Why data analytics should be taught to all planners

Written by Sean Lee



I had a fruitful summer at Singapore’s JTC Corporation. A government-linked company (think QUANGO), it owns and manages the nation’s industrial land. My time at the company’s Land Planning Division opened my eyes to the many compromises and deliberations government policymakers need to make in producing a landscape not only for industry to thrive, but one that safeguards the national interest in the long-run.


I was part of a team assessing and reformulating policies relating to foreign worker dormitories, a major issue during the COVID-19 outbreak here. We collated data from various government agencies, community organizations and private enterprises, extracting insights to inform policymaking. Getting to this stage in particular struck a chord with me – a combination of geospatial information processing software, data visualisation tools and statistics was used not just to exemplify our point, but also to bring it across.


This data-centric approach to deriving planning solutions got me thinking about two things. Firstly, emergent technologies like GIS fundamentally reshape how planners analyse and determine development decisions. Secondly, imparting technical data skills through formal planning education is vital in addressing widening skill and knowledge gaps that arise from these emergent technologies. Unfortunately, issues with the latter result in graduates’ inability to meet market demands, especially in terms of data handling tasks. This problem is not new, and nor will it go away. We need to evaluate formal planning education, incorporating this paradigm of “urban data analytics” to future-proof the planning profession.


Data-centric approaches will power the future of planning (credits: Nextrend Asia)


What exactly is urban data analytics, and why is it so important?


According to Kent Larson, director of the City Science Group in MIT’s Media Lab, “City planning consists, first of all, of insight—and insight is collecting and analyzing data so you know how things are in the world today”. It is already abundantly clear why data is important to planning. Yet most people are not acutely aware of the role of geospatial and urban data analytics, a process of taking in and making sense of data which in this digital age will be increasingly key in the way we plan:


  1. Geospatial and urban data analytics help identify potential gaps that planners potentially must address, as well as opportunities that planners can leverage upon. Data provides a better sensing of infrastructure requirements, so we can allocate resources for the future. For instance, clearly understanding how town demographics shift over time allows planners to account for projected demand for amenities and facilities supporting residents at the local level.

  2. Urban data analytics allows us to harness the power of Big Data. With the rise of Big Data, sensors increasingly supplant the human presence where it isn’t safe or possible, logging information about the environment and providing feedback in ways we traditionally cannot. We hear of urban planning concepts like the “Smart City” that integrates multiple information and communication technologies (ICT) for better management of municipal property. Facing a deluge of information, we need to filter out the noise, and this is where good urban data analytics skills can greatly help planners make sense of inputs, and optimise planning solutions.


"Smart cities" are enabled by effective urban data analytics (Credits: Urban Hub)



What can schools do?


Concrete steps therefore need to be taken to build up urban data analytics as a core competency amongst urban planning graduates. Schools and universities are ideal springboards for this action to take place.


Fortunately, planning programmes must incorporate spatial planning education to obtain RTPI accreditation, but this addresses one facet of the various competencies required for effective urban data analytics. Furthermore, it is noted that the RTPI grants universities a wide berth when deciding what to teach – at the undergraduate level, most provide an introduction to GIS (the Bartlett is one of them). There is no standardised “handbook” shared across tertiary institutions on spatial planning principles to be imparted. Our first step, then, should be to specify core competencies appropriate for different planning specialisations, perhaps differentiating levels of competency that undergraduate students and graduate students should master.


Apart from basic spatial planning education, schools should also target additional technical digital competencies the modern planner needs to succeed after graduation. For instance, introductory programming classes (like Python) not only train the mind for analytical and logical aspects of the data analytics process, but also provide a platform easing the learning curve that graduates eventually need to overcome in mastering modern geospatial software. Incorporating data visualisation software like Tableau into university modules underscore how data-driven insights are useless unless packaged and presented in a format easily understood by other built environment professionals, policymakers, and even the wider community.


Data visualisation programmes convey information in an accessible format (Credits: ESRI)



Lastly, the use of data analytics in the context of urban planning makes sense only when complemented with a strategy to transform findings and insights into data-driven solutions. For truly effective geospatial and data analytics, planners must formulate clear lines of hypothesis and inquiry, then match findings with options and choices, all the while deliberating on trade-offs. While it might be easier to teach theories relating to this critical thinking framework, the wide scope of planning and unique contexts which planning operates within points to the need for such education to be grounded in real-life operational work. Practical attachments to planning agencies or consultancies, where prospective planning graduates can get a good feel of how data-driven insights can be transformed into actionable policy mechanisms, should be a cornerstone of any formal planning programme.


Last Thoughts


Developing a new management model for cities that harnesses the latent potential of emerging technology is a societal imperative, necessary to address the great challenges of our era, from equity to climate change. Our schools determine the competencies of planning graduates, and should therefore take concrete action to keep the planning curriculum updated - not just to meet the rigours of the planning profession, but to nurture the next generation of high-calibre planners, fully capable of leveraging upon emerging technologies to drive planning into the future.



About Sean


Sean believes that Big Data and emerging technologies will positively transform collaborative planning. When not at the Planning Studio, he can often be found at one of the many student bars having a good time with his friends.


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