Driver’s Visual Engagement in Urban Streetscape

Representation of visual engagement in urban street through the streetscape is an important in urban design research as a city framework. Urban street are composed of various classifications and attract more attention from townspeople. Visual engagement is an important component for understanding a city. Human's perception of the nature environment and build environment gives a difference feeling to the visual. The city formed by many urban street that forms it. However, the establishment of this street still does not pay attention to the interests about visual on the street system as a city frameworks, but only pay attention to the function of the street. Urban street have regulations related to speed limits that are affect the visual of the driver. The importance of visual engagement driver’s when through the street to understanding of the city. The methodology in this study uses a post-positivist paradigm with quantitative strategies. Data collection tactics using Likert scale questionnaires was given to drivers or people who through the street by car with intensity once a week and data analysis uses SPSS application with descriptive statistics to identify factors that the most affect. The results of this study are to identify the factors of the urban streetscape that the most affect to the drivers when through the street. The visual elements factor that include the mass of the building, building entrance, textures, colours, and materials was establish the enclosure space on the street corridor. from these results identified a new factors that affect to visual engagement drivers

Visual Analytics for Regional Economic Environment Factors Based on a Dashboard Design

Abstract. Economic environment is vital for commercial investment, city planning and company strategy planning in urban areas. Mastering the economical trend may help the entrepreneurs, government officers and individuals in their decision-making process. In this study, we explore multiple geo-economic datasets using visual analytics methods for understanding the economic environment. More specifically, we user time-series Gross Domestic Product (GDP) data as an economic indicator of economic development and land use data to support the spatial analysis at a refined geographic scale. The spatiotemporal patterns of the regional economic environment are revealed both qualitatively and quantitatively. The work has a three-fold contributions: (1) we apply a grid-based spatial interpolation model to derive GDP values at a file granularity based on land use data; (2) we design a novel interactive dashboard for the GDP data exploration, which serves as a visual analytical tool between data and users; (3) we combine quantitative analysis with visualizations to strengthen the qualitative analysis. The feasibility of visual analytics methods and the dashboard design are tested in one of the most developed regions, Jiangsu Province, China. Both expected and unexpected economical patterns were extracted.</p

The urban heritage characterization using 3D geographic information systems. The system of medium-sized cities in Andalusia

The primary objective of this paper is to approach the use of the 3D Geographic Information Systems (3D GIS), as an instrumental tool that allows us to deal efficiently with the extensive amount of information that characterises a large part of the research carried out in the field of Urbanism and Regional Planning. Specifically, the study focuses on medium-sized cities in Andalusia, the most populous and the second largest region in Spain. The Andalusian urban system is substantially characterised by the historical importance of this type of cities within its territorial organisation, which dates back to more than two thousand years, and whose potential as sustainable and balanced stands out. In particular, it is intended to address features related to urban characterisation as medium-sized cities that have been declared as heritage sites, as well as, the integration of the cultural heritage into urban development planning as an active strategy by the cultural administration of the regional and local governments. In detail, this paper will analyse data relating to the development experienced, their characterisation through urban indicators or the evolution and traceability of their protection. In this sense, the use of 3D GIS will not only allow the efficient recording and the graphical representation of a significant amount of data resulting from the quantitative and qualitative analysis carried out but also model them using the third dimension to facilitate a cross analysis among the cities under study. Definitely, the aim is to demonstrate the suitability use of this technology in this type of scientific research.Ministry of Economy and Competitiveness of the Government of Spain HAR2016-79788-

Enhancing urban analysis through lacunarity multiscale measurement

Urban spatial configurations in most part of the developing countries showparticular urban forms associated with the more informal urban development ofthese areas. Latin American cities are prime examples of this sort, butinvestigation of these urban forms using up to date computational and analyticaltechniques are still scarce. The purpose of this paper is to examine and extendthe methodology of multiscale analysis for urban spatial patterns evaluation. Weexplain and explore the use of Lacunarity based measurements to follow a lineof research that might make more use of new satellite imagery information inurban planning contexts. A set of binary classifications is performed at differentthresholds on selected neighbourhoods of a small Brazilian town. Theclassifications are appraised and lacunarity measurements are compared in faceof the different geographic referenced information for the same neighbourhoodareas. It was found that even with the simple image classification procedure, animportant amount of spatial configuration characteristics could be extracted withthe analytical procedure that, in turn, may be used in planning and other urbanstudies purposes

Computer Analysis of Architecture Using Automatic Image Understanding

In the past few years, computer vision and pattern recognition systems have been becoming increasingly more powerful, expanding the range of automatic tasks enabled by machine vision. Here we show that computer analysis of building images can perform quantitative analysis of architecture, and quantify similarities between city architectural styles in a quantitative fashion. Images of buildings from 18 cities and three countries were acquired using Google StreetView, and were used to train a machine vision system to automatically identify the location of the imaged building based on the image visual content. Experimental results show that the automatic computer analysis can automatically identify the geographical location of the StreetView image. More importantly, the algorithm was able to group the cities and countries and provide a phylogeny of the similarities between architectural styles as captured by StreetView images. These results demonstrate that computer vision and pattern recognition algorithms can perform the complex cognitive task of analyzing images of buildings, and can be used to measure and quantify visual similarities and differences between different styles of architectures. This experiment provides a new paradigm for studying architecture, based on a quantitative approach that can enhance the traditional manual observation and analysis. The source code used for the analysis is open and publicly available