How to spot trends in mapping data

Key objective 

Visualising data helps in understanding relevant spatial dimensions, and thereby understanding the city you’re looking at more deeply. The visualisation itself helps to see the entities, lifestyles, and conditions hidden behind the dots and numbers. 

During our research for FABRIX, we had access to business point data, with which we could analyse the textile and clothing sector in Rotterdam and Athens, in combination with other layers of mapped information. For our analysis, this mostly concerned the structure of the two cities; aside from the rich information that can be gained from the point data alone, the urban growth patterns, demographic composition, centrality and connectivity of areas, and other urban qualities, have helped enrich the information we gathered from the point data. 

The biggest roadblock to understanding more from maps is not knowing what to look for or not finding creative ways with which to analyse the vast amount of data that is available. Here, we are going to cover some ideas to get you started with looking differently at business data you may have collected. While a lot can be done to understand a context directly using qualitative methods (directly visiting an area, interviewing businesses, and experiencing their context), we will cover what can be done with only mapped information at hand. 

What can you do with mapped data? Here are some possibilities

• Business activity x neighbourhood type: combine different activity codes with a zoning map to gain insight into why businesses are delegated to certain neighbourhoods. Do certain business clusters emerge in specific neighbourhoods? Do some neighbourhoods host a wide variety of business activities? What is the character of the neighbourhoods wherein these clusters emerge? In combination with socio-economic data, is there a population-driven reason for their business location (i.e. access to customers, suppliers, or collaborators)? (Note that conclusions from this overlap in one city or country are not necessarily transferable to other countries, as the level of strictness in zoning regulations and business activity may differ).

• Business activity x infrastructural networks: combine the point data with network/line data to understand the range of accessibility that a business has. Is it predominantly accessible by car? Are there public transport connections? Is the area accessible to delivery trucks or, depending on the activity, shipping containers? 

• Business locations x climate conditions: how are the spatial conditions that the businesses are located in, in relation to water and heat stress? What are the implications of rotting foundations or overheating studios to business operations in your case area? Is there enough shadow coverage outside to allow access and/or the use of public space for business activities (such as adding a sign or display rack)? 

• Business activities x ecosystem: combine the different industrial codes characterising the textile and clothing firms, to see if and how they together form clusters from production and consumption, to waste treatment. In this way, missing or dominant activities can be seen, in turn informing what characteristics of the complete value chain are located, and in which locations, from the local to global levels.