Urban Planning and Community Data Collection Efforts in the Developing World: Data as a Facilitator

The historical experience of the now-developed economies is that urbanization accompanied and fostered industrialization, economic growth, and productivity increases (Henderson, 2003; Montgomery 2008). But the process of urbanization now unfolding in the developing world seems much more mixed in outcomes, as the negative effects resulting from overcrowding, environmental degradation, and inadequate services can sometimes negate the productivity advantages of rapidly growing urban areas (Bloom et al., 2008). Central to the differentiated experience of urbanization in developing nations is the differentiated experience of the slums which are the principal spatial and social expressions of their rapid urban population growth (Arimah, 2010). While in some places and at some times slums have played a positive and dynamic role in urban development,  (Frankenhoof, 1967; Ulack, 1978) there is growing concern that the rapid pace of slums’ formation and growth in some urban areas are turning them into “poverty traps” (Glaeser, 2013; Marx et al., 2014). It is unlikely that most slums will transform themselves, so policy will need to play an important role in addressing the challenges and opportunities which slums present. But how can urban planning be effectively done in a “planet of sums” (Davis 2006)?

Addressing socioeconomic challenges in cities requires information that is hard to obtain, especially in poor informal settings, because it exists at the personal and neighborhood level, (UN-HABITAT, 2003a, 2003b; Baker, 2008). Information must be acquired and articulated from the bottom up across many different levels, from the neighborhood community to local service providers and governments. In this light, the problem of obtaining and coordinating information across levels of organization is an archetypal open-ended coordination problem: whenever individuals have common interests or goals, and their actions depend on actions of others, they must coordinate their actions in order to reach their goals (Schelling 1960).

The importance of involving those who are affected by urban planning in the planning process itself has been recognized before. Starting in the 1970s, it was appreciated that traditional forms of urban planning, especially when applied to informal neighborhoods, tend to lead to poor outcomes, often with tragic consequences (Turner & Fichter, 1972; Angel, 1983; Werlin, 1999).  Early studies on development in slum neighborhoods (Sudra, 1976; Schlyter & Schlyter, 1979) and novel urban theorizing (Turner, 1976) highlighted a role for urban planning as supporting, and not disrupting, processes of human development at the household and community levels.  While a shift in urban planning theory and practice has resulted in more constructive policies, many questions remain about the role of formal planning and, more generally, of local, national, and international agencies in promoting human development (Baker, 2008).

“Smart city” solutions to issues of human development, while sometimes useful, do not get to the core of the coordination problem. This is because the role of engineered solutions in cities is subordinate to human and social issues, in the sense that service provision and management provides the means to support socioeconomic life but does not determine it (Jacobs, 1961). Having more high quality data about the activities of citizens, households, and businesses in an urban environment, and having access to this data in a timely and open fashion, can undoubtedly be of great help in designing service delivery systems or managing infrastructure efficiently.  From driverless vehicles to software that runs subways systems like operating systems software run computers to dynamic bus scheduling to smart grids, technology can help run a city more efficiently. But over the long run, the parameters of urban engineered services, such as the quality of a service in terms of use of time, comfort, economic cost, etc., must continuously adapt to societal issues. And “big data” by itself does not identify the problems to be solved, nor the goals to be achieved.

Community organization and action is fundamental in solving the coordination problem underlying effective urban planning, with its generation of local knowledge and identifying priorities in an ongoing development program and its continuous assessment in terms of evaluation and sustainability (of costs, quality of service, maintenance, etc.).  How is this coordination problem to be solved effectively?  A community process which elicits trusted and verifiable data about the physical, social, and economic characteristics of neighborhoods and their needs is the simplest and most effective means to achieve this goal. Local knowledge must, however, be shared; otherwise, comparisons of experiences cannot occur, collective intelligence cannot emerge, and learning cannot accumulate. Such a process has the added benefit of creating a path of dialogue and inclusion to the urban poor and of responsive and knowledgeable government around practical issues for official organizations and private actors.

Facts and data are more commonly the language of official planners as well as of researchers and international organizations. But by collecting verifiable data about themselves, communities can enter a discussion about their own local development on their partners’ own terms, but with better information. To the extent that data is verifiable, it can facilitate less acrimonious discussions, negotiations, and planning. The act of verification and/or correction builds mutual trust and engagement.  In this process, city planners gain information about neighborhood communities that they would likely not have access to otherwise, allowing for more informed planning that includes community priorities and a more functional understanding of why certain services may be more important than others. It can also engage the community as observers and guarantors of the new service in a way that can help ensure sustainability, both financial and logistic. This approach is the common practice of organizations such as Slum/Shack Dwellers International (SDI)[1] and is a growing practice in many poor neighborhoods in cities throughout the global south.

Mobile communications technology, together with increased access to internet connectivity and other forms of social connectivity, makes community-data collection and aggregation easier (Hilbert & López, 2011; Holston, 2008; Sunderarajan, 2012). As a consequence, we can now rapidly solve problems by using “the crowd.” Wikipedia is a prime example of harnessing the knowledge of a crowd of dispersed experts. Crowdsourcing began as a method of for-profit companies to solve problems by leveraging free or cheap labor (motivated by prizes, a chance at fame, or a small payment) but has since spread into the realm of non-profits, governments, and other national institutions (Brabham 2009). The open-source software community uses a similar process to create more complex solutions (as in Linux, Android, and Mozilla Firefox) that rival the best commercial software available. However, whereas traditional crowd sourcing is much closer to the traditional top-down planning, open source development mirrors the bottom-up community development. The open-source process is much more ad-hoc, similar to a community-led process, where progress depends on who “owns” the project, the contributors, and what the software is being used for. As an example of successfully leveraging both crowdsourcing and open source software, consider mapping. When mapping first came to the web, professionals and experts controlled it. However, through the advancement of both open crowdsourcing technologies and more pervasive and accurate commercial GPS devices, geocoded data can now be collected accurately with little expert knowledge (Haklay 2010).  Slum dwellers throughout the world are making their communities visible by mapping them.

Essential to all of these open and crowd-sourcing solutions is the underlying social organization, technology, and communications platform. This is the “system” that allows data and knowledge to be preserved and to accumulate. The communications and wireless technology is an enabler for these processes. More important, though, is the existence of a common framework for communicating and sharing information using the technology. Without shared platforms, the contributions of individuals cannot be globally aggregated. We have seen this problem in the planning realm, where numerous surveys, assessments, censuses, and reports are conducted and produced, but often with only a local scope and without an explicit intent and process for the valuable information collected to be shared and compared. The inability to compare in turn translates into an inability to learn (Baker, 2008). Thus, a data platform is essential not just to house the data, but also to embody a process for how to standardization of data collection, advancing progress toward local goals while benefiting the global community.

References

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Arimah, B.C. (2010). The face of urban poverty: explaining the prevalence of slums in developing countries. Working Paper No. 2010.30, World Institute for Development Economics Research.

Baker, J. L. (2008). Urban poverty: a global view. Urban Papers; no. UP-5. Washington, DC: World Bank. http://documents.worldbank.org/curated/en/2008/01/9112288/urban-           poverty-global-view N PAPERS UP

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Sudra, T. L. (1976). Low-income housing system in Mexico City. PhD Thesis Massachusetts Institute of Technology.

Turner, J.F.C., Fichter, R. (1972). Freedom to Build: Dweller Control of the Housing Process.New York: Macmillan.

Turner, J.F.C. (1976). Housing by People: Towards Autonomy in Building Environments, Ideas in progress. London: Marion Boyars.

Ulack. R. (1978). The role of urban squatter settlements. Annals of the Association of American Geographers, 68: 535-550.

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Werlin, H. (1999). The Slum Upgrading Myth. Urban Studies, 36: 1523-534.

 

 

[1] http://www.sdinet.org/

About the Author
José Lobo is senior sustainability scientist at the Julie Ann Wrigley Global Institute of Sustainability and associate research professor at the School of Sustainability at Arizona State University.
Posted on September 21st, 2015.