NeoGeography and Web 2.0: concepts, tools and applications

Hudson-Smith, A; Crooks, Andrew; Gibin, Maurizio; Milton, Richard; Batty, Michael

doi:10.1080/17489720902950366

Cited by 86 publications

(46 citation statements)

References 14 publications

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“…For example, network science and GIS are leading the way in providing structure across unstructured big data that streetscapes often cast in the course of their everyday dynamics [420][421][422]. Indeed, these types of approaches may be what the community needs moving forward in an era of computational social science that is beginning to fuse a wealth of qualitative and observational work [21] with near-ubiquitous sensing [423] and big data capabilities [424,425].…”

Section: Discussionmentioning

confidence: 99%

Computational Streetscapes

Torrens

2016

Computation

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Abstract:Streetscapes have presented a long-standing interest in many fields. Recently, there has been a resurgence of attention on streetscape issues, catalyzed in large part by computing. Because of computing, there is more understanding, vistas, data, and analysis of and on streetscape phenomena than ever before. This diversity of lenses trained on streetscapes permits us to address long-standing questions, such as how people use information while mobile, how interactions with people and things occur on streets, how we might safeguard crowds, how we can design services to assist pedestrians, and how we could better support special populations as they traverse cities. Amid each of these avenues of inquiry, computing is facilitating new ways of posing these questions, particularly by expanding the scope of what-if exploration that is possible. With assistance from computing, consideration of streetscapes now reaches across scales, from the neurological interactions that form among place cells in the brain up to informatics that afford real-time views of activity over whole urban spaces. For some streetscape phenomena, computing allows us to build realistic but synthetic facsimiles in computation, which can function as artificial laboratories for testing ideas. In this paper, I review the domain science for studying streetscapes from vantages in physics, urban studies, animation and the visual arts, psychology, biology, and behavioral geography. I also review the computational developments shaping streetscape science, with particular emphasis on modeling and simulation as informed by data acquisition and generation, data models, path-planning heuristics, artificial intelligence for navigation and way-finding, timing, synthetic vision, steering routines, kinematics, and geometrical treatment of collision detection and avoidance. I also discuss the implications that the advances in computing streetscapes might have on emerging developments in cyber-physical systems and new developments in urban computing and mobile computing.

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Section: Discussionmentioning

confidence: 99%

Computational Streetscapes

Torrens

2016

Computation

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“…Moreover, some other terms such as "Neogeography" [3], or "wikification of GIS" [4] are utilized to describe this act; however, all these terms imply the same concept of turning passive consumers into active producers of geospatial information.…”

Section: Introductionmentioning

confidence: 99%

A Quality Study of the OpenStreetMap Dataset for Tehran

Forghani

Delavar

2014

IJGI

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There has been enormous progress in geospatial data acquisition in the last decade. Centralized data collection, mainly by land surveying offices and local government agencies, has changed dramatically to voluntary data provision by citizens. Among a broad list of initiatives dealing with user generated geospatial information, OpenStreetMap (OSM) is one of the most famous crowd-sourced products. It is believed that the quality of collected information remains a valid concern. Therefore, qualitative assessment of OSM data as the most significant instance of volunteered geospatial information (VGI) is a considerable issue in the geospatial information community. One aspect of VGI quality assessment pertains to its comparison with institutionally referenced geospatial databases. This paper proposes a new quality metric for assessment of VGI accuracy and as well as for quality analysis of OSM dataset by evaluating its consistency with that of a reference map produced by Municipality of Tehran, Iran. A gridded map is employed and heuristic metrics such as Minimum Bounding Geometry area and directional distribution (Standard Deviational Ellipse), evaluated for both VGI and referenced data, are separately compared in each grid. Finally, in order to have a specific output as an integrated quality metric for VGI, its consistency with ground-truth data is evaluated using fuzzy logic. The results of this research verify that the quality of OSM maps in the study area is fairly good, although the spatial distribution of uncertainty in VGI varies throughout the dataset.

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“…Thus, Volunteered Geographic Information (VGI) [4] is bringing the general public into the realm of map production functions traditionally reserved for official agencies. With all humans becoming potential contributors of geospatial information [4], this trend is affecting greatly the geospatial community VGI is following the development of Web 2.0 where users are contributing in more places and more often [5]. Another significant descriptive phrase is -crowdsourcing‖ which describes VGI in business terms, linking resources and work assignments as suggested by Howe [6].…”

Section: Introductionmentioning

confidence: 99%

Assessing Completeness and Spatial Error of Features in Volunteered Geographic Information

Jackson

Mullen

Agouris

et al. 2013

IJGI

Self Cite

115

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Abstract:The assessment of the quality and accuracy of Volunteered Geographic Information (VGI) contributions, and by extension the ultimate utility of VGI data has fostered much debate within the geographic community. The limited research to date has been focused on VGI data of linear features and has shown that the error in the data is heterogeneously distributed. Some have argued that data produced by numerous contributors will produce a more accurate product than an individual and some research on crowd-sourced initiatives has shown that to be true, although research on VGI is more infrequent. This paper proposes a method for quantifying the completeness and accuracy of a select subset of infrastructure-associated point datasets of volunteered geographic data within a major metropolitan area using a national geospatial dataset as the reference benchmark with two datasets from volunteers used as test datasets. The results of this study illustrate the benefits of including quality control in the collection process for volunteered data.

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NeoGeography and Web 2.0: concepts, tools and applications

Cited by 86 publications

References 14 publications

Computational Streetscapes

Computational Streetscapes

A Quality Study of the OpenStreetMap Dataset for Tehran

Assessing Completeness and Spatial Error of Features in Volunteered Geographic Information

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