Predicting Electric Vehicle Charging Demand using Mixed Generalized Extreme Value Models with Panel Effects

In the past 5 years Electric Car use has grown rapidly, almost doubling each year. To provide adequate charging infrastructure it is necessary to model the demand. In this paper we model the distribution of charging demand in the city of Amsterdam using a Cross-Nested Logit Model with socio-demographic statistics of neighborhoods and charging history of vehicles. Models are obtained for three user-types: regular users, electric car-share participants and taxis. Regular users are later split into three subgroups based on their charging behaviour throughout the day: Visitors, Commuters and Residents

Demographic risk factors for suicide among youths in the Netherlands

In 2000 to 2016 the highest number of suicides among Dutch youths under 20 in any given year was 58 in 2013. In 2017 this number increased to 81 youth suicides. To get more insight in what types of youths died by suicide, particularly in recent years (2013-2017) we looked at micro-data of Statistics Netherlands and counted suicides among youths till 23, split out along gender, age, regions, immigration background and place in household and compared this to the general population of youths in the Netherlands. We also compared the demographics of young suicide victims to those of suicide victims among the population as a whole. We found higher suicide rates among male youths, older youths, those of Dutch descent and youths living alone. These differences were generally smaller than in the population as a whole. There were also substantial geographical differences between provinces and healthcare regions. The method of suicide is different in youth compared to the population as a whole: relatively more youth suicides by jumping or lying in front of a moving object and relatively less youth suicides by autointoxication or drowning, whereas the most frequent method of suicide among both groups is hanging or suffocation

The BP Dependency Function: a generic measure of dependence between random variables

Measuring and quantifying dependencies between random variables (RV's) can give critical insights into a data-set. Typical questions are: `Do underlying relationships exist?', `Are some variables redundant?', and `Is some target variable Y highly or weakly dependent on variable X?' Interestingly, despite the evident need for a general-purpose measure of dependency between RV's, common practice of data analysis is that most data analysts use the Pearson correlation coefficient (PCC) to quantify dependence between RV's, while it is well-recognized that the PCC is essentially a measure for linear dependency only. Although many attempts have been made to define more generic dependency measures, there is yet no consensus on a standard, general-purpose dependency function. In fact, several ideal properties of a dependency function have been proposed, but without much argumentation. Motivated by this, in this paper we will discuss and revise the list of desired properties and propose a new dependency function that meets all these requirements. This general-purpose dependency function provides data analysts a powerful means to quantify the level of dependence between variables. To this end, we also provide Python code to determine the dependency function for use in practice

The Berkelmans-Pries Feature Importance Method: a generic measure of informativeness of features

Over the past few years, the use of machine learning models has emerged as a generic and powerful means for prediction purposes. At the same time, there is a growing demand for interpretability of prediction models. To determine which features of a dataset are important to predict a target variable Y, a Feature Importance (FI) method can be used. By quantifying how important each feature is for predicting Y, irrelevant features can be identified and removed, which could increase the speed and accuracy of a model, and moreover, important features can be discovered, which could lead to valuable insights. A major problem with evaluating FI methods, is that the ground truth FI is often unknown. As a consequence, existing FI methods do not give the exact correct FI values. This is one of the many reasons why it can be hard to properly interpret the results of an FI method. Motivated by this, we introduce a new global approach named the Berkelmans-Pries FI method, which is based on a combination of Shapley values and the Berkelmans-Pries dependency function. We prove that our method has many useful properties, and accurately predicts the correct FI values for several cases where the ground truth FI can be derived in an exact manner. We experimentally show for a large collection of FI methods (468) that existing methods do not have the same useful properties. This shows that the Berkelmans-Pries FI method is a highly valuable tool for analyzing datasets with complex interdependencies

Who didn't consult the doctor? Understanding sociodemographic factors in relation to health care uptake before suicide

Objective: This study aimed to establish differences between suicide decedents and a reference population across various health care settings.Methods: This population-wide registration study combined death statistics, sociodemographic data and health care data from Statistics Netherlands. From 2010 to 2016, 12,015 suicide cases and a random reference group of 132,504 were included and assigned to one of the three health care settings; mental health (MH) care, primary care or no care. Logistic regression analyses were performed to determine differences in suicide risk factors across settings.Results: In the 1–2 year period before suicide, 52% of the suicide decedents received MH care, 41% received GP care only and 7% received neither. Although sociodemographic factors showed significant differences across settings, the suicide risk profiles were not profoundly distinctive. A decreasing trend in suicide risk across health care settings became apparent for male gender, income level and being in a one-person or one-parent household, whereas for other factors (middle and older age, non-Western migration background, couples without children and people living in more sparsely populated areas), risk of suicide increased when health care setting became more specialized.Limitations: Because of the data structure, 18 months of suicide decedents’ health care use were compared with two years health care use of the reference group, which likely led to an underestimation of the reported differences.Conclusion: Although there are differences between suicide decedents and a reference group across health care settings, these are not sufficiently distinctive to advocat

The effect of local Suicide Prevention Action Networks (SUPRANET) on stigma, taboo and attitudes towards professional help-seeking: an exposure–response analysis

Purpose: In 2017, the European Alliance against Depression (EAAD) programme was introduced in the Netherlands through the creation of six local Suicide Prevention Action Networks (SUPRANET Community). This programme consists of interventions on four levels: (1) a public awareness campaign, (2) training local gatekeepers, (3) targeting high-risk persons in the community and (4) training of primary care professionals. This study aims to gain insight into the effectiveness of the SUPRANET programme on attitudinal changes in the general public by studying the exposure–response relationship. Methods: A repeated cross-sectional design, using general population surveys to measure key variables over time. The surveys were conducted in the six intervention regions (N = 2586) and in the Netherlands as a whole as a control region (N = 4187) and include questions on socio-demographic variables, brand awareness of the Dutch helpline, perceived taboo on suicide, attitudes towards depression and help-seeking. To examine the exposure–response relationship, regions were diff

The impact of a suicide prevention awareness campaign on stigma, taboo and attitudes towards professional help-seeking

Background: In 2017, the European Alliance against Depression (EAAD) was introduced in The Netherlands through the creation of six Suicide Prevention Action Networks (SUPRANET Community). The intervention was launched with a national suicide prevention awareness campaign. This campaign aims to encourage the general public to talk about suicide. This study aimed to gain insight into the effectiveness of the campaign in achieving attitudinal change in the general public, as stigmas related to mental health disorders and -services are an important reason for insufficient help-seeking. Methods: A repeated cross-section design, using general population surveys (N = 6,773) to measure key variables over time. The survey includes questions on socio-demographic variables, campaign visibility, brand awareness of the Dutch helpline, perceived taboo on suicide, attitudes towards depression and help-seeking. Results: The public awareness campaign was predominantly visible among the younger generation. Respondents who indicated having seen the public awareness campaign showed more openness towards seeking professional help and were considerably more likely to be familiar with the Dutch helpline than those who reported not having seen the campaign. Campaign awareness also seemed to relate to a higher perceived taboo on suicide and a lower estimation o