A Framework for understanding & classifying Urban Data Business Models

Governments’ objective to transition to ‘Smart Cities’ heralds new possibilities for urban data business models to address pressing city challenges and digital transformation imperatives. Urban data business models are not well understood due to such factors as the maturity of the market and limited available research within this domain. Understanding the barriers and challenges in urban data business model development as well as the types of opportunities in the ecosystem is essential for incumbents and new entrants. Therefore, the aim of this paper is to develop a framework for understanding and classifying Urban Data Business Models (UDBM). This paper uses an embedded case study method to derive the framework by analyzing 40 publicly funded and supported business model experiments that address pressing city challenges under one initiative. This research contributes to the scholarly discourse on business model innovation in the context of smart cities

Using SenseCam images in a multimodal fusion framework for route detection and localisation

Problem of structuring location data is solved by proposing a framework for classifying the data into often-traversed routes. It does not rely on any one source of location information, but can fuse data from multimodal localisation sources: SenseCam images, GPS data and WLAN signal strengths

A robust approach to model-based classification based on trimming and constraints

In a standard classification framework a set of trustworthy learning data are employed to build a decision rule, with the final aim of classifying unlabelled units belonging to the test set. Therefore, unreliable labelled observations, namely outliers and data with incorrect labels, can strongly undermine the classifier performance, especially if the training size is small. The present work introduces a robust modification to the Model-Based Classification framework, employing impartial trimming and constraints on the ratio between the maximum and the minimum eigenvalue of the group scatter matrices. The proposed method effectively handles noise presence in both response and exploratory variables, providing reliable classification even when dealing with contaminated datasets. A robust information criterion is proposed for model selection. Experiments on real and simulated data, artificially adulterated, are provided to underline the benefits of the proposed method

Are all hosts created equal? Partitioning host species contributions to parasite persistence in multihost communities

Many parasites circulate endemically within communities of multiple host species. To understand disease persistence within these communities, it is essential to know the contribution each host species makes to parasite transmission and maintenance. However, quantifying those contributions is challenging. We present a conceptual framework for classifying multihost sharing, based on key thresholds for parasite persistence. We then develop a generalized technique to quantify each species’ contribution to parasite persistence, allowing natural systems to be located within the framework. We illustrate this approach using data on gastrointestinal parasites circulating within rodent communities and show that, although many parasites infect several host species, parasite persistence is often driven by just one host species. In some cases, however, parasites require multiple host species for maintenance. Our approach provides a quantitative method for differentiating these cases using minimal reliance on system-specific parameters, enabling informed decisions about parasite management within poorly understood multihost communities