# Walkability Compass--A Space Syntax Solution for Comparative Studies
## 2 Method
### The Research Method
#### 2.2.3 Walkability Compass, Pattern and WebGIS Sharing Method (Steps 7-9)  
Since calculations were made at the single building scale, spatial data aggregation methods have been used for more broad-scale analysis and pattern detection. Step 7 uses 4 ha-sized hexagon tessellations to provide mean Gr, Re, St and PoP values. In urban studies, hexagon tessellations are well-recognised spatial generalisation methods. Due to their topological similarities to real-world irregular partitions, compact shape and simple recursive hierarchical nesting, they are used as spatial data aggregation units [64]. The larger the hexagon, the stronger the data generalisation is; thus, choosing the optimal size becomes a challenging task, especially in different-sized city comparative studies like this one. The earlier 200 m parameter used at the Space Syntax model validation stage (step 5) was our starting point. At the same time, for an urban unit of 200 m, [PERSON] et al. (2015) recommend a 4 ha hexagon size [64]; this was also used in this research. Hexagon tessellation was clipped to the city administrative boundary, and the mean values of Gr, Re, St and PoP were calculated. A further assumption regarding the walkability spatial pattern mapping task was made. So far, the literature does not provide any recommendations on this point, except straightforward hot and cold spot analysis not used in this study. Therefore, aprioriity assumptions were made. Initially, it was assumed that for values ranging from 0 to 1, 0.5 would be suitable and unambiguous to detect walkability hot spots; however, it turned out that in some case studies of cities, this value is represented by a single hexagon. For this reason, the threshold was decreased to 0.3. Hexagons with index values equal to or higher than 0.3 were mapped using the following symbology: hatched grey, light blue, purple and grey for Gr, Re, St and PoP, respectively. Finally, the mapped zones were counted in the manner presented in Figure 4. To complete the case study descriptions, patterns created by walkability zones were the object of initial typology. Proposed pattern typology, very initial at this stage, takes after patterns suggested by the American Planning Association (APS) [65] and is the subject of discussion and further development. The clustering effect has been examined using the Getis-Ord General G statistic and z-score [66] (step 8). Significantly, these calculations were based on non-aggregated datasets (uses points instead of hexagons). The Getis-Ord General G statistics interpret results in the context of the null hypothesis about no spatial clustering of feature values. As the null hypothesis is rejected (\(p<0.01\)), the z-score value is positive, the observed General G index is larger than the expected General G index, thus indicating that high values for the attribute are clustered in the study area. Conversely, suppose the z-score value is negative. Then, the observed General G index is smaller than expected, indicating that low values are clustered in the study area [67]. In other words, the G statistics' high values reveal walkability clusters tendency. The \"walkability compass\" consumes the 6 th step's calculations at the city-scale level by producing a graphical comparison of all analysed walkability aspects. The walkability compass visualises the synergies or Pearson correlation between the pairs of four walkability components: Gr-Re, Gr-St; Gr-PoP; Re-St; Re-PoP; St-PoP. The values closer to one show better synergy between ingredients, while close to zero--very little synergy. It allows us to evaluate not the numerical values of reachable destinations, people, or building perimeters, which could differ significantly between the cases, but the synergy between them, which could be high even within a low inhabitant density with just a few travel destinations if they are correctly allocated, and low in high population density zones with the random allocation of POI and bus stops. The last step (9 th) was the walkability results' interactive visualisation.