed, fascinating and user-friendly datasets ever. The Property Land Use Tax lot Output (PLUTO) dataset is essentially a record of every parcel of land in the city, what is on it and who owns it - but this is only part of it. See the full PLUTO data dictionary for more on this. Wired said the mapping elite were 'drooling' over it and there have been a few impressive visualisations already but I was keen to look at the data in more detail and then map land use patterns and get to grips with the dataset more generally. So, as an initial experiment, I mapped all 11 land use categories for the whole city in 3D (PLUTO has a field for number of floors so the maps below are extruded on this basis). Click on an image to enlarge and then flick through the images to compare land uses.

I've also put these images in a PowerPoint file in case anyone finds it useful... These visualisations in many ways tell us what many New Yorkers already know but the PLUTO data (n.b. I've used the ready-made MapPLUTO shapefile) offers everyone for the first time the opportunity to explore this open data and examine the geography of New York City as a whole in much more detail. 

Some further information about the dataset. There are 857,879 rows in the complete dataset and the MapPLUTO version has 85 fields so if you want to work with it then you better have a good computer. When you go to the download page you'll notice that the PLUTO dataset is available as one csv file while the MapPLUTO data is split into the five boroughs of New York City. 

This is an amazing resource but it is not perfect - as the Department of City Planning recognise when they say 'PLUTO is being provided ... for informational purposes only'. The data are only as good as the sources, and sometimes when you look closely things seem a little strange. For example, here's what you get when you chart the YearBuilt column for all buildings constructed since 1800 (click to enlarge). It's hard to tell but I reckon that from about 1980 onwards the YearBuilt column is pretty accurate but before that is is something of a best estimate - though I'd be happy to be proven wrong on this!

I'll probably come back and explore this again soon but that's all for now...


Footnote: 0.4% of tax lots and 1.0% of land remains unclassified. I produced the 3D maps in ArcScene and then annotated them in GIMP. I've just done these to explore at a basic level the characteristics of the dataset and the geography of land use in New York City.

Why I Like 3D Maps

I do quite a bit of spatial analysis and mapping in my academic research, and some of it ends up on this blog. Over the past few years I've done quite a few 3D* maps - most recently one of population density in China. A comment by map-guru James Cheshire made me think about the 3D issue, hence this post, which attempts to say a little more about why I like using the third dimension, as it were. Also, there's not much about this stuff online at present. The two images below show population density in Europe at NUTS3 level, with a colour scale running from red (high density) to blue (low density). Click the images to enlarge.

*N.B. Data for some parts of Italy, Germany, and the UK are missing, 

but that doesn't matter for now - this is just an example.

The reason I like the addition of the third dimension with this kind of dataset is that you can tell more about the differences between areas within the same statistical category. Essentially, it adds an additional dimension of information that you can not observe from the conventional 2D map above. This is particularly true of the most high density areas in the first map. There is of course an issue here about the relative size of areas and how this might change the population density of different places but that is a different matter since I'm not in control of NUTS3 definitions! For more on this kind of thing I'd recommend looking at Stan Openshaw's work and for more on the utility (or futility?) of choropleths generally Tobler (1973) is an excellent starting point. Gale and Halperin (1982) is also worth a look.

If we assume that the main purpose of a choropleth map is to present and discover spatial patterns then there is sometimes a strong case for using the vertical dimension and extruding polygons using a z-variable (I do this in ArcScene, in case anybody is interested). However, there are some complications and I don't think it is always appropriate to go 3D. For example, depending upon the spatial structure of your data extruded polygons in one area can obscure those in another. There is also the issue of the different size of areas and the way these might have an impact upon the level of extrusion - i.e. if we used 1km cells for the European population density map it would look rather different in 3D - though this is possibly another artifact of the modifiable areal unit problem, as described by Openshaw. I've patched together three different examples from my blog in the image below, just for comparison. Another option would be to follow the example of Ben Hennig and produce population-weighted cartograms.

When I produce these 3D maps (or visualisations) I'm not trying to create a geographically precise rendering of space but rather I'm attempting to draw attention to variations in a dataset in a way which 2D maps can only do to a limited degree. They are abstractions and simplifications but in terms of understanding the world I find it can be an improvement. There is a little bit about it in this Environment and Planning B paper I wrote but I plan to write more about this in the near future (the'near future' in geological terms of course). 

*Also known as 2.5D in the GIS world, but I'll put that to one side for now...