Buenaventura on the Colombian Pacific coast has experienced a wide range of threats, mainly due to the effects of coastal erosion and flooding. Globally, millions of people will experience increased vulnerability in the coming decades due to climate change. The change in the coastline (1986–2020) over time was analyzed with remote sensors and the Digital Shoreline Analysis System (DSAS) in conjunction with GIS. A total of 16 indicators were selected to quantitatively evaluate exposure, sensitivity, and adaptive capacity to construct a composite vulnerability index (COVI). The endpoint rate (EPR) of the change in the coastline was estimated. The results showed that 35% of the study area was stable, 18% of the coastline experienced erosion processes, and 47% experienced accretion. The COVI analysis revealed that coastal watersheds show great spatial heterogeneity; 31.4% of the area had moderate vulnerability levels, 26.5% had low vulnerability levels, and 41.9% had high vulnerability levels. This analysis revealed that the watersheds located in the northern (Málaga Bay) and central (Anchicaya, Cajambre, and Rapposo basins) parts of the coastal zone were more vulnerable than the other areas.

This study presents a methodology to estimate the seven indicators of the Setting and Infrastructure criterion of the UI GreenMetric World University Ranking based on three-dimensional data from a point cloud taken from an unmanned aerial vehicle (UAV). This study also estimated the potential aerial biomass, C and CO₂, stored in the green spaces of a university campus using photogrammetric data analyzed in a Geographic Information System (GIS). The method was based on isolating classified point clouds using digital surface models (DSMs) and ground control points (GCPs) considering the canopy height model (CHM), the allometric equation (DBH, p, h), the biomass conversion factor, and carbon dioxide equivalents (CO₂-e). The results confirmed that the national models for estimating the potential C reserves in natural forests are very close to reality and that the open space and green areas available to people on campus are adequate. The use of photogrammetric data facilitated the estimation of UI GreenMetric indicators from a highly detailed, low-cost three-dimensional model. The results of a case study revealed that the campus assimilates the CO₂ emissions it produces and generates a surplus.

Introduction: Emerging and re-emerging arboviral infections have become a public health challenge in the Americas due to their epidemic potential. Objective: To determine the spatio-temporal distribution of the dengue, Zika, and chikungunya viruses during an epidemic period in Cali, Colombia. Materials and methods: Multi-method descriptive ecological and exploratory study of confirmed and suspected cases reported to the epidemiological surveillance system between 2014 and 2016. Results: 40,168 cases were analyzed, and it was found that dengue was the most frequent arboviral infection (59.2 %). The most affected individuals were women (65 %) and those with a mean age of 34.5 years. Although arboviral infections spread out throughout the city, the three diseases were concentrated in significant groups located at the center-east and northeast areas of Cali (p<0.01; z=-203.7). Conclusions: This study identified critical zones for the three arboviral infections, which are located in areas with low socioeconomic status. Likewise, the results suggest that in addition to eco-epidemiological and bio-psychosocial factors, temperature, precipitation, and the aedic index may play an important role in the spatio-temporal behavior of these diseases. A multidisciplinary and collaborative approach is necessary, which must involve communities and authorities to implement effective control strategies, especially during epidemic periods.

Introducción: La identificación de áreas de alto riesgo de tuberculosis pulmonar (TBP) permite dirigir las intervenciones en salud pública a los grupos más vulnerables y a los lugares donde hay mayor transmisión. Objetivo: Determinar la distribución espaciotemporal de personas con edad ≥15 años afectadas por TBP en Cali, Colombia y describir sus características epidemiológicas. Métodos: Estudio observacional y ecológico. Se incluyeron sujetos con TBP activa confirmada entre el 2010 y 2017. Los análisis de información geográfica se realizaron en ArcGIS Desktop; con el análisis de Densidad de Kernel se presentaron mapas de calor. Se utilizó la herramienta Gi* Getis-Ord para el análisis de puntos calientes. Además, se describen sus características epidemiológicas. Resultados: La TBP afectó a personas de todas las comunas de Cali, pero se identificaron conglomerados de alto riesgo que permanecieron en el tiempo en las zonas centro y centro-oriente de la ciudad, donde también se orientaron la mayoría de casos de pérdida en el seguimiento y mortalidad. La incidencia promedio fue de 40,2 casos/100.000 habitantes y el 72% logró el éxito terapéutico. Los más afectados fueron los masculinos (63,3%) y el grupo etario 19 a 69 años (76%). El 16,5 % de pacientes se perdió en el seguimiento, el 1,9% presentó fracaso en el tratamiento y el 5,2% falleció. Conclusión: Se identificaron dos áreas críticas de concentración de casos de TBP (centro y centro-oriente de Cali), caracterizadas por la vulnerabilidad social. También, se observó una baja tasa de éxito en el tratamiento respecto a la meta del 85%. Esto subraya la necesidad de desarrollar estrategias integrales en toda la ciudad, con prioridad en áreas críticas.

Soil sealing remains an under-addressed threat to agricultural sustainability, particularly in rapidly urbanizing rural-urban interfaces. To address this challenge, a spatially explicit Soil Sealing Vulnerability Index (SSVI) was developed for 126,734.87 hectares of agricultural land in the Guachal and Amaime watersheds (GAWs), Valle del Cauca, Colombia. The SSVI integrates seven spatially referenced biophysical and institutional parameters—terrain slope, parcel size, road proximity, proximity to surface water bodies, agrological soil class, urban growth trends (2000–2024), and municipal land-use designations—using a multi-criteria analysis structured by expert consensus through the Analytic Hierarchy Process. With strong internal consistency, demonstrated by a Consistency Ratio (CR) of 2.51% that confirms the logical stability of expert judgments, the SSVI provides spatial support for decision-making in municipal land-use planning. Independent validation is deferred until sealing datasets become available, using a replicable concordance workflow. Results indicate that 42.54% of the GAWs area presents moderate vulnerability, 19.01% low vulnerability, 1.00% (1,270.1 ha) high vulnerability, and 37.44% corresponds to exclusion zones (e.g., urban cores and protected areas). Importantly, 938.7 ha of environmentally restricted soils and 228.8 ha of Mollisols fall within high-vulnerability zones, highlighting the model’s ability to identify policy-relevant risks. This study introduces the first spatially resolved SSVI tailored to Colombia’s regulatory landscape, demonstrating that vulnerability is more strongly influenced by institutional planning than by natural land constraints. Although technically replicable, effective application requires high-resolution spatial datasets and local expert participation. Integration into municipal planning instruments is essential to translate technical findings into policy action.