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[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.2.2″ background_color=”rgba(0,0,0,0)” custom_padding=”0px||0px||false|false”][et_pb_row column_structure=”1_5,1_5,1_5,1_5,1_5″ _builder_version=”4.2.2″ custom_margin=”-24px|auto||auto||” custom_padding=”0px||0px||false|false” link_option_url=”#navigation-strip”][et_pb_column type=”1_5″ _builder_version=”4.2.2″][et_pb_button button_url=”#introduction” button_text=”Introduction” button_alignment=”center” module_class=”full-width-btn” _builder_version=”4.2.2″ background_layout=”dark”][\/et_pb_button][\/et_pb_column][et_pb_column type=”1_5″ _builder_version=”4.2.2″][et_pb_button button_url=”#results” button_text=”Results” module_class=”full-width-btn” _builder_version=”4.2.2″ background_layout=”dark” hover_enabled=”0″][\/et_pb_button][\/et_pb_column][et_pb_column type=”1_5″ _builder_version=”4.2.2″][et_pb_button button_url=”#applications” button_text=”Applications” module_class=”full-width-btn” _builder_version=”4.2.2″ background_layout=”dark” hover_enabled=”0″][\/et_pb_button][\/et_pb_column][et_pb_column type=”1_5″ _builder_version=”4.2.2″][et_pb_button button_url=”#models” button_text=”Models” button_alignment=”center” module_class=”full-width-btn” _builder_version=”4.2.2″ background_layout=”dark” hover_enabled=”0″][\/et_pb_button][\/et_pb_column][et_pb_column type=”1_5″ _builder_version=”4.2.2″][et_pb_button button_url=”#publications” button_text=”Publications” button_alignment=”center” module_class=”full-width-btn” _builder_version=”4.2.2″ background_layout=”dark” hover_enabled=”0″][\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”introduction” _builder_version=”4.2.2″ background_enable_color=”off” animation_direction=”left” z_index_tablet=”500″ border_color_all=”#ffffff” border_width_top=”20px” border_width_right=”20px” border_width_left=”20px” border_width_top_tablet=”20px” border_width_top_phone=”20px” border_width_right_tablet=”20px” border_width_right_phone=”20px” border_width_left_tablet=”20px” border_width_left_phone=”20px” box_shadow_horizontal_tablet=”0px” box_shadow_vertical_tablet=”0px” box_shadow_blur_tablet=”40px” box_shadow_spread_tablet=”0px” locked=”off”][et_pb_row _builder_version=”4.2.2″ custom_padding=”||30px|||” animation_style=”slide” animation_direction=”left” animation_intensity_slide=”4%”][et_pb_column type=”4_4″ _builder_version=”3.26.6″][et_pb_text _builder_version=”4.2.2″ header_font_size=”35px” header_6_font=”|700|||||||” header_6_text_color=”#fe6223″ background_layout=”dark”]<\/p>\n
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The infrastructure network vulnerability, risk and resilience assessments are methodologies that take a \u2018system-of-systems\u2019 approach of modelling interdependent networks reliability and failure cascades. These system-of-systems models incorporate information at finer geographic scales with underlying physical, operational, social, and economic aspects associated with infrastructure systems. The methodological steps involve combining: (1) Spatailly correlated and probabilistic climate hazard events under current and future climate scenarios; (2) Spatial representations of network point and line assets with connectivity to capture failures cascade through the flow of resources (goods, information, etc.); (3) Direct loss measures of the exposures and damages of assets under varying severities of climate hazards; (4) Indirect socio-economic losses resulting from disruptions of infrastructure network services in terms of numbers and monetary values attached to household and business customers; (5) Further regional economic flow losses from production and labour disruptions to outputs of macroeconomic sectors; (6) Risk measures combining hazard probabilities, exposures and direct and indirect vulnerabilities across an exhaustive sampling of possible hazard and network failure and disruption scenarios; (7) Quantifiable options and costs for building resilience (to climate or any shock event) of individual assets and networks; (8) Performing a cost-benefit analysis to access the effectiveness of resilience options and select network assets to priortise for resilience investments.<\/p>\n
[\/et_pb_text][\/et_pb_column][et_pb_column type=”2_5″ _builder_version=”3.26.6″][et_pb_blurb image=”https:\/\/itrc-2020.ouce.ox.ac.uk\/wp-content\/uploads\/2020\/09\/themes_rr_fig1.png” _builder_version=”4.2.2″]<\/p>\n
Illustration of the system-of-systems risk and resilience assessment framework applied to multi-modal transport networks. Source: Oh, J.E., Espinet Alegre, X., Pant, R., Koks, E.E., Russell, T., Schoenmakers, R. and Hall, J.W. (2019).\u00a0<\/span>Addressing Climate Change in Transport: Volume 2: Pathway to Resilient Transport. World Bank, Washington DC. doi: http:\/\/dx.doi.org\/10.1596\/32412<\/a>.<\/p>\n [\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”3.26.6″][et_pb_column type=”4_4″ _builder_version=”3.26.6″][et_pb_button button_url=”#navigation-strip” button_text=”Back to top” button_alignment=”right” _builder_version=”4.2.2″ custom_button=”on” button_text_size=”14px” button_border_radius=”60px” button_font=”|700|||||||” button_icon=”%%0%%” button_icon_color=”#ffffff” button_on_hover=”off” background_layout=”dark”] [\/et_pb_text][et_pb_text _builder_version=”4.2.2″ header_font_size=”35px” header_3_font=”|700||on|||||” header_3_text_color=”#3c3c3c” header_4_text_color=”#3c3c3c” background_layout=”dark” custom_margin=”-40px|||||”]<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_3,2_3″ use_custom_gutter=”on” gutter_width=”1″ make_equal=”on” _builder_version=”4.2.2″ background_color=”rgba(0,0,0,0)” custom_margin=”||50px||false|false” custom_padding=”0px||0px||true|false” border_width_all=”1px” border_color_all=”#d6d6d6″ border_width_bottom=”5px” border_color_bottom=”#006f74″][et_pb_column type=”1_3″ _builder_version=”4.2.2″ background_color=”#006f74″ custom_padding=”30px|30px|30px|30px|true|true”][et_pb_blurb _builder_version=”4.2.2″ background_layout=”dark”]<\/p>\n Analysis to access direct exposures of infrastructure networks to multiple hazards provides the first kind of evidence towards understanding the spatial concentrations of network vulnerabilities. This shows the locations where choices are being made now that will lock in risks for decades to come, threaten the viability of infrastructure investments, and burden countries with escalating economic and human impacts and repair costs.<\/p>\n [\/et_pb_blurb][\/et_pb_column][et_pb_column type=”2_3″ _builder_version=”4.2.2″ background_color=”#ffffff” custom_padding=”30px|30px|30px|30px|true|true”][et_pb_blurb image=”https:\/\/itrc-2020.ouce.ox.ac.uk\/wp-content\/uploads\/2020\/09\/themes_rr_results_fig1-1.png” _builder_version=”4.2.2″]<\/p>\n Global scale analysis of multi-hazard exposures of roads and railways assets, showing (a) exposure for each region in the world; and . (b\u2013f) exposure based on income groups per hazard and per hazard intensity band. Source: Koks, E.E., Rozenberg, J., Zorn, C., Tariverdi, M., Vousdoukas, M., Fraser, S.A., Hall, J.W., Hallegatte, S. (2019). A global multi-hazard risk analysis of road and railway infrastructure assets.\u00a0Nature communications,\u00a010(1), pp.1-11. Doi: https:\/\/doi.org\/10.1038\/s41467-019-10442-3<\/a>.<\/p>\n [\/et_pb_blurb][et_pb_image src=”https:\/\/itrc-2020.ouce.ox.ac.uk\/wp-content\/uploads\/2020\/09\/themes_rr_results_fig1.png” align=”center” _builder_version=”4.2.2″][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_3,2_3″ use_custom_gutter=”on” gutter_width=”1″ make_equal=”on” _builder_version=”4.2.2″ background_color=”rgba(0,0,0,0)” custom_margin=”||50px||false|false” custom_padding=”0px||0px||true|false” border_width_all=”1px” border_color_all=”#d6d6d6″ border_width_bottom=”5px” border_color_bottom=”#006f74″][et_pb_column type=”1_3″ _builder_version=”4.2.2″ background_color=”#006f74″ custom_padding=”30px|30px|30px|30px|true|true”][et_pb_blurb _builder_version=”4.2.2″ background_layout=”dark”]<\/p>\n Assessments of failure probabilities of assets exposed to hazards helps indentify the initiating conditions for systemic network failures, which lead to disruptions of network services affecting users. Such evidence provides network planners with information on the assets that have largest impacts on network performance.<\/p>\n [\/et_pb_blurb][\/et_pb_column][et_pb_column type=”2_3″ _builder_version=”4.2.2″ background_color=”#ffffff” custom_padding=”30px|30px|30px|30px|true|true”][et_pb_image src=”https:\/\/itrc-2020.ouce.ox.ac.uk\/wp-content\/uploads\/2020\/09\/themes_rr_results_fig1.png” align=”center” _builder_version=”4.2.2″][\/et_pb_image][et_pb_blurb image=”https:\/\/itrc-2020.ouce.ox.ac.uk\/wp-content\/uploads\/2020\/09\/themes_rr_results_fig2.png” _builder_version=”4.2.2″]<\/p>\n Results showing (a) Fragility curves of railways bridge scouring in Great Britain derived from historical bridge failure observations with an assessed flood return period; (b) Rail passenger journeys disrupted for networks edge failures due to bridge scouring; (c) Estimated spatial distribution of daily average number of passenger journeys on the Great Britain rail network. Source: Lamb, R., Garside, P., Pant, R. and Hall, J.W., 2019. A Probabilistic Model of the Economic Risk to Britain’s Railway Network from Bridge Scour During Floods.\u00a0Risk Analysis<\/em>,\u00a039<\/em>(11), pp.2457-2478. Doi: https:\/\/doi.org\/10.1111\/risa.13370<\/strong><\/a><\/p>\n [\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_3,2_3″ use_custom_gutter=”on” gutter_width=”1″ make_equal=”on” _builder_version=”4.2.2″ background_color=”rgba(0,0,0,0)” custom_margin=”||50px||false|false” custom_padding=”0px||0px||true|false” border_width_all=”1px” border_color_all=”#d6d6d6″ border_width_bottom=”5px” border_color_bottom=”#006f74″][et_pb_column type=”1_3″ _builder_version=”4.2.2″ background_color=”#006f74″ custom_padding=”30px|30px|30px|30px|true|true”][et_pb_blurb _builder_version=”4.2.2″ background_layout=”dark”]<\/p>\n Interdependencies between infrastructure networks cause failure feedbacks into the initiating network, thereby triggering further failure cascades. These cascading failures set a chain of failure mechanisms leading to higher order disruptions across networks. Quantifying the degree to which network failures cascade in a systemic way helps under the influence of interdependencies that exist between different infrastructure networks.\u00a0\u00a0<\/p>\n [\/et_pb_blurb][\/et_pb_column][et_pb_column type=”2_3″ _builder_version=”4.2.2″ background_color=”#ffffff” custom_padding=”30px|30px|30px|30px|true|true”][et_pb_image src=”https:\/\/itrc-2020.ouce.ox.ac.uk\/wp-content\/uploads\/2020\/09\/themes_rr_results_fig1.png” align=”center” _builder_version=”4.2.2″][\/et_pb_image][et_pb_blurb image=”https:\/\/itrc-2020.ouce.ox.ac.uk\/wp-content\/uploads\/2020\/09\/themes_rr_results_fig3.png” _builder_version=”4.2.2″]<\/p>\n Analysis on interdependent electricity, telecoms, water, railway and road network in Great Britain shows how systemic failures originiating in electicity network create sequences of higher order cascading failures across all networks. Source Pant, R., Russell, T., Zorn, C., Oughton, E. and Hall, J.W. (2020).\u00a0<\/span>Resilience study research for NIC – Systems analysis of interdependent network vulnerabilities. Environmental Change Institute, Oxford University, UK. Available at https:\/\/www.nic.org.uk\/wp-content\/uploads\/Infrastructure-network-analysis.pdf<\/a>.<\/p>\n [\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_3,2_3″ use_custom_gutter=”on” gutter_width=”1″ make_equal=”on” _builder_version=”4.2.2″ background_color=”rgba(0,0,0,0)” custom_margin=”||50px||false|false” custom_padding=”0px||0px||true|false” border_width_all=”1px” border_color_all=”#d6d6d6″ border_width_bottom=”5px” border_color_bottom=”#006f74″][et_pb_column type=”1_3″ _builder_version=”4.2.2″ background_color=”#006f74″ custom_padding=”30px|30px|30px|30px|true|true”][et_pb_blurb _builder_version=”4.2.2″ background_layout=”dark”]<\/p>\n Due to network behaviours infrastructure disruptions create much larger impact footprints than the initiating hazard conditions. This further leads to indirect macroeconomic losses that extend to wider economic sectors in a much severe manner. There is an \u2018uplift\u2019 on the magnitudes of economic losses due to infrastructures that would not be seen if only the direct impacts were considered. Such evidence demonstrates the value of a systemic approach to disaster impact modeling. for multiple stakeholders beyond areas immediately impacted by the disaster event.<\/p>\n [\/et_pb_blurb][\/et_pb_column][et_pb_column type=”2_3″ _builder_version=”4.2.2″ background_color=”#ffffff” custom_padding=”30px|30px|30px|30px|true|true”][et_pb_image src=”https:\/\/itrc-2020.ouce.ox.ac.uk\/wp-content\/uploads\/2020\/09\/themes_rr_results_fig1.png” align=”center” _builder_version=”4.2.2″][\/et_pb_image][et_pb_blurb image=”https:\/\/itrc-2020.ouce.ox.ac.uk\/wp-content\/uploads\/2020\/09\/themes_rr_results_fig4a.png” _builder_version=”4.2.2″]<\/p>\n (a)<\/p>\n [\/et_pb_blurb][et_pb_blurb image=”https:\/\/itrc-2020.ouce.ox.ac.uk\/wp-content\/uploads\/2020\/09\/themes_rr_results_fig4b.png” _builder_version=”4.2.2″]<\/p>\n (b)<\/p>\n Analysis of electricity failure impacts of labour productivity in South-East England Figures showing: (a) the percentage of loss of labour productivity in areas flooded (left panel) and how these a amplified further when the electricity substations are flooded and disrupted as well (right panel); (b) Macroeconomic losses to different manufacturing and service sectors in the UK economy when considering direct flooding only (left) and with flood plus electricity disruptions (right). Source: Koks, E., Pant, R., Thacker, S. and Hall, J.W. (2019). Understanding business disruption and economic losses due to electricity failures and flooding. International Journal of Disaster Risk Science: 1-18. Doi: http:\/\/dx.doi.org\/10.1007\/s13753-019-00236-y.<\/p>\n [\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_3,2_3″ use_custom_gutter=”on” gutter_width=”1″ make_equal=”on” _builder_version=”4.2.2″ background_color=”rgba(0,0,0,0)” custom_margin=”||50px||false|false” custom_padding=”0px||0px||true|false” border_width_all=”1px” border_color_all=”#d6d6d6″ border_width_bottom=”5px” border_color_bottom=”#006f74″][et_pb_column type=”1_3″ _builder_version=”4.2.2″ background_color=”#006f74″ custom_padding=”30px|30px|30px|30px|true|true”][et_pb_blurb _builder_version=”4.2.2″ background_layout=”dark”]<\/p>\n Climate adaptation involves incorporating adaptation options to upgrade infrastructure assets to higher climate resilience design standards and assessing the benefit-cost ratios of such adaptation options. This helps build the case for investing in climate resilience which becomes stronger as economic impacts of disruptions increase. Such analyses are proving useful in providing scientific evidence to givernments to develop a national strategy for climate resilient innfrastrucutre plans, as part of contributions to the Nationally Determined Contributions, to meet the Paris Climate Agreement targets.<\/p>\n [\/et_pb_blurb][\/et_pb_column][et_pb_column type=”2_3″ _builder_version=”4.2.2″ background_color=”#ffffff” custom_padding=”30px|30px|30px|30px|true|true”][et_pb_image src=”https:\/\/itrc-2020.ouce.ox.ac.uk\/wp-content\/uploads\/2020\/09\/themes_rr_results_fig1.png” align=”center” _builder_version=”4.2.2″][\/et_pb_image][et_pb_blurb image=”https:\/\/itrc-2020.ouce.ox.ac.uk\/wp-content\/uploads\/2020\/09\/themes_rr_results_fig5.png” _builder_version=”4.2.2″]<\/p>\n Results from analysis of road network economic risks due to climate hazard vulnerability in Vietnam showing: (a) the criticality of the national roads highlighted through the mangitudes of economic losses associated with the disruptions of routes exposed to climate hazards; (b) how to prioritise climate resilience investments on national roads based on assessing the benefit-cost ratios of investments associated with climate proofing vulnerable roads. Source: Oh, J.E., Espinet Alegre, X., Pant, R., Koks, E.E., Russell, T., Schoenmakers, R. and Hall, J.W. (2019).<\/span>\u00a0<\/span>Addressing Climate Change in Transport: Volume 2: Pathway to Resilient Transport. World Bank, Washington DC. doi: <\/span>http:\/\/dx.doi.org\/10.1596\/32412<\/a>.<\/span><\/p>\n [\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_3,2_3″ use_custom_gutter=”on” gutter_width=”1″ make_equal=”on” _builder_version=”4.2.2″ background_color=”rgba(0,0,0,0)” custom_margin=”||50px||false|false” custom_padding=”0px||0px||true|false” border_width_all=”1px” border_color_all=”#d6d6d6″ border_width_bottom=”5px” border_color_bottom=”#006f74″][et_pb_column type=”1_3″ _builder_version=”4.2.2″ background_color=”#006f74″ custom_padding=”30px|30px|30px|30px|true|true”][et_pb_blurb _builder_version=”4.2.2″ background_layout=”dark”]<\/p>\n For high-level assessment at the national-scale the criticality hotspot maps showcase where concentrations of vulnerable assets of multiple infrastructures can lead to large-scale disruptive impacts at the national-scale. Such evidence is very useful for national policy makers to allocate resources across number of infrastructure networks to prepare for larger-scale low probability and high impact events.\u00a0\u00a0\u00a0<\/p>\n [\/et_pb_blurb][\/et_pb_column][et_pb_column type=”2_3″ _builder_version=”4.2.2″ background_color=”#ffffff” custom_padding=”30px|30px|30px|30px|true|true”][et_pb_image src=”https:\/\/itrc-2020.ouce.ox.ac.uk\/wp-content\/uploads\/2020\/09\/themes_rr_results_fig1.png” align=”center” _builder_version=”4.2.2″][\/et_pb_image][et_pb_blurb image=”https:\/\/itrc-2020.ouce.ox.ac.uk\/wp-content\/uploads\/2020\/09\/themes_rr_results_fig6a.png” _builder_version=”4.2.2″]<\/p>\n (a)<\/p>\n [\/et_pb_blurb][et_pb_blurb image=”https:\/\/itrc-2020.ouce.ox.ac.uk\/wp-content\/uploads\/2020\/09\/themes_rr_results_fig6b-231×300.png” _builder_version=”4.2.2″]<\/p>\n (b)<\/p>\n Hotspot maps showing high-level concentrations of regions with critical electricity, transport, water, waste and telecommunications assets in (a) England and Wales. Source: Thacker, S., Barr, S., Pant, R., Hall, J.W. and Alderson, D., 2017. Geographic hotspots of critical national infrastructure.\u00a0Risk Analysis<\/em>,\u00a037<\/em>(12), pp.2490-2505. Doi: https:\/\/doi.org\/10.1111\/risa.12840<\/strong><\/a>; (b) New Zealand. Source: Zorn, C., Pant, R., Thacker, S. and Shamseldin, A.Y. (2020).\u00a0<\/span>Evaluating the magnitude and spatial extent of disruptions across interdependent national infrastructure networks.\u00a0<\/span>ASME J. Risk Uncertainty Part B<\/em>, 6(2). 020904. Doi: http:\/\/dx.doi.org\/10.1115\/1.4046327<\/a>.<\/p>\n [\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”3.26.6″][et_pb_column type=”4_4″ _builder_version=”3.26.6″][et_pb_button button_url=”#navigation-strip” button_text=”Back to top” button_alignment=”right” _builder_version=”4.2.2″ custom_button=”on” button_text_size=”14px” button_border_radius=”60px” button_font=”|700|||||||” button_icon=”%%0%%” button_icon_color=”#ffffff” button_on_hover=”off” background_layout=”dark”] [\/et_pb_text][et_pb_text _builder_version=”4.2.2″ header_font_size=”35px” header_3_font=”|700||on|||||” header_3_text_color=”#ffffff” header_4_text_color=”#3c3c3c” background_layout=”dark” custom_margin=”-40px|||||”]<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”3_4,1_4″ _builder_version=”4.2.2″][et_pb_column type=”3_4″ _builder_version=”4.2.2″][et_pb_text _builder_version=”4.2.2″ background_layout=”dark”]<\/p>\n The risk and resilience models have been used in a wider range of applications over the years. We have worked closely with organisations responsible for infrastructure networks, understanding their needs and developing analytical methods that transform their decision making.<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.2.2″][et_pb_image src=”https:\/\/itrc-2020.ouce.ox.ac.uk\/wp-content\/uploads\/2020\/09\/themes_rr_app_fig1_res.png” _builder_version=”4.2.2″][\/et_pb_image][et_pb_image src=”https:\/\/itrc-2020.ouce.ox.ac.uk\/wp-content\/uploads\/2020\/09\/themes_rr_app_fig3.png” _builder_version=”4.2.2″][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”3.26.6″][et_pb_column type=”4_4″ _builder_version=”3.26.6″][et_pb_button button_url=”#navigation-strip” button_text=”Back to top” button_alignment=”right” _builder_version=”4.2.2″ custom_button=”on” button_text_size=”14px” button_border_radius=”60px” button_font=”|700|||||||” button_icon=”%%0%%” button_icon_color=”#ffffff” button_on_hover=”off” background_layout=”dark”] [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_3,1_3,1_3″ use_custom_gutter=”on” gutter_width=”2″ make_equal=”on” _builder_version=”4.2.2″ background_color=”rgba(0,0,0,0)” custom_margin=”||50px||false|false” custom_padding=”0px||0px||true|false” border_radii=”on|6px|6px|6px|6px” border_color_all=”#d6d6d6″ border_width_bottom=”0px” border_color_bottom=”#006f74″][et_pb_column type=”1_3″ _builder_version=”4.2.2″ background_color=”#ffffff” custom_padding=”30px|30px|30px|30px|true|true” border_radii=”on|6px|6px|6px|6px” border_width_all=”1px” border_color_all=”#d6d6d6″ border_width_bottom=”5px” border_color_bottom=”#fe6223″][et_pb_blurb title=”snkit” _builder_version=”4.2.2″ header_font=”|600|||||||” custom_margin=”||||false|false” custom_padding=”||20px||false|false”]<\/p>\n Spatial Networks Data Cleaning Toolkit<\/p>\n [\/et_pb_blurb][et_pb_button button_url=”https:\/\/github.com\/tomalrussell\/snkit\/” url_new_window=”on” button_text=”View on GitHub” 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background_color_gradient_overlays_image=”on” background_image=”https:\/\/www.itrc.org.uk\/wp-content\/uploads\/2020\/06\/digital-e1593371103180.jpg” min_height=”120px” height=”120px” custom_margin=”||||false|false” custom_padding=”10%||||false|false” link_option_url=”\/themes\/databases\/” header_6_font_size_phone=”10px” border_width_bottom=”2px” border_color_bottom=”#fe6223″]<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" [et_pb_section fb_built=”1″ _builder_version=”4.2.2″ background_color=”#636363″ background_color_gradient_direction=”160deg” background_image=”https:\/\/itrc-2020.ouce.ox.ac.uk\/wp-content\/uploads\/2019\/08\/shutterstock_790878583.jpg” background_blend=”multiply” custom_margin=”||||false|false” animation_style=”fade”][et_pb_row _builder_version=”3.26.6″][et_pb_column type=”4_4″ _builder_version=”3.26.6″][et_pb_text _builder_version=”4.2.2″ header_font_size=”35px” header_6_font=”|700|||||||” header_6_text_color=”#fe6223″ background_layout=”dark”] THEMES [\/et_pb_text][et_pb_text _builder_version=”4.2.2″ header_font_size=”35px” background_layout=”dark” custom_margin=”-40px|||||”] RISK AND RESILIENCE [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row module_id=”navigation-strip” _builder_version=”4.2.2″][et_pb_column type=”4_4″ _builder_version=”4.2.2″][et_pb_text _builder_version=”4.2.2″ header_4_text_color=”#ffffff” header_4_font_size=”17px”] The risk and resilience models at high spatial resolutions are state-of-the-art data-driven models of interconnected infrastructure networks and their socio-economic risks […]<\/p>\n","protected":false},"author":12,"featured_media":0,"parent":33438,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[],"tags":[],"class_list":["post-33518","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.itrc.org.uk\/wp-json\/wp\/v2\/pages\/33518","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.itrc.org.uk\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.itrc.org.uk\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.itrc.org.uk\/wp-json\/wp\/v2\/users\/12"}],"replies":[{"embeddable":true,"href":"https:\/\/www.itrc.org.uk\/wp-json\/wp\/v2\/comments?post=33518"}],"version-history":[{"count":17,"href":"https:\/\/www.itrc.org.uk\/wp-json\/wp\/v2\/pages\/33518\/revisions"}],"predecessor-version":[{"id":40282,"href":"https:\/\/www.itrc.org.uk\/wp-json\/wp\/v2\/pages\/33518\/revisions\/40282"}],"up":[{"embeddable":true,"href":"https:\/\/www.itrc.org.uk\/wp-json\/wp\/v2\/pages\/33438"}],"wp:attachment":[{"href":"https:\/\/www.itrc.org.uk\/wp-json\/wp\/v2\/media?parent=33518"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.itrc.org.uk\/wp-json\/wp\/v2\/categories?post=33518"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.itrc.org.uk\/wp-json\/wp\/v2\/tags?post=33518"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\n[\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”results” _builder_version=”4.2.2″ background_color=”#f1f9fa” custom_padding=”||60px|||” animation_direction=”left” hover_enabled=”0″ z_index_tablet=”500″ border_color_all=”#ffffff” border_width_top=”20px” border_width_right=”20px” border_width_left=”20px” border_width_top_tablet=”20px” border_width_top_phone=”20px” border_width_right_tablet=”20px” border_width_right_phone=”20px” border_width_left_tablet=”20px” border_width_left_phone=”20px” box_shadow_horizontal_tablet=”0px” box_shadow_vertical_tablet=”0px” box_shadow_blur_tablet=”40px” box_shadow_spread_tablet=”0px”][et_pb_row _builder_version=”3.26.6″ custom_padding=”||30px|||” animation_style=”slide” animation_direction=”left” animation_intensity_slide=”4%”][et_pb_column type=”4_4″ _builder_version=”3.26.6″][et_pb_text _builder_version=”4.2.2″ header_font_size=”35px” header_6_font=”|700|||||||” header_6_text_color=”#fe6223″ background_layout=”dark”]<\/p>\nRISK AND RESILIENCE<\/h6>\n
RESULTS<\/h3>\n
\n[\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”applications” _builder_version=”4.2.2″ background_color=”#006f74″ custom_padding=”||60px|||” animation_direction=”left” hover_enabled=”0″ z_index_tablet=”500″ border_color_all=”#ffffff” border_width_top=”20px” border_width_right=”20px” border_width_left=”20px” border_width_top_tablet=”20px” border_width_top_phone=”20px” border_width_right_tablet=”20px” border_width_right_phone=”20px” border_width_left_tablet=”20px” border_width_left_phone=”20px” box_shadow_horizontal_tablet=”0px” box_shadow_vertical_tablet=”0px” box_shadow_blur_tablet=”40px” box_shadow_spread_tablet=”0px”][et_pb_row _builder_version=”3.26.6″ custom_padding=”||30px|||” animation_style=”slide” animation_direction=”left” animation_intensity_slide=”4%”][et_pb_column type=”4_4″ _builder_version=”3.26.6″][et_pb_text _builder_version=”4.2.2″ header_font_size=”35px” header_6_font=”|700|||||||” header_6_text_color=”#fe6223″ background_layout=”dark”]<\/p>\nRISK AND RESILIENCE<\/h6>\n
APPLICATIONS<\/h3>\n
\n
\n[\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”models” _builder_version=”4.2.2″ background_color=”#ffffff” animation_direction=”left” hover_enabled=”0″ z_index_tablet=”500″ border_color_all=”#ffffff” border_width_top=”20px” border_width_right=”20px” border_width_left=”20px” border_width_top_tablet=”20px” border_width_top_phone=”20px” border_width_right_tablet=”20px” border_width_right_phone=”20px” border_width_left_tablet=”20px” border_width_left_phone=”20px” box_shadow_horizontal_tablet=”0px” box_shadow_vertical_tablet=”0px” box_shadow_blur_tablet=”40px” box_shadow_spread_tablet=”0px” locked=”off”][et_pb_row _builder_version=”3.26.6″ custom_padding=”||30px|||” animation_style=”slide” animation_direction=”left” animation_intensity_slide=”4%”][et_pb_column type=”4_4″ _builder_version=”3.26.6″][et_pb_text _builder_version=”4.2.2″ header_font_size=”35px” header_3_font=”|700||on|||||” header_3_text_color=”#3c3c3c” header_4_text_color=”#3c3c3c” background_layout=”dark” custom_margin=”-40px|||||”]<\/p>\nRELATED MODELS & DATASETS<\/h3>\n
\n[\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”publications” _builder_version=”4.2.2″ background_color=”#f1f9fa” animation_direction=”left” z_index_tablet=”500″ border_color_all=”#ffffff” border_width_top=”20px” border_width_right=”20px” border_width_left=”20px” border_width_top_tablet=”20px” border_width_top_phone=”20px” border_width_right_tablet=”20px” border_width_right_phone=”20px” border_width_left_tablet=”20px” border_width_left_phone=”20px” box_shadow_horizontal_tablet=”0px” box_shadow_vertical_tablet=”0px” box_shadow_blur_tablet=”40px” box_shadow_spread_tablet=”0px” locked=”off”][et_pb_row _builder_version=”3.26.6″ custom_padding=”||30px|||” animation_style=”slide” animation_direction=”left” animation_intensity_slide=”4%”][et_pb_column type=”4_4″ _builder_version=”3.26.6″][et_pb_text _builder_version=”4.2.2″ header_font_size=”35px” header_6_font=”|700|||||||” header_6_text_color=”#fe6223″ background_layout=”dark”]<\/p>\nRISK AND RESILIENCE<\/h6>\n
PUBLICATIONS<\/h3>\n
\n[\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”casestudies” _builder_version=”4.2.2″ background_color=”rgba(0,0,0,0)” animation_direction=”left” z_index_tablet=”500″ border_color_all=”#ffffff” border_width_top=”20px” border_width_right=”20px” border_width_left=”20px” border_width_top_tablet=”20px” border_width_top_phone=”20px” border_width_right_tablet=”20px” border_width_right_phone=”20px” border_width_left_tablet=”20px” border_width_left_phone=”20px” box_shadow_horizontal_tablet=”0px” box_shadow_vertical_tablet=”0px” box_shadow_blur_tablet=”40px” box_shadow_spread_tablet=”0px”][et_pb_row _builder_version=”3.26.6″ custom_padding=”||30px|||” animation_style=”slide” animation_direction=”left” animation_intensity_slide=”4%”][et_pb_column type=”4_4″ _builder_version=”3.26.6″][et_pb_text _builder_version=”4.2.2″ header_font_size=”35px” header_6_font=”|700|||||||” header_6_text_color=”#fe6223″ background_layout=”dark”]<\/p>\nRISK AND RESILIENCE<\/h6>\n
CASE STUDIES<\/h3>\n
\n[\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”researchthemes” module_class=”themes” _builder_version=”4.2.2″ background_color=”#006f74″ width=”100%” max_width=”98%” module_alignment=”center” min_height=”432px” custom_margin=”20px||||false|false” custom_padding=”16px||38px||false|false” z_index_tablet=”500″ border_color_all=”#AECC52″ border_color_top=”rgba(0,0,0,0)” border_color_bottom=”#fe6223″ border_width_top_tablet=”20px” border_width_top_phone=”20px” box_shadow_horizontal_tablet=”0px” box_shadow_vertical_tablet=”0px” box_shadow_blur_tablet=”40px” box_shadow_spread_tablet=”0px” global_module=”33572″][et_pb_row _builder_version=”4.2.2″][et_pb_column type=”4_4″ _builder_version=”4.2.2″][et_pb_text _builder_version=”4.2.2″ text_font=”||||||||” header_text_color=”#ffffff” header_3_font=”|700|||||||” header_3_text_color=”#ffffff” header_4_font=”|700|||||||” header_4_text_color=”#006f74″]<\/p>\nRESEARCH THEMES<\/h3>\n
ENERGY<\/h6>\n
TRANSPORT<\/h6>\n
WATER<\/h6>\n
DIGITAL COMMUNICATIONS<\/h6>\n
DEMOGRAPHICS<\/h6>\n
URBAN
DEVELOPMENT<\/h6>\nECONOMICS<\/h6>\n
INFRASTRUCTURE
GOVERNANCE<\/h6>\nNISMOD<\/h6>\n
RISK AND
RESILIENCE<\/h6>\nRESEARCH SOFTWARE ENGINEERING<\/h6>\n
DATABASES<\/h6>\n