Citizen security is essential for the well-being of citizens. In the current context, Ecuador is facing a growing crime wave that generates deep concern among citizens. This study focused on analyzing the spatial-temporal patterns of robberies to people in the area of Terminal Terrestre of Cuenca in the period 2020-2022, using spatial analysis methods (Nearest Neighbor Index, Kernel Density Estimation and Grid Cell Mapping) and temporal analysis, through Geographic Information Systems (GIS). The results of applying these techniques generated cartographic products in which it was possible to visualize and detect several concentrations of points (hot spots) in different sectors of the study area. Likewise, the temporal analysis supported by data clocks and the Grid Cell Mapping method showed that there is a higher incidence of crime at certain times of day and in specific zones of the study area.
Bioclimatic classification is essential for understanding and managing the territory in a more sustainable way, as well as in biodiversity conservation management and climate change assessment. The supervised Maximum Likelihood (ML) classification method is a powerful tool for bioclimatic classification. Therefore, the ML classifier was applied in the Caroni River basin, using data from global sources, from which averages of climatic variables were extracted. Geographic Weighted Regression (GWR) and the 90 m SRTM DEM were used to improve the scale. The Holdridge classification was applied, this presented an overall accuracy of 93 % the classes of premontane rainforest, very humid tropical forest and premontane rainforest denoted the worst performance, as there are transitive structures involved that the classification model could not capture.
Air pollution is mostly due to anthropogenic causes degrading the quality of breathable air. The larger dimensions of gaseous pollutants are still challenging to inspect. Satellite data can quantify the concentrations of atmospheric pollutants that are specifically beneficial in understanding air pollution. This paper attempts to understand the air pollutants in the Kathmandu district (Nepal) retrieved from the Sentinel-5P satellite data from 2019 to 2022: by correlating with the weather parameter and vegetation indices, observing the yearly concentrations of the gaseous pollutants over the days of the year and observing their spatial distribution. In the results, temperature showed a predominantly strong positive correlation with methane (CH4), carbon monoxide (CO) and ozone (O-3) and a negative correlation with nitrogen dioxide (NO2), formaldehyde (HCHO), sulphur dioxide (SO2) and aerosol. Precipitation was negatively correlated with all the pollutants except the O-3. Vegetation indices, Normalised Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), were negatively correlated with most of the gaseous pollutants signifying the importance of the vegetation towards mitigating the gaseous pollutants. Moreover, yearly trends of the gaseous pollutants and the spatial distributions were unique for all the gaseous pollutants. Lowest mean concentrations of the aerosol index, HCHO, NO2 and O-3 in different days of 2020 were suggestive towards the impacts of COVID-19 lockdown. In the broader context, proper vegetation and the remedial ways to maintain Kathmandu's temperature, around the city areas, is likely to moderate the gaseous pollutants in the city. This paper clearly points to the approach in utilising the satellite based data in air pollution inspections.
