###### Abstract  
Urban planning has a crucial role in helping cities meet the United Nations' Sustainable Development Goals and robust datasets to assess mobility accessibility are central to smart urban planning. These datasets provide the information necessary to perform detailed analyses that help develop targeted urban interventions that increase accessibility in cities as related to the emerging vision of the 15 Minute City. This study discusses the need for such data by performing a comparative urban accessibility analysis of two university campuses and their surrounding urban areas, here defined as the Stanford District, located in the San Francisco Bay Area in the United States, and Distrito Tec in Monterrey, Mexico. The open-source tool Urban Mobility Accessibility Computer (UrMoAC) is used to assess accessibility measures in each district using available data. UrMoAC calculates distances and average travel times from block groups to major destinations using different transport modes considering the morphology of the city, which makes this study transferable and scalable. The results show that both areas have medium levels of accessibility if cycling is used as the primary mode of transportation. Hence, improving the safety and quality of cycling in both cities emerges as one of the main recommendations from the research. Finally, the results obtained can be used to generate public policies that address the specific needs of each community's urban region based on their accessibility performance. urban accessibility; smart cities; smart mobility; open-source technology +
Footnote †: journal: Medicalon the particular needs of the area [2]. Nevertheless, creating and updating such datasets requires not only considerable financial and time investments, but in many cases requires appointing a specific entity (such as the Census authorities) to collect, process, handle, and publish the data. Consequently, many countries, especially Less Economically Developed Countries (LEDCs), are yet to develop such datasets, whilst More Economically Developed Countries (MEDCs), which already have datasets, encounter the continuous challenge of disaggregating and updating their data to understand and address upcoming challenges [3]. One example of a very data-hungry sector of urban transformation is transportation accessibility. Assessing urban accessibility has become an important part of the urban agenda in many cities due to the transformative potential of such an approach. The concept explores \"the extent to which the land-use-transport system enables (groups of) individuals or goods to reach activities or destinations by means of a (combination of) transport mode(s)\" [4]. This approach has also been supported recently by the \"15 Minute City\" concept, developed by [PERSON] [5]. However, most of the tools used to measure urban accessibility require up-to-date and fine-grained urban data to perform the analysis. Such is the case with Urban Accessibility Computer (UrMoAC), a software program developed by the German Aerospace Centre (DLR) to compute accessibility measures [6]. The objective of this study is to conduct a preliminary analysis of urban accessibility levels, using UrMoAC and public datasets, to main destinations in two study areas: Distrito Tec of the Technological Institute of Monterrey in Monterrey, Mexico, and the Stanford University District (i.e., Stanford District) in the San Francisco Bay Area in the United States of America. Both are university districts located in very large and important metropolitan areas within their countries. The significance of this work is rooted in its accounting of the current morphology of each city using open-source data and tools. The use of such accessible data allows for our proposed framework for automated urban accessibility analysis to be implemented in different urban centers, in this case impacted by university communities' accessibility needs, which makes it a replicable and scalable study approach.