Abstract
ABSTRACTBy the year 2050, it is expected that about 68% of global population will live in cities. To understand the emerging changes in urban structures, new data sources like social media must be taken into account. In this work, we conduct a feature space analysis of geo-tagged Twitter text messages from the Los Angeles area and a geo-spatial text mining approach to classify buildings types into commercial and residential. To create the feature space, broadly accepted word embedding models like word2vec, fastText and GloVe as well as more traditional models based on TF-IDF have been considered. A visual analysis of the word embeddings shows that the two examined classes yield several word clusters. However, the classification results produced by Naïve Bayes support vector machines, and a convolutional neural network indicates that building classification from pure social media text is quite challenging. Furthermore, this work illustrates a base toward fusing text features and remote sensing images to classify urban building types.
Highlights
A significant phenomenon in the twenty-first century is the migration from small- or middle-sized urban communities into mega cities
The recall at the commercial class yields a higher value, which could be a sign of learning commercial class text attributes
If one considers the results of the Support vector machines (SVM) model, it is apparent that the recall number of the residential class is low compared to the Naïve Bayes result
Summary
A significant phenomenon in the twenty-first century is the migration from small- or middle-sized urban communities into mega cities. By the year 2050, around 68% of people will live in metropolises (Taubenböck & Wurm, 2015; United Nations, 2018) These developments lead to fundamental changes in urban city structures. In order to observe and to understand these dynamic changes of settlement patterns, city structures or the temporal development of building areas are going to need the adoption of new and dynamic sources of information augmenting visual and morphological information available from remote sensing. In this context, social media data promise to provide useful insights into the human aspects of urban dynamics that do not necessarily manifest in morphology. Social media provides a very timely source of information given that users feed social media platforms with a sheer number of different kinds of information every second
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