Data Driven Urban Planning: Prerequisite for the City of Knowledge?

At the current times of datafication, we can no more think of building, governing, or using cities without data bundled with myriads of digital systems and applications. In marketing speech, data is a merchandise and panacea: it allegedly provides solutions to current problems from climate change and traffic chaos to terrorism and obesity.

Data as such, however, just refers to any unorganized information: it must be organized to provide useful knowledge to tackle urban challenges. Consequently, the order of the day is data-driven planning – planning that is based on data, or more likely, knowledge gathered by analyzing the organized data. Although all (good) planning has always been based on thorough analytics, the data analytics and computing that emerged in the 1990s are only accelerating today. Numerous applications to organize, steer, and apply data in city governance from digital twins, CIMs, BIMs, e-planning tools, digital platforms, and dashboards to dynamic simulations have reached today a great potential in knowledge production.

However, they often concentrate on drawing knowledge from quantitative (big) data. In the world of datafication and digitalization, qualitative aspects of urban life are often in a lesser role, which narrows the overview gained with these tools. Moreover, an essential issue with data-driven planning and design is the openness of data. Despite the optimistic marketing speech, there are severe concerns. On the one hand, data is often unavailable, expensive, fragmented, or too coarse; on the other, we willingly share our phone data with hidden third parties without hesitance causing many privacy concerns. However, sharing often appears to be the price for many services in today’s society.

In this trans-disciplinary session, we discuss about current means, methods, theoretical foundation, and limitations of extracting knowledge from urban data for more informed urban planning and governance to build sustainable cities with a higher quality of life. In this request, we ask, for example,

  • How are the digital systems (CIM, BIM, simulations, e-planning tools, and so on) currently developed and applied to promote human, societal, or ecological aspects of knowledge-creation? What are their downsides, and desirable future trends?
  • What could be the role of soft sciences regarding data organization and data-driven planning? How to combine hard and soft approaches in taming (big) data, and provide a multifaceted understanding of society overall? 
  • What is the role of sharing personal data in knowledge creation in the city? How can we improve access to high-quality data for data-driven planning and research without hurting individual privacy?

We invite scholars from the fields of urban studies, planning and design, governance, sociology, geography, engineering, and beyond to present their methodological, theoretical, philosophical, or practical work.

Language of the session is English.


Jenni Partanen
TalTech, Tampere University

Kimmo Lylykangas


How equitable is evidence-based decision-making? On smart cities and planning for older people

Christoph Fink
Digital Geography Lab, University of Helsinki

Elias Willberg
Digital Geography Lab, University of Helsinki

Aaro Tupasela
Faculty of Social Sciences, University of Helsinki

Juanita Devis Clavijo
Research Center for Studies in Media, Innovation and Technology, Vrije Universiteit Brussel

Marjut Salokannel
Faculty of Social Sciences, University of Helsinki

Tuuli Toivonen
Digital Geography Lab, University of Helsinki

Urban planners, practioners and academics alike, are in strong support of evidence-based decision-making. Advances in computing technology and capacities has lead to the development of smart city initiatives, and former data vaults, such as spatial data infrastructure(s) (SDI), have evolved into Urban Digital Twins: dynamic computer models mirror every detail of a city’s physical, organisational, and social realities to inform policymakers, planners, and decision-makers ‘in real-time’ – possibly forecasting the impact of anticipated decisions and changes. Such smart city infrastructures share a critical limitation with other data science approaches: what cannot be measured, cannot be recorded.

It is, for instance, greatly challenging to represent older people, or other vulnerable or marginalised groups, in smart city systems: Typically, the data that are collected and analysed, emphasise the interests of the majority group, and technological, ethical and legal limitations lead to practical data scarcity on minority groups. This is further aggravated by a legacy of, among others, transport planning models assuming a representative ‘average resident’ who often implicitly ends up white, middle-aged, male, and middle-class. More often than not, neither model nor data take marginalised groups and the most vulnerable residents into consideration.

In the ongoing URBANAGE project, we looked at how to overcome underrepresentation of older people in smart city initiatives and digital city twins. We developed strategies for engagement and co-creation, and experimented with different forms of inclusive rights-based urban decision-making. We also took a closer look at urban mobility and accessibility, and examined how accessibility models for the Helsinki metropolitan region vary if their parameters are adjusted to reflect older people’s mobility preferences and restrictions. In this presentation, we highlight that seemingly minor differences between the everyday realities of individual city residents can have a tremendous impact on the ‘evidence’ we base planning decisions on, and outline practical and conceptual steps towards more equitable planning for more sustainable urban futures.

User perspective on a novel 3D tool for co-planning urban (green) environment

Pilvi Nummi
Aalto University, Department of Built Environment and Tallinn University of Technology, FinEst Centre for Smart Cities

Petri Kangassalo
Tallinn University of Technology, FinEst Centre for Smart Cities

Viktorija Prilenska
Tallinn University of Technology, FinEst Centre for Smart Cities

Xunran Tan
Aalto University, Department of Built Environment

Easily accessible information on diverse aspects of environment is an essential precondition for data-driven planning. City information models already provide comprehensive information on the built environment, but data describing the green environment lags behind. There is a risk that the values of the green environment will remain secondary in planning as data-driven planning becomes more common.

In GreenTwins, we are developing a digital plant library and a new 3D tool (Virtual Green Planner, VGP) for co-planning of urban environment and urban green infrastructure (UGI). In this case, co-planning refers to activities for searching alternative planning solutions together with different stakeholders and assessing the impact of plans in communication. VGP includes functions for viewing plans, commenting on plans, creating own plans, and analyzing the impacts of plans. The plant library provides individual plants and plant covers to be used in VGP for visualization and simulation.

Throughout the development process, we have studied end users’ perspectives on VGP and the plant library for participatory UGI planning. We ask now, how the users consider the purpose of Virtual Green Planner, and do they find it useful. In this presentation we focus on the usefulness of VGP for communicative, knowledge-informed urban planning.

Although VGP is still in the development phase and includes only limited functionalities, the tool is considered useful for different tasks, like brainstorming, quick visualization, co-creating plans, and communicating them for different stakeholders. It appears that there is a growing awareness about the importance of the urban vegetation and its positive impact on the well-being of the citizens. Therefore, the tools for co-planning of urban, and specifically, urban green environment, might be of high demand in the near future.

Redefining neighborhood boundaries using participatory mapping data and activity spaces: Bringing together soft spaces and spatial analysis

Kamyar Hasanzadeh (presenter), Eva Purkarthofer, Anna Kajosaari & Marketta Kyttä
Department of Built Environment, Aalto University

Neighborhoods can be described as small divisions of urban areas in which the inhabitants receive or perceive similar socioeconomic effects and neighborhood services (Goodman, 1977). Usually, neighborhoods are defined as static administrative divisions with precisely outlined limits. At the same time, a large body of literature shows that the everyday life of residents is rarely fully contained by these static boundaries. In fact, the space containing the everyday activities and mobilities of individuals, i.e., activity space, is usually subject to variation in both size and structure (Hasanzadeh et al., 2018). This results in a potential mismatch between administrative boundaries and the spatial delineation of problems that planning seeks to address (Laatikainen et al., 2018).

Although this mismatch is extensively discussed in the literature, it has rarely been addressed in a data-driven way to reshape the definition of neighborhoods in planning. This study introduces a novel aggregate model of individualized activity spaces based on Public Participation GIS (PPGIS) data collected from city of Espoo to capture the variable and fuzzy character of the spaces people frequent in their everyday lives.

The study shows that neighborhoods can be more fluid and fuzzier that what we often assume in research and planning. This not only challenges our current understanding of neighborhoods, but also sheds new light on residents’ use of urban space. In this study, we will go through our findings from city of Espoo and discuss how the theoretical concept of soft spaces can be used together with spatial analysis to create new functional neighborhood areas.


Goodman, A. C. (1977). A Comparison of Block Group and Census Tract Data in a Hedonic Housing Price Model. Land Economics, 53(4), 483.

Hasanzadeh, K., Laatikainen, T. E., & Kyttä, M. (2018). A place-based model of local activity spaces: individual place exposure and characteristics. Journal of Geographical Systems, 20(3), 227.

Laatikainen, T. E., Hasanzadeh, K., & Kyttä, M. (2018). Capturing exposure in environmental health research: challenges and opportunities of different activity space models. International Journal of Health Geographics, 17(1), 29.

Advances in data collection practises for the GHG accounting informing spatial planning in Europe

Kimmo Lylykangas
Tallinn University of Technology, University of Iceland

Currently, the best practice for the GHG quantification supporting spatial planning in regional and local scale seems to be conducting two independent analyses, applying territorial and consumption-based GHG accounting approaches. The most common approach, territorial GHG accounting, has its limitations that are widely recognized. Consumption-based GHG accounting (CBA) can provide complementary information that helps understand the global climate impacts of the residents’ consumption. Sub-national GHG accounting practice has adopted some methods and approaches from national GHG inventories; yet the sub-national GHG inventories are usually based on local data. 

As regions, cities and municipalities are learning to apply the two approaches and interpret the results, new trends can be recognized in the data collection for sub-national GHG accounting:  

  • Bottom-up data collection is replacing top-down annual data from statistics; 
  • Annual inventories are developing towards real-time monitoring practice; 
  • Data collection is automated;  
  • Carbon footprint assessment practice provides accurate data on embodied (material-related) emissions for both buildings and infrastructure. 

These changes have an impact on data accessibility and transparency of impact assessment in spatial planning. It seems that it is data collection, rather than calculation methods, that would benefit from harmonization. 

The role of quantitative analysis and other reasearch in knowledge management of housing policy in Helsinki

Jasmin Bayar & Reetta Kosonen
City of Helsinki, City Executive Office, Economic Development and Planning Division

In the city of Helsinki housing research plays a significant role in knowledge management and therefore functions in a key role as a tool for supporting decision-making. The emerging research needs can be derived from the city’s Implementation Programme on Housing and Related Land Use as well as the city strategy. The Housing Unit of the Helsinki City Executive Office prepares and manages the city’s housing policy and suburban regeneration. In addition, the unit produces statistics, studies and reports supporting housing, construction and regional development.

The role of research and statistical data in supporting the city’s housing policy plays an important role. Therefore, the research results are also used to evaluate the effectiveness of the housing policy targets and to systematically measuring them. There exist a lot of data for statistical analysis and monitoring development. Usuallt the data time series are long which makes it possible to examine changes over the past years or for a longer period of time.

The use of statistics-based information is also associated with challenges, for example in terms of the data update cycle, as many statistics are updated once a year. In addition, there are challenges associated with database updates if the new information obtained through the updates is not comparable with previous information. The city’s regular residential surveys are an important source of information for mapping residents’ experience information but the number of survey responses is most of the times too low for more detailed analysis of developments in specific areas.

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