###### Abstract
The computation of urban accessibility is performed only considering sustainable transportation modes, aiming to see how prepared each area is to shift from using cars as the main mode of transport to active mobility (walking or cycling). Due to the lack of data, it was not possible to compute measurements with public transport, as there is no General Transit Feed Specification (GTFS) [7] data available for the city of Monterrey. Naturally, this is an important limitation of the current research, as commuting distances in both study areas go well beyond walkable or cyclable distances. Nevertheless, the results intend to demonstrate the immense capability of urban analysis tools to understand the current state of cities, as well as to identify underperforming areas based on the variables of interest. Additionally, social preference is discussed, as the authors of the paper are well aware that individuals tend to frequent certain destinations not only based on proximity, but also on preference [8]. It has been noted in the urban accessibility field that most of the reported studies explore highly specific topics at particular scales. Although this type of approach is able to model to a certain extent urban accessibility, a multiscale approach is preferred since urban systems are not isolated and their dynamics are the result of a more complex network structure [9]. Another important feature of our work is the transferable approach, validated with a comparison between different cities. It has been previously noted that a drawback of urban indicators is that they are often specific and not applicable to other cities [10]. Examples of recent urban accessibility studies were presented in [11; 12]. In [11], an analysis of the urban accessibility of museums (for pedestrians) in Seoul was performed using travel angle and walking distance. The results of the study showed that, although museums were within walking distance (2 km), they were isolated from primary spaces where citizens walk. Although the study showed interesting results, the authors mentioned that future research should consider other traveling modes. In [12], the availability of urban green spaces (UGSs) in Ilorin (Nigeria) within a 300 m distance was evaluated. The authors obtained an availability indicator as a ratio of the UGS area (in a ward) divided by the population of that ward. The results showed that there was an alarming lack of UGSs in the city, which should be addressed to follow the path of a sustainable and liveable city. Contrary to the aforementioned studies, which focused on one travel mode, one scale of analysis, and one type of destination, one important remark is that our proposed work follows a multiscale approach since multiple scales (blocks, districts, city), travel modes (walking, bicycle), and types of destinations (schools, hospitals, parks) are included, as well as a comparison of all these factors between two cities of two different countries. Finally, the research serves as a reflection of the importance of governments and decision-makers actively engaging and investing in developing high-quality datasets that enable academics and practitioners to understand how the urban structure of a given city is performing and the local community's needs [13]. The research also demonstrates the great value of having standardized tools and data formats to compare how different urban areas are performing in terms of urban accessibility [14]. The structure of this article begins with the contextualization of the study areas in Section 2. Section 2.2 describes the methodology used, including the delimitation of the study areas and the software and the data used. With this information, the results obtained from each urban district are shown in Section 3. Finally, a discussion is presented in Section 4, and the conclusions derived from the study are presented in Section 5.