Crowdsourcing urban form and function

Crooks, Andrew; Pfoser, Dieter; Jenkins, Andrew; Croitoru, Arie; Stefanidis, Anthony; Smith, Duncan; Καραγιώργου, Σοφία; Efentakis, Alexandros; Lamprianidis, George

doi:10.1080/13658816.2014.977905

Cited by 179 publications

(103 citation statements)

References 46 publications

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“…For example, research discussed above [57] was able to capture system dynamics at an extremely high resolution, but lacked essential data required to substantiate the behavioral assumptions embedded in the agents. With the advent of Big Data, modelers potentially have access to a wealth of information that might go some way to resolving the critical issues of model evaluation and accurate behavioral simulation [82].…”

Section: Abm For City Simulationmentioning

confidence: 99%

“…In both of these cases the issues often center around data availability and hence "big" sources have the potential for innovation in urban modeling. The proliferation of Internet-enabled devices such as smart phones has enabled individuals and third-party organizations to begin to capture digital information about aspects of peoples' lives that have historically gone undocumented [82]. This "datafication" [86] might not only includes an individual's precise location in time and space, but also their thoughts, feelings, moods, and behaviors.…”

Section: Big Datamentioning

confidence: 99%

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“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

Heppenstall

Malleson

Crooks

2016

Systems

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Cities are complex systems, comprising of many interacting parts. How we simulate and understand causality in urban systems is continually evolving. Over the last decade the agent-based modeling (ABM) paradigm has provided a new lens for understanding the effects of interactions of individuals and how through such interactions macro structures emerge, both in the social and physical environment of cities. However, such a paradigm has been hindered due to computational power and a lack of large fine scale datasets. Within the last few years we have witnessed a massive increase in computational processing power and storage, combined with the onset of Big Data. Today geographers find themselves in a data rich era. We now have access to a variety of data sources (e.g., social media, mobile phone data, etc.) that tells us how, and when, individuals are using urban spaces. These data raise several questions: can we effectively use them to understand and model cities as complex entities? How well have ABM approaches lent themselves to simulating the dynamics of urban processes? What has been, or will be, the influence of Big Data on increasing our ability to understand and simulate cities? What is the appropriate level of spatial analysis and time frame to model urban phenomena? Within this paper we discuss these questions using several examples of ABM applied to urban geography to begin a dialogue about the utility of ABM for urban modeling. The arguments that the paper raises are applicable across the wider research environment where researchers are considering using this approach.

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Section: Abm For City Simulationmentioning

confidence: 99%

Section: Big Datamentioning

confidence: 99%

“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

Heppenstall

Malleson

Crooks

2016

Systems

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View full text Add to dashboard Cite

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“…Second, trajectory patterns reveal the underlying structure of each scene as it is defined through human activities. As such they communicate peoples' perceptions of the space allowing us to move from a pure geometric approach (e.g., using floor plans) that considers only form, to one that takes into account how people use the space i.e., its function as well [27]. Using such information to inform an ABM allows us to tailor its application to the particularities of various scenes.…”

Section: Introductionmentioning

confidence: 99%

Walk This Way: Improving Pedestrian Agent-Based Models through Scene Activity Analysis

Crooks

Croitoru

et al. 2015

IJGI

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Pedestrian movement is woven into the fabric of urban regions. With more people living in cities than ever before, there is an increased need to understand and model how pedestrians utilize and move through space for a variety of applications, ranging from urban planning and architecture to security. Pedestrian modeling has been traditionally faced with the challenge of collecting data to calibrate and validate such models of pedestrian movement. With the increased availability of mobility datasets from video surveillance and enhanced geolocation capabilities in consumer mobile devices we are now presented with the opportunity to change the way we build pedestrian models. Within this paper we explore the potential that such information offers for the improvement of agent-based pedestrian models. We introduce a Scene-and Activity-Aware Agent-Based Model (SA 2 -ABM), a method for harvesting scene activity information in the form of spatiotemporal trajectories, and incorporate this information into our models. In order to assess and evaluate the improvement offered by such information, we carry out a range of experiments using

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“…For example, our understanding of relationships between functional elements of populated areas and the characteristics of the VGI corresponding to these areas is still in its infancy. Specifically, the relationships between urban form and function and how it affects VGI contributions [23,89] should be further examined. Moreover, with the increasing availability of Internet access, it would also be interesting to explore the impact of such access on VGI contributions in developing countries.…”

Section: Discussionmentioning

confidence: 99%

Authoritative and Volunteered Geographical Information in a Developing Country: A Comparative Case Study of Road Datasets in Nairobi, Kenya

Mahabir

Stefanidis

Croitoru

et al. 2017

IJGI

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Abstract:With volunteered geographic information (VGI) platforms such as OpenStreetMap (OSM) becoming increasingly popular, we are faced with the challenge of assessing the quality of their content, in order to better understand its place relative to the authoritative content of more traditional sources. Until now, studies have focused primarily on developed countries, showing that VGI content can match or even surpass the quality of authoritative sources, with very few studies in developing countries. In this paper, we compare the quality of authoritative (data from the Regional Center for Mapping of Resources for Development (RCMRD)) and non-authoritative (data from OSM and Google's Map Maker) road data in conjunction with population data in and around Nairobi, Kenya. Results show variability in coverage between all of these datasets. RCMRD provided the most complete, albeit less current, coverage when taking into account the entire study area, while OSM and Map Maker showed a degradation of coverage as one moves from central Nairobi towards rural areas. Furthermore, OSM had higher content density in large slums, surpassing the authoritative datasets at these locations, while Map Maker showed better coverage in rural housing areas. These results suggest a greater need for a more inclusive approach using VGI to supplement gaps in authoritative data in developing nations.

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Crowdsourcing urban form and function

Cited by 179 publications

References 46 publications

“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

Walk This Way: Improving Pedestrian Agent-Based Models through Scene Activity Analysis

Authoritative and Volunteered Geographical Information in a Developing Country: A Comparative Case Study of Road Datasets in Nairobi, Kenya

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