**3. Challenges of central urbanism methodologies**

Urbanism during the twentieth and beginning of twenty-first century was formed by large-scale centrally planned developments. In the 1960s until early 2000s, several urban analytics models incorporated computational tools that introduced automation and standardization, in order to visualize and understand the urban space. One of the most widespread used tools that revolutionized mapping since the 1960s was geographic information system (GIS), which enabled the

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*Human-Centered Approaches in Urban Analytics and Placemaking*

investments related to urban space and infrastructure.

therefore they do not involve user participation.

economic segregation.

association of geo-location with information. GIS systems are able to visualize time and space paths as static models using models of space and time that show the entire path within a geographic space and a fixed domain of time [4]. This has greatly lowered the cost of data accumulation and improved the accuracy of the results [5]. The main data source employed in central urban model is census data from government databases, which with the use of GIS tools can be visualized and mapped. Central urbanism presents certain challenges, which derive from two sources: the first is related with technical aspects of data sets and data handling and the second with socioeconomical aspects that influence the fluctuation of capital and

1. Centrally planned urbanism refers mainly to the broad picture of the urban environment; as a result, it does not address local details adequately. Centralized, top-down approaches do not derive in resilient conditions as they usually favor certain economic interests. In contradiction to bottom-up approaches, central-based urbanism does not rely on evidence-based methodologies, and

2.In addition to the above, centralized approaches involve money-oriented developments, which do not respond to local citizens' needs; in fact, they usually undermine them. As they largely follow the dictates of social and economic elites, they are based around uneven development and exclusion, increasing

3.Centrally planned urbanism is based on limited data sets and assumptions, which fail to address cities as arrays of social complex relations. Such assumptions engendered vehicular domination over walkability, maximized urban density, and homogenized urban districts all at the expense of residents' quality of life. It appears that there is hardly any empirical data or residents' input that provide insight into most central-based master plan developments. Central urban models are guided by a set of specified constraints that perform in a simplified environment disconnected from real facts; thus, they may not capture complex dynamics of socioeconomic flux. One explanation for this is the difficulty of adequately incorporating the breadth of social theory needed to account for the range of urban mechanisms. For instance, even the analysis of the relationships that occur in a park of a business district neighborhood during day and nighttime quickly becomes a complicated problem to describe through census data. These models are constrained by their inability to theoretically ground mechanisms of neighborhood change and translate them into a data set. They are limited by a lack of empirical detail, in their specifications of data attributes.

The challenges listed above derive from the fact that the processes employed mask a great deal of heterogeneity between urban areas. This resulted from deficiencies in the data sets and short time-scale of the analysis, factors that designated the low predictive capacity of the models, and the insufficiency to fully understand

As cities are becoming more instrumented and networked, more data is being generated about the urban environment and its residents, allowing urban designers to access the local scale fabric of the city, opening up new research directions for understanding the city. Going beyond traditional data sources, such as census, which is fairly static and updated only every, designers are encouraged to engage with other types of data that capture the ephemeral side, such as, people's desires, problematic, trends, etc. It is important for designers and planners to recognize the

neighborhood dynamics, which remain ambiguous and conflicting.

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

#### *Human-Centered Approaches in Urban Analytics and Placemaking DOI: http://dx.doi.org/10.5772/intechopen.89675*

*Sustainability in Urban Planning and Design*

**2. Participatory design**

where the first focuses on the integration of human perspective in neighborhood evaluation and the other on active, contextualized user participation in placemaking and neighborhood reformation. Both processes address human perception as an effective means in capturing the dynamics of space, as well as a mean to drive the change itself. User participation is the agency upon which, local resilience is formed by balancing the power between stakeholders and community members. We support that user-centric approaches improve society well-being and user satisfaction,

In order to improve policy-making and the health of communities, collaborations often extend beyond the level of academic research, to that of the user level. Recent research suggests that researchers create more innovative concepts when taking advantage of user input than working purely with existing data sets. Humans are positioned as the major contributors to changing environments [2]; therefore, human factor should be addressed and included when conceptualizing urban analysis methodologies. This approach has a political dimension of user empowerment and democratization, and it is called participatory design approach. Participatory approaches link together all stakeholders (e.g. employees, researchers, customers, citizens, end users), in an attempt to improve human well-being, user satisfaction, accessibility, sustainability, and livability. As participatory processes are more and more supported by information technology, this enables both sides, users, and researches to understand and collect diverse knowledge, for example, opinions, ideas, objectives, statements, etc.; however, it increases the complexity and the handling of information when it comes to decision-making. Regarding user participation, the possibilities of digitalization should be regarded as an opportunity to accompany the social transformation toward a digital society in the information age of the twenty-first century [3]. A participatory process involves the side of the researcher or organizer and the side of the participants. In this chapter, we present two different directions of the above relationship: indirect user participation and direct user participation. In the first case, the users seek no personal interest in the process; however, they state their opinion regarding a real matter, which is proven useful in understanding urban dynamics. This process involves two stages that depict different processes. The results are then combined in a series of maps. The second case is a deliberate process in which the interested party (citizens) is involved in the policy-making toward the satisfaction of their needs. The process involves the construction of a digital platform that is user driven. This approach builds upon participatory action research by moving beyond participants' involvement and producing solutions to problems rather than documenting the results as a resource database. Further stages may then focus on community brainstorming,

modeling and prototyping, and implementation in community spaces.

Urbanism during the twentieth and beginning of twenty-first century was formed by large-scale centrally planned developments. In the 1960s until early 2000s, several urban analytics models incorporated computational tools that introduced automation and standardization, in order to visualize and understand the urban space. One of the most widespread used tools that revolutionized mapping since the 1960s was geographic information system (GIS), which enabled the

**3. Challenges of central urbanism methodologies**

toward more democratic and sustainable urban environments.

**150**

association of geo-location with information. GIS systems are able to visualize time and space paths as static models using models of space and time that show the entire path within a geographic space and a fixed domain of time [4]. This has greatly lowered the cost of data accumulation and improved the accuracy of the results [5]. The main data source employed in central urban model is census data from government databases, which with the use of GIS tools can be visualized and mapped.

Central urbanism presents certain challenges, which derive from two sources: the first is related with technical aspects of data sets and data handling and the second with socioeconomical aspects that influence the fluctuation of capital and investments related to urban space and infrastructure.


The challenges listed above derive from the fact that the processes employed mask a great deal of heterogeneity between urban areas. This resulted from deficiencies in the data sets and short time-scale of the analysis, factors that designated the low predictive capacity of the models, and the insufficiency to fully understand neighborhood dynamics, which remain ambiguous and conflicting.

As cities are becoming more instrumented and networked, more data is being generated about the urban environment and its residents, allowing urban designers to access the local scale fabric of the city, opening up new research directions for understanding the city. Going beyond traditional data sources, such as census, which is fairly static and updated only every, designers are encouraged to engage with other types of data that capture the ephemeral side, such as, people's desires, problematic, trends, etc. It is important for designers and planners to recognize the opportunities for making better sense of public space through technology. One of the key benefits of adopting a data-driven approach to urban analytics surveys is the ability to see a combination of datasets in context with each other and to detect temporal and spatial patterns.
