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UID:pretalx-qgis-uc2026-PEJYSG@talks.osgeo.org
DTSTART;TZID=CET:20261006T123000
DTEND;TZID=CET:20261006T130000
DESCRIPTION:Road traffic crashes remain a persistent global challenge\, cau
 sing millions of fatalities each year and severely injuring many more. Bey
 ond their human toll\, crashes impose significant socio-economic costs wor
 ldwide\, yet traditional approaches to identifying hazardous locations oft
 en rely on simple frequency-based metrics\, which fail to capture spatial 
 dependence and variations in crash severity. However\, crash events are no
 t spatially random in many urban contexts\; instead\, they exhibit strong 
 clustering patterns driven by recurring geometric\, operational\, and beha
 vioural factors.\nIdentifying the spatial distribution of traffic crash ho
 tspots provides valuable insights into the root causes of crash occurrence
  over the area under study. This knowledge supports decision-makers in ass
 essing road safety risks and implementing targeted countermeasures to redu
 ce crash rates. Nevertheless\, identifying areas with high crash potential
  remains a complex task.\nIn this regard and within this study\, we aim to
  address these limitations by developing a comprehensive spatial analytics
  framework for assessing crash severity patterns. The proposed methodology
  integrates severity-weighted crash indices and applies a suite of spatial
  statistical techniques to detect clustering behaviour and identify micro-
 scale hotspots. Specifically\, the framework employs Average Nearest Neigh
 bor Distance (ANND) to evaluate the statistical significance of spatial cl
 ustering\, Global Moran’s I to assess overall spatial autocorrelation\, 
 and Local Moran’s I to identify significant local clusters\, including H
 igh-High (HH) and Low-Low (LL) severity areas\, as well as spatial outlier
 s. \nThe framework is designed and developed within the QGIS environment a
 s an experimental plugin\, offering a user-friendly interface and a stream
 lined workflow for performing advanced spatial statistical analyses. The s
 olution is scalable and transferable to different geographic contexts\, pr
 ovided that appropriate input data are available. The methodology is demon
 strated through a case study in the greater Limassol area in southern Cypr
 us\, where it is used to analyse crash patterns and evaluate spatial risk.
 \nResults reveal strong clustering of crash locations based on ANND analys
 is\, while Global Moran’s I indicates weak or non-significant global spa
 tial autocorrelation of crash severity. This suggests that severe crashes 
 do not form broad regional patterns but instead emerge as highly localized
  phenomena. Local Moran’s I analysis confirms the presence of small yet 
 significant High-High severity hotspots and several spatial outliers (High
 -Low and Low-High)\, typically located along major arterial corridors\, ne
 twork transition points\, and mixed-use zones. A comparative analysis of p
 re- and post-COVID periods shows a substantial reduction in crash frequenc
 y and cluster intensity\, indicating measurable impacts from altered mobil
 ity patterns and the introduction of automated speed enforcement measures.
  Overall\, the findings highlight the importance of localized\, severity-a
 ware spatial analysis for modern road safety management. The proposed fram
 ework provides actionable insights for black-spot identification\, targete
 d interventions\, dangerous goods routing\, and integration into digital t
 win and intelligent transport systems (ITS) decision-support environments\
 , supporting data-driven strategies aligned with Vision Zero objectives.
DTSTAMP:20260724T214414Z
LOCATION:Bridge Cinema
SUMMARY:A QGIS-Based Spatial Analytics Framework for Severity-Aware Road Cr
 ash Hotspot Detection - Andreas M. Georgiou
URL:https://talks.osgeo.org/qgis-uc2026/talk/PEJYSG/
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