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Melbourne Pedestrian Sensor Data Analysis

Hands-on Exploratory Data Analysis of Pedestrian TrafficThe workflow implements the following steps:Loading our dataset from a CSV file and applying some data-cleaningsteps for our analysis.Using the Pivot & GroupBy for convenient aggregation operations.Using the data visualization nodes to create interactive datavisualizations.Perform exploratory data analysis of pedestrian traffic counts using apublic dataset in Melbourne city. Pivot aggregation to find the total number of pedestrians forevery sensor across the years Visualize the number of active sensors across each year Top 10 sensors by traffic in 2022 Yearly traffic across all sensors Extract sensor names with readings for every year Yearly Traffic for Sensors Active All Years Monthly Traffic for Sensors Active in 2019, 2020, and 2021 Hourly Traffic for Flinders La-Swanston St (West) in2020 Hourly pedestrian traffic for three sensors in Sep-2019 Distribution of daily pedestrian traffic for Bourke Street Mall (North) in 2019, 2020, and 2021 Reading and preparing the data for analysis Reading Melbournecity pedestrian data CSV fileConvert Date_Time field to proper date & time formatOrder the datasetby Date_TimeDistinct numberof sensors acrosseach yearTotal number of pedestrians for every sensor across the yearsFiltering outMelbourne Centralsensor countFilter forYear 2022Pedestrian trafficfor every sensorin 2022Top 10sensors by traffic in 2022Node 13Node 14Node 15Total pedestriancount by yearsNode 17Node 18Distinct numberof years forevery sensorSensor namescovering all14 yearsNode 22Node 23Node 24Node 25Total number of pedestrians for every month across the yearsNode 35Filtering years2019, 2020, 2021Node 37Node 39Node 41Node 42Node 44Node 45Filter forSeptember 2019Filter forSensor NameHourly pedestrian countacross the 3 sensorsNode 50Filter for Sensor_Nameand Years 2019, 2020, 2021Createdaily DatecolumnDaily pedestriancount acrosseach yearNode 57Node 58Node 59Distinct numberof yearsNode 62 CSV Reader String to Date&Time Sorter GroupBy Pivot Row Filter Row Filter GroupBy Top k Row Filter Bar Chart Line Plot Number to String GroupBy Number to String Line Plot GroupBy Row Filter ReferenceRow Filter GroupBy Number to String Line Plot Pivot Number to String Nominal ValueRow Filter Line Plot Column Combiner Rule Engine Column Resorter Row Filter Line Plot Row Filter Rule-basedRow Filter Pivot Line Plot Rule-basedRow Filter Date&Time to String GroupBy Box Plot Number to String Conditional BoxPlot (JavaScript) GroupBy Table Rowto Variable Hands-on Exploratory Data Analysis of Pedestrian TrafficThe workflow implements the following steps:Loading our dataset from a CSV file and applying some data-cleaningsteps for our analysis.Using the Pivot & GroupBy for convenient aggregation operations.Using the data visualization nodes to create interactive datavisualizations.Perform exploratory data analysis of pedestrian traffic counts using apublic dataset in Melbourne city. Pivot aggregation to find the total number of pedestrians forevery sensor across the years Visualize the number of active sensors across each year Top 10 sensors by traffic in 2022 Yearly traffic across all sensors Extract sensor names with readings for every year Yearly Traffic for Sensors Active All Years Monthly Traffic for Sensors Active in 2019, 2020, and 2021 Hourly Traffic for Flinders La-Swanston St (West) in2020 Hourly pedestrian traffic for three sensors in Sep-2019 Distribution of daily pedestrian traffic for Bourke Street Mall (North) in 2019, 2020, and 2021 Reading and preparing the data for analysis Reading Melbournecity pedestrian data CSV fileConvert Date_Time field to proper date & time formatOrder the datasetby Date_TimeDistinct numberof sensors acrosseach yearTotal number of pedestrians for every sensor across the yearsFiltering outMelbourne Centralsensor countFilter forYear 2022Pedestrian trafficfor every sensorin 2022Top 10sensors by traffic in 2022Node 13Node 14Node 15Total pedestriancount by yearsNode 17Node 18Distinct numberof years forevery sensorSensor namescovering all14 yearsNode 22Node 23Node 24Node 25Total number of pedestrians for every month across the yearsNode 35Filtering years2019, 2020, 2021Node 37Node 39Node 41Node 42Node 44Node 45Filter forSeptember 2019Filter forSensor NameHourly pedestrian countacross the 3 sensorsNode 50Filter for Sensor_Nameand Years 2019, 2020, 2021Createdaily DatecolumnDaily pedestriancount acrosseach yearNode 57Node 58Node 59Distinct numberof yearsNode 62CSV Reader String to Date&Time Sorter GroupBy Pivot Row Filter Row Filter GroupBy Top k Row Filter Bar Chart Line Plot Number to String GroupBy Number to String Line Plot GroupBy Row Filter ReferenceRow Filter GroupBy Number to String Line Plot Pivot Number to String Nominal ValueRow Filter Line Plot Column Combiner Rule Engine Column Resorter Row Filter Line Plot Row Filter Rule-basedRow Filter Pivot Line Plot Rule-basedRow Filter Date&Time to String GroupBy Box Plot Number to String Conditional BoxPlot (JavaScript) GroupBy Table Rowto Variable

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Created by: krish
Created at: 2024-12-07
On NodePit since: 2024-12-13
Last update: 2024-12-17
Created with KNIME version: v5.4.0

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