Developing a Flexible Framework for Spatiotemporal Population Modeling

Martin, David; Cockings, Samantha; Leung, Samuel

doi:10.1080/00045608.2015.1022089

Cited by 59 publications

(49 citation statements)

References 38 publications

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“…The redistribution of population to destination centroids is governed by a temporal profile specific to that activity class (e.g., school hours at a school site) and a known capacity for each centroid obtained from ancillary datasets. Further detail on the modelling framework and software is available separately [23,38,39]. This paper demonstrates an example application.…”

Section: Spatiotemporal Population Modellingmentioning

confidence: 99%

“…A major refinement in this approach, adopted by this paper, is the inclusion of seasonally varying overnight visitor population estimates developed by Newing et al [22]. These have been integrated within the flexible Population 24/7 data framework [23] which can be used to produce spatiotemporal gridded population estimates using variable kernel density estimation methods.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Impact of Seasonal Population Fluctuation on Regional Flood Risk Management

Smith

Newing

Quinn

et al. 2015

IJGI

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Human populations are not static or uniformly distributed across space and time. This consideration has a notable impact on natural hazard analyses which seek to determine population exposure and risk. This paper focuses on the coupling of population and environmental models to address the effect of seasonally varying populations on exposure to flood risk. A spatiotemporal population modelling tool, SurfaceBuilder247, has been combined with LISFLOOD-FP flood inundation model outputs for a study area centred on the coastal resort town of St Austell, Cornwall, United Kingdom (UK). Results indicate strong seasonal cycles in populations and their exposure to flood hazard which are not accounted for in traditional population datasets and flood hazard assessments. Therefore, this paper identifies and demonstrates considerable enhancements to the current handling of spatiotemporal population variation within hazard exposure assessment and disaster risk management.

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Section: Spatiotemporal Population Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing the Impact of Seasonal Population Fluctuation on Regional Flood Risk Management

Smith

Newing

Quinn

et al. 2015

IJGI

Self Cite

View full text Add to dashboard Cite

show abstract

“…McPherson et al, 2004;Freire & Aubrecht, 2012), or (2) multiple time-slice models that account for continuous variation in human activities over the course of a day (e.g. Aubrecht et al, 2014;Martin et al, 2015).…”

Section: Vgdi Input For Dynamic Modelling Of the "Population" Componentmentioning

confidence: 99%

Volunteered Geo-Dynamic Information for Health-Risk Exposure Assessment - A FRESHER Case Study

Aubrecht¹,

Steinnocher²

2016

giforum

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In this paper we discuss the use of volunteered geo-dynamic information (VGDI) for assessing exposure to health risks and improving analysis of associated dynamic aspects in urban settings. VGDI is introduced as an alternative and complementary data source to traditional geodata whereby both spatial and temporal aspects are highlighted. Within the FRESHER project several health-related parameters are modelled, including air pollution and access to fast food restaurant locations. We discuss how Foursquare data (and VGDI in general) can benefit integrative smart urban analytics and provide sample illustrations for a test case area in Lisbon, Portugal. Keywords:foursquare, spatio-temporal analysis, population dynamics, health-risk exposure IntroductionVolunteered geographic information (VGI) has attracted increased attention in the past couple of years in the context of integrative spatial analytics, especially in relation to urban areas. When the term VGI was coined by Goodchild (2007), widespread citizen participation and engagement in the voluntary creation of geographic information was still considered an "exotic" prospect. Things have clearly changed in that regard (Goodchild et al., 2016) and the notion of ubiquitous sensing (Gartner et al., 2007) is becoming more and more today's reality. Data from different sensors are analysed to extract additional multi-level information and improve our understanding of inter-relations in the environment. This approach, recently referred to as Urban Informatics -or Urban Geoinformatics when focusing mainly on spatial aspects -is driving the agenda forward regarding big urban data analysis and advancing the smart city vision. With regard to VGI, urban areas are of particular relevance as levels of use and contribution are highest there. However, the issue of representativeness is still considered one, if not the major, factor impacting applicability of VGI for operational use and input in decision support systems. Nevertheless, it has been widely agreed that the usefulness of VGI can in many ways outplay its inherent weaknesses. Addressing those weaknesses and capitalizing on the unique potential of VGI are crucial focuses of scientific research in the domain of VGI and Urban (Geo-)Informatics.

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“…Several studies presented frameworks and practices of spatio-temporal population mapping by measuring people's movement between daytime and nighttime [34,97] and spatio-temporal patterns of commutes between home and work [98,99]. The frameworks are extensible to other data for better accuracy and broader regions.…”

Section: People Mobilitymentioning

confidence: 99%

Reviews of Geospatial Information Technology and Collaborative Data Delivery for Disaster Risk Management

Miyazaki

Nagai

Shibasaki

2015

IJGI

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Due to the fact that geospatial information technology is considered necessary for disaster risk management (DRM), the need for more effective collaborations between providers and end users in data delivery is increasing. This paper reviews the following: (i) schemes of disaster risk management and collaborative data operation in DRM; (ii) geospatial information technology in terms of applications to the schemes reviewed; and (iii) ongoing practices of collaborative data delivery with the schemes reviewed. This paper concludes by discussing the future of collaborative data delivery and the progress of the technologies.

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Developing a Flexible Framework for Spatiotemporal Population Modeling

Cited by 59 publications

References 38 publications

Assessing the Impact of Seasonal Population Fluctuation on Regional Flood Risk Management

Assessing the Impact of Seasonal Population Fluctuation on Regional Flood Risk Management

Volunteered Geo-Dynamic Information for Health-Risk Exposure Assessment - A FRESHER Case Study

Reviews of Geospatial Information Technology and Collaborative Data Delivery for Disaster Risk Management

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