## 2 Study Area
### Step 1: Measuring PSI Indicators at the Census Block Level  
The first step of PSI estimation is to identify a set of accessibility-related indicators. A rights-based approach [59; 60] was used to identify multidimensional socioeconomic indicators to estimate the PSI composite index. The renowned Ecuadorian _Buen Vivir_ concept was proposed to create a framework of Good Living analysis in Ecuador based on human rights and nature rights [60]. In this framework, the basic needs of the human beings, such as access to good education, a proper healthcare system, and other desirable services, ensure conditions of dignity and well-being of citizens, and supports the development of capabilities that create equal opportunities for all. Therefore, based on a thorough literature review of PSI analysis [18; 30; 61; 62; 63], we integrated the _Buen Vivir_ concept and selected different accessibility variables that appropriately represent the public services and which were available from statistical records. The following seven indicators were collected to estimate neighborhood context: (1) number of households without access to drinking water (_NonDri_), (2) number of households without access to the sewerage system (_NonSew_), (3) number of households without access to the public electricity grid (_NonEle_), (4) number of households without access to the garbage collection service (_NonCol_), (5) limited access to healthcare services (_Dist_H_), (6) limited access to educational services (_Dist_E_), and (7) limited access to green areas (_Green_). The information used to calculate _NonDri_, _NonSew_, _NonEle_, and _NonCol_ was extracted from the 2010 Ecuadorian Population and Housing Census. The indicators _Dist_H_ and _Dist_E_ were calculated as the Euclidean distance to the nearest healthcare service (or educational service) from the centroid of each of the census blocks. The _Green_ indicator was calculated as the ratio of greenspace per unit area (census block). The indicators _NonDri_, _NonSew_, _NonEle_, and _NonCol_ were transformed into ratios (number of households without access to a specific public service versus the total number of households). The indicators above were chosen based on a thorough literature review [59; 60]. The chosen indicators were applied to evaluate the social dimensions of public services inequalities, and are fundamental to understanding PSI disadvantages. After normalizing the indicators using a min-max normalization, these indicators were used as inputs for the neighborhood aggregations using six different clustering methods, as explained in Step 2. These indicators were further used to construct a PSI composite index to represent neighborhood context at census block and the different aggregated neighborhood levels which were produced in Step 2. The PSI composite index was built by aggregating the weighted indicators. Multicollinearities in the data were evaluated based on variance inflation factors. Indicator weights were calculated by means of principal component analysis, following guidelines published by the Organization for Economic Co-operation and Development [64]. Step 2: Classifying Census Blocks Into Hierarchical Categories of Neighborhoods Using Six Clustering Methods  
The clustering methods applied in this research were selected after a thorough literature review. All methods have been applied to define neighborhoods in previous studies. The focus of this research is by no means to exhaustively explore the various capabilities of the clustering algorithms, but rather to explore the MAUP when formulating functional neighborhoods. Therefore, we first assumed that households sharing similar quality of life characteristics will have similar access to public services. Six unsupervised clustering methods were thus separately applied to homogenize the census blocks into different groups of hierarchically aggregated neighborhood units based on seven accessibility-related indicators. These seven indicators are related to health, education, employment, and housing conditions.