A Spatial Approach to Surveying Crime‐problematic Areas at the Street Level

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.Reaching far beyond the realm of geography and its related disciplines, spatial analysis and visualization tools now actively support the decision-making processes of law enforcement agencies. Interactive mapping of crime outperforms the previously manual and laborious querying of crime databases. Using burglary and robbery events reported in the urban city of Manchester, England, we illustrate the utility of graphical methods for interactive analysis and visualization of event data. These novel surveillance techniques provide insight into offending characteristics and changes in the offending process in ways that cannot be replicated by traditional crime investigative methods. We present a step-wise methodology for computing the intensity of aggregated crime events which can potentially accelerate law enforcers’ decision making processes by mapping concentrations of crime in near real time

Cycle superhighways in the study area of Inner London.

<p>Cycle superhighways in the study area of Inner London.</p

The distribution of risk indicators over Inner London’s street segments (<i>n</i> = 51,216 segments).

<p>The distribution of risk indicators over Inner London’s street segments (<i>n</i> = 51,216 segments).</p

Effect estimates of negative binomial bicycle theft models for the seasonal variables ^a at four threshold distances of risk exposure measurement (models are adjusted for risk factors).

<p>Effect estimates of negative binomial bicycle theft models for the seasonal variables <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163354#t002fn003" target="_blank">^a</a> at four threshold distances of risk exposure measurement (models are adjusted for risk factors).</p

Seasonal bicycle theft statistics for Inner London from May 2013 to April 2016 (<i>n</i> = 36,987 events).

<p>Seasonal bicycle theft statistics for Inner London from May 2013 to April 2016 (<i>n</i> = 36,987 events).</p

Flowchart of the experimental design to identify risk indicators for regression modeling of bicycle theft.

<p>Flowchart of the experimental design to identify risk indicators for regression modeling of bicycle theft.</p

Incidence rate ratios (IRRs) and 95% confidence intervals of negative binomial models.

<p>Estimates correspond to effects of risky and risk-mitigating amenities and socioeconomic factors on bicycle theft. Effects are measured using bicycle theft counts (May 2013 to April 2016) for 51,216 street segments. Models account for the seasonal effects shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163354#pone.0163354.t002" target="_blank">Table 2</a> and assess risk exposure over four threshold distances: (a) 160 m—Model 1; (b) 320m -Model 2; (c) 480m—Model 3; and (d) 640m—Model 4. The commuter-adjusted population is modeled as an offset variable.</p

Applying Data Mining in Graduates’ Employability: A Systematic Literature Review

Envisaging an adequate IT/IS solution that can mitigate the employability problems is imperative because nowadays there is a high rate of unemployed graduates. Thus, the main goal of this systematic literature review (SLR) was to explore the application of data mining techniques in modeling employability and see how those techniques have been applied and which factors/variables have been retained to be the most predictors or/and prescribers of employability. Data mining techniques have shown the ability to serve as decision support tools in predicting and even prescribing employability. The review determined and analyzed the machine learning algorithms used in data mining to either predict or prescribe employability. This review used the PRISMA method to determine which studies from the existing literature to include as items for this SLR. Hence, 20 relevant studies, 16 of which are predicting employability and 4 of which are prescribing employability. These studies were selected from reliable databases: ScienceDirect, Springer, Wiley, IEEE Xplore, and Taylor and Francis. According to the results of this study, various data mining techniques can be used to predict and/or to prescribe employability. Furthermore, the variables/factors that predict and prescribe employability vary by country and the type of prediction or prescription conducted research.  Nevertheless, all previous studies have relied more on skill as the main factor that predict and/or prescribe employability in developed countries and none studies have been conducted in unstable developing countries. Therefore, the need to conduct research on predicting or prescribing employability in such countries by trying to use contextual factors beyond skill as features

Predicting Employability of Congolese Information Technology Graduates Using Contextual Factors: Towards Sustainable Employability

Predicting employability in an unstable developing country requires the use of contextual factors as predictors and a suitable machine learning model capable of generalization. This study has discovered that parental financial stability, sociopolitical, relationship, academic, and strategic factors are the factors that can contextually predict the employability of information technology (IT) graduates in the democratic republic of Congo (DRC). A deep stacking predictive model was constructed using five different multilayer perceptron (MLP) sub models. The deep stacking model measured good performance (80% accuracy, 0.81 precision, 0.80 recall, 0.77 f1-score). All the individual models could not reach these performances with all the evaluation metrics used. Therefore, deep stacking was revealed to be the most suitable method for building a generalizable model to predict employability of IT graduates in the DRC. The authors estimate that the discovery of these contextual factors that predict IT graduates’ employability will help the DRC and other similar governments to develop strategies that mitigate unemployment, an important milestone to achievement of target 8.6 of the sustainable development goals