**2. Methodologies**

#### **2.1 Green space analysis**

Using the geographic information on green space types from the open-source data of Zurich2 . Open Street Map, and introducing another two parameters, ownership and accessibility, green spaces has been grouped into three types:


Onsite observation and measurement helped clarify obscure situations and adjust the size of the green spaces.

The study pre-observed the whole district to deposit primary impressions of the green-space use in the area. Two observations emerged: that people usually walked to public green spaces; and that, people seldom visited community green spaces belonging to other neighbourhoods, although these spaces were open and furnished with similar facilities. These impressions generated two premises for the study:


<sup>2</sup> The open-source data of Zurich classified green spaces into eleven types. These space types suggested that parameters of this classification included ground and flora types, such as forests, meadows and swamps; functions, such as agricultural fields, sports fields and cemeteries; and location, such as street greenery and greenery around the residence.

<sup>3</sup> The Categorisation of Public Open Space Based on Size and Coverage Area in *SDG Indicator 11.7.1 Training Module: Public Space* defines the service coverage radius of city public open space and neighbourhood green spaces as 800 and 400 m. [17]

The study employed distance matrices and nearest-neighbourhood tools in QGIS to analyse green space connectivity within the 800-m radius. The accumulated connections towards public green spaces suggest the role of each space in the entire district (**Figure 2**).

To structure further onsite observation, the study selected four representative public green space clusters with high connectivity that cross the whole district. Each cluster comprises one or more large green spaces in the middle, namely Grünau, Lindenplatz, Bachwiesen and Süsslerenanlage. The service areas tool in QGIS generated the 400-m coverage area of these four clusters. Together with their around streets or physical boundaries, the coverage areas shaped four subsite cases (**Figures 3** and **4**). The study further measured the size of three green-space types, building footprints, grey surface, and their composition patterns.

#### **Figure 2.**

*Public green spaces' connectivity and significance in Altstetten and Albisrieden. Each centroid represents a public green space. The colour variation of the centroids from yellow to dark green demonstrates the number of connections accumulated to each centroid from small to large.*

#### **Figure 3.**

*Four selected subsite cases based on the connectivity of public green spaces and their 400-m service coverage.*

*Urban Greenery as a Tool to Enhance Social Integration? A Case Study of Altstetten-Albisrieden... DOI: http://dx.doi.org/10.5772/intechopen.109736*

**Figure 4.** *Four selected subsite cases in the 3D model.*

## **2.2 Space use analysis**

The study obtained space use information through onsite un-participatory observation and open-question interviews. Observed activities and interactions were recorded in photos and videos and marked as point features in QGIS. Details of space users and their activities were fastened as attributes of point features. **Table 1** displays these attributes.

The research team observed all public green spaces, embraced grey spaces where playgrounds or other exercise facilities are installed, and most community green spaces in the four subsites. Due to the lack of access permits and the concern about privacy, the researchers could not observe activities in most private spaces. The observation periods were 14:00–18:30 on weekdays and Sundays when weather conditions and outdoor temperatures were similar.



#### **Table 1.**

*Space-use activity attributes.*

Attributes such as start time, end time, location and activity types were exploited for activities frequency and duration, which were visualised using heat maps to indicate points of interest (POIs) in the subcases. Information such as language, age, gender, and user types contributed to the crossing-case discussion of how green-space structure influences daily space use and the potential of these spaces for social integration.
