## 5 Discussion  
In this research, we (1) aggregated census blocks to generate meaningful neighborhood (cluster) units in relation to public services inequality using six clustering algorithms, and (2) examined the  
Figure 3: The log-likelihood statistic of the multilevel models regarding PSI contextual values and citizens’ perceptions of: (**a**) the self-perceived health status; (**b**) perceptions of neighborhood safety; (**c**) perceptions of neighborhood social cohesion. internal neighborhood differences of citizens' perceptions related to health status, neighborhood safety, and neighborhood social cohesion. Regarding the question of whether the internal variability of individual perceptions can be explained by the neighborhood context, our results suggest that this is strongly influenced by the zoning scale. Furthermore, there is no decisive way to identify the meaningful neighborhoods that are consistent representations of place in relation to citizens' perceptions. Our findings show that different types of clustering methods allow us to aggregate the neighborhood units that effectively reflect the internal heterogeneities of citizens' perceptions. Therefore, it is worthwhile to test the efficiency and sensitivity of these clustering algorithms in a hierarchical way when they are selected for the neighborhood analysis. Previous studies [83; 84] have showed that if clustering algorithms generate highly similar partitions, it is a good indicator that the actual structure of the dataset has been discovered. In this study, although six different clustering algorithms have served to explore new spatial delineations of neighborhoods in a hierarchical way, our findings also imply that the seven census-based accessibility indicators selected to represent PSI are reasonable for interpreting and understanding public services inequalities in Quito. Particularly, these indicators are related to the fundamental human rights that a population should have: access to clean water, electricity, education, healthcare, environmental protection, and so on [20; 85]. They can be linked to the measure of social deprivation and health-related social dimensions [20; 60], as well as to enhance the understanding of a variety of place interpretations [86; 87]. The applied methods on indicators collection and processing are available and transferable to other cities since they are conceptually similar [61]. We should note that there is not one ideal dataset to represent socially meaningful neighborhoods. The combinations of different social indicators may lead to different alternative neighborhoods and different relations of these neighborhoods with citizens' perceptions. Therefore, it is crucial to integrate different socioeconomic variables to study neighborhoods and social perceptions of place in the future. The framework presented in this study converts the static statistical data into a graphic diagram in a transferable and feasible way. Therefore, different kinds of composite indicators based on multiple dimensions of socioeconomic-environmental features should be further applied to examine the uncertainty and sensitivity of indicators in neighborhood identifications. Our results also suggest that neighborhood contexts do not strongly influence variances in perceptions of neighborhood social cohesion. Thus, it can be said that, in this study area, the perceptions of neighborhood social cohesion are deeply dependent on social cooperation and the conditions of other neighborhoods. However, within each neighborhood, there are marked differences in perceptions of neighborhood social cohesion between citizens. This statement is consistent with the idea that social cohesion is an indicator of inequality at the individual level, but not at the neighborhood level [88; 89]. Unlike the case of perceptions of neighborhood social cohesion, external heterogeneities of neighborhood contexts strongly influence internal variances of self-perceived health status and perceptions of neighborhood safety.