Mapping Transnational Crime in El Salvador: New Trends and Lessons from Colombia

Since El Salvador’s civil war formally ended in 1992 the small Central American nation has undergone profound social changes and significant reforms. However, few changes have been as important or as devastating as the nation’s emergence as a central hub in the transnational criminal “pipeline” or series of recombinant, overlapping chains of routes and actors that illicit organizations use to traffic in drugs, money weapons, human being, endangered animals and other products. The erasing of the once-clear ideological lines that drove the civil war and the ability of erstwhile enemies to join forces in criminal enterprises in the post-war period is an enduring and dangerous characteristic of El Salvador’s transnational criminal evolution. Trained, elite cadres from both sides, with few legitimate job opportunities, found their skills were marketable in the growing criminal structures. The groups moved from kidnapping and extortion to providing protection services to transnational criminal organizations to becoming integral parts of the organizations themselves. The demand for specialized military and transportation services in El Salvador have exploded as the Mexican DTOs consolidate their hold on the cocaine market and their relationships with the transportista networks, which is still in flux. The value of their services has risen dramatically also because of the fact that multiple Mexican DTOs, at war with each other in Mexico and seeking to physically control the geographic space of the lucrative pipeline routes in from Guatemala to Panama, are eager to increase their military capabilities and intelligence gathering capacities. The emergence of multiple non-state armed groups, often with significant ties to the formal political structure (state) through webs of judicial, legislative and administrative corruption, has some striking parallels to Colombia in the 1980s, where multiple types of violence ultimately challenged the sovereignty of state and left a lasting legacy of embedded corruption within the nation’s political structure. Organized crime in El Salvador is now transnational in nature and more integrated into stronger, more versatile global networks such as the Mexican DTOs. It is a hybrid of both local crime – with gangs vying for control off specific geographic space so they can extract payment for the safe passage of illicit products – and transnational groups that need to use that space to successfully move their products. These symbiotic relationships are both complex and generally transient in nature but growing more consolidated and dangerous.

Estimation of average annual daily traffic (AADT) using geographically weighted regression (GWR) method and geographic information system (GIS)

Annual average daily traffic (AADT) is important information for many transportation planning, design, operation, and maintenance activities, as well as for the allocation of highway funds. Many studies have attempted AADT estimation using factor approach, regression analysis, time series, and artificial neural networks. However, these methods are unable to account for spatially variable influence of independent variables on the dependent variable even though it is well known that to many transportation problems, including AADT estimation, spatial context is important. ^ In this study, applications of geographically weighted regression (GWR) methods to estimating AADT were investigated. The GWR based methods considered the influence of correlations among the variables over space and the spatially non-stationarity of the variables. A GWR model allows different relationships between the dependent and independent variables to exist at different points in space. In other words, model parameters vary from location to location and the locally linear regression parameters at a point are affected more by observations near that point than observations further away. ^ The study area was Broward County, Florida. Broward County lies on the Atlantic coast between Palm Beach and Miami-Dade counties. In this study, a total of 67 variables were considered as potential AADT predictors, and six variables (lanes, speed, regional accessibility, direct access, density of roadway length, and density of seasonal household) were selected to develop the models. ^ To investigate the predictive powers of various AADT predictors over the space, the statistics including local r-square, local parameter estimates, and local errors were examined and mapped. The local variations in relationships among parameters were investigated, measured, and mapped to assess the usefulness of GWR methods. ^ The results indicated that the GWR models were able to better explain the variation in the data and to predict AADT with smaller errors than the ordinary linear regression models for the same dataset. Additionally, GWR was able to model the spatial non-stationarity in the data, i.e., the spatially varying relationship between AADT and predictors, which cannot be modeled in ordinary linear regression. ^