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

#### **Figure 2.**

*Sustainability in Urban Planning and Design*

from 2000 to 2012 and is related to tenure status, median household income, median home value, and employment rate. For every data set of the above, we calculated the delta value (amount of change or difference) between the years 2000 and 2012 and remapped the values to a numerical range between 0 and 1, which corresponded to a gray scale ranges from white (255, 255, 255) to black (0, 0, 0). White color represents no change, whereas black color represents the highest amount of change. The delta value was plotted in the context of San Francisco Bay Area, and the result is four maps, each for one data set. The four maps, which derived from the process described above, represent the amount of change in tenure, median household income, median home value, and employment rate were weighted and integrated into a single map

*Geo-located 3D space in the software processing. Natural elements and census data for San Francisco Bay Area. Public resources for homeless population for the city of San Francisco (figure was created by the author) [20, 21, 22].*

In addition to the data sets related to tenure status, we included the census data of artists' employment rate as it is considered a key indicator of the early stages of a gentrification process. Surveys in the field of urban renovation have established

that represents the amount of change of all four data sets (**Figure 3**).

**156**

**Figure 1.**

*Census data for the city of San Francisco, total population, income, vacant housing, home value, unemployed population (figure was created by the author) [18].*

the artists community as an agent of urban gentrification, for the reason that low-income artists tend to revalorize unproductive spaces because they are affordable and, as a result, increase the attractiveness of the neighborhood. Artists make the first move into post-industrial, post-welfare neighborhoods, and soon they attract the hipster movement before, eventually, being displaced by them and their new middle-class neighbors. Both participate in the cycle of exploring, developing

**Figure 3.**

*San Francisco Bay Area, census GIS data comparison of tenure, median household income, median home value, and employment rate from 2000 to 2012 overlapped with artists' employment rate (figure was created by the author) [19, 20, 22].*

new potential sites for capital investment. Hence, the combined data set of the four census categories is overlapped with artists' employment rate census data set (**Figure 4**). Regarding the services that are directly related to the urban quality, such as accidents and pedestrian network continuity and status, transportation, walkability, and car dependency street trees and parks, schools, education points, medical and religious spaces, the data sets are divided in two categories. The first depicts amenities such as access to education, religion, health, and green areas, as well as the street trees that definitely improve the urban environment in terms of walkability, microclimate, and aesthetic. The second depicts car dependency zones, reported car injuries location, pavement condition, and parking spaces (**Figures 5**–**7**). The source of the data sets mentioned above was mainly government websites, and the data were provided in.csv format. Data were imported to Microsoft Excel, in order to calculate and process the key indicators that derive from more than one data set and the delta values from the comparison of the data set over a period of time. After the calculation, the information was then imported in grasshopper and processing for visualization in context, as a series of maps.

**159**

**5.3 Method 2: open-data**

**Figure 4.**

The database is articulated by tracing certain populations and services categories that reflect activity and flux of the built environment. The targeted data sets involve artists and their recent activity in Oakland, industrial buildings, loft residencies, yoga and fitness studios, fashionable cafes, as well as crime reports from 2010 to 2013 (**Figures 8** and **9**). The data accumulation derives from open data platforms by defining an equivalent keyword query. The artist population is considered as the frontline of gentrification [2]; therefore, tracing their activity would provide useful insight, combined with a survey on loft residencies, which usually attract the artist community and on certain amenities that appeal to the same target group. The survey on industrial buildings helps in the formation of a forecast model of potential

*San Francisco Bay Area, census GIS data comparison from 2000 to 2012 overlapped with businesses related to* 

*artists from Google places (figure was created by the author) [19, 20, 22].*

*Human-Centered Approaches in Urban Analytics and Placemaking*

*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*

#### **Figure 4.**

*Sustainability in Urban Planning and Design*

new potential sites for capital investment. Hence, the combined data set of the four census categories is overlapped with artists' employment rate census data set (**Figure 4**). Regarding the services that are directly related to the urban quality, such as accidents and pedestrian network continuity and status, transportation, walkability, and car dependency street trees and parks, schools, education points, medical and religious spaces, the data sets are divided in two categories. The first depicts amenities such as access to education, religion, health, and green areas, as well as the street trees that definitely improve the urban environment in terms of walkability, microclimate, and aesthetic. The second depicts car dependency zones, reported car injuries location, pavement condition, and parking spaces (**Figures 5**–**7**). The source of the data sets mentioned above was mainly government websites, and the data were provided in.csv format. Data were imported to Microsoft Excel, in order to calculate and process the key indicators that derive from more than one data set and the delta values from the comparison of the data set over a period of time. After the calculation, the information was then imported in grasshopper and processing for

*San Francisco Bay Area, census GIS data comparison of tenure, median household income, median home value, and employment rate from 2000 to 2012 overlapped with artists' employment rate (figure was created by* 

**158**

**Figure 3.**

*the author) [19, 20, 22].*

visualization in context, as a series of maps.

*San Francisco Bay Area, census GIS data comparison from 2000 to 2012 overlapped with businesses related to artists from Google places (figure was created by the author) [19, 20, 22].*

### **5.3 Method 2: open-data**

The database is articulated by tracing certain populations and services categories that reflect activity and flux of the built environment. The targeted data sets involve artists and their recent activity in Oakland, industrial buildings, loft residencies, yoga and fitness studios, fashionable cafes, as well as crime reports from 2010 to 2013 (**Figures 8** and **9**). The data accumulation derives from open data platforms by defining an equivalent keyword query. The artist population is considered as the frontline of gentrification [2]; therefore, tracing their activity would provide useful insight, combined with a survey on loft residencies, which usually attract the artist community and on certain amenities that appeal to the same target group. The survey on industrial buildings helps in the formation of a forecast model of potential

#### **Figure 5.**

*Oakland green areas, street trees, transport, and education (figure was created by the author) [17].*

transformation of industrial building envelopes to loft residencies. The chosen data sets describe adequately the artists community in the sense that it is commonly known that upcoming artists are mostly freelancers or seeking for a job, and in order to settle their studio, exhibition space, etc, they actively pursue real state, as well as specific lifestyle preferences. This activity regarding real estate hunting cannot be described by census data, simply because it is volatile and constantly shifting. Methods that employ open data platforms such as Google Places and "craigslist.org," however, can capture the activity of such groups very accurately as every activity is geo-located. The key difference between the census data analysis and this method is that the data derives from open-data platforms by defining a key word query. Despite the fact that the two methods are referring to the same target group, in this case, artists, the data are a result of a significantly different process and source. The open-data method, using Google API and "craigslist.org," involved multiple requests at a daily basis, in order to collect all the necessary data. The keyword queries were related to temporal

**161**

**Figure 6.**

*by the author) [17].*

**5.4 Method 3: crowdsourcing**

*Human-Centered Approaches in Urban Analytics and Placemaking*

requests and offers regarding real estate for artists' studios, gallery spaces, events, artists' resources, artwork sale, exhibitions, FAQ, etc. The second set includes crime reports posted from civilians for the years 2010 and 2013, depicting a significant decrease in reported crimes during that period. The data accumulated was formatted in.csv format and visualized as nodes on the same context (**Figures 8** and **10**).

*Oakland, quality of pedestrian network, pavement condition, injuries, and parking spaces (figure was created* 

The second method involves a human-based approach, as a crowdsourcing process. In this method, the crowdsourcing process was achieved via a human-based outsourcing platform called "Amazon Mechanical Turk." The "Amazon Mechanical Turk" platform is a crowdsourcing Internet marketplace, operated by "Amazon," which enables individuals to coordinate the use of human intelligence and perform

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

#### **Figure 6.**

*Sustainability in Urban Planning and Design*

transformation of industrial building envelopes to loft residencies. The chosen data sets describe adequately the artists community in the sense that it is commonly known that upcoming artists are mostly freelancers or seeking for a job, and in order to settle their studio, exhibition space, etc, they actively pursue real state, as well as specific lifestyle preferences. This activity regarding real estate hunting cannot be described by census data, simply because it is volatile and constantly shifting. Methods that employ open data platforms such as Google Places and "craigslist.org," however, can capture the activity of such groups very accurately as every activity is geo-located. The key difference between the census data analysis and this method is that the data derives from open-data platforms by defining a key word query. Despite the fact that the two methods are referring to the same target group, in this case, artists, the data are a result of a significantly different process and source. The open-data method, using Google API and "craigslist.org," involved multiple requests at a daily basis, in order to collect all the necessary data. The keyword queries were related to temporal

*Oakland green areas, street trees, transport, and education (figure was created by the author) [17].*

**160**

**Figure 5.**

*Oakland, quality of pedestrian network, pavement condition, injuries, and parking spaces (figure was created by the author) [17].*

requests and offers regarding real estate for artists' studios, gallery spaces, events, artists' resources, artwork sale, exhibitions, FAQ, etc. The second set includes crime reports posted from civilians for the years 2010 and 2013, depicting a significant decrease in reported crimes during that period. The data accumulated was formatted in.csv format and visualized as nodes on the same context (**Figures 8** and **10**).
