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Power BI and DAX for Crash Safety Performance Dashboards

30 Chapters

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Crash safety performance dashboards

objectives, stakeholders, and key metrics such as fatality rate, injury severity, and crash frequency.

performancedashboards

Data sources for crash safety

police reports, hospital records, vehicle telematics, and road inventory data; schema design and data quality considerations.

hospitaltelematics

Power BI fundamentals for crash safety

importing data, creating relationships, and building a basic dashboard layout with crash location maps.

importingcreating

DAX basics for crash metrics

calculated columns and measures for crash counts, injury severity distribution, and year-over-year comparisons.

Time intelligence in DAX for crash trends

YTD, MTD, rolling averages, and period comparisons to monitor safety performance over time.

intelligenceDAX

Geospatial analysis in Power BI

mapping crash hotspots, clustering, and integrating road network data for spatial safety insights.

Geospatialanalysis

Crash severity analysis with DAX

calculating severity indices, KABCO scale distributions, and conditional formatting for high-risk locations.

severityanalysis

Filtering and slicing crash data

using slicers, cross-filtering, and DAX filter functions to explore crash patterns by vehicle type, road class, and weather.

Filteringcross-filtering

Building interactive crash dashboards

bookmarks, drill-through, and tooltips to enable detailed investigation of crash events.

Buildinginteractive

DAX measures for crash rates

normalizing crashes by traffic volume (VMT) and population, and calculating crash rate per million vehicle-kilometers.

Identifying crash hotspots with DAX and Power BI

ranking locations, calculating cluster densities, and using heat maps for prioritization.

Identifyinghotspots

Integrating vehicle safety data

merging crash data with vehicle specifications (e.g., NCAP ratings) to analyze safety feature effectiveness.

Integratingspecifications

DAX for injury severity modeling

calculating injury probability, severity scores, and comparing outcomes across crash types.

Crash data visualization best practices

choosing charts for crash frequency, severity, and trends; avoiding misleading visuals.

visualizationpractices

Power BI data modeling for crash safety

star schema design with fact tables (crashes) and dimensions (date, location, vehicle, person).

modelingdimensions

DAX advanced filtering

using CALCULATE, FILTER, and KEEPFILTERS to create dynamic crash segments for targeted analysis.

Crash safety KPIs and scorecards

designing measures for leading and lagging indicators, and setting targets with conditional formatting.

Time series analysis of crash data

seasonality, trend decomposition, and forecasting crash frequencies using DAX and Power BI analytics.

analysisseasonality

Crash cost analysis with DAX

estimating economic costs of crashes by severity, and aggregating costs for cost-benefit analysis of safety interventions.

Building a crash safety performance dashboard from scratch

step-by-step from data import to final report with DAX measures.

Buildingperformance

DAX for comparative analysis

comparing crash metrics across regions, time periods, and vehicle types using ratios and difference measures.

Integrating external data for context

weather, road conditions, and traffic volume data to enrich crash analysis and identify risk factors.

Integratingexternal

Crash data storytelling with Power BI

using annotations, narrative visuals, and report tooltips to communicate safety insights effectively.

storytellingannotations

Performance optimization for large crash datasets

DAX best practices, aggregation tables, and incremental refresh for responsive dashboards.

Performanceoptimization

DAX patterns for crash safety

common patterns like ranking, percentile calculations, and moving averages applied to crash data.

Crash safety dashboard for different audiences

tailoring views for policymakers, traffic engineers, and law enforcement using row-level security.

dashboarddifferent

Validating and testing DAX measures

ensuring accuracy of crash counts, rates, and trends through cross-validation with source systems.

Automating crash data refresh and distribution

Power BI dataflows, scheduled refresh, and sharing dashboards with stakeholders.

Automatingdistribution

building a national crash safety performance dashboard with Power BI and DAX, including data integration and KPI design.

buildingnational

Future directions

integrating real-time crash data, predictive analytics with DAX, and emerging trends in crash safety dashboards.

directionsintegrating