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Data was always a fundamental ingredient for urban design projects. Urban designers have used data about places and people in their work for a long time. Traditional sources of data such as surveys or administrative data are core elements of urban projects. At the same time, the vast amount of data that has resulted from the evolution of internet technology and telecommunications provide an opportunity for multi-source, multi-scale, multi-time data, big data. Urban designers interpret data and transform them into information. From this point of view, design is a data and information-intensive process.

Scroll down to find information on:

  • What are big data and its relevance for urban design?

  • Types of urban big data

  • Why would I use big data in a mixed-methods approach?

  • What are some challenges I should be aware of?

In this section, you will find more information on:

research design

mixed methods



What are Big Data and relevance to urban design

What IS big data and ITS relevance TO urban design

Urban big data can tell us a great deal about life in cities. As cities are becoming more automated - ’smarter’, data on social, economic, and environmental interactions is being generated in real-time from different sensors. 

Big data becomes relevant in the early stages of urban design as it helps urban designers understand places. Its granularity can give an insight into the complexity of everyday life, but as it is always produced through interpretation, conclusions can be subjective and need to be interpreted carefully.

Type of urban data

type of urban data

Some examples of real-time, fine-grained data that is routinely generated about cities and their inhabitants by a range of public and private organisations, include:

  • Utility companies 

  • Transport providers 

  • Mobile phone operators 

  • Travel and accommodation websites 

  • Social media sites

  • Crowdsourcing of knowledge 

  • Government bodies and public administration 

  • Financial institutions and retail chains

  • Private surveillance and security firms 

  • Emergency services 

  • Home appliances and entertainment systems 

Why would I use Big Data in a mixed-methods approach?

Why would I use big data in a mixed-methods approach?

Big Data by itself cannot answer all the questions about our cities. Often, we get the most value from Big Data when we combine it with qualitative forms of research. For example, data from the Census and surveys give us a benchmark against which we can judge the quality (validity) of new data sources. We combine traditional and new forms of data to get the best of both or link multiple sources of big data to enhance the quality of the information.

It is important to learn how to use Big Data and how to isolate hidden data patterns to transform data into information. These data patterns when mixing with qualitative data can reveal socio-economic structures.

What are some challenges I should be aware of?

what are some challenges i should be aware of

There are a number of challenges to be considered when working with big data. Watch this 4 min clip below by Dr Jens Kandt, who gives an overview of some opportunities, technical challenges, ethical challenges and solutions for using big data in an urban context.

Some other limitations mentioned by interviewees for this project include:

  • The high cost in accessing some data sets as a barrier

  • Quality of data. For example, one interviewee explained her experience working with data in India where the lack of monitoring of data collection leads to highly subjective datasets and incomplete information filled with human error

  • Concerns about data protection in relation to Brexit

  • Focus group participants mentioned VPN issues when accessing UK datasets from abroad

  • Digital divide. Big data is often said to represent all of the population, but think carefully who might be left out, e.g. people who might not be able to access digital devices easily, or may not be very familiar with them, or refuse to use them.

  • How much does big data capture non-monetised ways of exchange?

Lastly, keep in mind that data is constructed and is based on our own understanding of reality. Imposing strong questions on seemingly neutral big sets of data might lead you to new conclusions. If you are interested in exploring this more, you might want to look to descriptive statistics on spatial data.

research design

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