Classifying urban models

Multilevel Growth Mixture Models for Classifying Groups

This article introduces a multilevel growth mixture model (MGMM) for classifying both the individuals and the groups they are nested in. Nine variations of the general model are described that differ in terms of categorical and continuous latent variable specification within and between groups. An application in the context of school effectiveness research is presented. Schools are classified i...

Towards Classifying Reactions of SBML-Models

Biological processes are often described by models. With the increasing amount of such biological models, researchers encounter the question how similar these various models are and whether it is possible to group them according to their features. The complexity of the question leads to the problem that only partial solutions exist until now. In addition, manually studying similarities of a lar...

“Urban vs. Regional Divide: Comparing and Classifying Digital Divide”

This paper presents a comparative study on digital divide between a region and its main metropolitan area. The exercise shows how barriers to technology access and usage may vary in terms of both nature and intensity. The paper also proposes a taxonomy of the different types of digital divides that may be present inside a region.

Classifying Tracked Moving Objects in Outdoor Urban Scenes

Object classification in far-field video sequences is a challenging problem because of low resolution imagery and projective image distortion. We approach the problem by identifying discriminative object features for classifying vehicles and pedestrians in far-field video captured by a static, uncalibrated camera. Using these features, we design a scene-invariant classification system that is t...

Augmented Statistical Models for Classifying Sequence Data