KNIME_​project_​DV08_​T05 (1)

Imports the hospitalisation dataset into KNIME for analysis.

Road User Trend (2011-2021)Includes only 5 major road user groups most common in Australia and directly relevant to Q4 analysis.

Dataset ImportImports national hospitalised injury data from BITRE raw dataset into KNIME.

Filters records where the cause of injury is related to traffic incidents.

Removes rows containing “Missing” values to improve data quality and accuracy.

Groups data by calendar year and calculates total hospitalisations, bed days, and died cases.

Calculates the death rate percentage using died cases divided by total hospitalisations.

Calculates yearly total hospitalisations for percentage comparison.

Visualises the percentage distribution of hospitalisations across selected age groups over time.

Combines grouped datasets using calendar year to support percentage calculations.

Visualises yearly trends of hospitalisations and death rate percentage over time.

Compares yearly changes in major counterparties associated with First Nations road crash hospitalisations between 2011 and 2021.

Transforms counterparty categories into separate columns for clearer grouped bar chart comparison.

Imports the national road crash hospitalisation raw dataset into KNIME for gender-based injury analysis.

Groups hospitalisation data by calendar year and age group, then calculates total hospitalisations.

Removes “Not applicable” road user records and incomplete sex categories to ensure cleaner and more reliable gender comparison analysis.

Calculates the percentage share of hospitalisations for each age group.

Pivot by Road UserPivots data so each road user becomes a separate column for year-by-year trend comparison in Line Plot.

Visualises yearly percentage differences between traffic counterparties.

Convert Calendar YearConverts Calendar year from integer to string for proper X axis display in Line Plot visualisation.

Removes rows with missing age group values to improve data quality.

Restructures yearly male and female hospitalisation totals into separate columns for long-term gender gap trend analysis.

Groups data by calendar year, sex, and road user type to calculate total hospitalisations and aggregated bed day severity measures across the decade.

Classifies key age groups (17–25 and 65+) to focus the analysis on vulnerable populations.

Converts calendar year values into string format for clearer categorical display in visualisations.

Transforms grouped road user data into separate male and female columns for direct injury severity comparison.

Filters the dataset to include only traffic-related injury records.

Compares male and female injury severity patterns across major road user categories using aggregated bed day totals.

Visualises the long-term difference between male and female road crash hospitalisations from 2011 to 2021 to examine whether the gender gap changed over time.

Group by Year and Road UserGroups hospitalisation data by Calendar year and Road user, calculates total Sum ofHospitalisations for each group.

Combines grouped datasets using calendar year for comparative analysis.

Imports the hospitalisation dataset into KNIME for analysis.

Calculates the percentage share of hospitalisations for each counterparty category.

Imports First Nations road crash hospitalisation data from the BITRE raw dataset into KNIME for counterparty trend analysis.

Groups hospitalisation data by calendar year and counterparty, then calculates total hospitalisations.

Calculates yearly total hospitalisations for percentage comparison.

Filters the dataset to include only traffic-related injury records.

Aggregates hospitalisation totals by calendar year and counterparty type to analyse changes over time.

Handle Missing ValuesReplaces any missing hospitalisation values with zero to ensure clean data before GroupBy aggregation.

Remove Not ApplicableFilters out rows where Road user is "Not applicable" to focus analysis on actual road user categories.

Imports the hospitalisation dataset into KNIME for analysis.

Removes non-relevant rows, header rows, and less informative categories to focus on meaningful First