Benchmarking Regression Models Under Spatial Heterogeneity

Machine learning methods have recently found much application on spatial data, for example in weather forecasting, traffic prediction, and soil analysis. At the same time, methods from spatial statistics were developed over the past decades to explicitly account for spatial structuring in analytical and inference tasks. In the light of this duality of having both types of methods available, we explore the following question: Under what circumstances are local, spatially-explicit models preferable over machine learning models that do not incorporate spatial structure explicitly in their specification? Local models are typically used to capture spatial non-stationarity. Thus, we study the effect of strength and type of spatial heterogeneity, which may originate from non-stationarity of a process itself or from heterogeneous noise, on the performance of different linear and non-linear, local and global machine learning and regression models. The results suggest that it is necessary to assess the performance of linear local models on an independent hold-out dataset, since models may overfit under certain conditions. We further show that local models are advantageous in settings with small sample size and high degrees of spatial heterogeneity. Our findings allow deriving model selection criteria, which are validated in benchmarking experiments on five well-known spatial datasets

Towards a Framework for Predictive Maintenance Strategies in Mechanical Engineering – A Method-Oriented Literature Analysis

Industrial machines are amongst Germany’s main export products and contribute to the increasing revenue of Mechanical Engineering. However, in the course of globalization, services for such machines have become costly and inflexible due to long distances between vendors and customers. Consequently, companies seek to avoid unexpected failures and long down times by the development of data-based “smart” service solutions, including Predictive Maintenance (PM). In contrast to reactive or preventive measures, PM refers to the proactive planning of required maintenance services based on data sampled from the machinery. Although PM has been conceptualized decades ago and various methods have been proposed ever since, there is no standard strategy. By analyzing existing literature, we shed light on the knowledge base in PM. We provide an overview of methods and discuss their respective context, including preconditions and applications. Our work constitutes a first step towards a framework that guides the implementation of PM-strategies

Where you go is who you are -- A study on machine learning based semantic privacy attacks

Concerns about data privacy are omnipresent, given the increasing usage of digital applications and their underlying business model that includes selling user data. Location data is particularly sensitive since they allow us to infer activity patterns and interests of users, e.g., by categorizing visited locations based on nearby points of interest (POI). On top of that, machine learning methods provide new powerful tools to interpret big data. In light of these considerations, we raise the following question: What is the actual risk that realistic, machine learning based privacy attacks can obtain meaningful semantic information from raw location data, subject to inaccuracies in the data? In response, we present a systematic analysis of two attack scenarios, namely location categorization and user profiling. Experiments on the Foursquare dataset and tracking data demonstrate the potential for abuse of high-quality spatial information, leading to a significant privacy loss even with location inaccuracy of up to 200m. With location obfuscation of more than 1 km, spatial information hardly adds any value, but a high privacy risk solely from temporal information remains. The availability of public context data such as POIs plays a key role in inference based on spatial information. Our findings point out the risks of ever-growing databases of tracking data and spatial context data, which policymakers should consider for privacy regulations, and which could guide individuals in their personal location protection measures

Vehicle-to-grid for car sharing -- A simulation study for 2030

The proliferation of car sharing services in recent years presents a promising avenue for advancing sustainable transportation. Beyond merely reducing car ownership rates, these systems can play a pivotal role in bolstering grid stability through the provision of ancillary services via vehicle-to-grid (V2G) technologies - a facet that has received limited attention in previous research. In this study, we analyze the potential of V2G in car sharing by designing future scenarios for a national-scale service in Switzerland. We propose an agent-based simulation pipeline that considers population changes as well as different business strategies of the car sharing service, and we demonstrate its successful application for simulating scenarios for 2030. To imitate car sharing user behavior, we develop a data-driven mode choice model. Our analysis reveals important differences in the examined scenarios, such as higher vehicle utilization rates for a reduced fleet size as well as in a scenario featuring new car sharing stations. These disparities translate into variations in the power flexibility of the fleet available for ancillary services, ranging from 12 to 50 MW, depending on the scenario and the time of the day. Furthermore, we conduct a case study involving a subset of the car sharing fleet, incorporating real-world electricity pricing data. The case study substantiates the existence of a sweet spot involving monetary gains for both power grid operators and fleet owners. Our findings provide guidelines to decision makers and underscore the pressing need for regulatory enhancements concerning power trading within the realm of car sharing

Immediate effects of a very brief planning intervention on fruit and vegetable consumption: A randomized controlled trial

Action planning interventions can effectively promote fruit and vegetable (FV) consumption, but not much is known about the day-to-day translation of intervention planning into action. In this randomized controlled trial, immediate intervention effects of a very brief planning intervention on FV consumption during the following 13 days were investigated. After a 13-day pre-intervention diary, N = 206 participants (aged 19-66 years) were randomly allocated to a waiting-list control condition or a planning condition, where they formed one FV plan. Participants from both conditions completed a 13-day post-intervention diary. Self-reported daily FV consumption, FV-specific self-efficacy, and action control were assessed. Segmented linear mixed models estimating a discrete change (i.e. "jump") between diary phases showed a positive "jump" of FV intake and self-efficacy in the planning condition when compared to the control condition. For action control, such effects were not observed. Changes in study variables throughout the post-intervention phase did not differ between both conditions. Present findings extend previous evidence on action planning interventions by showing that increases in self-regulatory (i.e. self-efficacy) and behavioral (i.e. FV intake) outcomes can occur very rapidly and already on the first day for which behavioral increases were planned