Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

The present project studies the pattern of spatial variations of employment accessibility in the Mexico City Metropolitan Area. The question is to assess differences in employment accessibility according to transportation mode (car or public transit) used and between the formal employment sector and total employment (formal + informal sectors). Two indicators were explored at Traffic Analisys Zones (TAZ) level: gravity-based job accessibility (GBM) and the indicator developed by Shen (1998). We explored two data sources of travel times: the 2017 Household Origin Destination Survey and the region’s travel demand model TRANUS.  Thus, there are four aspects of comparison in this evaluation of job accessibility: sources of travel time data (Tranus vs HODS17), methods (Shen vs GBM), type of employment data (total vs formal) and transportation modes (Car vs Transit).  Notice that when doing each comparison, the rest of the aspects are kept constant. In the first two, absolute values of accessibility are not comparables. Both sources of travel time data followed different approaches in gathering the data, while both methods have different units as well as upper and lower limits. For this reason, when comparing job access results between sources of time data or between methods the objective is to determine the consistency and robustness of the results in base of the job access ranking among TAZs. Thus, the Spearman Rank Correlation (SRC) is the appropriate indicator to check the consistency in the accessibility estimations.Then, in the other two aspects of comparison (Total employment vs formal employment; Car vs Transit) absolute values and rankings of accessibility are relevant. These two aspects represent direct variables embedded in both accessibility equations and therefore offer insights about how these factors impact its estimation. Overall, the purpose of analyzing the importance of these variations was to select those estimations with the highest consistency between travel-time sources but included further differences according to employment-type data and transportation modes. This information offers insights into the nuances of these aspects in the disparity of intra-metropolitan accessibility.Our exploration of accessibility using the GBM shows an important variation in the metropolitan pattern according to employment data, travel-time sources and transportation mode. As a general description, jobs-rich areas in the inner city have the highest accessibility with a decrease in accessibility with increasing distance from the urban center, however this negative relationship is not as clear as in the Shen´s type model. This is an expected result with GBM since this model focuses on the supply side of the jobs market, i.e. employment urban cores are predominant areas of accessibility. Total employment dramatically increases accessibility and gives more consistent results between travel-time sources than does formal employment, probably due to the reinforcement of the role of land use in the estimation. Accessibility is always higher for car drivers than for transit users with TRANUS, while for HODS17 this remains true for the most part but with a few exceptions.The Shen´s indicator shows a more consistent spatial pattern of accessibility (spearman correlations close to 1) regardless of travel-time data, and transportation mode choice, demonstrating the robustness of the method. In general, the spatial pattern of accessibility in relation to the urban center is a line with a negative slope. The resulting Shen´s accessibility landscape was compared with the urban structure cited in the literature. Results show that areas with the highest employment accessibility are within the central agglomeration and the associated corridors along main highways at its perimeters, according to the urban structure reported by Suárez and Delgado (2009). The disparity in terms of location means that access in the TAZ with the highest accessibility record is 26% higher than the metropolitan accessibility average. As expected, the inclusion of total employment increases accessibility in comparison with only formal employment. Commuting by car reduces travel time, and although this increases accessibility overall, the increase is negligible when comparisons are made with the increment of accessibility from formal to total employment, or with the difference between the higher and lower ends of job access by public transit. These comparisons show that as opposed to transportation mode, locations of residence, in direct relation with its closeness to the urban employment centers, is the main factor affecting access to employment. As en example of how results can be used to guide goverment interventions we identified priority areas for accessibility improvement as those TAZs with the worst accessibility index and highest marginalization in both the State of Mexico and CDMX.

Studying the CCUS helps to describe relationships between man and nature through a spatial interface, such as satellite images and Geographic Information Systems (GIS). This allows the monitoring of regional and global changes by looking at the distribution patterns of landscape covers and the effects on the available resources. This also makes it possible to use tools such as the Models of Change of Cover and Land Use (CCUS, in Spanish), which identify the quantity and/or areas susceptible to change, in addition to preventing environmental degradation conditions. These models mainly identify patterns of change by using remote perception data, population censuses, statistical analyses, and expert knowledge, allowing to generate parameterization of transitions between categories. Once the prospective model has been developed, the simulation or accuracy of the prediction can be evaluated, ideally through statistical methods produced to determine the accuracy of the projection of a model. Through the validation of CCUS models, we can establish the certainty of the changes that are foreseen. Therefore, we consider it important to find new methods to evaluate predictive CCUS models, and in this study, evaluates the accuracy of the predictions considering the present information to define the certainty of models that have already met the prediction time. Therefore, the objective of this work is to determine the predictive capacity of three CCUS models in the Baja California peninsula, Mexico, generated in 2008. The main input was the predictive models calculated for the period 1978-2003, produced from satellite images. Subsequently, through the mapping update method, three maps of land covers and uses were obtained for 2018, used to generate change maps for the period 2003-2018 and thus evaluate the assertiveness of the change surfaces projected by the predictive models. This was done with remote sensing tools, geographic information systems, statistical analysis software (R), and change detection (DINAMICA-EGO). The mapping update procedure required a total of four Landsat-8 scenes with a spatial resolution of 30; this method produces segments corresponding to units of the landscape units and avoids producing isolated pixels. Changes are detected through these segments based on atypical spectral responses compared to other objects in the image. This procedure combines digital image classification and visual interpretation processing to produce maps for different times based on existing mapping enhancement processes. Once the 2018 CCUS maps were obtained, the reliability of each 2018 map was evaluated, as well as the reliability of the user and producer of each map. Finally, the predictive models elaborated in 2008 were evaluated by observing the changes between the 2003 map and the updated 2018 map for each study site. Subsequently, matrices of cover changes between years 2003-2018 were elaborated in DINAMICA EGO, allowing us to determine the area (hectares) of change by category for each site and the total loss by category between years. From this, we estimated the area of xeric shrubland that changed, corresponding to the dependent variable in all predictive models. Finally, the current data of change for each model were compared with the prediction for each study site. The 2018 CCUS maps showed a reliability figure above 96% in the three locations. The predictions from CCUS models in 2008 were very close to those observed in 2018 in two cases; Santo Domingo showed an assertiveness of 77% (26 more hectares lost vs. prediction) and San Quintín, 86% (17 less hectares lost vs. prediction), while Tijuana showed an assertiveness of only 35% (13 less hectares lost vs. prediction). The methodology used is a proposal that helps to know the actual degree of certainty of the CCUS predictive models and the generation of updated cartography. This evaluation not only allows determining assertiveness but, under a more thorough and rigorous investigation, it will make it possible to identify the direct and indirect factors that led to change, thus contributing expert knowledge for future predictions.

Fresh water is a fundamental resource for environmental and social processes, essential for the emergence and development of life. Mapping and monitoring surface water is therefore of great importance for understanding hydrological processes and managing water resources. This study was conducted in the largest mountain range in Mexico, the Sierra Madre Occidental (SMO), spanning the states of Chihuahua, Sonora, Sinaloa, Durango, Nayarit, Zacatecas and Jalisco. The SMO has an area of 251,648 km2 and elevations ranging from 300 m to 3,347 m. Due to its size, orography and geographical location, this region which constitutes the main source of water for northern Mexico, contains a wide variety of ecosystems, which in turn promote high species diversity. The objectives of this study were: 1) to detect water bodies in the SMO using Sentinel-2 satellite images with high spatial resolution, and 2) to make an inventory of water bodies in the SMO by vegetation type. In this study, 120 Sentinel-2 satellite images were used. The satellite has a multispectral sensor with a spatial resolution of 10 m. An atmospheric correction was carried out for each image using the dark object subtraction method. The normalized difference water index (NDWI) was used to detect and delimit water bodies. Before the validation process, the water bodies that had been detected were cross-tabulated against the polygons of the different vegetation types. These vegetation types were classified as follows: forest class, which includes pine, oak, pine–oak, oak–pine and cloud forest; tropical forest class, which includes low and medium deciduous tropical forest; forest with secondary herbaceous and shrubby vegetation class; scrub class; grassland class and chaparral class. The polygons were obtained from the INEGI 1:250,000 vectorial Series VI data on land use and vegetation. The number of water bodies (and their area) detected in each vegetation class were obtained through geoprocessing using the ArGIS 10.7 program. Estimates of the areas of the water bodies were validated by estimating the kappa index, and by means of confusion and error matrices. These were used to calculate the areas of the water bodies and their confidence intervals for each vegetation class. A total of 26,394 water bodies were detected. The vegetation type with the most water bodies was forest, with 46.86%, followed by grasslands, with 21.47%. The water bodies detected had areas ranging from 43 m2 to 64 km2. Pixel values from the NDWI associated with water bodies ranged from 0.1 to 0.8. The median was close to 0.3, and the quartiles were 0.2 and 0.4. The kappa index values indicated good and excellent agreement for the precision of water body detection in the different vegetation types. The lowest value, K = 0.62, was associated with pine–oak and cloud forest vegetation types. This was due to shadows that were mistaken for water bodies (251 shadows). The highest kappa index values, K = 0.91, were obtained for grasslands, where very few shadows (13 shadows) were confused with water bodies. The overall precision was 0.738, and the error matrix showed that the class with the most errors of commission was grassland, with a user accuracy value of 0.227. The class that had the most errors of omission was scrub, with a producer accuracy value of 0.351. This study makes a substantial contribution to the 800 water bodies previously reported for the SMO in the Series VI data for land use and vegetation from 2016.

On a global scale, tourist mobilities have increased exponentially since World War II. The Caribbean region is one of the most attractive spaces. The Yucatan Peninsula is a laboratory to study the tourist phenomenon. Whilst for about a decade the tourist phenomenon was previously concentrated on the coast, the inland areas have become touristified, as shown in the case of the Mainland Caribbean and particularly for the Yucatan Peninsula, which is clearly marked by an accelerated tourism. In terms of intensity, tourist activity shows its highest levels long the coast of Quintana Roo, tends to decrease towards the west and south, and registers its lowest values in Campeche, southern Yucatan and southern (inland) Quintana Roo. This regionalization proposal allows us to explain glimpse these differences in greater detail. Thus, the Cancun-Riviera Maya traspaís is a region that reveals a production model showing the differentiated space of the Caribbean coast. Starting from a critical review of the production of space in the Cancun region —the Riviera Maya and its interior hinterland— this article argues for the existence of two opposing models, and reveals specific nuances between them. Cancun and the Riviera Maya, located in the Mexican Caribbean, concentrate the largest tourist region in Latin America, developed over the last fifty years. With Cancun as the “integrally planned” center, the tourism market spreads across the territory giving rise to a new region defined by this activity. This is the case of the hinterland region of Cancun-Riviera Maya, which for the last fifteen years has known a great tourist effervescence; in many Mayan towns in this region, local societies have chosen tourism as a complementary or main economic activity. The territorial structure model in the Mexican Caribbean has the following characteristics: a) occupying a large space, usually in a linear manner, and linked to a real estate business that is developed as the central axis for capital accumulation, that becomes even more important than the tourism; b) the privatization of resources destined for tourism, and specifically, the beaches; and c) a complete functional and social segregation of space. The incorporation of the hinterland, however, modifies the dominant paradigm for the production of touristic spaces in the region: while the coastal model relies on the privatization of spaces that were previously public, in the hinterland, the companies involved in the collective production of tourism transform privately owned spaces into public ones. The rural communities of predominantly Mayan origin in the Cancun-Riviera Maya region, enter into the tourist activity claiming authenticity as an argument for the tourism. In a similar manner, tourism activities coexist alongside the local population, in their everyday and residential spaces. The development of this model across the territory is not linked to real estate and therefore the use of space is precise and opportune. Without denying the existence of nuances between these two antithetical conceptions, the case of the Cancun-Riviera Maya transfer presents the ways in which the indigenous population is inserting itself into tourism, by means of a concept that integrates it into their daily life and activities and that allows for an intercultural coexistence that can be beneficial for both groups of actors.

Degradation of soil quality due to fertility loss and erosion can hamper food security and self-sufficiency, which would mostly impact small subsistence farmers and exacerbate food poverty. Degradation affects nearly half of Mexican soils, but its effects on food self-sufficiency have been poorly investigated.This study conducted an initial cartographic and statistical analysis of the relationship between erosion in rain-fed agricultural soils, whose production destination is self-consumption, and food deficiency in Mexico.Country-wide, cartographic and statistical data from official sources were used, including series V of the INEGI Land-Use and Vegetation chart (scale 1: 250,000), the map of human-induced soil degradation (scale 1: 250,000), results from the VIII Agricultural, Livestock and Forestry census (2007), and Poverty reports (2010, 2012 and 2014).By overlaying such maps, we identified the polygons classified as rain-fed agriculture, affected by water erosion, and whose production was entirely or partially intended for self-consumption. This information was correlated with food deficiency data per municipality.To examine the relationship between the surface area dedicated to rain-fed annual crops, affected by water erosion, and the population with limited access to food, a linear correlation analysis was carried out using Pearson’s coefficient. Our early results show that 16% of rain-fed agricultural land is affected by erosion to some extent. These areas are distributed across 27 states.Agricultural Production Units dedicated to rain-fed agriculture, affected by erosion and whose production is intended for self-consumption (UPAE), occur in 41% of the municipalities of Mexico; 56% of these are entirely dedicated to self-consumption. In terms of yield and diversity, agricultural production is the main source of income for small, self-consumption producers. Land is the primary asset from which they are able to obtain products of high nutritional quality.Food poverty is unevenly distributed across the country. The worst levelsare concentrated in four states: State of Mexico, Veracruz, Puebla, and Jalisco.We found a moderate positive correlation (r = 0.65) between erosion in rain-fed agricultural soils dedicated to self-consumption and lack of food access. Although these results do not imply a causal relationship, they clearly show the linkage between erosion in rain-fed agricultural soils and food deficiency among the population at an aggregate level in Mexico. The states that most contribute to this correlation are Guerrero, Michoacán, Guanajuato, and State of Mexico.Soil degradation affects the food security of producers as well as the sovereignty of the country. These concerns, which are stated as three sustainable development goals (SDGs), demand comprehensive attention in order to implement policies to improve soil quality and develop sustainable agricultural practices, accompanied by rural development.

Forest soil degradation is a problem that the Mexican government has addressed in recent years through the implementation of targeted forest restoration programs; however, there is limited information on how this implementation process has been at the local level. Therefore, the objective of this work is to analyze the implementation of the soil conservation program in the agrarian nuclei of the Nevado de Toluca Flora and Fauna Protection Area (APFFNT), as well as the perception that these ejidos and communities have about said Program. The analysis methods used were spatial statistics and the assessment focused on the subjective perception of the participants. The results show the formation of a spatial cluster of agrarian nuclei with the largest amount assigned around the APFFNT corezone, with the variable surface area of the agrarian nucleus having the highest relationship with the amount assigned above the level of erosion. The perception analysis of the agrarian nuclei indicates that the most serious problem of soil degradation is the decrease in fertility, that the lowest levels of participation of the agrarian nuclei are at the beginning of the program and that the lowest impacts of the project are the low participation of indigenous groups, the low reduction of labor migration and the low infiltration of water from the works. In conclusion, the aspects that must be restructured are: include actions aimed at the most eroded areas (agricultural areas adjacent to the forest), consider the degree of erosion in the allocation of amounts, generate strategies to attack the causes of degradation and implement actions that raise the participation of the agrarian nuclei.

Mexico, where farms range from subsistence to industrial systems, is a good example of the wide global diversity in farm systems in terms of agricultural technology, economic strategies and socio-cultural characteristics. This paper studies national-scale data to analyse three maize production systems: small-scale (< 2 ha per farm), medium-scale (5-10 ha per farm), and large-scale (>50 ha per farm). Farm-scale data from the Agricultural National Survey of 2014 were used at national and state scale to investigate trends and differences among the systems in relation to (1) socio-cultural characteristics (use of maize for self-sufficiency and sales; land tenure, type of seed and use of family labour), (2) agricultural technology (crop yields, irrigation system, agrochemicals use and labour requirements) and (3) economic characteristics (governmental programmes, insurance, credits and production costs). The results show that some characteristics are intrinsic to the type of system but that others deviate from the trends or patterns reported in the literature. For instance, (1) most farmers from the three systems use agrochemicals and (2) a large share of the crop area of large-scale systems (22%) consists of social land tenure (“ejido”). Furthermore, some of these trends show geographical deviations. Farms differ in their response to social, economic and technological aspects, and this emphasizes the need for an interdisciplinary approach in the design of political strategies that are context specific.

Drones allow obtaining detailed aerial information more autonomously and economically, compared to other sources of remote sensing. This has motivated the civil and Community use of drones around the world, both for audiovisual and cartographic purposes. Therefore, governmentshave had to legislate its use and address various concerns such as aviation security, privacy and data protection. In Mexico, five Mandatory Circulars were issued (2010–2017) and then an Official Standard (2019). Despite its great potential, the community use of drones has not been debated or incorporated into these regulations. Therefore, the objective of this article is to review the Mexican regulatory framework on drones and analyze their impacts on the community use of said technology. The documentary review and its analysis with communities in central Mexico reveal that the legislation does not favor its community use, particularly when it is for mapping purposes. This limitation is due to diverse bureaucratic and economic requirements, as well as the absence of aeronautical cartography of easy access and consultation. In this situation, we discuss the challenges of incorporating the community use of drones into the regulatory framework. Furthermore, we suggest that airspace is an integral part of the territorial rights of indigenous and local communities, and that its legal recognition would favor its community use. Finally, we suggest that the community use of drones could greatly benefit from a regulatory approach based on the risk of operation, establishing requirements and limitations more in line with the heterogeneity of airspace and its traffic. Much of our analysis is also useful for the civil use of drones in Mexico.

The aridity and geographic isolation of the region currently occupied by the State of Baja California Sur have determined its status as a sparsely populated region for most of its history, which has allowed the ecosystems therein to remain pristine or little impacted. Nevertheless, the State has not been immune to the global trend of vigorous population growth in coastal regions. This trend became evident in La Paz city since the 1990s (population as of 1995: 158,800; as of 2015: 253,021 inhabitants), caused by a tourism-driven economy that has led to accelerated, unplanned urban sprawling (urban area as of 1995: 44.7 km2; as of 2017: 85.7 km2). The objective of this study was to identify and analyze anthropic effects on the geomorphology and morphodynamics of the coastal zone, starting with the geomorphological reconstruction of the likely original conditions, followed by an analysis of the population growth and economic activities leading to urban expansion, to finally elucidating their effects on and the evolutionary response of the coastal zone. We used linear regression methods for analyzing geomorphological and morphodynamic changes from historical documents, particularly those that are, or can be, georeferenced such as maps, aerial photographs, and satellite images. This allows utilizing both methods over long-term, large (kilometer) spatial scales. Linear regression methods used all the shoreline location data points available to identify changes over time and estimate evolutionary rates. One distinctive advantage of linear regressions is the possibility of using coastlines as delineated from aerial photographs or historical maps, since beach profiles encompassing long-term, large (kilometer) spatial scales are usually not available. The study area comprises two distinct geomorphological units — the alluvial plain and the coastal zone — subject to increasing anthropic activity as evidenced by urban expansion over time. This affects the geomorphological environment, causing major changes in the physical environment, such as fragmentation in parcels and streets, deforestation, and land leveling, as well as the diversion and filling of streambeds. The direct effects of anthropogenic activities on the coastal zone lead to the alteration and partial or total loss of coastal ecosystems such as coastal plains, marshes, mangrove swamps, beaches, and dunes, caused by the construction of tourism infrastructure including hotels, marinas, docks, golf courses, and buildings ranging from residential areas to car junkyards. At the same time, the effects of human activities on the alluvial plain indirectly impact the coastal zone through the reduction of the effective runoff area caused by unabated urban expansion. This, in turn, impairs the sedimentary balance that gave rise to alluvial coastal environments, in which the intensity of the dynamic agents (such as wind, tides, and waves) acting on the coastal zone remained constant. These agents have been acting with the same intensity but, in the absence of sedimentary material, conditions have changed sufficiently to affect the morphodynamics of the coastal fringe of the La Paz lagoon. The evolutionary trend of the La Paz lagoon shows that it has experienced an erosive process at a rate of ‑0.00227 km2 over 44 years (from 1973 to 2017, or 0.3 m/year). Paradoxically, the poor or nil urban growth planning is not only causing the loss of the resources that support tourism activities, but is also exacerbating the vulnerability to flooding events in ​​La Paz city and surrounding suburbs. Therefore, it is crucial to properly weigh the benefits of continuing building hotels, marinas, golf courses, residential areas, and other types of infrastructure, versus those of conserving marshes, mangrove swamps, beaches, and dunes, whose proper use and management would contribute to the sustainable economic and environmental development of the region.

Identifying environmental hazards is the first step in risk assessment to define safety or mitigation actions. However, while experts make a diagnosis based on quantitative risk assessment, ordinary people use the information from the physical and communication environment to assess their own safety status. This leads to biases between expert and everyday visions of risk, which can increase it physically when, in the face of any event, people are either unprepared or show exaggerated responses because of the socially attenuated or magnified perception of risk, respectively. Hence the importance of risk perception in disaster prevention. Recent studies on the subject indicate that emotions play a central role in how ordinary people make judgments about their safety. These studies report that fear or alarm is the main driver of public perception and acceptance or rejection of risk within a wide range of hazards. Considering the above, this article explores the effect of fear on the social identification and assessment of urban hazards, and its relationship with other variables that influence the perception of risk. We aimed to answer the following question: Do people identify the potential hazards in their environment or consider them as severe because they fear them? This question involves exploring the influence of fear on the social identification of hazards and its weight on the extent of hazard assigned by subjects. To this end, we conducted a survey called Local Risk Perception: Mexicali City, applied to a random sample of 390 households in the city of Mexicali, Baja California, Mexico. From this, we used data regarding six variables that record, for a list of 36 hazards: Whether the inhabitants identify them in the area where they live; the level of fear and the degree of hazard assigned; whether they have experienced any direct damages, whether they recall recent events related to them, and whether they perceive an increase in the severity of the phenomenon. Besides, regarding the hazards on the list, we included two objective variables that record expert assessment: the level of exposure and the urban hazard, both calculated from the information provided by the Atlas of Risks for Mexicali. Finally, Pearson's correlation coefficients were calculated for all possible pairs of the eight variables involved. Among the key findings, the correlation analysis showed that the identification of the hazards in the area, the experience of damages, and the memories of recent events showed the strongest relationship of all the variables involved, evidencing the weight of direct experience of adverse effects on people's judgment on the local hazards. In addition, the statistical test indicated that fear is not significantly related to all the variables involved and is poorly related to most of them, revealing that fear marginally influences or is affected by the variables considered in this study. The above support that people can identify the hazards in the local environment because they have witnessed their presence on the site or have been directly affected by them, rather than because they are afraid. Our findings are contrary to the remarks in the literature on the influence of fear on risk assessment. According to our study, this negative emotion is insufficient to explain the fact that the inhabitants of Mexicali identify the presence of particular hazards in their neighborhood or the hazard level assigned to them, despite having suffered direct damages or recalling any past event. Instead, we found that direct experience regarding damages and losses is a major source of information in the learning and assessing the local hazards. Hence, from a risk perception perspective, it is necessary to approach the mitigation of urban risk from specific strategies that contemplate the array of relevant local hazards.