GISRUK 2023

University of Glasgow

Filter displayed posters (97 keywords)

Sponsor GISRUK 2023 2 Traffic flow 2 geospatial 2 show more… Aerial 1 Aerial images 1 Bias 1 Building footprints 1 COVID-19 1 Classification 1 Cluster Analysis 1 Crime 1 Deep Learning 1 Deep learning 1 Flash flood susceptibility 1 France 1 Fusion 1 GIS 1 Geodemographic 1 Geospatial Analysis 1 Google Earth Engine 1 Habitat fragmentation 1 Historical maps 1 Human mobility 1 Hybrid geographies 1 In-app data 1 K-means 1 Lake District 1 Land Use 1 Land use change 1 Leicester 1 London 1 Low Traffic Neighbourhood 1 Machine Learning 1 Multimode 1 PGIS 1 Participatory Mapping 1 Pixel based analysis 1 Remote sensing 1 ResNet 1 SHAP 1 Segmentation 1 Social Media Analysis 1 Spatial Durbin model 1 Spatiotemporal Data 1 Sustainable Mobility 1 Temporal Geodemographics 1 Temporal-spatial 1 Topographic map 1 Tourism 1 Transport 1 Tree Preservation Order 1 Tree-based Ensemble Learning 1 UK census 1 Urban areas 1 Urban big data 1 User Generated Content (UGC) 1 Variables per km of route 1 Zonal Statistics 1 anti-immigration sentiment 1 betting shop 1 bicycle trajectories 1 big data 1 commons 1 commuting 1 crime attractors and generators. 1 cycling 1 data processing 1 data visualisation 1 early career 1 gambling 1 geodemographics 1 in-app data 1 inequity 1 innovation 1 land tenure 1 mobility 1 natural language processing 1 neocartography 1 post-disaster 1 public transport 1 risky facilities 1 rural geography 1 satellite data 1 social disorganisation theory 1 social media 1 space-time cube 1 spatial interaction 1 start-up 1 support 1 survey 1 temporal analytics 1 traffic analysis 1 transportation 1 trust 1 urban flooding; impact assessment; traffic simulation 1 urban inequalities 1 walking 1

Show posters with Recorded Presentation

Detecting bias within location histories collected from a panel of mobile applications

Hamish Gibbs, Rosalind M. Eggo, James Cheshire

Abstract: Location data collected by mobile applications is typically aggregated from a heterogeneous sample of mobile devices with varying demographic and behavioral characteristics. In this paper, we present a method for detecting homogenous groups of mobile devices relevant to specific scientific domains. We apply this method to an anonymized in-app location dataset of ~4,500,000 mobile devices in the United Kingdom, to detect homogeneous groups of devices relevant to applications including transport planning and infectious disease modeling.
Presented by: Hamish Gibbs <hamish.gibbs.21@ucl.ac.uk>
Institution: Department of Geography, University College London
Other Affiliations: Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine
Keywords: Human mobility, Bias, In-app data

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Geovation from Ordnance Survey

Luke Hampson

Abstract: Geovation is here to maximise positive impact by bringing people together and unlocking their potential so that we have a commercially, socially and environmentally sustainable future. Geovation can support you with your next step, turning your research into a commercial proposition.
Presented by: Luke Hampson
Institution: Geovation from Ordnance Survey
Keywords: geospatial, innovation, support, start-up, Sponsor GISRUK 2023

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Leicester Temporal Geodemographic Classification

Nouh AL-Mahrouqi and Stefano De Sabbata

Abstract: The city of Leicester (UK) has seen large socio-demographic changes over the past century, gaining complex social, economic and multicultural geographies. Such changes are reflected in the data collected through the census and can be illustrated by developing a temporal geodemographic classification. This paper presents the results of a Master's dissertation project aimed at developing a temporal geodemographic classification for Leicester's urban area, including the Leicester Local Authority District, as well as the Oadby and Wigston Local Authority District, analysing its changing socio-economic structure over three censuses.
Presented by: Nouh Al Mahrouqi and Dr. Stefano De Sabbata
Institution: School of Geography, Geology and the Environment, University of Leicester
Keywords: Temporal Geodemographics, Leicester, Cluster Analysis, K-means, UK census

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Urban Big Data Centre

Martin Shannon

Abstract: The Urban Big Data Centre is delighted to sponsor GISRUK 2023 and warmly welcomes all delegates to Glasgow.
Presented by: UBDC <ubdc@glasgow.ac.uk>
Institution: UBDC at the University of Glasgow
Keywords: Sponsor GISRUK 2023

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Using Interactive Space-Time Cube Visualisation for Pattern Mining in Bicycle Trajectories and Traffic-Related Parameters

Andreas Keler, Sylvia Ludwig and Chenyu Zuo

Abstract: This work focuses on the visualisation of bicycle trajectories at an urban junction in Munich, Germany, using a Space-Time cube (STC) approach. The trajectories are obtained from a traffic observation using computer-vision-based approaches and pre-processed for analysis. A GUI implementation in MATLAB is introduced for evaluating the usefulness of the STC technique for transport planning and engineering purposes, with a focus on evaluating traffic safety. The visual patterns are evaluated by experts based on the quality of five interactive components of the implemented STC GUI.
Presented by: Andreas Keler
Institution: Applied Geoinformatics, University of Augsburg
Other Affiliations: 2) Chair of Traffic Engineering and Control, Technical University Munich 3) Chair of Cartography and Visual Analytics, Technical University Munich
Keywords: data processing, data visualisation, space-time cube, traffic analysis, bicycle trajectories

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Spatial-temporal Transformation of Habitat Causes the Northward Migration of Wild Elephants in Yunnan: Based on Google Earth Engine

Donghao Jiang and Yue Zhang

Abstract: From 2020 to 2021, a herd of wild elephants migrated more than 500 kilometers northward from their habitat (Yunnan, Southwest China) became an international hotspot. The study analysis the main habitat (Mengyang Reserve and surrounding areas) of this group of wild elephants from the natural environment, changes in land use types, and habitat fragmentation and found the rare hot and dry weather before the wild elephant migration and the habitat destruction caused by human activities in the past ten years contributed to this migration.
Presented by: Donghao Jiang
Institution: University of Manchester
Other Affiliations: Department of Geography
Keywords: Temporal-spatial, Land use change, Google Earth Engine, Habitat fragmentation

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Trust in post-blast Beirut: combing survey and satellite data

Elisabetta Pietrostefani

Abstract: On the 4th of August 2020, a large amount of ammonium nitrate stored at the port of the city of Beirut exploded causing at least 200 deaths and over 7,000 injuries. This study explores the impact of the blast on trust in local institutions by analysing the degree of damage to buildings in two heavily affected neighbourhoods using satellite imagery and spatial survey data. I combine Maxmar WorldView-2 data from before and after the blast with survey data from 2018 and 2021 to evaluate the changing level of trust in governance post-disaster. The Beirut Blast had both physical and physiological channels of influence. This paper focuses on how different types of exposure to the blast changed measures of trust in local institutions. I adopt two methodological strategies, a pixel-based supervised classification of affected areas using a Random Forest classifier, and a difference-in-difference fixed effects framework to evaluate the effects of the blast on measures of trust. Results reveal that trust in institutions was already declining before the blast, and residents of heavily damaged buildings had less trust in institutions, even after accounting for socio-economic heterogeneity. Furthermore, the blast had a significant negative effect on trust in institutions. These findings highlight the need for effective disaster management policies and strategies that address the psychological and physical impact of disasters on affected communities.
Presented by: Elisabetta Pietrostefani
Institution: University of Liverpool
Other Affiliations: London School of Economics and Political Science
Keywords: trust, post-disaster, satellite data, survey, urban inequalities

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Modified Point-Voxel CNN on Aerial Lidar Data

Fauzy Othman and Phil Bartie

Abstract: This research uses modified Point Voxel CNN as a method to segment point cloud data directly in 3D coordinates. In addition to xyz coordinates, geometric properties were calculated from the point cloud and added as attributes, used in training and inference. By combining voxel and multi layer perceptron methods, neighbouring point features are captured and learnt in this deep learning method. This method consumes low GPU memory size with significantly lower training time. Future work will explore further improvement in f1-score.
Presented by: Fauzy Othman
Institution: Heriot-Watt University
Other Affiliations: PETRONAS
Keywords: Deep Learning, Fusion, Aerial, Classification, Segmentation

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Spatial Patterns of Transportation Mobility by Mode and Associated Neighbourhood Characteristics in Dublin, Ireland

Kevin Credit

Abstract: This paper explores the spatial patterns of walking, cycling, public transport, and automobile mobility in Dublin using a novel methodology that queries all neighbourhood-to-neighbourhood travel times within the region (by mode). These travel times are converted to travel costs and divided by Euclidean distance to create relative measures of mobility (cost-distance ratios). In terms of city-wide modal trade-offs, cycling outcompetes automobile travel through trips of 2.4km or less, while public transport outcompetes all other modes for trips longer than 8.1km. Additionally, analysing the neighbourhood characteristics of the origins and destinations for these ratios shows that neighbourhoods with more walkable built environments tend to have better observed mobility for the walking and cycling modes.
Presented by: Kevin Credit
Institution: Maynooth University
Keywords: transportation, spatial interaction, commuting, public transport, walking, cycling, inequity, mobility

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Variable construction process to understand cyclists' route choice from GPS records of mobile applications

Laura Daniela Ramírez-Leuro, Lenin Alexander Bulla-Cruz

Abstract: This document summarizes the process for the treatment of GPS records of mobile applications and builds variables to understand the route decisions of cyclists. Therefore, improve public policies of sustainable mobility from a geographical analysis of road safety, infrastructure, and public safety (thefts) parameterized by km of the route traveled, which are subsequently analyzed statistically. Finally, this case study is based on the GPS records of the Biko mobile application in Bogotá, Colombia. The process can be replicated in any city with the available information.
Presented by: Laura Daniela Ramirez Leuro
Institution: Department of Urban Planning, Technische Universität Berlin
Other Affiliations: Department of Civil and Agricultural Engineering, Faculty of Engineering Universidad Nacional de Colombia
Keywords: Sustainable Mobility, Geospatial Analysis, Variables per km of route

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Poster: Water telling stories Community cartographies for the survival of the amphibian culture and sustainable development in the Lower Sinú wetlands, Department of Córdoba, Colombia

Water telling stories Community cartographies for the survival of the amphibian culture and sustainable development in the Lower Sinú wetlands, Department of Córdoba, Colombia

Luis Miguel Sánchez Zoque & Daneris Alberto Herrera Mestra

Abstract: This article presents the experience of appropriation of the hybrid geographies approach as a support for initiatives to transform socio-environmental and territorial conflicts in the Ciénaga Grande del Bajo Sinú, in the Colombian Caribbean region, where communities are working to position their vision of the territory vis-à-vis State institutions, in order to advance in the construction of proposals that guarantee the sustainable permanence of the way of life of peasant, fishing, indigenous and Afro- descendant communities in the face of the radical transformations that the ecosystem in which they live and from which they derive their livelihoods.
Presented by: Luis Miguel Sánchez Zoque <lmsanchezz@unal.edu.co>
Institution: Maestría en Desarrollo rural Universidad Autónoma Metropolitana, México.
Other Affiliations: Movimiento Social el Agua Contando Historias
Keywords: Hybrid geographies, neocartography,rural geography, land tenure, commons

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Long-time series, small-area statistics for sustainable transport

Malcolm Morgan, and Philips I.

Abstract: Transport policy often suffers from a paucity of data on which to build a robust evidence base. Long- time series, small-area transport statistics over a large geographical area can provide insights into the effects of a broad range of transport policies. Unfortunately there are important gaps in the UK’s small-area statistics that limit their usability and effectiveness for answering key policy questions. This paper reviews the state of data in the UK and reports early progress on filling data gaps.
Presented by: Malcolm Morgan
Institution: Institute for Transport Studies, University of Leeds, UK
Keywords: Transport, Land Use, Zonal Statistics, Spatiotemporal Data, Geodemographic

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Using Twitter data to analyse the spatial patterns of online anti-immigration sentiment in the UK

Matt Mason and Francisco Rowe

Abstract: There is a growing academic literature examining anti-immigration sentiment posted onto social media platforms, with evidence emerging of its impact on rises in “real physical” incidents of hate. Despite this, little is understood about the spatial pattern of the production of online anti-immigration content and contextual factors contributing to shaping its spatial configuration. This study aims to use Twitter data and natural language processing to analyse spatial patterns of online sentiment towards immigration across sub-regional areas of the UK, and identify key demographic and contextual factors associated with the production of anti-immigration sentiment on social media platforms.
Presented by: Matt Mason
Institution: Geographic Data Science Lab, The University of Liverpool
Keywords: anti-immigration sentiment, social media, natural language processing, early career

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The use of in-app data to drive geodemographic classification of activity patterns

Mikaella Mavrogeni, Paul Longley, Justin van Dijk

Abstract: We use location data from multiple mobile phone applications to describe daily, weekly, seasonal and annual activity patterns. Geodemographics, or ‘the analysis of people by where they live’, provides an organising framework, extended to represent the ways in which neighbourhood residents interact with workplaces, recreational and leisure destinations and transport infrastructure. We evaluate how in-app location data can be incorporated into geodemographic analysis to better understand the flux of activity patterns that characterise densely populated areas throughout the day. Limitations and net benefits of in- app location data are critically assessed to evaluate the ways in which activity-based geodemographics are robust, effective and safe to use when characterising the population at large.
Presented by: Mikaella Mavrogeni
Institution: Department of Geography University College London
Keywords: geodemographics, big data, temporal analytics, in-app data, geospatial

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Betting shop provisioning and crime patterns in England

Oluwole Adeniyi, Ferhat Tura and Andy Newton

Abstract: Critics alleged that there is a link between gambling provisioning and crime and scholars argue that gambling outlets serve as attractors of anti-social and criminal behaviours. This study compared the patterns of betting shops with different categories of crime in England. using a spatial longitudinal approaches b at neighbourhood level. Spatial analysis and multilevel negative binomial model results identified significant effect of betting shops on crime, even after accounting for ethnic heterogeneity, concentrated disadvantage, and other neighbourhood factors. Our findings suggest risk factors of crime including betting shops converge in similar areas, and this creates opportunities for more crimes.
Presented by: Oluwole Adeniyi
Institution: School of Management, Nottingham Business School, Nottingham Trent University
Other Affiliations: 2) Department of Social Sciences and Social Work, Bournemouth University 3) Department of Criminology and Criminal Justice, School of Social Sciences, Nottingham Trent University
Keywords: Crime, gambling, betting shop, social disorganisation theory, risky facilities, crime attractors and generators.

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Identifying Tree Preservation Order Protected Trees by Deep Learning in Greater London Area

Qiaosi Li, Mingkang Wang, Yang Cai, Qianyao Luo, Zian Wang, Qunshan Zhao

Abstract: Tree Preservation Order (TPO) is used to protect specific trees from damage and destruction, which is determined in high subjectivity. This research collected and analyzed TPO data, aerial images, geographic data, and socio-economic data in the Greater London area and developed a multi-input deep learning (DL) framework to classify TPO-protected and non-TPO-protected trees. The synergy use of aerial images and GIS data with the fusion model of ResNet50 and multilayer perceptron network produced the best classification accuracy of 87.32%. The result indicated the robustness of the multi- input DL model to identify the social attributes of trees compared with the single-input DL model.
Presented by: Qunshan Zhao <Qunshan.Zhao@glasgow.ac.uk>
Institution: Urban Big Data Centre, School of Social and Political Sciences, University of Glasgow
Keywords: Aerial images, Deep learning, ResNet, Tree Preservation Order

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The Potential of Social Media Analysis for Tourism in Indonesia

Raidah Hanifah, Alexis Comber, Nicholas Malleson and Victoria Houlden.

Abstract: Nowadays, social media are recognized as a media for information aggregation rather than just a sociability and gathering platform. Because of the exponential growth of social media usage, there has been an explosion of social media data produced by its users as UGC (User Generated Content), which can be stored in a variety of formats, including textual data, images, videos, audio, and geolocations. This potential can be used to explain various phenomena and solve problems in the tourism sector. This study will examine the use of social media data to gain a better understanding of Indonesian tourism.
Presented by: Raidah Hanifah
Institution: University of Leeds
Other Affiliations: Institut Teknologi Sumatera
Keywords: Social Media Analysis, Tourism, User Generated Content (UGC)

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Finding Building Footprints in Over-detailed Topographic Maps

Robin Roussel, Ali Asadipour and Sam Jacoby

Abstract: Building footprints are a key component of many GIS applications, including morphological and street view based analysis. Crowdsourced data such as OpenStreetMap (OSM) is widespread but not consistently detailed enough to reliably extract footprints of individual buildings, while topographic building maps such as the Ordnance Survey MasterMap Topography Layer (MTL) may split footprints into multiple polygons or include constructions without an address. We propose a method to determine which topographic building polygons can be unambiguously matched to individual footprints of buildings with an address, to enable integration with address-based data sources such as transactions or energy performance certificates. The results suggest that this method recovers significantly more building footprints than what can be obtained from OSM.
Presented by: Robin Roussel
Institution: Computer Science Research Centre, Royal College of Art
Other Affiliations: School of Architecture, Royal College of Art
Keywords: Topographic map, Building footprints

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Participatory Mapping?

Timna Denwood and Jonathan Huck

Abstract: This research explores the potential of developing Participatory Mapping interfaces that do not include an explicit map or other spatial contextualisation, using a case study in the Lake District, UK. Whilst there are countless examples of the successful deployment of map-based interfaces for collecting spatial information from citizens, in some cases, a map may not be an appropriate tool and other types of visual representations more suitable. We will demonstrate that the uncritical use of spatial contextualisation can impact both the usability of the interface for the user and robustness of the resultant data for the researcher.
Presented by: Timna Denwood
Institution: University of Manchester
Other Affiliations: MCGIS
Keywords: PGIS, Participatory Mapping, Lake District

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Quick Mapping of historical urbanization in France, case study of Scan Histo 1950 using spatial pixel based approach

Walid Rabehi, Rémi Lemoy, Marion Le Texier

Abstract: Automatic detection of human settlement and cities forms on historical maps remains a challenging topic regarding the complexity of the maps semiology, but also because of the general conditions of maps (ink degradation, color change...). The aim of this contribution is to establish an urban layer of France based on historical maps of 1950 using pixel-based approach and multitemporal landcover map. This work will use the produced layers to test whether large cities are more or less parsimonious in terms of land use than smaller cities, at different periods.
Presented by: Walid Rabehi <walid.rabehi@univ-rouen.fr>
Institution: UMR IDEES 6266/CNRS, University of Rouen
Other Affiliations: University of Montpellier
Keywords: Pixel based analysis, Remote sensing, Historical maps, Urban areas, France

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The Influence of Low Traffic Neighbourhood Scheme on Multimodal Traffic Flow in London

Xianghui Zhang, Tao Cheng

Abstract: This study aims to investigate the influence of Low Traffic Neighbourhood (LTN) Scheme deployed since the COVID-19 pandemic on multimodal traffic flow in London. We adopt a mobile phone application dataset to investigate the changes in multimodal traffic flow generated by the general public following the introduction of LTNs. Three LTNs located in London are explored between 4th May and 30th August 2020. The analysis approved that LTN scheme could encourage residents to take cycling and restrict through-traffic, but the influence varies across areas, travel modes and groups and may affect by specific measures.
Presented by: Xianghui Zhang
Institution: SpaceTimeLab for Big Data Analytics, Department of Civil, Environmental and Geomatic Engineering, University College London
Keywords: Multimode, Traffic flow, Low Traffic Neighbourhood, London

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Poster: Using ensemble learning and interpretable machine learning to assess flash flood susceptibility - A GIS-based Watershed Similarity Analysis

Using ensemble learning and interpretable machine learning to assess flash flood susceptibility - A GIS-based Watershed Similarity Analysis

Zheng Guan[1,2], Xiaoxiang Zhang[1,2] and Jing Yao[3,4]

Abstract: This research aims to assess flash flood susceptibility, to identify watersheds with similar susceptibility risks, and to analyse the contribution of conditioning factors in Jiangxi Province, China, using four tree-based ensemble learning approaches and an interpretation tool (SHapley Additive exPlanations, SHAP). Results show that flash flood susceptibility areas in Jiangxi Province are scattered and the ensembles can efficiently perform flash flood susceptibility mapping. A comprehensive assessment of flash flood susceptibility will provide direction for flood prevention and mitigation, as well as obtaining the factors that have the greatest impact on flash floods in order to prevent them in terms of their formation mechanisms.
Presented by: Xiaoxiang Zhang <xiaoxiang@hhu.edu.cn>
Institution: 1. College of Hydrology and Water Resources, Hohai University, Nanjing, China;2. Center for Geospatial Intelligence and Watershed Science (CGIWaS), Hohai University, Nanjing, China;3. Urban Big Data Centre, University of Glasgow, Glasgow, UK;4. Centre for Sustainable Healthy and Learning Cities and Neighbourhoods, School of Social and Political Sciences, University of Glasgow, Glasgow, UK
Keywords: Flash flood susceptibility, Tree-based Ensemble Learning, Machine Learning, GIS, SHAP

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Dynamic Flood Impact Assessment on Urban Road Networks A case study of Beijing, China

Yimeng Liu, Alistair Ford, Richard Dawson, Saini Yang

Abstract: Urban transport networks are vulnerable to surface water flooding, leading to impacts on economic activities, social well-being and the environment. This paper describes a flood-impact-assessment method to comprehensively assessed the economic impacts of traffic disruption from flooding in terms of time delay, fuel consumption and pollutant emission. Urban flooding in Beijing is simulated using the CADDIES-2D model, and impacts are propagated onto the transport network for assessment using the SUMO agent-based transport model. Comparing the economic damage of the baseline traffic scenario with that of three flooded scenarios, it’s demonstrated that rainfall occurring at 7 a.m. induces four times more cost than the baseline. The rain of the same intensity and duration occurring at 8 or 9 a.m. lead to a cost increase for 37.33% and 13.21% respectively.
Presented by: Yimeng Liu
Institution: School of Engineering, Newcastle University
Other Affiliations: Beijing Normal University, Beijing
Keywords: urban flooding; impact assessment; traffic simulation

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Understanding Urban Traffic Flows in Response to COVID-19 pandemic with Emerging Urban Big Data in Glasgow

Yue Li, Qunshan Zhao and Mingshu Wang

Abstract: This research applies spatial Durbin model to analyse traffic flow distributions via various factors in the urban areas and traffic flow data. The results show that the overall built environment within a buffer area has more significant impact on urban traffic flow compared to the nearby location within a few meters. Areas with more young and white dwellers are associated with more traffic flows. With the influence of COVID-19, residents prefer to spend their daily life in their local neighborhood rather than having long distance travel. The initial findings from this research provide evidence of developing 20- minute city via active travel for achieving net-zero carbon target.
Presented by: Yue Li
Institution: Urban Big Data Centre, University of Glasgow
Other Affiliations: School of Geographical and Earth Sciences, University of Glasgow
Keywords: Traffic flow, Urban big data, COVID-19, Spatial Durbin model

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