Much of the physical activity and built environment literature has focused on composite walkability indices based on the D variables– design, density, diversity, destination accessibility, and distance to transit. This literature, however, has largely ignored the microscale streetscape features that affect the pedestrian experience. Five street level urban design qualities were recently identified and defined for quantitative measures although these measures are mostly through subjective field observation. View related features such as long sight line and proportion of sky have not yet been objectively measured due to the limitation of data and method. This study uses both 2D and 3D GIS to objectively measure street level urban design qualities in Buffalo, New York and tests their correlation with observed pedestrian counts and Walk Scores. Our results showed that 3D GIS helped to generate objective measures on view related features. These objective measures can help us better understand the influence of street level urban design features on walkability for designing and planning healthy cities.
Affinity propagation (AP) is a clustering algorithm for point data used in image recognition that can be used to solve various problems, such as initial class representative point selection, large-scale sparse matrix calculations, and large-scale data with fewer parameter settings. However, the AP clustering algorithm does not consider spatiotemporal information and multiple thematic attributes simultaneously, which leads to poor performance in discovering patterns from massive spatiotemporal points (e.g., trajectory points). To resolve this issue, a multidimensional spatiotemporal affinity propagation (MDST-AP) algorithm is proposed in this study. First, the similarity of spatial and nonspatial attributes is measured in Gaussian kernel space instead of Euclidean space, which helps address the multidimensional linear inseparability problem. Then, the Davies-Bouldin (DB) index is applied to optimize the parameter value of the MDST-AP algorithm, which is applied to analyze road congestion in Beijing via taxi trajectories. Experiments on different datasets and algorithms indicated that the MDST-AP algorithm can process multidimensional spatiotemporal data points faster and more effectively.
This article introduces an interdisciplinary collaboration that brings together sympathetic trends in qualitative geographic visualization (from the perspective of one author who is a geographer) and contemporary generative artistic practices (from the perspective of the other author, who is an artist and theorist)—attempting to represent a diverse array of creative and multi-modal data through generative and participatory digital methods. We present how this convergence expands categories of meaning, allowing us to explore experiential/embodied as well as creative/imaginative engagements with everyday geographies distinct to a digital age. The article mediates on the idea of mapping the imagination and the ways we imagine quotidian spaces, as well as possibilities for new methods for the analysis and representation of spatial and emotional complexity. We particularly explore strategies of integrating multiple technologies and multiple-modes of representation for mapping and re-mapping complexities of social and creative living in order to help provide alternate ways to imagine, represent and engage different forms of embodied and imaginative geographies. This article presents a case study with the artist Andrew Buckles, in Seattle, Washington, correlating representational and participatory digital data including geospatial, temporal, audio, video as well as electroencephalography readings from brainwave sensors.
This research investigates current and potentially desired opportunities available for children’s afterschool activities in the U.S. Buffalo metropolitan area. By analyzing and geographically visualizing travel paths, excluded children’s activity space, and existing activity opportunities in the 3D view using GIS, the study looks at how children’s activity opportunities are limited by any socio-spatial factors such as racial distribution,
median income, current transportation system and geographical distribution of activity opportunity. Especially, it focuses on finding out if there have been children’s unequal activity opportunities between the city and the suburban area. There is an abundance of research that has looked at accessibility to opportunities based on the transportation system. However, only few studies have focused on children’s mobility even though their mobility is typically constrained and tied to those of adults in the household. With more direct engagement with children and representation of their activity space in GIS, this article is intended to discuss transport exclusion and related socio-spatial constraints from the perspective of children.
A number of approaches for integrating GIS and qualitative research have emerged in recent years, as part of a resurgence of interest in mixed methods research in geography. These efforts to integrate qualitative data and qualitative analysis techniques complement a longstanding focus in GIScience upon ways of handling qualitative forms of spatial data and reasoning in digital environments, and extend engagements with ‘the qualitative’ in GIScience to include discussions of research methodologies. This article contributes to these emerging qualitative GIS methodologies by describing the structures and functions of ‘computer-aided qualitative GIS’ (CAQ-GIS), an approach for storing and analyzing qualitative, quantitative, and geovisual data in both GIS and computer aided data analysis software. CAQ-GIS uses modified structures from conventional desktop GIS to support storage of qualitative data and analytical codes, together with a parallel coding and analysis process carried out with GIS and a computer-aided data analysis software package. The inductive mixed methods analysis potential of CAQ-GIS is demonstrated with examples from research on children’s urban geographies.
A growing number of geographers are conducting mixed methods research involving the integration of quantitative and qualitative data in GIS. Contributing to these efforts, this chapter describes software-level modifications that adapt GIS to enable inclusion of qualitative data as well as interpretive codes associated with these data. These innovations enable GIS to serve as a platform for dynamically integrating quantitative and qualitative data throughout the analysis process. Further, this chapter shows how GIS may be meshed with computer-aided qualitative analysis software (CAQDAS) to support inductive interpretive analysis. The value of GIS is in its ability to represent both qualitative and quantitative data along with their spatial information, and the value of CAQDAS lies in its ability to provide better means of storing, managing, and analyzing qualitative data. The system described here enables researchers to take advantage of all of these capabilities as they are working with multiple forms of data. Further, the linkage between GIS and CAQDAS that I have developed enables researchers to carry out many different forms of analysis, such as exploratory data visualization, conventional forms of spatial analysis, grounded theory, and other approaches.
Community is an ambiguous concept, and the meanings of community as a subject of study have received a great deal of attention across various disciplines. This paper discusses how children’s diverse meanings of community shape and are shaped by the social, cultural, and physical environments of their everyday lives. To explore these meanings I combine principles of child-centered research and qualitative geovisualization into a research methodology. I demonstrate that this integration displays the transformative nature of qualitative analysis and visualization to support interpretive analysis of various forms of qualitative and spatial data together, and offers us a hybrid methodological framework for gaining insights into the diverse meanings of community held by the children. The main case study is drawn from a multi-year research collaboration called the Children’s Urban Geography (ChUG), in which I participated along with children who lived in a relatively poor but emerging multi-cultural Hispanic neighborhood in Buffalo, NY.
The popularity of geotagged social media has provided many research opportunities for geographers and GIScientists in the digital age. This article reviews innovative approaches to studying spatially linked social media, and applies lessons taken from qualitative GIS and geographic visualization to improve these approaches. I introduce the idea of “code clouds” as a potential technique for the qualitative geovisualization of spatial information. Code clouds can depict and visualize analytic codes, or codes identifying key ideas and themes, that are generated through digital qualitative research. Rather than transforming qualitative forms of data into categories or numbers, code clouds attempt to preserve and represent the context of data as a visualized outcome of qualitative analysis. Professor Jung use examples from an exploratory case study of geotweets in King County, WA, to demonstrate how code clouds can be applied to the production of meanings through qualitative geovisualization.