**4. Challenges of participatory design**

A new generation of researchers has been deriving evidence-based rules for urbanism, which benefits from user participation [6]. These rules replace outdated working assumptions that have created dysfunctional urban conditions. Recent methodologies in urban research validate human scale urbanism and collaborative approaches. In order to provide a better understanding of the contradictory approaches, we will list some of the main challenges of centralized urbanism. Moving beyond the form-oriented framework of centrally based urbanism, we should also refer to certain challenges that the participatory approach entails.

The growing desire of involving participants in the process represents certain challenges that need to be addressed for successful decision-making [7]. Building user participation systems in response to the complexity requires a combination of data, which is fit for use and decision support tools. We list some of the key barriers that are present in user participation approaches.


Based on the above, opening a channel for sharing knowledge and opinions is not necessarily sufficient for building a system that takes the most advantage of user input. The objective is to achieve a balanced relationship between extensive information and clarity, in order to ensure that all the data and their interconnections are handled to their entirety. We need to build human-computer interaction in a way that it facilitates user orientation and comprehension of the framework, defines the

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*Human-Centered Approaches in Urban Analytics and Placemaking*

described as an instance of organized complexity [1].

scopes of the user and the researcher, and translates the user input into a quantifiable entity. Therefore, we refer to a software workflow/application that ingrates user input in a form of binary data that can be easily quantified, categorized, and visualized. To avoid oversimplification of the process, the insight of the researcher is crucial, in order to extract valuable, subjective information in a simple format.

**5. Case study 1: urban analytics through crowdsourcing methodologies**

The first example is a mapping process of the gentrification and displacement rate and livability levels in the neighborhoods of Oakland in the San Francisco Bay Area. Before analyzing the methodology of the example, we should first understand the notions of neighborhood and gentrification as addressed in this chapter, which will provide clarity regarding the reasoning behind the example methodology. The neighborhood is often understood as the physical building block of the city for both social and political organization [8] and thus combines physical and nonphysical characteristics. Early scholars have described neighborhoods as defined, closed ecosystems, characterized only by their physical elements, such as size, density, demographics, etc. that would get disrupted by external factors, such as new residents. Moreover, neighborhood change has been regarded as a natural process of population relocation and competition for space, until a state of equilibrium could be reestablished. Based on these ideas, neighborhoods were presented as a deterministic model and categorized based on simplified criteria such as their residents' financial status, etc. However, neighborhoods are not introverted, autonomous clusters, and the mechanisms of neighborhood change do not rely on exclusively external factors. According to Jacobs [1], nowadays, people identify a neighborhood by a landmark in the city because it has become intimate from daily use or encounter. The key that creates the notion of a neighborhood is diversity and identity. She argues that people tend to avoid visiting places that do not represent any variation either in function or esthetics [1]. Although the modern way of living has urged people to be more mobile than previously, people tend to pay attention to district that surrounds their home if it meets the certain criteria that fit their lifestyle. The stability of a neighborhood relies on its capacity to absorb opportunities and sustain its diverse character. In this paper, the term neighborhood can be

The notion of gentrification can be described as one category of neighborhood change and is broadly defined as the process of improving and renovating previously deteriorated neighborhoods by the middle or upper class, often by displacing low-income families and small businesses. The first documented use of the term "gentrification" [9] describes the influx of a "gentry" in lower income neighborhoods. Owens identifies nine different types of neighborhoods that are experiencing upgrading: minority urban neighborhoods, affluent neighborhoods, diverse urban neighborhoods, no population neighborhoods, new white suburbs, upper middle-class white suburbs, booming suburbs, and Hispanic enclave neighborhoods [10]. Gentrification does not only rely on a singular cause, as it may emerge when more than one condition is present. It is a complicated process that does not rely on binary and linear explanations. Early studies identified two main categories that cause gentrification: private capital investment for profit-seeking and people flow that refers to individual lifestyle preferences [11]. Gentrification does not necessarily result in negative effects, as it can also operate as a tool for revitalization. When revitalization occurs from existing residents, who seek to improve their

*DOI: http://dx.doi.org/10.5772/intechopen.89675*

**5.1 Introduction**

scopes of the user and the researcher, and translates the user input into a quantifiable entity. Therefore, we refer to a software workflow/application that ingrates user input in a form of binary data that can be easily quantified, categorized, and visualized. To avoid oversimplification of the process, the insight of the researcher is crucial, in order to extract valuable, subjective information in a simple format.
