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).

 

Figure 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 P^1/4 /ū ^1/2

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 (∆T_u) in Milan can be expressed through the increase in the city radius (R) as follows:

∆T_U-R = 0.39 ln (R/R₀) + 0.87

Equation 6 Relationship between Milan city radius and the UHI

or

∆T_U-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]

References

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

 

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)

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…

Epilogue

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 21^st 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

Swachh Bharat: An urban reform

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.

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!

Introduction

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 = ∆T_U-R = T_U – T_R

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.

References

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

Footnotes

[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

Design thinking for Bucharest business district areas. Experimental workshop – outside view @Kaleidoscope

Guest authors: Irina Paraschivoiu (Urban INC), Anamaria Vrabie (Urban INC), Silje Klepsvik (Kaleidoscope Nordic), Miia-Liina Tommila (Kaleidoscope Nordic).

This is the second of two posts based on an experimental workshop developed by Urban INC and Kaleidoscope (first post here, more details at the bottom).

Outside view @Kaleidoscope: the place

What can be done without lengthy municipal processes, and who should initiate change? What is the identity of the Pipera and Dimitrie Pompeiu districts, and how can the identified problems be turned into opportunities? These are some of the questions we wanted to highlight in the workshop held at Urban INC.

It was evident from the interviews that social arenas, green elements and more outdoor activities would be highly valued. The opportunity to sit on a bench in the sun, have a place to meet a friend away from the shopping centre, or have a place to go for a walk in pleasant surroundings, or just being safe on the pedestrian sidewalk would give increased well-being and higher input throughout their working day. Understanding these people’s daily needs and obstacles is the key element in any further development of the area.

The interviews confirmed the many challenges the area is confronted with, but also revealed possibilities and that people’s perception of the area is not solely negative. Many enjoy the modern architecture and the aesthetic of the lighted buildings, some do take their bike to work even though the bike lanes are few and incoherent, and although mostly located in the shopping centre, there are many eateries to choose from. But most importantly, there is an abundance of highly competent and resourceful people working in the area! This is the real asset.

Walking through the area we also discovered several urban qualities, some more hidden than others. There is a diverse mix of modern glass buildings, former industrial buildings and wasteland. The new clean surfaces next to the old and ruff makes an intriguing combination, and some leftover green spots have a great potential. Being accessible from the center by subway, tram and bus pose a major advantage, and with small improvements and a change in mindset the area could turn problems into possibilities.

Bicycle paths and pedestrian networks have an enormous potential to provide the area with increased coverage and improve the safety, health and well-being of those who work there. In addition, public spaces for social interaction could give the area a real boost, by integrating work life with social life and offer more than just being a workplace.

This leads us to the question if public space could be the driving force for community building and a more cohesive development of  the Pipera and Dimitrie Pompeiu districts. Public spaces tend to act as an activator, and trigger local initiative and innovation. In public venues entrepreneurs see the opportunity to start something. This attract users and consumers, making the location more valuable, and when social commitment starts occurring, it brings a myriad of  positive synergies.

Roadmaps to business district heaven

The emerging business areas in the North of Bucharest have been growing fast and the local authorities have not proven the capacity to support this trend by putting in place adequate access to utilities or through master planning and long term envisioning. The administrative complexity of the city of Bucharest and its metropolitan area have also had detrimental effects in taking advantage of the private sector’s locational decisions and their benefits for the city. We have seen in our interviews and field research that governance boundaries translate into physical boundaries which come at great cost for the companies and employees who are key drivers for the city’s economic growth.

However, we found there is potential to surpass existing challenges, if there is a clear understanding of the existing problems and opportunities, as well as an imagination of alternative scenarios. We found that design thinking methods can be a powerful tool to accelerate understanding and mapping of local problems and can provide a fast track to solution design.

As a combined result of the different approaches and scenarios thought out in the workshop, Kaleidoscope worked out a series of collages illustrating one possible chain of incremental change. This scenario is linked to the power of renewal which is embedded in fixing the missing links in the area. What if the short term action was only to remove obstacles and fences along the way, and paint a bicycle lane network connecting Pipera and Dimitrie Pompeiu district internally? Even in a guerrilla manner, in order to raise awareness around the conditions of public space? Could a stunt have the power to change people’s mindset and their behaviour? This immediate action could potentially function as a kick-starter for a long term vision where a welcoming public space with focus on the pedestrians and soft mobility rises as a new typology.

Pipera currently

Pipera short term change. Copyright: Kaleidoscope Nordic

Pipera long term vision. Copyright: Kaleidoscope Nordic

The workshop was designed as a part of the project Urban Insights: Building partnership for user-centred design, financed through the NGO Fund in Romania via EEA Grants 2009-2014 and managed by the Foundation for Civil Society Development. The content of this material does not necessarily reflect the official position of the EEA Grants 2009-2014.

Photos: Kaleidoscope Nordic

Urban INC is a platform space for experiments, learning and scaling of new solutions for cities. Urban INC works towards formulating meaningful insights on urban dynamics in Romania, bringing together stakeholders and citizens to experiment, learn and scale new solutions.

Kaleidoscope is a Norwegian-Finnish architecture office creating architecture with a local presence and a Nordic resonance, working in a variety of scales and approaches to urban issues. Kaleidoscope is also a member office of the Finnish urbanist expert network Uusi Kaupunki collective, specialising in participatory urban planning processes.

Online participatory mapping for spatial planning

Spatial planning is all about putting things on the map: existing spaces and places, as well as spaces and places to come. Yet how much of mapping for spatial planning actually engages the supposed beneficiaries of planning? This post considers online mapping technologies that can be easily used by non-experts, particularly online mapping surveys for public engagement.

MinStad, online participatory mapping in 3D in Gothenburg, Sweden. Courtesy of Agency9.

Value of online participatory mapping

The main stake for online participatory mapping is to allow virtually anyone to participate in mapping places of interest, as well as places of disinterest that would benefit from upgrading or transformation of some sort. Insodoing, online participatory mapping enables the crowds of non-expert to express contrasting interests and views about places and spaces. This can make for variegated maps that bring to the fore the inner tensions of dwelling together and sharing spaces. Such was the case in an online mapping survey carried out as background public consultation for the Helsinki 2050 master plan, where two distinct groups of participants emerged: those supporting urban infill and those preferring the status-quo. This ability of online mapping surveys to address contentious planning issues is simultaneously a major challenge and benefit of online participatory mapping for public engagement. Participatory mapping in the form of Public Participation GIS (PPGIS) can enable local communities to put their issues on the map, facilitate resource management, stand up for their rights, and/or speak the same language as planners (e.g. see the PPGIS.net development projects).

Beyond GIS

Geographical Information Systems (GIS) are the backbone of spatial plans. Yet their use remains limited to those who have had adequate training, typically people who need to work with GIS on a daily basis. Traditional GIS is opening up though. QGIS is a powerful, open-source alternative to proprietary GIS software marketed by firms such as ESRI. Because it is open-source, QGIS is often used for different forms of participatory action research (e.g. here) as is the open-source, and crowdsourced Open Street Map (see for example the extreme citizen science projects at UCL, and humanitarian OSM). QGIS and OSM projects are however typically expert led, or requires solid basic training for which many people may not have sufficient time, patience or spatial skills. Open Street Map, although crowdsourced, is mostly updated by a minority of keen mappers, for a majority of passive users (i.e. that do not contribute content, as is often the case with many “wikis”). Furthermore, maps made with QGIS do not provide shared, instantaneous interactivity to many simultaneous online users, in the way that online mapping survey software do. Even much PPGIS can require the intervention or leadership of mapping experts.

Some online mapping software

With Web 2.0 functionalities, cloud-based data storage, and increasingly interoperable geospatial data (i.e. compatibility across data formats and visualisation platforms), participatory mapping technologies are now more powerful than ever. A former post on the blog highlights some of these online mapping tools, such as Carto (formerly CartoDB), Fulcrum or Harvard University’s free, intuitive map-making software World Map. A great Open Source online software is Geojson.io: you can make great maps on the fly, draw on them, put place markers, add simple attributes to the added features, share the maps with others, and export the maps in multiple formats, or directly to Github for easy collaboration on projects. ESRI’s Story Maps allow users to create beautiful and simple picture-based maps and narratives, but are otherwise limited.

Online mapping surveys

Online mapping surveys, in the form of web-based Public Participation GIS, have been used in many places the world over to engage people in spatial planning, particularly in cities. They are meant to be easy to use for virtually anyone acquainted with Google Maps or Bing Maps. Although a lot of online mapping survey services enable ordinary people to participate in mapping, and some source codes for software are Open Source, most of them remain license-based (Software as a Service –SaaS). Most existing online mapping services have emerged since the 2010s, so this is a rather new phenomenon for spatial planning.  Here are some examples.

Maptionnaire

The research-based software Maptionnaire is one of the most famous PPGIS to have been applied in many planning contexts, mostly in Finland, but also internationally. It has received extensive coverage in the academic literature, as most people running the company are (or were until recently) researchers at Aalto University. Over time, it has been used to engage thousands of urban residents, and has also been customised to engage target groups in urban planning (e.g. children and older residents).

Carticipe

This software has been used in many French cities, including Lille, Marseille, Strasbourg, as well as smaller councils/municipalities. Of late, it has been used to engage local residents in choosing alternative routes for a new fast train line linking Paris and Normandy. Feel free to play with the demo for Paris (in French). Given its range of functionalities (e.g. like/dislike comments, view who contributes what, see places in google street view etc., commenting options) and unique user interface, the tool deserves to be used in other countries too.

Social Pinpoint

Based in Australia, Social Pinpoint tool has been used in many different planning contexts, especially in smaller councils. The range of projects is quite impressive, and the user interface can provide a lot of background information to users and enable significant interactivity for users.

Mapping for Change

While the above have mostly been used in urban planning, Mapping for Change has been used for more diverse uses, including making community maps (e.g. comprehensive maps of local community assets, local climate change mitigation initiatives) as well as citizen science projects (e.g. monitoring air pollution in London). Like Social Pinpoint, this online mapping survey software has mostly been used to engage smaller communities and neighbourhoods, or by small local councils and boroughs. Check out their wide range of completed and current projects.

CityPlanner

As online GIS is growing more powerful, 3D and 4D (3D over time) GIS are becoming increasingly available on the web, thanks to WebGL technology. Making use of Open Street Map 3D, CityPlanner is a software that, at its best, can function as a 3D planning platform and urban social media. The main flagship project for public engagement, and best all-bar-none identified by myself so far, is MinStad, used and augmented by the City of Gothenburg. It allows to visualise planning proposals in 3D, view historical pictures of the city, and read other users’ personal narratives about their life in the city. Most importantly, it allows users to make comments and submit ideas in 2D (e.g. cycle routes, areas) and 3D (blocks, volumes), as well as demolish features in the 3D environment (I haven’t managed to make that work though…). You can then share comments on social media, as you would with Social Pinpoint or Carticipe. CityPlanner is also used internally by many local councils in Sweden for project management and collaboration, in a similar manner as ESRI CityEngine.

Toward Mapping 4.0

The above online mapping surveys enable to engage urban residents in a powerful way that complements more traditional methods for public engagement. While this is a great achievement in itself, the future lies in increased interoperability between all digital platforms, with a significant potential to link all stages, aspects, and spatial scales of place-making processes. Terms such as “Urban Information Systems” will likely best encapsulate the potentialities of the digital participatory planning platforms of the future, which will connect and stretch well beyond either online mapping surveys or professional planning, construction and design tools, and enable an interconnected synergy between these. The shape of such “Urban Information Systems” to come will be the theme of my next post.