## 3 Analysis and Results
### Monterery Metropolitan Area
Figure 6: Comparison of the performance of transport modes to access the closest public school at the metropolitan level. Figure 7: Average travel time (minutes) to the closest public hospital by foot. Figure 8 compares the performance of both transport modes (walking and bicycle) to access the closest public hospital. For this specific variable, the differences between one mode and the other are more evident. This is related to the lower number of destinations available at the metropolitan level (in contrast with Figure 6, the number of hospitals is evidently lower than that of schools). Hence, faster transport modes, such as bicycles, will outperform walking by a considerable margin: for 75% of the AGEBs to reach the closest public hospital travelling by foot, it takes 46.8 min; in contrast, it only takes 14.7 min by bicycle. Figure 9 shows the number of economic activities that employ 51 persons or more (main employment centres) that can be accessed within a 15 min travel time by bicycle. It can be appreciated that most of the AGEBs with higher levels of accessibility (+73 destinations) are located in the central areas of the city, with a gradual decline towards the outskirts. At first sight, such a result might be perceived as unusual, considering that the MMZ is an industrial city and that most industries are located in suburban areas. Nevertheless, industries usually have very large complexes that occupy vast areas of land and make them separate from one another, reducing access to them in short periods. In contrast, even though they are smaller in size and number of employees, the economic activities that concentrate in central areas of the city (such as commerce and services) require smaller areas of land compared to industries and are located closer to each other. The reduced number of main employment centres at the northern, western, eastern, and southeast AGEBs implies that residents in those areas have to travel long distances and periods of time to central areas to work, relying heavily on motorised modes of transport. This traffic has high economic, environmental and social costs for the city and its residents, and can be tackled by creating new clusters of economic activities in areas with low access and by improving transport networks, especially public transport. It is important to isolate and analyse each particular case, as some of these areas lack main employment centres given that, historically, they were independent of the city, but the sprawl has reached  
Figure 8: Comparison of the performance of transport modes to access the closest public hospital at the metropolitan level. them and forced them to integrate with its economy. Some other areas were lacking main employment centres on purpose, as many of the AGEBs located to the southeast of the MMZ were designed as high-income gated communities (especially golf clubs) with only residential land use. As all the residents from these areas require accessing the opportunities located within central areas, they generate demand for public infrastructure that is extremely expensive and that should not exist if planning policies were to assure land use diversity and accessibility parameters before authorising building permits. The three variables previously presented demonstrate how complex urban areas truly are and how inaccurate generalisations can be. The present exercise thus serves as a preliminary approach to demonstrate that every city faces many different and particular challenges. Consequently, any solution, in order to generate a beneficial change, must be based on a full understanding of the specific desired conditions in the city. The current research demonstrates that using accessibility measures is extremely useful for identifying specific needs from different areas of the city and that despite the accessibility measure chosen, the data obtained are relevant. The information generated can be used as a departure point for prioritising interventions and public policies depending on the performance of each area on a given variable.