Disaggregation Dilemma - Part 2...(from static land-use color codes to something like OSM features indexing)

Information density at lowest levels of disagregation

Let us continue the discussion on information loss and data aggregation by comparing the maps of different planning levels that I showed in the first part of the blog, with their corresponding scales on OpenStreetMap and google satellite imagery. 

Instead of starting from the top (the city level), let's start from the bottom (the layout level) this time, and remember the words of Prastacos again- 

that computerisation basically allows us to maintain data at the lowest level of disaggregation and then readily aggregate it as the need arises.

There is of course no restriction on further zooming into the osm (OpenStreetMap) or the satellite imagery to study the area on greater detail. Similary, one can zoom out and reduce the scale to any extent to study larger areas. One does not have to stay restricted to certain categories of pre-defined map-scales (and needless to say, we also get our freedom from the scanned copies of water-soaked blue-prints that the government generously shares as "open data" and get a feel of the power of the REAL open data).

 

What is information loss due to aggregation ?

If we zoom into the area of the layout plan in the City level land use plan, then this is all the detail that we could possibly get -

 

Now compare the above level of detail (left) with what we saw in the case of the osm image (right) -

 

The above comparison is a simple visual representation of the amount of information loss that happens when spatial data is aggregated to higher levels using non-computerised cartographic methods.

There was simply no other way - in the absence of computers - than to prepare maps of different scales; covering different geographical extents in order to show different planning levels.

However, none of those limitations remain if one is working with digital spatial data and computers - there is no information loss at higher levels of aggregation of the same map. 

The trouble is that we continue to operate with the same methodology even when we have computers and geo-spatial software at out disposal.

Coding spatial information - learning from OSM features

When one understands the fundamental manner in the way in which computerisation allows aggregation and disaggregation of data, then one can also understand that the manner in which land and building uses were coded in earlier non-computerised map-making systems are no longer adequate or relevant.

Incidentally, a very powerful and effective alternative to older methods of land-use coding has already started appearing in the form of the "Map Features" of OpenStreetMap. 

Here is a description of the system from osm's wiki page -

OpenStreetMap represents physical features on the ground (e.g., roads or buildings) using tags attached to its basic data structures (its nodes, ways, and relations). Each tag describes a geographic attribute of the feature being shown by that specific node, way or relation.

Most features can be described using only a small number of tags, such as a path with a classification tag such as highway=footway, and perhaps also a name using name=*. But, since this is a worldwide, inclusive map, there can be many different feature types in OpenStreetMap, almost all of them described by tags.

The osm feature indexing system is extremely thorough and exhaustive and designed for use by the computer. Have a look at the difference between a typical color coded land use system and the osm map features system below.

This is how land uses are color coded in a typical land-use plan -

And this is how the osm map features indexing system looks like -

Just a casual glance is enough to see the power of this feature indexing system. It lists the various types of uses as key-value pairs, states what osm map elements they belong to, provide a clear description of each feature, the rendering and also photographs of typical examples.

The wealth of information that gets collected and maintained using such an indexing system is truly mind-boggling.

The analytical opportunities such systems open up can help us go toe-toe with the most complex urban problems that we face - and win.