Extracting Information from an Urban Network by Combining a Visibility Index and a City Data Set

Abstract

Cities can be represented by spatial networks, and the mathematical structure that defines a spatial network is a graph. Taking into account this premise, this paper is focused on analysing information on an urban scale by combining a new ray-casting visibility index with a data set of the urban street network. The visibility index provides information about the most visible buildings or areas. We relate this index with other data extracted from the city, with the aim of generating and analysing information about urban elements. To corroborate this idea, real data are analysed. The dataset is related to the heritage conservation of the buildings of the Villaflora suburb, located in the city of Quito (Ecuador). This information is processed, together with the visibility index, with the aim of determining the conservation degree of the urban areas most visually exposed to pedestrians or visitors. The combination of both values—heritage conservation and visibility index—is carried out by means of two new indices, I P and I N , which are defined using two-variable exponential functions.

Urban Multi-Source Spatio-Temporal Data Analysis Aware Knowledge Graph Embedding

“…It is an important task to understand the potential laws behind multi-source spatio-temporal data. The target of data analysis is to examine potential laws behind active data and the many external-influence data of city residents, including predicting the possibility for future development [4] and the state of aggregation of the region [5], explaining their practical significance [6] and abnormal road surface recognition [7]. Urban multi-source spatio-temporal data analysis can not only understand the practical significance of data existence from the perspective of the human-land relationship, but also provide a connective point for the construction of new smart cities and the integration of big data development strategies.…”

“…First, in different scenarios, it is difficult to comprehensively consider data from different types, sources, and meanings [2]. Second, traditional data analysis methods cannot adapt to high-dimensional data from daily life, and results of multi-source spatio-temporal data analysis cannot be interpretable [6]. Many stumbling blocks hinder the development of data analysis in smart cities.…”