The classification of streets on road networks has been focused on the vehicular transportational features of streets such as arterials, major roads, minor roads and so forth based on their transportational use. City authorities on the other hand have been shifting to more urban inclusive planning of streets, encompassing the side use of a street combined with the transportational features of a street. In such classification schemes, streets are labeled for example as commercial throughway, residential neighborhood, park etc. This modern approach to urban planning has been adopted by major cities such as the city of San Francisco, the states of Florida and Pennsylvania among many others. Currently, the process of labeling streets according to their contexts is manual and hence is tedious and time consuming. In this paper, we propose an approach to collect and label imagery data then deploy advancements in computer vision towards modern urban planning. We collect and label street imagery then train deep convolutional neural networks (CNN) to perform the classification of street context. We show that CNN models can perform well achieving accuracies in the 81% to 87%, we then visualize samples from the embedding space of streets using the t-SNE method and apply class activation mapping methods to interpret the features in street imagery contributing to output classification from a model.

The classification of streets on road networks has been focused on the vehicular transportational features of streets. Examples of street labels include arterials, major roads, minor roads and so forth based on their transportational use. City authorities on the other hand have been shifting to more wholistic planning of streets. The modern approach towards designing and planning streets is more inclusive of the street context, meaning the side use of a street combined with the transportational features of a street. Several city authorities are developing new classification schemes for street context that extends traditional classification methods to include the use of land on the sides of a street. In those classification schemes, streets are labeled for example as commercial throughway, residential neighborhood, park etc. This modern approach to urban planning have been adopted by major cities such as the city of San Francisco, the state of Florida and Pennsylvania among many others. Currently, the process of labeling streets according to their contexts is manual and hence is tedious and time consuming. It will require manual labor to regularly relabel streets as the city develops. In this paper, we propose an approach to deploy advancements in computer vision towards modern urban planning. We propose a framework for automatic classification of street contexts. Using labeled street imagery we train deep convolutional neural networks (CNN) to perform the classification of street context. We show that CNN models can perform well achieving accuracies in the 81% to 87% range. We test various CNN architectures and report on their performances.

This paper presents a data analysis framework to uncover relationships between health conditions, age and sex for a large population of patients. We study a massive heterogeneous sample of 1.7 million patients in Brazil, containing 47 million of health records with detailed medical conditions for visits to medical facilities for a period of 17 months. The findings suggest that medical conditions can be grouped into clusters that share very distinctive densities in the ages of the patients. For each cluster, we further present the ICD-10 chapters within it. Finally, we relate the findings to comorbidity networks, uncovering the relation of the discovered clusters of age densities to comorbidity networks literature.