Tableau Public for Data Visualization using Shape Files
By: Angel Das
Tags: Tableau, Analytics, Data Visualization, Python, Data Science

Tableau Public for Data Visualization using Shape Files

Analyzing Pedestrian Traffic Volume — Melbourne Cohort

Image Credits — Developed by the Author using Tableau

Problem Context

Pedestrian activity in any country is a symbol of vibrancy and vitality. As often stated, “Walking is important to a city”, the pedestrian activity is an indication of economic prosperity, safety, and convenience of people residing in a city. An automated pedestrian counting system developed by the City of Melbourne enabled the development of an information repository that can be utilized to analyze the activity of residents across different parts of the city. The pedestrian counting sensors installed in various locations across the city transmit data to a central server which can then be utilized towards better decision making and planning for the future. This project aims to deep dive into the data gathered using these sensors to address two key questions:

How does pedestrian traffic volume vary in different parts of Melbourne? How does the day of the week and time affect overall pedestrian traffic volume?

Analytical Approach

A five-step iterative process starting with data gathering, attribute synthesis, data wrangling, hypothesis generation, and data visualization was used to derive key findings and insights about the questions at hand. The detailed process of the same is outlined in Figure 1 below.

Figure 1. Illustrates the analytical process followed to answer analyze the problem context. Note that the data used for this problem content was collated and made available beforehand. Image Credits — Developed by the Author using Power Point

Data collation & Attribute Synthesis

Two separate datasets used for this analysis are outlined below:

Pedestrian Counting System — Sensor Locations: This contains the spatial coordinates of pedestrian sensor devices located around the City of Melbourne, extracted from data.melbourne.vic.gov.au Pedestrian Counting System — 2019: This contains the hourly pedestrian counts of each sensor during 2019, extracted from data.melbourne.vic.gov.au

The data dictionary, i.e. information about different data attributes are included in the Jupyter Notebook and can also be found here. The Pedestrian Counting System — Sensor Locations dataset is at a Sensor ID level (primary key) whereas the Pedestrian Counting System — 2019 dataset is at a Sensor ID, date-time level (primary keys).

Data Wrangling

Data wrangling is defined as the process of structuring, cleaning, and transforming the data into a format that can facilitate better decision making in quicker turn around times. The data was obtained in a tabular format hence restructuring wasn’t required. However, a Jupyter notebook was developed to look for data anomalies, missing values, outliers, and other data issues. The findings from the wrangling exercise are outlined below:

Missing Value & Outliers

The Pedestrian_Counting_System_-_Sensor_Locations.csv file had information for 66 different sensor ids of which the direction information was missing for four sensors. On further deep dive, it was seen that these sensors were either removed or are inactive. The information about these sensors can be seen in Figure 2 below. The “note” column has missing values for the majority of the sensors.

Figure 2. Snapshot of sensors with missing information for columns direction_1 and direction_2. Image Credit s— Developed by the Author using Jupyter Notebook

None of the columns from the Pedestrian_Counting_System_2019.csv dataset had missing values. A quick description/summary statistic of the numerical columns exhibited that the figures are in range. The column “Hourly_Counts” indicated significant skewness of the pedestrian counts. The 75th percentile and maximum value of the pedestrian count recorded are significantly different and can be attributed to events, holidays, seasonal trends, etc. The summary statics of Sensors with at-least 7% outliers are shown in Figure 3 below.

Box plot — Image Credit s— Developed by the Author using Jupyter Notebook

Figure 3. Summary statistics and boxplot of Sensors with 7% outliers. Note for the sake of simplicity 7% is considered as a threshold. Since pedestrian count can differ based on time, seasonality, events, and other factors we won’t be treating the outliers for this analysis. Image Credit s— Developed by the Author using Jupyter Notebook

Sensor ID — Miss match

On further deep dive, 8 sensors IDs were obtained that are part of the Pedestrian_Counting_System_-_Sensor_Locations.csv dataset however they are absent in the Pedestrian_Counting_System_2019.csv dataset. Findings below:

Sensor IDs 16, 38, 32, and 13 which were originally identified as removed or inactive doesn’t have pedestrian count information in the Pedestrian_Counting_System_2019.csv dataset Sensor IDs 63, 64, 65 and 66 were installed in the year 2020 hence doesn’t have pedestrian count information in the year 2019 hence are missing in the Pedestrian_Counting_System_2019.csv dataset Sensor IDs 15 (State Library) and 33 (Flinders St-Spring St (West)) are marked as R (Removed) and I (Inactive) but has a pedestrian volume from 2019. These sensors are retained with the hypothesis that the sensors were removed recently

Hypothesis Formulation

A top-down approach is used to break the problem into multiple factors (hypothesis). Based on data availability these factors are then analyzed using visualizations in Tableau to identify key findings or trends. The treemap for hypothesis synthesis can be found here.

Data Exploration in Tableau

Data were explored in Tableau using visualizations to check for data anomaly. Figure 4 shows that the pedestrian traffic volume collected by the sensors is skewed towards the left and the box plot also shows the presence of outliers. As discussed earlier, outliers are not treated as multiple factors like seasonality, events, time, and day of the week impacts the pedestrian volume recorded. Figure 5 shows that the pedestrian volume is not recorded for a few of the sensors, e.g. Sensors 32, 63–66, etc. as illustrated in the bar graph below. Since most of the analysis is based on pedestrian volume by regions and date-time, these sensors weren’t dropped.