Data present us with novel ways to look at our environment. This applies to investment opportunities, regulatory frameworks and market as much as day-to-day business. Cities, being the major economic and educational hubs of most states, present an interesting case study. While they can be viewed as a single unit, on closer inspection there's often a complex topology with multiple parameters.
They also generate quite a bit of data which in Bulgaria goes unused.
Recently I explored this pool of unused data with several Bulgarian cities. There is an impressive amount of open data already available. I downloaded the data for the 6 biggest cities from the national cadastre - Sofia, Plovdiv, Burgas, Varna, Ruse and Stara Zagora. The cadastre's GIS system offers different layers presenting entities including administrative borders, building plot ownership, infrastructure, individual buildings and even units. I focused on the buildings and compiled a dataset with their shapes and metadata.
Understanding the data is always a bigger challenge. Often we expect to crunch the numbers and get a straight-forward result. When dealing with the convergence of big data sets, however, that is often not possible. Instead, we can visualize the data in an appealing way adding contrast to the aspects we are most interested in.
Here I used open-source tools to visualize various aspects of the buildings. In Sofia, you see how tall they are and in Stara Zagora - what is the floor space they have. While these maps were primarily made for esthetic purposes, they also present a glimpse into the living and working space distribution as well as the topology of the buildings in these cities. Other variations show administrative practices like building permit date and cadastre registration timestamps, building ownership type and intended use.
There is so much one can do with those maps. Using them gives us the general context of the area. While inadvertently data resolution is lost, such maps utilize the strongest human skill - innate pattern recognition. Presented with huge amounts of visual data we are able to extract new information no algorithm can. Coupled with interactive tools and appealing visual aids, this process can be improved greatly. Unlike standardized indexes and algorithms, however, such visual analysis may not present us with concrete answers. It will certainly focus our attention to details we may have missed and allow us to ask the right questions and seek the right information.
Data-driven decisions are gaining more traction in both business and our personal lives. Opening up data in public and private institutions provide big opportunities for reducing risk and finding new ventures. Data about air pollution, infrastructure and transportation quality, administrative practices, building density, even school and park proximity helps make important decisions: where to set up an office, where to buy a house, how to shape your transportation habit. Visualizing it makes those decisions at least in part easier.