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Climate change is a problem that concerns all of us. Despite the information produced by organizations such as the Expert Team on Climate Change Detection and Indices and the United Nations, only a few cities have been planned taking into account the climate changes indices. This paper aims to study climatic variations, how climate conditions might change in the future and how these changes will affect the activities and living conditions in cities, specifically focusing on Mexico city.

In this approach, two distinct machine learning regression models, k-Nearest Neighbors and Support Vector Regression, were used to predict variations in climate change indices within select urban areas of Mexico city. The calculated indices are based on maximum, minimum and average temperature data collected from the National Water Commission in Mexico and the Scientific Research Center of Ensenada. The methodology involves pre-processing temperature data to create a training data set for regression algorithms. It then computes predictions for each temperature parameter and ultimately assesses the performance of these algorithms based on precision metrics scores.

This paper combines a geospatial perspective with computational tools and machine learning algorithms. Among the two regression algorithms used, it was observed that k-Nearest Neighbors produced superior results, achieving an R² score of 0.99, in contrast to Support Vector Regression, which yielded an R² score of 0.74.

The full potential of machine learning algorithms has not been fully harnessed for predicting climate indices. This paper also identifies the strengths and weaknesses of each algorithm and how the generated estimations can then be considered in the decision-making process.

Up-to-date, the simulation of pedestrian behavior is used to support the design and analysis of urban infrastructure and public facilities. The purpose of this paper is to present a microscopic model that describes pedestrian behavior in a two-dimensional space. It is based on multi-agent systems and cellular automata theory. The concept of layered-intelligent terrain from the video game industry is reused and concepts such as tracing, evasion and rejection effects related to pedestrian interactive behavior are involved. In a simulation scenario, an agent represents a pedestrian with homogeneous physical characteristics such as walking speed and height. The agents are moved through a discrete space formed by a lattice of hexagonal cells, where each one can contain up to one agent at the same time. The model was validated by using a test that is composed of 17 real data sets of pedestrian unidirectional flow. Each data set has been extracted from laboratory-controlled scenarios carried out with up to 400 people walking through a corridor whose configuration changed in form of the amplitude of its entrance doors and the amplitude of its exit doors from one experiment to another. Moreover, each data set contained different groups of coordinates that compose pedestrian trajectories. The scenarios were replicated and simulated using the proposed model, obtaining 17 simulated data sets. In addition, a measurement methodology based on Voronoi diagrams was used to compute the velocity, density and specific flow of pedestrians to build a time-series graphic and a set of heat maps for each of the real and simulated data sets.

The approach consists of a multi-agent system and cellular automata theory. The obtained results were compared with other studies and a statistical analysis based on similarity measurement is presented.

A microscopic mobility model that describes pedestrian behavior in a two-dimensional space is presented. It is based on multi-agent systems and cellular automata theory. The concept of layered-intelligent terrain from the video game industry is reused and concepts such as tracing, evasion and rejection effects related to pedestrian interactive behavior are involved. On average, the simulated data sets are similar by 82 per cent in density and 62 per cent in velocity compared to the real data sets. It was observed that the relation between velocity and density from real scenarios could not be replicated.

The main limitations are presented in the speed simulations. Although the obtained results present a similar behavior to the reality, it is necessary to introduce more variables in the model to improve the precision and calibration. Other limitation is the dimension for simulating variables at this moment 2D is presented. So the resolution of cells, making that pedestrian to occupy many cells at the same time and the addition of three dimensions to the terrain will be a good challenge.

In total, 17 data sets were generated as a case study. They contain information related to speed, trajectories, initial and ending points. The data sets were used to calibrate the model and analyze the behavior of pedestrians. Geospatial data were used to simulate the public infrastructure in which pedestrians navigate, taking into account the initial and ending points.

The social impact is directly related to the behavior analysis of pedestrians to know tendencies, trajectories and other features that aid to improve the public facilities. The results could be used to generate policies oriented toward developing more consciousness in the public infrastructure development.

The general methodology is the main value of this work. Many approaches were used, designed and implemented for analyzing the pedestrians’ behavior. In addition, all the methods were implemented in plug-in for Quantum GIS. The analysis was described with heat maps and statistical approaches. In addition, the obtained results are focused on analyzing the density, speed and the relationship between these features.

In this chapter, we describe the challenge and experience to spread out information and communication technologies (ICT) and how to implement them progressively, in order to improve administrative, academic, and pedagogical issues. As a case study, institutional strategies to tackle these issues in the higher education institutions (HEI) that belong to the Instituto Politécnico Nacional of Mexico (IPN) are described. The main challenge of HEI to incorporate cutting-edge technology in order to form professionals according to educational models and needs of the worldwide is pointed out. Thus, the intended focus of this work is to elevate the discourse and generate awareness about information and communication technologies to translate the language for being legible and understandable to deans, directors, and general authorities of HEI so that they can clearly make decisions with respect to the benefits that current technologies bring to the education institutions, and thus they can keep in mind investing important economic resources in future and mature technologies. Nowadays, HEI of the IPN are facing problems regarding the allocation of budgetary resources, so the strategies and analytical discussion of how ICT can envision the growth of HEI is a very timely issue to be presented to the Mexican government.

Over the past few decades, the higher education landscape underwent dramatic changes, causing strain on higher education institutions (HEI) worldwide. The academic debate is filled with very well-argued accounts of these developments. Advances in information and communication technology (ICT) add an additional layer to this already complex reality. Against this backdrop, it is necessary that the question of managing and administering HEI today be rethought. Emerging technologies such as artificial intelligence (AI) and cognitive computing, augmented, virtual and mixed reality (R+), Internet of Things (IoT), blockchain, bear the promise to effectively assisting HEI administrators in navigating their institutions through the period of profound change. This chapter offers a brief account of that.

The paper enriches the understanding of the principal challenges faced in future lawyers' education in Mexico considering global trends, particularly from the perspective of skills creation in diverse areas of legal practice.

The framework used draws on trends identified within an international collaborative research study in which both authors participated, titled “Developing a Blueprint for Global Legal Education”. This current paper stems from the premise that these recommendations can be further developed and better utilised if explored within a specific context. The methodology designed for this research consisted of two main components: a thorough analysis of the norms that regulate the education system and the professional practice in Mexico, and an extensive literature review that provided insights into the state of global trends in legal education.

This paper reveals that in Mexico having a well-designed and comprehensive legal framework is the first step to promote the creation of high-quality educational models.

The study analyses the current situation in Mexico within four global trends: (1) regulation of legal education and access to the profession; (2) building professional practice skills; (3) internationalisation of education and (4) incorporation of technology and responsible innovation.

The reflections are intended to promote better training of law students in the skills required to face the various challenges that the legal profession currently involves. This is under an approach that analyses global challenges and identifies the best practices to connect learning processes with in-demand professional skills.