Mapping environmental crime to characterize human impacts on islands: an applied and methodological research in Canary Islands.

Environmental crimes are a global issue due to the damage they cause to landscapes and ecosystems. This study focused on characterizing environmental crimes in the Canary Islands (Spain). Four categories of environmental crimes related to construction, mining and tilling, solid waste, and liquid waste) were defined and analysed. A total of 28 databases were generated, corresponding to each of the 7 major islands and each environmental crime typology. Each database was linked to information on land use and the socioeconomic and physical characteristics of the territory. For each database, firstly a descriptive statistical analysis was conducted, followed by the generation of a regularized Random Forest model with the aim of identifying characteristics that may be related to the location of environmental crimes. The results showed that, in most cases, proximity to residential accommodations, agricultural areas and industrial zones act as the main explanatory features of the distribution of environmental crimes. Furthermore, a marked pattern of concentration of environmental crimes in the coastal belt of the islands was observed, mainly associated with urban-tourist development since the 1960s and 1970s.

Spatiotemporal analysis of the housing bubble's contribution to the proliferation of illegal landfills - The case of Gran Canaria.

Illegal landfills are the source of many impacts that can alter the environment and represent a public health risk. This study investigates their spatiotemporal distribution in two representative areas of Gran Canaria: northwest (Zone A) and east (Zone B). Illegal landfill occurrence was simulated between 2000 and 2018, to estimate and spatially locate the surface growth of illegal landfills based on cellular automata, cellular automata-Markov and multiobjective land allocation models. The proliferation of illegal landfills in 2018 was simulated following the calibration and validation of the proposed models. Models' accuracy was assessed using Kappa index and landscape metrics. The cellular automata-Markov model had the best performance. The model simulations predicted an increase of 52.3 ha and 81.5 ha affected by illegal landfills in Zone A and Zone B for 2018, respectively. The interannual growth rate of surfaces affected by illegal landfills for the period between 2000 and 2006 was 4.5% and 9.5% and between 2006 and 2012 it was 6.6% and 6.7%, for Zone A and Zone B respectively. The growth of illegal landfills between 2000 and 2006 was higher in urban areas, construction sites, and industrial zones, and may be closely related to the process of urban expansion linked to the real estate boom. The latter would have a deep impact on the landscape due to the proliferation of illegal construction and demolition waste. The growth rate of illegal landfills in urban environments fell during the later period of urban expansion. Overall, simulation outputs showed the model's ability to correctly reproduce the distribution patterns for illegal landfill proliferation. Even though the simulated spatial location of illegal landfills was not highly accurate, the models built in this study provide an informative tool to policy makers to aid the process creating policies for environmental protection as well as territorial planning.

Characterization and mapping of illegal landfill potential occurrence in the Canary Islands.

The proliferation of illegal landfills (IL) has a negative impact, especially for ecologically sensitive areas or those attractive for tourists. This research focuses on the drivers of the IL spatial distribution in archipelagic environments for mapping the IL potential occurrence. 286 and 153 illegal landfills localizations were identified through fieldwork in the islands of Gran Canaria (GC) and La Palma (LP), respectively. The characterization of IL was carried out from a set of features (177) such as: waste type, control and surveillance, socioeconomic, accessibility, distance to elements of interest, visibility and physiographic. Feature selection was performed using the Discriminant Analysis technique (DA). The DA model selected 10 and 9 features for GC and LP, respectively. The GC IL potential occurrence was mainly related to the greenhouse density, type of cadastral plot and distance to the coast. For the case of LP, the following features were selected: population density, distance to natural protected areas, distance to urban areas, slope and Normalised Difference Vegetation Index (NDVI). Different potential illegal landfill occurrence maps were obtained: (i) likelihood of occurrence of IL; and (ii) areas potentially affected by IL, based on the application of ROC (Receiver Operating Characteristics) curves and success rate. ROC was equal to 0.973 and 0.979 in LP and GC, respectively. Success rate was equal to 81.58% considering an affected area of 21.95% in LP, whereas success rate was equal to 87.32% in GC considering 20.10% affected area.