## 2 Materials and Methods
### Methodology
#### 2.2.3 Data Processing  
For both study areas, urban blocks were the origins for all accessibility measurements. The datasets that contained the urban blocks were downloaded from INEGI for the MMA and from the United States Census Bureau for the BA; this is specified in Table 3.  
The destinations considered for the computation were public education centers (public elementary, middle, and high schools), health care facilities (general acute care hospitals and primary care clinics), supermarkets, and public parks. The datasets were obtained from the National Statistical Directory of Economic Units (DENUE, by its acronym in Spanish) and Urban and Rural Geostatistical Cartography by Blocks, both sources published by INEGI, for the MMA, whereas for the BA, several sources were used: United States Census Bureau, California State Geoportal, California Health and Human Services Open Data, and OpenStreetMap (OSM). The datasets used for the destinations of both study areas are presented in Table 4.  
It is important to mention that there are socioeconomic factors, such as social preference and willingness to pay, that can affect people's decision to travel to one destination over another. In addition, public services often have access limitations related to building capacity, opening hours, working days, and demand, among others. Such variables will be briefly discussed, as their complete analysis goes beyond the scope of this research project, given that the objective of the research is to provide a preliminary approach to urban accessibility based on the availability of land-uses and transportation networks at different scales according to public datasets. Hence, only public health care facilities and schools were taken into account for the computation, as public facilities are available to everyone regardless of their income. The road networks of both study areas were downloaded from OSM and are presented in Table 5. \begin{table}
\begin{tabular}{c c} \hline
**Mode** & **Speed (km/h)** \\ \hline Walking & 3.6 \\ Cycling & 13 \\ Motorized vehicles & The road’s speed limit \\ Public transport & Time schedule from GTFS \\ \hline \end{tabular}
\end{table} 
Table 2: Speeds of transport modes used for computation, as defined in the UrMoAC tool. \begin{table}
\begin{tabular}{c c c c} \hline
**Variable** & **Database** & **Institution** & **URL (All Data Was Accessed on 10 January 2022)** \\ \hline Blocks (MMA) & INEGI & INEGI & [[https://www.inegi.org.mx/app/biblioteca/ficha.html?upc=702825218867](https://www.inegi.org.mx/app/biblioteca/ficha.html?upc=702825218867)]([https://www.inegi.org.mx/app/biblioteca/ficha.html?upc=702825218867](https://www.inegi.org.mx/app/biblioteca/ficha.html?upc=702825218867)) \\ Blocks (BA) & 2020 Census & United States Census Bureau & [[https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html](https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html)]([https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html](https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html)) \\ \hline \end{tabular}
\end{table} 
Table 3: Urban blocks’ datasets used for the computation of urban accessibility in this study. In order to make the individual results from each block readable, understandable, and useful, they were aggregated into areas and then mapped with QGIS. For the MMA, Basic Geostatistical Areas (AGEBs) were used; for the BA, block groups were used. Both are widely utilized units of analysis for cartographic purposes (see Table 6). All the datasets collected for the study (origins, destinations, road network, and aggregation areas) were uploaded to PostgreSQL. Once this step was completed, UrMoAC, with access to the database, ran the computations. In order to do so, for each accessibility measure, it is necessary to write a command that specifies the variables to use, the transport mode, and the constraints for each measurement.