A spatially-explicit harmonized global dataset of critical infrastructure

Nirandjan, Sadhana; Koks, Elco; Ward, Philip J.; Aerts, Jeroen

doi:10.1038/s41597-022-01218-4

Cited by 37 publications

(26 citation statements)

References 46 publications

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“…However, particular attention needs to be paid to data quality, when OSM data is utilized in global studies or to derive global data products, e.g. to derive a "global" dataset on critical infrastructure 19 , or when using "big data" in comparing urban morphology across the globe 20 . When unaccounted for, spatial bias can lead analysts and researchers to draw general conclusions which are only valid for well-represented (well-mapped) areas 21 .…”

Section: Introductionmentioning

confidence: 99%

Investigating the digital divide in OpenStreetMap: spatio-temporal analysis of inequalities in global urban building completeness

Herfort

Lautenbach

Albuquerque

et al. 2022

Preprint

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OpenStreetMap (OSM) has evolved as a popular geospatial dataset for global studies, such as monitoring progress towards the Sustainable Development Goals (SDGs). However, many global applications turn a blind eye on its uneven spatial coverage. We utilized a regression model to infer OSM building completeness within 13,189 urban agglomerations home to 50% of the global population. Our results reveal that for 1,510 cities OSM building footprint data exceeds 80% completeness. Humanitarian mapping efforts have significantly improved completeness, especially in low SHDI regions. The digital divide in OSM has receded, but a strong spatial bias associated with subnational human development index, city size and World Bank region remains. In consequence, global studies will provide misleading results if the biases in OSM's coverage are not accounted for. We recommend combining completeness maps with socio-demographic information to guide mapping to ensure that "nobody is left behind" as encouraged by the SDGs.

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Section: Introductionmentioning

confidence: 99%

Investigating the digital divide in OpenStreetMap: spatio-temporal analysis of inequalities in global urban building completeness

Herfort

Lautenbach

Albuquerque

et al. 2022

Preprint

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“…To perform this analysis, we make use of the available geospatial information of roads through OpenStreetMap (OSM). As mentioned in previous studies (Meijer et al 2018, Nirandjan et al 2022, OSM provides good coverage of country road networks, and can be considered one of the most consistent geospatial datasets to be used for a global analysis on road networks (Koks et al 2019). In 2017, Barrington-Leigh and Millard-Ball (2017) estimated that approximately 80% of the OSM road network was complete.…”

Section: Network Preparationmentioning

confidence: 93%

A global assessment of national road network vulnerability

Koks

Rozenberg

Tariverdi

et al. 2023

Environ. Res.: Infrastruct. Sustain.

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Every country relies on a well-functioning road system. However, we do not have a clear understanding yet of the vulnerability of each of these road networks to different forms of disruption. In this study, we aim to better understand how road networks are affected by different disruptive events, to identify hotspots of road network vulnerabilities, and to better target where and what type of future investments can be made to develop more resilient networks. To do so, we developed a fully open source modelling framework to expose over 200 country road systems across the world to random, local, and targeted disruption schemes. For each country, we assessed the impact of such disruptions on intra-country travel activities and regional accessibility. The results highlight the vulnerability of road systems in mountainous and small-island countries owing to the limited availability of alternative routes. Additionally, we find that, on average, low-income countries experience a collapse of road-system services with much fewer disruptions, relative to high-income countries, due to the lack of redundancy in their systems. While the value of goods and services disrupted may be higher in wealthier countries, the results highlight that from an equity perspective, transport infrastructure investments are more desired in low-income country networks.

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“…Factors such as economic status, age, gender, marital status, experience of flooding, and household size can all affect disaster resilience [61][62][63]. Disaggregating this information from global datasets (such as [64] and [65]) will pose similar problems to the issues we identified with the census derived population estimates in this study. For certain camps, the UNHCR has begun reporting population demographics.…”

Section: The Usefulness and Limitations Of Our Approachmentioning

confidence: 95%

Unknown risk: assessing refugee camp flood risk in Ethiopia

Bernhofen,

Blenkin,

Trigg

2023

Environ. Res. Lett.

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The number of global refugees has been rising annually for the last decade. Many of these refugees are housed within camps, in temporary structures, vulnerable to the impacts of flooding. The flood risk of refugees is not well understood. Flood risk guidance available for camp planners and managers is vague, and existing flood risk data is often lacking in the remote areas where camps are typically located. We show how global data should, and should not, be used to assess refugee flood risk in Ethiopia; a country hosting 725,000 refugees, primarily from 4 neighbouring countries, in 24 camps. We find that global population datasets, typically used in national flood risk assessments, do not accurately capture camp populations. Even the most accurate global population datasets are missing three fifths of camp flood exposure. We propose, and test, alternative approaches for representing exposure that combine reported estimates of camp population with data on camp area, building footprints, and population density. Applying these approaches in our national flood risk assessment, we find that 95.8% of camps in Ethiopia are exposed to flooding of some degree and between 143,208 (19.8%) and 182,125 refugees (25.2%) are exposed to a 1% Annual Exceedance Probability flood (100-year return period). South Sudanese refugees are the nationality most exposed to flooding, but Eritrean refugees are the nationality most exposed to flooding with a high risk to life. Promisingly, we find that many camps may be set up in such a way that reduces the exposure of refugees to flooding. Our study demonstrates that global data, augmented with local data, can be useful for understanding the flood risk of refugee camps. The consistent scalable approach can be used as a first-order analysis of risk, identifying risk hotspots, and helping to prioritize further detailed analyses to inform within-camp adaptation.

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A spatially-explicit harmonized global dataset of critical infrastructure

Cited by 37 publications

References 46 publications

Investigating the digital divide in OpenStreetMap: spatio-temporal analysis of inequalities in global urban building completeness

Investigating the digital divide in OpenStreetMap: spatio-temporal analysis of inequalities in global urban building completeness

A global assessment of national road network vulnerability

Unknown risk: assessing refugee camp flood risk in Ethiopia

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