Big data, agents and the city

Crooks, Andrew; Malleson, Nick; Wise, Sarah; Heppenstall, Alison

doi:10.4324/9781315270838-17

Cited by 5 publications

(3 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In view of the above problems, this research attempts to take South China University of Technology (Wushan campus, hereafter referred to as SCUT) as an example, to obtain real-time and dynamic data through coupling GIS model and ABM 1 [1,3], and to simulate the effects of different epidemic prevention and management measures, providing more intuitive guidance on epidemic prevention and management strategies for universities.…”

Section: Introductionmentioning

confidence: 99%

Research on Epidemic Prevention and Management Measures in University Based on GIS and ABM – Taking South China University of Technology (Wushan Campus) as an Example

Ming-xi

2021

Proceedings of the 2021 DigitalFUTURES

View full text Add to dashboard Cite

Prevention and management of epidemic is a protracted war. As large community in city, universities are key regions in the anti-epidemic period. However, the current epidemic prevention and management measures in many universities do not compatible with the spatial form and the characteristics of the population, likely to lead to waste of resources and cause conflicts. The research simulates campus environment by constructing GIS model, and simulates the behavior of campus crowd by ABM. Under the coupling effect of the two, the real-time calculation of the spread of epidemic in universities can be calculated in real-time, making up for the deficiency of GIS model which can only do static data analysis. On this basis, research takes South China University of Technology as an example and assumes three epidemic prevention management measures, i.e. closed-off management, zoning management and self prevention, respectively to simulate the spread of the epidemic, sum up the results of different management measures and provide certain suggestions.

show abstract

Section: Introductionmentioning

confidence: 99%

Research on Epidemic Prevention and Management Measures in University Based on GIS and ABM – Taking South China University of Technology (Wushan Campus) as an Example

Ming-xi

2021

Proceedings of the 2021 DigitalFUTURES

View full text Add to dashboard Cite

show abstract

“…However, the emergence of smart cities and the associated data deluge has led to a diverse range of new data sources that could potentially be used to better understand ambient populations. The rise of these big data streams therefore presents an opportunity to improve the calibration and validation of geographical simulation models in urban areas [10,11]. Possible datasets include, for example, footfall data (i.e.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the ambient population using hourly population footfall data and an agent-based model of daily mobility

Crols

Malleson

2019

Geoinformatica

Self Cite

View full text Add to dashboard Cite

The ambient population, i.e. the demographics and volume of people in a particular location throughout the day, has been studied less than the night-time residential population. Although the spatio-temporal behaviour of some groups, such as commuters, are captured in sources such as population censuses, much less is known about groups such as retired people who have less documented behaviour patterns. This paper uses agent-based modelling to disaggregate some ambient population data to estimate the size and demographics of the constituent populations during the day. This is accomplished by first building a model of commuters to model typical 9-5 workday patterns. The differences between the model outputs and real footfall data (the error) can be an indication of the contributions that other groups make to the overall footfall. The research then iteratively simulates a wider range of demographic groups, maximising the correspondence between the model and data at each stage. An application of this methodology to the town centre of Otley, West Yorkshire, UK, is presented. Ultimately this approach could lead to a better understanding about how town-and city-centres are used by residents and visitors, contributing useful information in a situation where raw data on the populations do not exist.

show abstract

“…This rise in popularity has been aided by technological advances such as the emergence of accessible development platforms, greater processing power and storage capacity [6]. However, it could also be argued that the proliferation and availability of new and novel forms of micro-level data have equally contributed to its uptake [7]. Social scientists find themselves, for the first time, in a data rich era with access to a variety of detailed geo-referenced data sources (e.g., social media, mobile phone data, census information, etc.)…”

mentioning

confidence: 99%

Guest editorial for spatial agent-based models: current practices and future trends

Heppenstall

Crooks

2019

Geoinformatica

Self Cite

View full text Add to dashboard Cite

Big data, agents and the city

Cited by 5 publications

References 1 publication

Research on Epidemic Prevention and Management Measures in University Based on GIS and ABM – Taking South China University of Technology (Wushan Campus) as an Example

Research on Epidemic Prevention and Management Measures in University Based on GIS and ABM – Taking South China University of Technology (Wushan Campus) as an Example

Quantifying the ambient population using hourly population footfall data and an agent-based model of daily mobility

Guest editorial for spatial agent-based models: current practices and future trends

Contact Info

Product

Resources

About