UX for the city

In this post I explore how the notion of User Experience (UX), a key dimension of Human Computer Interaction (HCI), can enhance urban planning. Applying user-centred design and management approaches to cities, in turn, can also enrich the field of HCI.

Cities are made by people. Yet, are cities made for people? Or rather: for whom, and by whom? Couldn’t urban designers, decision-makers and residents meet up for lunch more often? Everyone has a stake in nurturing supportive urban environments. There are many incentives to increase dialogue and co-production among all stakeholders in spatial planning (e.g. Rittel and Webber 1973, Healey 2012, Albrechts 2013). Two examples:

Greater collaboration could help address growing divides between people. Inequalities are growing in many parts of the world, including the UK (e.g. income disparities, rising homelessness, and related health disparities). The richest 1% are thriving, but for how long? In many cities, the polarisation between gated communities and slums seems anything but sustainable.

Austerity calls for increased engagement. In contexts of austerity in local government, as in the UK, involving users in the design and management of public space could help explore the pros and cons of privatisation and public-private partnerships. For example, funds for public parks in British local councils are melting like ice-cream in summer heat, potentially jeopardising their future provision of ecosystem services to urban residents. In Newcastle, the city council is exploring ways of engaging the public and community groups more actively in the actual maintenance of green urban parks, since its budget for parks and recreation has shrunk tenfold between 2011 and 2016.

The Big Society” is becoming catch-all term for key government services being slowly devolved to the public — compare David Cameron’s launch of the Big Society programme in 2010, and Theresa May’s (even gloomier?) “shared society”. For better or worse, this new way of delivering public service cannot happen without end-users.

UX matters

The notion of “user experience” (UX) emerged in the field of Human Computer Interaction to make computer systems more “humane”. It was a reaction against excessively technical and performance-based approaches to software and website usability, in order to include such fuzzy aspects as users’ feelings (Hassenzahl and Tractinsky 2006). The picture below sums up neatly the difference in experience between a product that is pleasant to use, and one that isn’t.

Bad UX (I hate everything and you!!) vs Great UX (love my life! And you! And puppies!)

Picture credits: Bad UX vs. Great UX, by Jane Aldrich (reproduced with permission) https://userexperiencerocks.wordpress.com/2014/03/24/good-ux-it-does-a-body-good/

User experience is an essential add-on to the more traditional notions of utility and usability. In a blog titled “Usability 101”, Jakob Nielsen defines utility as “whether a design provides the features you need” and usability as “how easy and pleasant these features are to use”. While utility is about getting things done, usability is about pleasant user experiences. A product that meets both criteria can be considered useful.

In a nutshell, “UX” means considering users’ needs, aspirations, and personal and cultural identities in product design and evaluation. It requires considering how these affective/emotional and socio-cultural dimensions influence the way technology is being appropriated by users. It also means recognising the social life of technology: that the use of technology is all about experience, rather than just fool-proof functionality and performance. In other words, users’ adoption of technology is as much about “being”, self-actualisation and social expression as it is about getting things done (Hassenzahl 2004, McCarthy and Wright 2004).

Advocates of collaborative product design (e.g. Wright and McCarthy 2010) also consider that product design should start with users’ needs, particularly those of disadvantaged groups in society, for example in the form of applications that support healthcare, education or mobility. Participatory design of technology is best achieved by engaging end-users as co-designers in iterative cycles of product development and evaluation.

UX for the city

User-centred urban design and management enable to make cities for people, especially if based on the active, iterative participation of end-users. For example, user participation can enhance place-making processes, and complement the legacy of expert observations of end-users by the like of Jan Gehl and William Holly White.

Carefully designed and implemented digital technologies have a central role to play in delivering user-friendly cities. From civic hackathons and gamification to participatory budgeting, living labs, and augmented reality, there are now wide arrays of techniques and methods to engage residents in expressing their needs and preferences. These technologies allow to design with and for users.

Here UX becomes recursive: both the means of engaging users and the deliverables of having engaged them (e.g. urban development projects, plans, and strategies) can embody a strong participatory approach to effectively meet user needs and aspirations. In other words, user experiences should become integral to both processes and outcomes.

Some noteworthy efforts for considering user experience in urban planning include opportunities for more children-friendly planning in Scotland, as well as a recent framework developed by Arup.  However, fully considering children’s experiences does not fall short of challenges in terms of political will and societal awareness. Unicef considers that children’s needs should be at the heart of all decision-making in cities. Case studies show that dialogue and co-exploration of issues and solutions can be preliminary steps toward empowerment (e.g. in Wrexham town, North Wales).

Engagement initiatives need not be top-down. 12-year old Roman set up a Minecraft lounge for everyone in his neighbourhood in Winnipeg, Canada, enabling all participants to learn, have fun and make valuable design suggestions.

Designing for user experience is not without difficulties however. Notions such as “equity” or “fairness” in planning remain slippery or ill-defined (Attoh 2011), which has consequences for sectoral applications, such as green park accessibility (Rigolon 2016), as well as more strategic orientations, such as urban resilience (Meerow and Newell 2016). Multicultural planning, or planning for diversity rather than difference, also brings its own set of challenges: different individuals and communities often have different needs, and fulfilling them is important to improve the experience of all city users. Failing to do so can exacerbate socio-economic differences and divides.

Measuring UX, in planning as in HCI, can also be tricky, because loaded with assumptions.

Notwithstanding challenges, some cities are already formally applying UX to city planning, such as Gainesville, Florida.

Closing the loop

If the notion of user experience derived from Human Computer Interaction, supported by a wide array of digital and physical technologies, can enhance place-making, then this quality can also enrich the field of HCI. Designing cities with and for a diversity of users is difficult yet essential, especially in terms of social, cultural, environmental and intergenerational equity.

Improving UX in the city by means of useful and engaging technology provides many opportunities for HCI to address social concerns, and contribute to close some of the gaps between people. “UX for the city” can become a recursive entreprise, as user experience can be advantageously coded into all aspects and at all scales of system design, evaluation and optimisation.


Albrechts, L. (2013). “Reframing strategic spatial planning by using a coproduction perspective.” Planning Theory 12(1): 46-63.

Attoh, K. A. (2011). “What kind of right is the right to the city?” Progress in Human Geography 35(5): 669-685.

Hassenzahl, M. (2004). “The interplay of beauty, goodness, and usability in interactive products.” Hum.-Comput. Interact. 19(4): 319-349.

Hassenzahl, M. and N. Tractinsky (2006). “User experience – a research agenda.” Behaviour & Information Technology 25(2): 91-97.

Healey, P. (2012). “Re-enchanting democracy as a mode of governance.”

McCarthy, J. and P. Wright (2004). “Technology as experience.” interactions 11(5): 42-43.

Meerow, S. and J. P. Newell (2016). “Urban resilience for whom, what, when, where, and why?” Urban Geography: 1-21.

Rigolon, A. (2016). “A complex landscape of inequity in access to urban parks: A literature review.” Landscape and Urban Planning 153: 160-169.

Rittel, H. W. J. and M. M. Webber (1973). “Dilemmas in a general theory of planning.” Policy Sciences 4(2): 155-169.

Wright, P. and J. C. McCarthy (2010). Experience-centered design: designers, users, and communities in dialogue. San Rafael, Calif., Morgan & Claypool.

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Urban Heat Island for Beginners: Part 3 *

The Vertical Profile of UHI

Any change in the surface energy budget and in the surface temperatures produces effects on urban heat island (UHI) that can be noticed also in its vertical profile. The difference of temperatures between urban core and rural site origins different momentum at different heights in urban canopy layer (Oke, 2006). The vertical difference of temperatures at different heights is mostly visible during nights (Landsberg, 1981). A quite common phenomenon is the crossover effect of temperature in the sky layers. It is the steeper increase of the air temperature over rural sites than urban ones that determines at a variable height a crossover point in which the rural air temperature is higher than the urban one.

The increase in temperature at different heights is mainly due to the more stable climatic condition on rural areas, while urban areas are characterized by a relative instability (Santamouris, 2006).

Typically, the vertical profile of the urban heat island is influenced by wind especially during nights. Summers, in 1964, expressed the magnitude of the UHI in Montreal as inversely dependent on wind speed (Landsberg, 1981). However, Landsberg (1981) affirmed that since every urban area is characterized by a different morphology and a different climatology is not possible to generalize the results obtained for other cities.

Oke (1976) shows the relation between the UHI and the wind speed in Vancouver. By his study it results a residual, although small, UHI also under high velocity wind conditions. In contrast, in correspondence of weak wind speed it is possible verify a stronger UHI.

Wind speed in an urban context is heavily influence by the urban morphology (Shahgedanova, Burt, & Davies, 1997; Montavez, Rodriguez, & Jimenez, 2000; Gaffin, et al., 2007).

At the beginning of the twentieth century, Kremsen focused his research on the decrease in wind speed in urban areas. He observed in Berlin during a decade a wind speed drop of 24% (Landsberg, 1981). Almost the same decrease was observed also in New York City. In Maryland it has been observed that wind speed was about 70% weaker than that recorded in the airport (Landsberg, 1981). Many authors have attributed the wind speed decrease to the increase in urbanization (e.g., Bacci & Maugeri, 1992; Brunetti, Mangianti, Maugeri, & Nanni, 2000).

The wind speed not only varies in the urban pattern in dependence on its density, but it also varies with the height according to the urban roughness.

Taylor found the expression of the vertical wind profile in a neutrally stratified atmosphere (Landsberg, 1981):


Equation 1 Vertical wind profile

Where k is the von Kàrmàn’s constant, its value is about 0.4; h is the height of the measurement; z0 is the superficial roughness; u* is the friction velocity given by:

ū* = τ/ρ

Equation 2 Friction velocity expression

τ is the surface shearing stress and ρ is the atmospheric density. z0 can be expressed as follows (Landsberg, 1981):

z0=h average/2A

Equation 3 Urban superficial roughness

Where h average is the mean height of buildings; A is the ratio between the cross section beaten by the wind and the area of buildings in that area (Landsberg, 1981).

Nakamura and Oke (1988) suggest a simplified expression for wind velocity in street canyons:

ūcanyon = p ūroof

Equation 4 Mean canyon wind speed

Where ūcanyon is the horizontal mean wind speed measured in the street canyon center at a height of 0.06H; p is a factor depending on H/W and ūroof is the wind speed at a height of 1/2H above the roof level. For a wind velocity up to 5 m s-1 Nakamura and Oke found an aspect ratio (H/W) of about 1, p ≈ 2/3.

Canyon effect at micro scale can channel wind and maximize its velocity; this is due to a wrong design of street dimensions. Oke (1988) found the mathematical correlation between the wind velocity and H/W ratio of a street canyon. The high decrease in the wind, or, on the contrary the high increase in the wind speed can be a hazard for population. Oke (1988) found that a ratio H/W of about 0.65 can ensure the best comfort for people. If the high wind velocity can be a hazard for population, on the other hand, the excessive decrease in wind speed can provoke the stagnation of pollutants with effects on human health.

The urban canyons geometry also plays a crucial role in the energy balance. The canyon surfaces are important because by their characteristics depend the amount of energy absorbed and re-radiated. Street canyon characterized by a high ratio between height and width provokes the trapping of solar radiations and the increase in temperatures especially during night (Santamouris, 2006). Although, typically, just a little amount of solar radiation reaches the canyon surface, the emitted radiation depends on the sky view factor (SVF).

The sky view factor is defined as the “openness of a site within an urban setting”  (Grimmond, 2007). Such a factor plays an important role in the thermal behavior of the street canyon in the urban environment. In the urban canyons both the pavement and the building façades are involved in the thermal balance. During the day, the surface temperature mainly depends on the solar radiation reaching the canyon, thus depends also on the aspect ratio (H/W) and on orientation, and depends also on the thermal and optic characteristics of the building materials. At night, the façade temperature is governed by the radiative balance. Its value depends on the SVF.



Bacci, P., & Maugeri, M. (1992). The urban heat island of Milan. Il nuovo cimento, 15 (4)

Brunetti, M., Mangianti, F., Maugeri, M., & Nanni, T. (2000). Urban heat island bias in Italian air temperature series. Nuovo Cimento, 23 (4)

Gaffin, S. R., Rosenzweig, C., Khanbilvardi, R., Parshall, L., Mahani, S., Glickman, H., et al. (2007). Variations in New York city’s urban heat island strength over time and space. Theoretical and Applied Climatology

Grimmond, S. (2007). Urbanization and global environmental change: local effects of urban warming. The Royal Geographical Society, 83-88

Landsberg, H. E. (1981). The Urban Climate (Vol. 28). New York: International Geophysics Series

Montavez, J. P., Rodriguez, A., & Jimenez, J. I. (2000). A Study of the Urban Heat Island of Granada. International Journal of Climatology, 20, 899-911

Nakamura, Y., & Oke, T. (1988). Wind, temperature and stability conditions in an E-W oriented urban canyon. Atmospheric Environment, 22 (12), 2691-2700

Oke, T. R. (1976). The distinction between canopy and boundary layer urban heat islands. Atmosphere, 14, 268-277

Oke, T. R. (1988). Street Design and Urban Canopy Layer Climate. Energy and Buildings, 11, 103-113

Oke, T. R. (2006). Instruments and Observing Methods – Initial Guidance to Obtain Representative. (W. M. Organization, Ed.) Retrieved August 30, 2010, from World Meteorological Organization: http://www.wmo.int/pages/index_en.html

Santamouris, M. (2006). Environmental design of urban buildings. An integrated approach.London: Earthscan

Shahgedanova, M., Burt, T. P., & Davies, T. D. (1997). Some aspects of the three-dimensional heat island in Moscow. International Journal of Climatology, 17, 1451-1465

* Rearranged text from: Susca, T. (2011). Evaluation of the Surface Albedo in a LCA Multi-scale Approach. The Case Study of Green, White and Black Roofs in New York City. Ph.D. Thesis

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Urban Heat Island for Beginners: Part 2 *

The Two Dimension of the UHI

UHI is mainly dependent on the modification of the urban energy fluxes and, as a consequence, on the synoptic conditions in urban and rural sites. UHIs are characterized by two dimensions: an extension and a vertical profile. Typically, the UHI is plotted with closed isotherms indicating the area of the urban surface characterized by high temperatures (Oke, 2006).


Figure 1 Urban heat island isotherms. Credit: United States Environmental Protection Agency

The cross-section – that is also the icon through which the UHI is represented – typically possesses ‘cliffs’ at the urban-rural fringe and a ‘peak’ in correspondence of the most built-up core of the cities (Pon, 2007).


Urban_heat_island_Celsius-640x356Figure 2 Urban heat island cross section. Credit: Geography

The UHI Extension

As demonstrated in many studies, the UHI dimension depends on the city-core extension, the most densely urbanized areas of the city (e.g., Unger, Sümeghy, Gulyás, Z., & Mucs, 2001). In the city core the natural energy balance is mostly modified by the substitution of natural materials with man-made ones (Oke, 1982). At meso-scale, in calm weather conditions, UHI has the shape of a dome at urban boundary layer; while, in case of steady regional airflow its shape is a plume at planetary boundary layer (Oke, 2006; Shahmohamadi, Che-Ani, Ramly, Maulud, & Mohd-Nor, 2010).



Figure 3 Urban plume. Credit: modified after Oke, 1987

The variation of the natural energy balance determined by the local urban morphology (Santamouris, 2006; He, Liu, Zhuang, Zhang, & Liu, 2007) provokes climatic variations also at local scale (i.e., urban canopy layer).

The effects of UHI at different scales can be summarized as follows:

  1. Micro-scale: the micro scale, in the urban pattern, is representative of the dimension of a building, a street or a courtyard. Thus, its dimension is of just few meters or less. Every modification in the energy budget at this scale, gives rise to a modification in air temperature, considered in the local and instantaneous change in punctual urban climate (Oke, 2006);
  2. Local scale: the local scale does not take into account the micro scale changes in temperature but considers the topography and the climate of neighborhoods. Commonly this scale has an extension ranging from one to several kilometers (Oke, 2006);
  3. Mesoscale: at this case, the information about the climatic conditions are at the urban scale, for this reason all the city have to be monitored by several stations. The scale is of several kilometers (tens kilometers) (Oke, 2006).

Oke defined an empirical correlation between the magnitude of the UHI and population for both American and European cities (Landsberg, 1981). For the North American evaluation, Oke plotted the data about UHI recorded in ten settlements in Quebec and eight cities in the other states. He determined through a regression analysis the following relationship valid for the North-American cities:

ΔT(U-R) max=3.06 logP – 6.79

Equation 1 Maximum UHI intensity in North-American cities. Source: (Oke, 1976)

The relationship between population and UHI maximum intensity (equation 2) explains 96% of the variance with a standard error of ±0.7°C (Oke, 1976).

For the European cities the same relationship is explained by the following formula:

ΔT(U-R) max=2.01P – 4.06

Equation 2 Maximum UHI intensity in European cities. Source: (Landsberg, 1981)


Figure 4 Relationship between urban population and the maximum UHI (Source: Oke, 1982)

Oke analyzed different cities characterized by topographic elements of minimal influence on UHI (Oke, 1982). Population was chosen as a synthetic parameter in order to evaluate the increase of urban temperature because it is characteristic not only of the number of people living in a city but also of anthropogenic heat release and alteration of surface conditions. The difference between the regression analysis carried out for the North-American cities and the European ones is the symptom of how differently cities have been built up in the two continents (figure 4). Moreover, Oke (1973) found an empirical correlation between UHI and population (P) and mean wind speed (ū):

ΔT(U-R) =0.25 P1/41/2

Equation 3 Relationship between population, mean wind speed and UHI Intensity

Bonan (2002) revised the previous relations between UHI and population as follows:

ΔT(U-R) =5.21 Log10(P) – 11.24

Equation 4 Relation between UHI and population in North-American cities

ΔT(U-R) =3.02 Log10(P) – 3.29

Equation 5 Relation between UHI and population in European cities

Many other studies focused on the relationship between urban sprawl and urban temperatures. The analyses of the temperatures of the last 148 years, conducted by Bacci and Maugeri (1992), reveal an increase in the difference between urban and rural temperatures of 1.4°C in the last decades in Milan. Bacci and Maugeri also analyzed the relation between the urban sprawl and the UHI in Milan. According to the authors, the UHI (∆Tu) in Milan can be expressed through the increase in the city radius (R) as follows:

∆TU-R = 0.39 ln (R/R0) + 0.87

Equation 6 Relationship between Milan city radius and the UHI


∆TU-R = 0.86 (R/R0)0.40

Equation 7 Relationship between Milan city radius and the UHI

Where T is the air temperature expressed in degrees Celsius [°C], R is expressed in [km], and R0 is equal to one kilometer.

Crisci et al. conducted the regression analysis of the temperature series recorded in Florence. Furthermore, the authors measured the influence of urbanization on the temperature series by regression analysis. The temperature data series in Florence were recorded by the Peretola Observatory placed on a rural site, and by the Ximeniano Observatory, an urban site, in the time periods: 1951-1974 and 1975-1994. By the regression analysis, the authors found that the mean difference between the rural temperatures and the urban ones was approximately 0.7°C for the period 1951-1974, besides for the period 1975-1994, the mean difference was 1.2°C. The increase in temperatures was not constant during the period 1878-1997, but was particularly concentrated in the periods 1920-1950 and 1951-1997. During the period 1920-1950, the increase in temperatures was only evident in the summer months, while in the period 1951-1997, the warming was evident throughout the whole time period. The authors attributed the rise in temperatures to both global and urban warming. Most of all, the increase in temperatures has been more evident since the early sixties (the economic and building boom time period).

The correlation between the increase in the urban temperature and the urban sprawl in Rome is confirmed by Bonacquisti et al. (2006). Comparing the minimum temperatures in the period 1964-75 (the period of the most consistent urban growth) with 1831-1910, the authors observed a rise of 1°C. Moreover, the authors affirm that the UHI in Rome mainly depends on the thermal properties and on the urban geometry, but it is not strongly influenced by anthropogenic heat releases. Colacino and Lavagnini (1982), in their study, attribute the presence of the UHI in Rome not only to the modification of the urban structure but also to the distance from the coastline. The maximum temperatures recorded during summer and winter in Rome, in an inland suburban area and in a coastal site, were compared. The results of their inquiry show that the sea breeze mitigates the maximum temperatures both during summer and during winter. The effects of the sea breeze are visible up to a distance of approximately 10 km.

[….To be continued]


Bacci, P., & Maugeri, M. (1992). The urban heat island of Milan. Il nuovo cimento, 15 (4).

Bonacquisti, V., Casale, G. R., Palmieri, S., & Siani, A. M. (2006). A canopy layer model and its application to Rome. Science of the Total Environment (364), 1-13.

Colacino, M., & Lavagnini, A. (1982). Evidence if the Urban Hest Island in Rome by Climatological Analyses. Arch. Met. Geoph. Biokl. (31, Ser. B), 87-97.

Crisci, A., Gozzini, B., Maracchi, G., & Meneguzzo, F. (n.d.). http://www.clima.ibimet.cnr.it/attachments/gilia/La_serie_storica_delle_temperature_medie_mensilli_di_Firenze.pdf. Retrieved March 2009

He, J. F., Liu, J. Y., Zhuang, D. F., Zhang, W., & Liu, M. L. (2007). Assessing the effect of land use/land cover change on the change of urban heat island intensity. Theoretical and Apllied Climatology, 90, 217-226.

Landsberg, H. E. (1981). The Urban Climate (Vol. 28). New York: International Geophysics Series.

Oke, T. R. (1973). City size and the urban heat island. Atmospheric Environment, 7, 769-779.

Oke, T. R. (1976). The distinction between canopy and boundary layer urban heat islands. Atmosphere, 14, 268-277.

Oke, T. R. (1982). The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society, (108), 1-24.

Oke, T. R. (2006). Instruments and Observing Methods – Initial Guidance to Obtain Representative. (W. M. Organization, Ed.) Retrieved August 30, 2010, from World Meteorological Organization: http://www.wmo.int/pages/index_en.html

Pon, B. (2007). High Temperatures. Retrieved September 12, 2010, from Heat Island Group: http://heatisland.lbl.gov/HighTemps/

Santamouris, M. (2006). Environmental design of urban buildings. An integrated approach. London: Earthscan.

Shahmohamadi, P., Che-Ani, A. I., Ramly, A., Maulud, K. N., & Mohd-Nor, M. F. (2010). Reducing urban heat island effects: A systematic review to achieve energy consumption balance. International Journal of Physical Sciences, 5 (6), 626-636.

Unger, J., Sümeghy, Z., Gulyás, Á., Z., B., & Mucs, L. (2001). Land-use and meteorological aspects of the urban heat island. Meteorology Applied, 8, 189-194.

* Rearranged text from: Susca, T. (2011). Evaluation of the Surface Albedo in a LCA Multi-scale Approach. The Case Study of Green, White and Black Roofs in New York City. Ph.D. Thesis


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John Friedmann (1926-2017)

Not only was John Friedmann (1926-2017) one of the brightest minds in the history of planning research and action, he was also a great friend of the YAs. In 2015, while I was finalising the organisation of the YA conference in Palermo, his wife, Leonie Sandercock, who we had invited as keynote speaker, suggested me: ‘John is travelling with me, why don’t you ask him if he wants to help?’

We had no budget left, and I was quite puzzled by the idea of asking. But the opportunity to have John Friedmann in the conference was too thrilling: so I went on asking whether he’d like nonetheless to chair a session and a workshop on the research-theory-action nexus (with Laura Saija). He was happy to join us, and contributed a great deal to the success of the conference. No one of us will ever forget his generosity, strength and charm; and the amazing histories of planning he told us during that workshop.

We decided to remember John Friedmann by collecting some short comments from members of the YA community. Below, you’ll find very different statements, which share the energy John transmitted to whom met or read him.

We’d love to receive more statements, and we’ll publish them on a rolling basis on this page. Please send your contribution (150-200 words) to yamail@aesop-youngacademics.net and simone.tulumello@ics.ulisboa.pt.

(Simone Tulumello)

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Urban Information Systems

In this post I discuss Urban Information Systems, and how they fit into wider society.


Urban Information Systems – a modern day “Pandora’ s Box”? Photo by Ludovic Bertron on Flickr, Creative Commons Attribution 2.0.

What are they?

Urban information systems centralise and mediate diverse types of information that have been extracted from urban data. In turn, these arrays of information can help people produce relevant knowledge and facilitate better decision-making in spatial planning and business.

In the absence of any authoritative definition, “urban information systems” encompass the following information management systems: Decision Support Systems (DSS), Planning Support Systems (PSS), Spatial Planning Support Systems (SPSS), and City Information Models. These often rely on spatially-explicit technologies such as Geographic Information Systems (GIS) and multifunctional 3D visualisation platforms that allow to display and overlay diverse types of data. Urban Information Systems enable to monitor and visualise resource use, flows, and processes so as to aid decision-making, with the promise to support more sustainable urban management and strategic planning .

Urban information systems can be closely connected to modelling software that can simulate floods, mobility, urban densification, physical connectivity, as well as perform complex multivariate spatial analyses (e.g. Models can be descriptive (describe current processes), predictive (e.g. simulate changes in the environment and compare land use scenarios) and prescriptive (e.g. visualise required changes in the environment or in behaviours to meet specific goals and targets, such as through backcasting). While urban models and Planning Support Systems typically have different objectives and target audiences , the increasing compatibility of data, software and hardware now allows for greater fluidity between modelling and decision-support outputs.

Data, the IoE, and smart cities

At the core of Urban Information Systems is data. More traditional forms of urban data include cadastre data, real estate, tax, incomes, crime statistics, health statistics, construction management (e.g. BIM), planning applications, energy use… Urban data also includes the fuzzy notion of “Big Data”: data collected through myriads of physical sensors to monitor environmental conditions and processes (e.g. climate, air quality, water, energy, noise, traffic, people…), data collected through diverse mobile and stationary devices (e.g. smartphones, computers, drones), social media, and consumer behaviour data. Some Big Data is Open Data, but not always. Many local authorities and research clusters are now deploying networks of sensors in the urban environment (Mone, 2015), from Chicago and Newcastle to Nairobi, leveraging affordable real-time data.

The complex assemblage of interconnected infrastructures, networks and devices that mediate the production, distribution, processing and storage of data are powered by the Internet of Things (IoT), with its heavy reliance on cloud computing, storage capacity and services; these constitute the backbone of smart cities (Jin, Gubbi, Marusic, & Palaniswami, 2014). The IoT becomes the “Internet of Everything” (IoE) as it pervades and monitors all parts of our daily lives. Such relentless data accumulation is often portrayed as enabling to improve life in cities, thanks to the work of the emerging workforce of expertly trained data scientists (Power, 2016b), as well as low-cost business- and government-led crowdsourcing and procurement initiatives, such as civic hackathons (Johnson & Robinson, 2014).

3D visualisation platforms

Visualising everything on a single platform promises to be a highly effective way of understanding and coordinating disparate trends in the urban environment. Integrated PSS technologies for collaborative project management and public engagement now exist to mediate more effective communication and decision-making across a wide variety of stakeholders. For example, a projected aim of the Virtual Newcastle Gateshead 3D city information model, developed at Northumbria University, is to become openly accessible to all local stakeholders to allow more effective communication and collaboration between local authorities, construction professionals, businesses, non-profit organisations and the public.  Challenges include putting the model online, and improve the quantity and quality of data that could leverage more applications for use in the city-region.

There are also institutional and organisational hurdles to adopting 3D Urban Information Systems. Delivering a keynote at the GISRUK 2017 conference in Manchester, Andrew Hudson-Smith (Director of CASA at UCL) shared how the pioneer 3D city model of London which they developed in the early 2000s never quite caught on with Ken Livingstone, then Mayor of London. The planning times were simply not ripe. In a similar vein, my former supervisor at KTH, Stockholm, also told me how, despite the increasing availability of fancy 3D GIS visualisations, local planners in Sweden still rely on the good-old paper local plans as statutory documents. The enticing text and image descriptions of which actually bring life to such flat plans, on the other hand, are not legally binding.

Similarly, Marco te Brömmelstroet has shown on the basis of an experiment that while Planning Support Systems (PSS) can improve planning processes when they are actually taken up, they do not systematically improve the quality of planning outcomes. Furthermore, even as PSS become more user-friendly, they are not necessarily more useful to planners, because of organisational and political factors within planning administrations which PSS-developers still need to consider in product-design and deployment. In the absence of true iterative collaborative processes between industry and academics, the implementation gap for Urban Information Systems is set to persist (M. te Brömmelstroet, 2016; Marco te Brömmelstroet, 2016).

Welcome to the Machine / Ghost in the shell

The data being produced about urban environments and their inhabitants is now becoming so large that it outpaces the capacity to derive meaning from it. As the volume, velocity and diversity of data production and distribution increases exponentially, machine learning is becoming a core facilitator of the data-driven smart(-er?) city. The smart-cities hype builds on the Big Data hype itself, where innovation is fostered almost for its own sake, driven by fluid forms of governance, and where social inclusiveness is potentially held on the back burner (see former posts in this blog). The very act of learning to speak and think in the language of the machine (i.e. coding languages) is becoming increasingly valued in-and-of-itself, as observed by Yuval Noah Harari in Sapiens, and it certainly serves the smart city well. Digital literacy and data science are the backbone of tomorrow’s urban information systems. The process of “sensing the city” through all possible means is making the physical, digital and human make-up of cities increasingly fused or “cyborg-like” (see Gandy, 2005; Mitchell, 2003).

In this context, the ubiquitous digital technologies that facilitate life in cities also allow to monitor flows and processes with increasing minutiae and sophistication, for good or evil. Not only can human constituents be tracked physically, they can also be profiled based on the information they provide by interacting with urban as well as digital environments, such as social media. Dan Power (2016a), expert in the field of Decision Support Systems, argues that the societal push towards big data analytics and ubiquitous computing runs the risk of being usurped by totalitarian-minded power mongers, in the style of George Orwell’s 1984. Big Data invites Big Brother? Complex Urban Information Systems are also central to dystopian movies such as Brazil and novels such as Fahrenheit 451. Other researchers such as Stephen Graham and Louise Amoore also warn against the powerful surveillance uses of smart and networked technologies. Will machines take control over human decisions? The highly automated junk financial transactions did largely contribute to precipitate the global economic crunch in 2008…


Today’s breed of Urban Information Systems is relatively new, but older insights could come to the rescue. An engaging article by Edward Hearle (1968) provides some useful reflections for effective urban management information systems that can help modern cities separate the wheat from the chaff in the “smart cities” and “Big Data” agendas, and ween off data-addiction. Information derived from data is great, but how about insight and wisdom?

The post thus begs the following questions: Do urban information systems hold the promise of supporting more sustainable cities? Or are they the 21st century version of Pandora’s box? Probably both. Urban information systems can mediate an effective and participatory coordination of urban metabolisms, just as they risk being hijacked by some mustachioed Big Brother supported by a fear-stricken constituency. Urban Information Systems may be just a cog in the great wheel of neoliberal urbanisation (see David Harvey). Following Pandora’s lead, we can and must hope for the better (but must also work hard to achieve it). The types of information that technology can mediate are a mean to improve spatial planning, rather than an end. In my understanding, the information that matters most in using Urban Information Systems is what our intentions and motivations are as individuals and communities.

Gandy, M. (2005). Cyborg urbanization: Complexity and monstrosity in the contemporary city. International Journal of Urban and Regional Research, 29(1), 26-49. doi:10.1111/j.1468-2427.2005.00568.x
Hearle, E. F. R. (1968). Urban management information systems. Socio-Economic Planning Sciences, 1(3), 215-221. doi:10.1016/0038-0121(68)90010-4
Jin, J., Gubbi, J., Marusic, S., & Palaniswami, M. (2014). An Information Framework for Creating a Smart City Through Internet of Things. IEEE Internet of Things Journal, 1(2), 112-121. doi:10.1109/JIOT.2013.2296516
Johnson, P., & Robinson, P. (2014). Civic Hackathons: Innovation, Procurement, or Civic Engagement? Review of Policy Research, 31(4), 349-357. doi:10.1111/ropr.12074
Mitchell, W. J. (2003). Me[plus plus]: the cyborg self and the networked city. Cambridge, Mass: MIT.
Mone, G. (2015). The new smart cities. Commun. ACM, 58(7), 20-21. doi:10.1145/2771297
Power, D. J. (2016a). “Big Brother” can watch us. Journal of Decision Systems, 25(sup1), 578-588. doi:10.1080/12460125.2016.1187420
Power, D. J. (2016b). Data science: supporting decision-making. Journal of Decision Systems, 25(4), 345-356. doi:10.1080/12460125.2016.1171610
te Brömmelstroet, M. (2016). PSS are more user-friendly, but are they also increasingly useful? Transportation Research Part A: Policy and Practice, 91, 166-177. doi:10.1016/j.tra.2016.05.012
te Brömmelstroet, M. (2016). Towards a pragmatic research agenda for the PSS domain. Transportation Research Part A: Policy and Practice. doi:https://doi.org/10.1016/j.tra.2016.05.011

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Swachh Bharat: An urban reform

swachh bharat

A framework on objectives of Swachh Bharat

Source: http://www.ndtv.com/photos/news/two-years-on-what-is-the-status-of-swachh-bharat-abhiyan–22957

Modi Government’s ‘swachh bharat’ (here) is an initiative taken by the central government of India that literally means “clean India”. The reform formulated by Prime Minister Modi is influenced by Gandhiji’s one moto: “Quit India, Clean India” during British Colonisation period. Modi government has widely campaigned for such “Clean India” movement (here). While the policy reform is primarily meant to contribute towards public health, in the blog, I argue that there are wider benefits to be reaped from the policy, especially in the light of contemporary urban issues in Indian context, related to resource depletion and sustainability. Moreover, Prime Minister Modi emphasizes on the fact that such a movement require wide public participation, it is part of a bigger global movement known as Healthy Cities movement promoted by World Health Organisation (WHO). In the later part of the blog, I discuss ‘Healthy Cities Movement’ in detail.

Open Defecation Free Cities

One of the main components of Swachh Bharat was to make cities open defecation free; this is, to deliver and maintain individual and public toilets, open defecation being a common urban issue in Indian cities. For the state of Maharashtra, Regional Centre for Urban and Environmental Studies, All India Institute of Local Self Government (here) has been instrumental in delivering ODF cities. Performance Assessment System (PAS) project (here) at the Centre of Water at CEPT University, Ahmedabad played a key role in the project in the state of Gujarat and Maharashtra. It is a five-year Action Research Project funded by Bill and Melinda Gates Foundation. The project is on developing appropriate tools and methods to measure, monitor and improve delivery of water and sanitation services in urban India. While open defecation has been a typical issue in Indian cities, many government policies on providing community toilet and individual toilet have failed to achieve the outcome in low income communities for various reasons including people started using those such hard infrastructures for domestic purposes, and still chose to continue open defecation. The policy document on open defecation refers to removing indignity and improving public health. In the context of India, especially for women, there are safety and violence issues related with open defecation. Specially there are at least more number of such cases reported nowadays, and safety of women has become one important issues. The program is expected to contribute towards women safety.

Waste water recycling

While there is an obvious component of hygiene related to controlling open defecation, there are wider implications. Many old cities do not have separate storm water system and sewage system. Cities in India are facing water crisis as a combined impact of climate change and fast urbanisation. There is rapid urbanisation rate and depletion of resources. Many old cities are now replacing the traditional urban water system with wastewater recycling system and grey water harvesting to deal with water crisis. The assumption is that collected rainwater, i.e., the storm water can be recycled for other uses without treatment. However, the high rate of open defecation jeopardise such a possibility of reusing storm water. Hence, it is imperative that cities are ODF so that cities can successfully operate its water reuse and recycle system. While access to water is a serious issue across the globe, it is acknowledged that urban water system is interconnected and has to be maintained as an integrated one. ODF cities will not only contribute towards clean city and public health, but also to efficiency of the water recycling system. The concept of urban metabolism can be applied here, as it deals with inflow of resources to the city, and outflow of resources from the city, which goes back to the natural environment. Scholars from pure science background are using the concept of urban metabolism to measure the quality and quantity of groundwater table in Bangalore.

Solid Waste Management

Another component of Swachh Bharat is door-to-door waste collection programme. However, there is no mention of segregating waste at the source in the policy document. The website indicates that a higher percentage of our waste can be recycled though. There is also no mention of policies to encourage little waste generation, or punishing for generating waste beyond a limit. However, for contemporary Integrated Township project, private sector developers and managers use such concept. The Magarpatta City model in Maharashtra practices such segregation of waste at source by providing containers of different colours, which is efficient and unique. Surprisingly, many old cities have a traditional system of dumping the large chunk of non-segregated solid waste from the whole municipal area at a single location, where it is manually segregated by informal and low-income workers. This leads to serious health hazard for them. Implementing such practice of segregation of solid waste at source will be a milestone. Although this is challenging in old cities, it is being practiced in bits and pieces. For instance, one municipal district of Kolkata had partially implemented the same, and was awarded an international award (here). The city of Curitiba demonstrates a wonderful example of the segregated solid waste collection at the source for the whole city.

Healthy Cities Project
World Health Organisation’s ‘Healthy Cities project’ is a global movement (here). It engages local governments in health development through a process of political commitment, institutional change, capacity-building, partnership-based planning and innovative projects. I would like to connect the national reform on Swachh Bharat with the international movement on Healthy Cities Project, reference to water supply and sanitation sector is mandatory to any traditional healthy city and public health debate.

Citizen participation
For the success of Swachh bharat, Modi has called on the mass for participation, as without their participation, it is not possible to achieve this. While this is a very noble step, I believe no single decision is taken in isolation. Many scholars have written about how the public sector participation windows are captured by elite and upper middle classes at the local government level, in spite of the legal framework being in place. Although private sector entrepreneurs are working in the area of solid waste management with innovative technological solutions, they repeatedly mention that operation in the area of solid waste collection and management is territorial in nature, and hence, often encountered with illegal actors. Finally, to achieve the objectives of swachh bharat, the local governments will have to address the informal and illegal workers’ Right to the City, which has been raised by New Urban Agenda, and have been an issue of conflict with the local governments.

To conclude, each of the issues mentioned above, as ODF cities, solid waste management, and Right to the City, are way deeper, and can contribute towards blogs on their own.

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Urban Heat Island for Beginners: Part 1 *

The urban population is increasing worldwide and the urban expansion or the increase in urban density can affect both the global and the local climate having consequences, in turn, on human health. It is, therefore, important to increase the awareness of policy makers, urban decision makers and citizens about urban climate pathologies. In particular, I report in the following a brief and simplified explanation about the urban heat island effect, a very common urban climate phenomenon. The text in the following has been simplified in order to reach not only researchers or people already in the field, but, more importantly, citizens who might have no scientific background in such field, but that might be the most interested stakeholder in this theme. For this reason I decided to entitle this post “Urban Heat Island for Beginners”.

The description of the urban heat island phenomenon has been split into chapters that you will find published on this same blog in the coming months.

Enjoy the first part of the reading!


At the beginning of the twentieth century, 15% of the world inhabitants lived in cities. Nowadays, about 50% of the world population lives in urban areas which are approximately 2.8% of the total land of our planet (Millennium Ecosystem Assessment, 2005). The rise of the urban inhabitants has led to urban sprawl, especially in developing countries (United Nations, 2004), and – as demonstrated in previous studies (e.g., Bacci & Maugeri, 1992) – urban sprawl is often correlated to the increase of the urban temperature compared to the rural surroundings, the so-called Urban Heat Island (UHI) effect (Landsberg, 1979; Frumkin, 2002).

The development of UHI mainly depends on the modification of the natural energy balance in urban areas. The modification of the natural energy balance is due to several factors such as urban canyons (Landsberg, 1981), substitution of natural materials with artificial ones featured by different thermal properties (Montavez, Rodriguez, & Jimenez, 2000), substitution of green areas with impervious surfaces which limit evapo-transpiration (Takebayashi & Moriyama, 2007; Imhoff, Zhang, Wolfe, & Bounoua, 2010; Lougeay, Brazel, & Hubble, 1996), and urban albedo decrease (Akbari & Konopacki, 2005). Another cause of the increase in urban temperature is the distribution of buildings that, in most cases, provokes an abatement of wind speed and a consequent reduction of heat dissipation (Morris & Simmonds, 2001).

The urban heat island is usually defined as the difference between the urban temperature and its rural surroundings, with temperatures recorded in the canopy layer [1] (equation 1), but often is also described through the difference of temperatures recorded in the boundary layer [2] for example through the use of towers, balloons or aircrafts.

UHI = ∆TU-R = TU – TR

Equation 1 UHI intensity general expression

Furthermore, other parameters, commonly used to depict UHI phenomenon, are the difference of surface temperatures (Imhoff, Zhang, Wolfe, & Bounoua, 2010) or vegetation index (Gallo, McNab, Karl, Brown, Hood, & Tarpley, 1993; Gallo & Owen, 1999; Weng, Dengsheng, & Jacquelyn, 2004).

It has been observed that UHI phenomenon consistently amplified over time for the enhancement of industrial activities and urbanization. Brunetti et al. (2000) investigated the historical series of temperatures in Italy, which shows that the increase of the UHI phenomenon in Italy (0.2°C/100 years) is higher than the global one (0.1°C/100 years).

Many other studies justified the rise of urban temperature not only with the climatic phenomenon, but also with the change in the urban structure (e.g., Bacci & Maugeri, 1992; Bonacquisti, Casale, Palmieri, & Siani, 2006). Gaffin et al. (2007), analyzed the historical series of temperatures recorded in New York City – from the beginning of the twentieth century to present – in order to study the temporal evolution of the UHI. The authors have reconnected the increase of the urban temperature during time to a significant drop of wind speed due to a change in the urban structure, in particular due to the increase of the height of buildings and the expansion of the city core.

Brief history of UHI

The UHI was detected and measured for the first time at the beginning of ninetieth century by Luke Howard. Luke Howard – known also as ‘the father of meteorology’ – was also a pioneer in urban climatology. From 1820 through 1833 he compared the temperatures he surveyed in at Plaistow, a village 6.4 Km far from London and at Tottenham with those recorded by the Royal Society at Somerset House and he recognized the urban heat island in London (Landsberg, 1981). Howard found a difference of temperatures between the ‘urban’ and ‘rural’ sites and he attributed the increase of the urban temperature to the high use of fuel and to the anthropogenic heat (Santamouris, 2006) [3]. About twenty years later, Renou detected the urban heat island in Paris. Renou mainly noted the difference of temperatures between urban and rural sites, especially during the afternoon, the increase of the urban temperatures also during winter and the heavily decrease in the wind speed in the urban context (Landsberg, 1981). After Howard and Renou a large number of important studies have been carried out and have contributed to decrypt and understand the urban heat island phenomenon. Tony Chandler recognized in 1959 the spatial characteristics of the UHI in London. He showed that the hot area in London occupied the built-up area in the city and increased its magnitude in the most densely urbanized areas while it was weaker in the greener areas generating the cool heat island. In ‘The Climate of London’, Howard underlined that in the period from 1794 through 1799, the difference of temperatures between urban and rural sites was about 3°C and that in the period from 1811 through 1816 that difference increased to 4.5°C (Howard, 1833). Notwithstanding the first observations were, in some cases, elementary, they constituted the first step in the detection of an increasing urban pathology and the roots for more refined further studies.


Akbari, H., & Konopacki, S. (2005). Calculating energy-saving potentials of heat-island reduction strategies. Energy Policy, 33, 721-756

Bacci, P., & Maugeri, M. (1992). The urban heat island of Milan. Il nuovo cimento, 15 (4)

Bonacquisti, V., Casale, G. R., Palmieri, S., & Siani, A. M. (2006). A canopy layer model and its application to Rome. Science of the Total Environment (364), 1-13

Brunetti, M., Mangianti, F., Maugeri, M., & Nanni, T. (2000). Urban heat island bias in Italian air temperature series. Nuovo Cimento, 23 (4)

Frumkin, H. (2002). Urban Sprawl and Public Health. Public Health Reports, 117

Gaffin, S. R., Rosenzweig, C., Khanbilvardi, R., Parshall, L., Mahani, S., Glickman, H., et al. (2007). Variations in New York city’s urban heat island strength over time and space. Theoretical and Applied Climatology

Gallo, K. P., & Owen, T. W. (1999). Satellite-based adjustments for the urban heat island temperature bias. Journal of Applied Meteorology, 38, 806-813

Gallo, K. P., McNab, A. L., Karl, T. R., Brown, J. F., Hood, J. J., & Tarpley, J. D. (1993). The use of NOAA AVHRR data for assessment of the Urban Heat Island effect. Journal of Applied Meteorology, 5 (32), 899-908

Howard, L. (1833). The climate of London, deduced from meteorological observations. London: Joseph Rickerby

Imhoff, M. L., Zhang, P., Wolfe, R. E., & Bounoua, L. (2010). Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sensing of Environment, 114, 504-513

Imhoff, M. L., Zhang, P., Wolfe, R. E., & Bounoua, L. (2010). Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sensing of Environment, 114, 504-513

Landsberg, H. E. (1979). Ampmospheric changes in a growing community (the Columbia, Maryland experience). Urban Ecology, 4, 53-81

Landsberg, H. E. (1981). The Urban Climate (Vol. 28). New York: International Geophysics Series

Lougeay, R., Brazel, A., & Hubble, M. (1996). Monitoring Intra-Urban Temperature Patterns and Associated Land Cover in Phoenix; Arizona Using Landsat Thermal Data; Geocarto International, 11, 79-98

Millennium Ecosystem Assessment. (2005). Millennium Ecosystem Assessment, Ecosystems and Human Well-being: Current State and TrendsAssessment. Washington, DC: Island Press

Montavez, J. P., Rodriguez, A., & Jimenez, J. I. (2000). A Study of the Urban Heat Island of Granada. International Journal of Climatology, 20, 899-911

Morris, C. J., & Simmonds, I. (2001). Quantification of the Influences of Wind and Cloud on the Nocturnal Urban Heat Island of a Large City. American Meteorological Society, 40, 169-182

Oke, T. R. (1982). The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society, (108), 1-24

Santamouris, M. (2006). Environmental design of urban buildings. An integrated approach. London: Earthscan

Takebayashi, H., & Moriyama, M. (2007). Surface heat budget on green roof and high reflection roof for mitigation of urban heat island. Building and Environment (42), 2971-2979

United Nations. (2004). World Urbanization Prospects: The 2003 Revision. New York: United Nation Publication

Weng, Q., Dengsheng, L., & Jacquelyn, S. (2004). Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89, 467-483


[1] The canopy layer is defined by the mean urban roughness that is due to mean building height and urban vegetation height

[2] The boundary layer is a meso-scale internal layer determined by the urban characteristics (Oke, 1982)

[3] Although a limit in Howard’s measurements was the lack of simultaneity

* Rearranged text from: Susca, T. (2011). Evaluation of the Surface Albedo in a LCA Multi-scale Approach. The Case Study of Green, White and Black Roofs in New York City. Ph.D. Thesis

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